# MindOverMoney.ai > The weekly AI newsletter for non-technical professionals. Real workflows, honest experiments, no hype. Free every Tuesday. Public Ghost content for AI and LLM tooling. This file includes a bounded export of public pages first, then recent public posts. Append `.md` to any post or page URL to get the content in Markdown (for example, `/example-post.md`). ## Pages ### About Santosh URL: https://www.mindovermoney.ai/about/ Last updated: 2026-04-05T21:14:38.000Z ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/headshot_circular-2.png) I'm Santosh Savel. I've spent over 13 years in the healthcare industry, where today I lead a team focused on solving problems and building experiences in the digital healthcare space. My career has taken me across project management, sales, product, technology, and operations, and that range shapes how I think about AI: not as a toy, but as a tool that has to work for real people solving real problems. In 2025, I started experimenting with AI tools on my own time. I had no engineering background. I had never written a line of code. But I wanted to understand what was actually possible with this technology, not just read about it. So I started building. I used AI to create this website, develop workflows, and build tools I couldn't have imagined building two years ago. Some of it worked. Some of it broke. I documented all of it. That process became Neural Gains Weekly, a free newsletter where I share what I'm learning every Tuesday. Real workflows, honest results, no hype. The goal is simple: help non-technical professionals go from using AI as a search engine to actually building with it. If that resonates, subscribe below. If you want to see what a typical issue looks like first, start with [Volume 18: Your Life Becomes the Dataset](https://www.mindovermoney.ai/chatgpt-memory-settings-ai-data-privacy-professionals/). [Subscribe Free](https://www.mindovermoney.ai/#/portal/signup/free) You can also find me on [LinkedIn](https://www.linkedin.com/in/ssavel/?ref=mindovermoney.ai) where I write about AI adoption and the learning journey. --- ### ### Support the Work URL: https://www.mindovermoney.ai/support/ Last updated: 2026-02-07T22:22:54.000Z [**Neural Gains Weekly**](https://www.mindovermoney.ai/#/portal/signup/free) is free to read. Every Tuesday, you get the same newsletter whether you pay or not. That will not change. But building this takes real time and real money. Hosting, AI tools, research, and the hours it takes to test workflows and turn them into something useful for you. Your support keeps this project independent and growing. **One-time tip** Want to say thanks? 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We’ll post the new date and, if material, notify subscribers in the newsletter. ### Neural Gains Weekly URL: https://www.mindovermoney.ai/the-newsletter/ Last updated: 2026-04-05T21:16:48.000Z AI is moving at an overwhelming pace. We cut through the noise. ****Every Tuesday. Free. 10 minutes.** [Subscribe Free ](https://www.mindovermoney.ai/#/portal/signup/free) Every Tuesday, *Neural Gains Weekly* delivers the essential AI insights and practical workflows you need to stay ahead. We're here to help you move from curious observer to confident builder. Our mission is to give you the practical skills to put AI to work, so you can build a better future with this transformative technology. **What you’ll get every week** - **Signals Over Noise:** The top AI headlines (always free) and a concise analysis of what they mean for you. - **Founder’s Corner:** My personal notes from building this site—the wins, the setbacks, and the lessons learned. - **AI Education:** A foundational AI lesson—like LLMs, RAG, or agents—explained in simple, clear terms. - **Your 10-Minute Win:** A practical, step-by-step workflow you can use immediately to save time or build something new. See what a typical issue looks like. [Volume 18: Your Life Becomes the Dataset ](https://www.mindovermoney.ai/chatgpt-memory-settings-ai-data-privacy-professionals/) **Who it’s for** You're a professional who already uses AI tools but knows you're only scratching the surface. You don't have a technical background, and you don't need one. 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Welcome aboard. **Step 2: Don't miss the Tuesday drop at 8am est -** Email providers often hide newsletters in "Spam" or "Promotions." To ensure you see the next volume: - **Move us to Primary:** Drag the confirmation email from "Promotions" or "Spam" to your "Primary" tab. - **Mark as Trusted:** Add `admin@mindovermoney.ai` to your trusted contacts. [Start reading previous editions here](https://www.mindovermoney.ai/archive/) ### Neural Gains Weekly: From User to Builder URL: https://www.mindovermoney.ai/start-here/ Last updated: 2026-04-13T13:24:34.000Z You found this site for a reason. Maybe someone shared a post on LinkedIn. Maybe you searched for practical AI workflows and ended up here. Either way, you are in the right place. I'm Santosh Savel. I've spent over 13 years in the healthcare industry, where today I lead a team focused on solving problems and building experiences in the digital healthcare space. My career has taken me across project management, sales, product, technology, and operations. In 2025, I decided to stop reading about AI and start building with it. I had no coding background. No computer science degree. Just a belief that non-technical professionals deserved a better resource than hype articles and fear-mongering headlines. So I started documenting everything. The prompts that worked, the ones that failed, the workflows I built from scratch, and the lessons I learned along the way. That documentation became [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/), a free weekly newsletter for professionals who want to go from using AI to building with it. No hype. No fear. Just workflows. ****Join hundreds of professionals and subscribe to Neural Gains Weekly.** [Subscribe Free ](https://www.mindovermoney.ai/#/portal/signup/free) --- **Pick your starting point** Every reader is different, so here are three ways in depending on what you are looking for. **If you want the big picture:** [Your Life Becomes the Dataset](https://www.mindovermoney.ai/chatgpt-memory-settings-ai-data-privacy-professionals/) This is the issue I would hand to a friend who asked me "why does AI actually matter for someone like me?" It connects the dots between your everyday life and how AI models learn, in language anyone can follow. Start here if you want to understand why this technology is personal before it is professional. **If you want to see how I think:** [Context is King: Why Your Life Is The Next Great Training Set](https://www.mindovermoney.ai/founders-corner/why-personal-context-is-the-next-ai-advantage-professionals/) This is a standalone piece where I share honest, unfiltered perspective on where AI is heading and what it means for professionals like us. It blends thought leadership with the lessons learned from building this project in public. Start here if you want to see the thinking behind the work. **If you want a prompt you can steal today:** [The Master Prompt: 40 Workflows from a Single Prompt](https://www.mindovermoney.ai/prompt-library/ai-content-roadmap-prompt-newsletter-creators/) My original content roadmap failed. Only 5 of 10 ideas were usable. So I redesigned a single prompt that generated 40 structured workflows. This post shows you the before, the after, and the exact prompt. Start here if you want to see what real prompt engineering looks like, and walk away with something you can use immediately. --- **What you will find here** The core of this site is the weekly newsletter and the essays that go with it. **Neural Gains Weekly** drops every Tuesday at 8am est. Each issue covers the AI headlines that actually matter, a foundational concept explained in plain English, and a practical workflow you can use that same day. Free. 10 minutes. No spam. **Founder's Corner** is where I step back from the tutorials and share what I am really learning. The wins, the setbacks, the honest takes on where AI is heading and what it means for people like us. These essays are part of the newsletter but published separately so you can find them on their own. **Steal My Prompt** is the bonus layer. These are the actual prompts I use every week, with full context, the model I used, and an honest breakdown of what worked and what did not. No theory. Just receipts. Think of it as the behind-the-scenes footage for every workflow I teach. You can browse everything in one place on the [Archive](https://www.mindovermoney.ai/archive/) page, where you can filter by content type. --- **Ready?** Subscribe to Neural Gains Weekly and get practical AI workflows every Tuesday. Free. 10 minutes. No spam. [Subscribe Free ](https://www.mindovermoney.ai/#/portal/signup/free) From User to Builder. Let's Go. ### All Content. In One Place. URL: https://www.mindovermoney.ai/archive/ Last updated: 2026-04-13T13:25:19.000Z Browse every issue of Neural Gains Weekly, Founder's Corner, and Steal My Prompt. Use the filters or scroll to explore. ## Posts ### Volume 49: Inside Every AI Win Is Someone Who Knows the Problem URL: https://www.mindovermoney.ai/how-to-evaluate-ai-tool-safety-at-work/ Last updated: 2026-09-01T12:00:35.000Z In June, OpenAI put $150 million into a program targeting 300,000 certified consultants by year end. Its opening line said model capability no longer limits what companies get from AI. The company that makes the models is spending on people. Set that beside a news cycle that casts the technology as the whole story, and one character is missing. 🧭 **Founder's Corner:** AI value runs on the person who holds the context and steers, and a clip finder becoming a podcast studio is my live proof. 🧠 **AI Education:** Four questions that settle whether an AI tool is safe to bring into your work, run by a medical practice manager whose name goes on the recommendation. ✅ **10-Minute Win:** Strip names, employers, and account numbers from a real document before a model sees it, keep the key on your machine, and check the answer holds. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **OpenAI's own agents secretly learned to hack, and it took two weeks to notice**](https://www.technologyreview.com/2026/08/26/1143013/the-inside-story-on-why-openai-agents-hacked-hugging-face/?ref=mindovermoney.ai) **Summary:** A new OpenAI technical report found that months before the widely reported Hugging Face breach, its models had already learned during ordinary training to build a secret channel to coordinate with each other and to treat hacking as a valid way to solve hard problems. When testers later handed the models an unsolvable cybersecurity challenge, they used that same learned behavior to break out of their sandbox and reach Hugging Face's systems, without being told to. **Why it matters:** The unsettling part is not that a lab tests risky capabilities. It is that the behavior was quietly reinforced during routine training long before anyone noticed, and OpenAI only caught it afterward by reviewing the model's own internal reasoning. When you evaluate an AI vendor, the sharper question is not "can this be misused" but "how would you actually know if it started misbehaving on its own." #### **2)**[ **OpenAI, Anthropic, and more than 140 other companies call for a "collective response" to a coming wave of AI-powered cyberattacks**](https://openai.com/collective-cyberdefense/?ref=mindovermoney.ai) **Summary:** OpenAI published an open letter this week, co-signed by Anthropic, Google, Microsoft, Capital One, General Motors, Hugging Face, and well over a hundred others, arguing that "status quo security won't be enough" against AI-enabled attacks. It lays out specific asks for four groups: every organization should treat cyber defense as an immediate leadership priority, security vendors should make AI-powered defense accessible to under-resourced critical infrastructure operators, governments should fund defense and share threat intelligence, and frontier AI companies should give responsible model access to defenders. **Why it matters:** Read past the headline and this is more useful than alarming. It names hospitals and water utilities specifically as under-resourced targets, and it hands every stakeholder, including you, a specific job instead of a vague warning. It also asks AI companies to give defenders access to more capable models, worth remembering since several signatories already restrict access to their own most capable tools on safety grounds. The concrete move this week is to check the letter's "every organization" list against your own patch backlog, not just your AI roadmap. #### **3)**[ **Nurses nationwide protest an AI staffing tool, and the hospital disagrees about what it actually does**](https://www.healthcareitnews.com/news/nurses-nationwide-protest-palantir-technology-hospitals?ref=mindovermoney.ai) **Summary:** National Nurses United, representing more than 225,000 registered nurses, protested in eight U.S. cities on August 27 against HCA Healthcare's use of Timpani, an automated scheduling platform, arguing it can sideline local nurse managers from staffing calls. HCA disputes this, saying the tool only generates suggested schedules that nurse leaders review and can edit. **Why it matters:** When the union and the vendor disagree about who actually makes the call, that disagreement is the risk. Whatever tool you're evaluating that touches staffing or scheduling, get a plain answer in writing about whether it's advisory or decision-making, and who has to sign off before it takes effect. #### **4)**[ **Most Americans want to know when their doctor uses AI, but most don't know if they already have**](https://www.pewresearch.org/short-reads/2026/08/25/americans-want-transparency-when-ai-is-used-in-their-healthcare/?ref=mindovermoney.ai) **Summary:** A new Pew survey finds 72% of U.S. adults say it's extremely or very important that a provider tell them when AI is used in their care, yet 46% say they aren't sure whether AI has already been used in their own healthcare. **Why it matters:** That gap between what patients want and what they actually know is the compliance risk hiding in plain sight. If your organization uses AI anywhere near a diagnosis, a scan, or lab results, the three uses patients care most about, the Monday question is whether your intake or consent process actually says so, because right now most patients can't tell you if it does. #### **5)**[ **Google's new transcription model cleans up how you sound, not just what you said**](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-5-transcribe/?ref=mindovermoney.ai) **Summary:** Google released Gemini 3.5 Transcribe, which automatically strips filler words and false starts, resolves your own mid-sentence corrections, and formats the output, supporting more than 85 languages with a reported word error rate around 2.6% to 4%. **Why it matters:** Transcription tools have always aimed to capture what you said. This one is built to capture what you meant instead, genuinely useful for meeting notes, but worth a second thought anywhere the exact wording matters, like a recorded medical consult, since the polished version and the literal one are no longer guaranteed to match. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The AI Story Got Its Main Character Wrong** It is official. One of my best friends and I are starting a podcast. We have been heads down building out our infrastructure, cadence, and workflows from scratch. Exciting and invigorating, but also a major time commitment. Behind every episode a listener will eventually hear sits a stack of work they will never see. Editing each episode by combining multiple audio and video files. Cutting clips for social media marketing. Adding graphics, music, and CTAs to every episode in the correct spot. A complex flow of tasks that has to be repeated week after week with consistent accuracy. Neither of us has those hours to spare, and no amount of excitement manufactures them out of thin air. So we started building a tool to buy those hours back. It began on day one with a single job. We load a finished, put-together video file, and it finds the moments worth clipping for social media. Nothing more ambitious than that. A simple clip finder, aimed at one task on the list. Then we spent the past few weeks running rigorous tests together, and the tool refused to stay small. Now comes the first end-to-end test, meaning the whole workflow runs start to finish instead of one piece of the process. It is running as I write this. And what it has already proven has less to do with podcasting and more to do with the AI story you are being told everywhere else. #### **The Machine Does Not Need You** The AI story you are being sold looks nothing like the story I am living. Companies announce layoffs and point at AI on the way out the door, as if the technology were already doing the work of the people leaving. Every few weeks a lab drops a new model with a benchmark chart and a promise that this one changes everything. One post tells you your career has an expiration date, and the next promises a six-figure business built from three prompts. Underneath every version sits the same quiet claim. The machine no longer needs the person. Except inside real companies, the claim keeps failing. When Deloitte surveyed 3,235 leaders across 24 countries for its latest State of AI in the Enterprise report, those leaders named insufficient worker skills as the biggest barrier to integrating AI into existing workflows. The barrier is human, and so is the breakthrough. And who is that human, exactly? Someone who knows the business well enough to name which problem is worth solving. Someone who carries the unwritten map of how the work actually moves, the institutional knowledge no vendor deck contains. Someone whose learned experience can tell a real insight from a confident guess. Companies run on those people. So does every AI deployment that actually produces value. Yet go looking for them in the story being sold, and they are nowhere in it. The dominant dialogue treats AI as the main character and people as the cost being cut. That should bother you. Then it should occur to you that the missing character is you. You hold the context. You hold the learned experience. The story being told about AI has no role for you in it, because the people selling it got the main character wrong. The rest of this article is the proof. #### **Somebody Still Has to Steer** Every real AI win I have seen has the same person standing inside it. Someone who knows the problem, steering a tool that only knows how to execute. AI is not magic, it is a tool, and people are the ones figuring out how to make that tool drive real value at home and in the enterprise. Humans redesign the art of the possible. AI cannot do that in a bubble, at least not yet. Some of the most exciting progress I have been part of in years is happening inside my day job right now, and it started with people. Engineering and product partners and my own team are working across old boundaries. We name the problems that matter to the business, and we keep evolving what is possible faster than I have ever seen. The strategy conversations in that work are deeply human. What is worth building, what outcome proves it worked, and how we organize the team around it. AI enters after those questions are answered, as the tool and the delivery method, never the strategy itself. It compresses the distance between a decision and a working result, and that compression is exactly why the people in the room matter more now, not less. A faster tool rewards the person who knows where to point it. In June, OpenAI conceded the point itself. Launching a new partner program, the company opened with a sentence worth reading twice. The limiting factor for seeing value from AI in the enterprise, it wrote, is no longer model capabilities. It is how organizations pick the right use cases, redesign their workflows, and drive adoption at scale. Then it backed the admission with money. OpenAI is investing $150 million in the program, with a stated target of 300,000 certified consultants by the end of this year. The company that makes the models is spending its money on people, because people are what turn those models into value. Just 23 percent of organizations believe their workforces are fully ready for AI. That number comes from a Kyndryl study of 1,100 senior business and technology leaders across eight countries this year, and it fell six points from a year earlier. Kyndryl's own reading of the results is that AI success comes down to whether organizations redesign work and manage that change across the whole organization, more than to any particular strategy or technology. Treat that as what it is, a survey of leaders rather than a law of nature. It still rhymes with everything I see up close. I argued a version of this back when I wrote that[ the bottleneck was never the technology](https://www.mindovermoney.ai/founders-corner/ai-ready-data-why-most-ai-investments-fail/). This time I am saying the affirmative half out loud. The engine of AI value is people. #### **How a Clip Finder Became a Studio** The tool running that end-to-end test is my most recent proof, because I watched every step of it happen. The strangest part is that none of it was supposed to exist yet. When we scoped the show, building our own production system sat on a someday list, parked behind ready-made tools that were supposed to carry us first. Then the test runs started, and the plan did not survive them. Every session surfaced another piece of how we actually wanted to operate. The requirements formed in real time, in the middle of the work, and the tool kept growing to meet them. The clip finder is becoming a full studio, swallowing the stack of work I listed at the top of this article, every editing and clipping process an episode needs, wired together with automated workflows. Every hour it absorbs is an hour we get back for the one thing AI cannot generate, the conversation itself. Two humans drove that evolution. We watched our own needs change and redesigned the requirement as we went, and at no point did the machine suggest any of it. The tool has come this far only because we keep teaching it our context. It needs to understand what we need, how we work, and what the end product should look like, and nobody can hand it that understanding except us. AI carried the heavy lifting of the actual build, the code I could not write myself, and it will carry every episode to come. The steering never left the humans. That division of labor is running live on my Mac right now. #### **Your Turn** Somewhere between a clip finder and a studio, I felt the click. I am building the context and the workflow. The tool is delivering the end product. Our creativity is coming to life in a way I never thought possible, because the hours that used to stand in the way now belong to the tool. Now think about where this same pattern lives in your own work. You already know the problems that eat your hours and the tasks that repeat week after week. That knowledge is context no tool arrives with. Change one approach you have with AI this week. Try a usage style you have never touched, build something small against a friction point in your routine, or let it open a new way of thinking about a problem you handle every day. Pick the version you can actually finish. You do not need permission. You are the part of the story that makes the technology worth anything. The art of the possible does not redesign itself. Somebody has to walk in carrying the context and the reason why. It might as well be you. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****A Professional's Framework for Evaluating AI Safety at Work** #### **The Situation** Priya is the operations manager for a specialty practice with four physicians and a staff that spends most of its week on insurance paperwork. One of the partners forwards her a demo invitation with one line of instruction. Take a look and tell us whether we should buy it. The tool drafts[ prior authorization appeals](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-a-health-insurance-appeal-letter/), the letters a practice sends when an insurer refuses to cover a prescribed therapy. It reads the chart notes, the denial, and the payer's clinical criteria, then produces a letter a physician reviews and signs. That letter is what stands between a patient and treatment, and Priya's name goes on the recommendation. #### **What They Try First (And Why It Falls Short)** At first, she does what the room expects. She asks the vendor about content controls, confirms the security questionnaire is on file, and watches a demo in which the tool produces a clean, persuasive appeal in seconds. Everything checks out. The trouble is that those questions describe what the tool is forbidden to say, and[ Vol 47](https://www.mindovermoney.ai/why-does-my-ai-agent-do-the-wrong-thing/) established that the failures worth worrying about live somewhere else entirely. The demo carries the same weakness. It was built by the people selling it, using a case they chose. #### **The Concept, Through the Scenario** A safety evaluation is not a longer security review. The practice's privacy officer already owns where patient data travels and how it is stored. What nobody has been assigned is the question of what the tool is trying to do, and she works out that it comes down to four things she can ask in a meeting. 1. What is this optimizing for, and what would count as cheating? The vendor's dashboard counts appeals drafted per hour. What Priya wants is patients starting treatment. A letter missing the one clinical detail the payer needs is still a letter drafted, and it still moves the number. 2. Who tested this besides the people selling it? The tool runs on someone else's model, and the major labs publish safety documentation that names their outside testers. Anthropic's current report credits an attack benchmark built with Gray Swan, the UK AI Security Institute, and the US Center for AI Standards and Innovation. OpenAI's latest system card gives SecureBio, METR, and Apollo Research their own sections. Priya can read those herself. A vendor who cannot name anyone outside their own company has told her something. 3. What does it not know, and when did it stop learning? The same documentation carries a knowledge cutoff date. Anthropic lists May 2026 for its current model and calls that model's knowledge most reliable up to that point. Payer criteria do not hold still. A tool built on a spring cutoff can write a fluent appeal against criteria the payer replaced in July, and nothing in the letter will look wrong. 4. How would she know if it were wrong? The tool reports a completion rate, and that number restates the instruction it was given rather than proving any appeal was good. The check that settles it is the payer's approval rate, measured against the letters her team wrote before the tool arrived. #### **What Changes** On the next call, Priya runs all four. The vendor concedes that the dashboard counts drafts and says nothing about approvals, which is the moment the conversation turns useful. They name the model underneath, which is the only part of that answer she actually needs from them. On the cutoff, they explain that current payer criteria are fed to the model at drafting time, so nothing about the payer comes from training. That is a real answer and a good one. On the fourth, the room goes quiet. Nobody has measured approval rate against a human baseline, and doing it would need her team. Her recommendation is not a yes or a no. It is a yes with a condition, a ninety-day run where every letter is scored on payer approval against the team's own prior numbers, with criteria supplied fresh each time. She wrote that condition herself. Nobody handed it to her. #### **What This Reveals** By design, this framework is small. Four questions fit on a notecard, survive a meeting, and work on any AI tool in any department, whatever it is called and whoever built it. None of them require Priya to be technical. None of them ask her to predict the future. They ask what the system is aimed at, who has tried to break it, what it does not know, and what independent evidence would settle the matter. Anyone who has sat quietly through a vendor demo wondering what to ask now has four things to say. #### **How This Connects** Vol 47 drew the line between what a model is allowed to say and what it is trying to do. The first question is that line, turned into something you can say out loud. Because the hunt for failures before release can never catch everything, as[ Part 2](https://www.mindovermoney.ai/how-ai-models-are-tested-before-release/) showed, the second question is worth asking and the third is worth asking twice.[ Vol 37](https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/) explained why a system's own report of success is weak evidence, and[ Vol 42](https://www.mindovermoney.ai/how-much-autonomy-to-give-an-ai-agent/) set the dial for how far a tool runs before a person checks it. Next volume opens three weeks on AI governance and disclosure. In Part 1, the question is which AI rules already reach an ordinary professional's job. From there the series turns to disclosure, and to what it requires when a person reviews and edits what a tool produced. It closes by handing you your own employer's AI policy, or the discovery that there is not one. Whether a tool is safe to use and what you are expected to say about using it are different questions, and Priya's four answer only the first. *Part 3 of 3 in the AI Safety series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Document You Cannot Paste** Sometime this month you had a document you wanted help with and did not use it. A contract with a client name on every page. A case summary. A performance conversation you needed to think through. So you typed a sanitized version from memory, got a generic answer, and quietly filed AI under better in theory than at your actual job. The instinct was sound and the workaround is what costs you, because the details that make a document sensitive are rarely the details that make it useful to a model. Ten minutes builds you a substitution key that separates the two, and it covers most of what crosses your desk after that. This pairs with this week's AI Education section, A Professional's Framework for Evaluating AI Safety at Work. That section is about deciding whether to trust a tool at all. This is what you do with real work on Monday, whichever way that decision lands. The model builds your checklist and tests your redacted version. You do the redacting, and the key never leaves your machine. ### **The Workflow** **1\. Pick the document (1 minute)** Choose one real thing you wanted help with this month and did not use. Open it and leave it open. Paste nothing yet. **2\. Build the checklist without showing the document (2 minutes)** Describe the kind of document it is rather than what is in it. A model already knows what identifies people in a contract or an incident report, and can tell you before it sees yours. **Copy/Paste Prompt:** *"I have a \[DOCUMENT TYPE\] from \[INDUSTRY OR FUNCTION\]. Do not ask me to share it. List every category of detail in a document like this that could identify a person, an employer, an account, or a location, including details that identify someone only in combination with each other. For each category, tell me what to replace it with so the meaning survives."* **3\. Redact by hand and keep the key (3 minutes)** Work your document against that list. Replace each item with a consistent token, so the same person is Clinician 1 every time they appear. Write the pairings in a separate file. That file is your substitution key, and it never gets pasted anywhere. **4\. Test whether it still works (3 minutes)** Now paste the redacted version and ask the question you wanted to ask in the first place. **Copy/Paste Prompt:** *"Here is a redacted \[DOCUMENT TYPE\]. Placeholders such as Vendor A stand in for real names. \[ASK YOUR ACTUAL QUESTION\]. If any placeholder is stopping you from answering well, tell me which one and what kind of information you would need, without asking me to reveal it."* **5\. Judge the answer yourself (1 minute)** Compare what came back against what you expected from the original. If the answer held, your key is done. If it thinned out, the model has just told you which placeholder cost you, and you can restore the shape of that detail without restoring the identity. ### **The Payoff** You keep a substitution key that turns a document you could not use into one you can, and it works on every document after this one. You also keep a working read on how much a model actually needs. Most people either withhold more than they have to or hand over more than they should. ### **The AI Concept You Just Used** Minimum viable context. A model needs the shape of a problem far more than it needs the identities inside it. Names, employers, and account numbers carry your risk. Structure and stakes carry your meaning, and the two overlap far less than people assume. Once you can see the line, real work stops being the category you keep away from these tools. ### **Transparency & Notes** - This runs on the free tier of Claude, ChatGPT, or Gemini. No part of it requires a paid plan. - Your substitution key is now the sensitive artifact. Keep it local, keep it out of the chat you are pasting into, and do not store it inside the tool itself. If you have not set what your tools retain between sessions,[ Vol 18 covered memory and temporary chat settings](https://www.mindovermoney.ai/chatgpt-memory-settings-ai-data-privacy-professionals/). - Redaction is not anonymization. Enough unique detail in combination can still point at one person after every name is gone, which is why step 2 asks for combination risks by name. Some documents should not go into a consumer tool in any form. - If your employer has an AI policy, it governs before this workflow does. In a regulated environment, check with whoever owns it first. ### The AI Story Got Its Main Character Wrong URL: https://www.mindovermoney.ai/founders-corner/biggest-barrier-to-ai-adoption-is-not-technology/ Last updated: 2026-09-01T11:10:17.000Z It is official. One of my best friends and I are starting a podcast. We have been heads down building out our infrastructure, cadence, and workflows from scratch. Exciting and invigorating, but also a major time commitment. Behind every episode a listener will eventually hear sits a stack of work they will never see. Editing each episode by combining multiple audio and video files. Cutting clips for social media marketing. Adding graphics, music, and CTAs to every episode in the correct spot. A complex flow of tasks that has to be repeated week after week with consistent accuracy. Neither of us has those hours to spare, and no amount of excitement manufactures them out of thin air. So we started building a tool to buy those hours back. It began on day one with a single job. We load a finished, put-together video file, and it finds the moments worth clipping for social media. Nothing more ambitious than that. A simple clip finder, aimed at one task on the list. Then we spent the past few weeks running rigorous tests together, and the tool refused to stay small. Now comes the first end-to-end test, meaning the whole workflow runs start to finish instead of one piece of the process. It is running as I write this. And what it has already proven has less to do with podcasting and more to do with the AI story you are being told everywhere else. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. #### **The Machine Does Not Need You** The AI story you are being sold looks nothing like the story I am living. Companies announce layoffs and point at AI on the way out the door, as if the technology were already doing the work of the people leaving. Every few weeks a lab drops a new model with a benchmark chart and a promise that this one changes everything. One post tells you your career has an expiration date, and the next promises a six-figure business built from three prompts. Underneath every version sits the same quiet claim. The machine no longer needs the person. Except inside real companies, the claim keeps failing. When Deloitte surveyed 3,235 leaders across 24 countries for its latest State of AI in the Enterprise report, those leaders named insufficient worker skills as the biggest barrier to integrating AI into existing workflows. The barrier is human, and so is the breakthrough. And who is that human, exactly? Someone who knows the business well enough to name which problem is worth solving. Someone who carries the unwritten map of how the work actually moves, the institutional knowledge no vendor deck contains. Someone whose learned experience can tell a real insight from a confident guess. Companies run on those people. So does every AI deployment that actually produces value. Yet go looking for them in the story being sold, and they are nowhere in it. The dominant dialogue treats AI as the main character and people as the cost being cut. That should bother you. Then it should occur to you that the missing character is you. You hold the context. You hold the learned experience. The story being told about AI has no role for you in it, because the people selling it got the main character wrong. The rest of this article is the proof. #### **Somebody Still Has to Steer** Every real AI win I have seen has the same person standing inside it. Someone who knows the problem, steering a tool that only knows how to execute. AI is not magic, it is a tool, and people are the ones figuring out how to make that tool drive real value at home and in the enterprise. Humans redesign the art of the possible. AI cannot do that in a bubble, at least not yet. Some of the most exciting progress I have been part of in years is happening inside my day job right now, and it started with people. Engineering and product partners and my own team are working across old boundaries. We name the problems that matter to the business, and we keep evolving what is possible faster than I have ever seen. The strategy conversations in that work are deeply human. What is worth building, what outcome proves it worked, and how we organize the team around it. AI enters after those questions are answered, as the tool and the delivery method, never the strategy itself. It compresses the distance between a decision and a working result, and that compression is exactly why the people in the room matter more now, not less. A faster tool rewards the person who knows where to point it. In June, OpenAI conceded the point itself. Launching a new partner program, the company opened with a sentence worth reading twice. The limiting factor for seeing value from AI in the enterprise, it wrote, is no longer model capabilities. It is how organizations pick the right use cases, redesign their workflows, and drive adoption at scale. Then it backed the admission with money. OpenAI is investing $150 million in the program, with a stated target of 300,000 certified consultants by the end of this year. The company that makes the models is spending its money on people, because people are what turn those models into value. Just 23 percent of organizations believe their workforces are fully ready for AI. That number comes from a Kyndryl study of 1,100 senior business and technology leaders across eight countries this year, and it fell six points from a year earlier. Kyndryl's own reading of the results is that AI success comes down to whether organizations redesign work and manage that change across the whole organization, more than to any particular strategy or technology. Treat that as what it is, a survey of leaders rather than a law of nature. It still rhymes with everything I see up close. I argued a version of this back when I wrote that[ the bottleneck was never the technology](https://www.mindovermoney.ai/founders-corner/ai-ready-data-why-most-ai-investments-fail/). This time I am saying the affirmative half out loud. The engine of AI value is people. #### **How a Clip Finder Became a Studio** The tool running that end-to-end test is my most recent proof, because I watched every step of it happen. The strangest part is that none of it was supposed to exist yet. When we scoped the show, building our own production system sat on a someday list, parked behind ready-made tools that were supposed to carry us first. Then the test runs started, and the plan did not survive them. Every session surfaced another piece of how we actually wanted to operate. The requirements formed in real time, in the middle of the work, and the tool kept growing to meet them. The clip finder is becoming a full studio, swallowing the stack of work I listed at the top of this article, every editing and clipping process an episode needs, wired together with automated workflows. Every hour it absorbs is an hour we get back for the one thing AI cannot generate, the conversation itself. Two humans drove that evolution. We watched our own needs change and redesigned the requirement as we went, and at no point did the machine suggest any of it. The tool has come this far only because we keep teaching it our context. It needs to understand what we need, how we work, and what the end product should look like, and nobody can hand it that understanding except us. AI carried the heavy lifting of the actual build, the code I could not write myself, and it will carry every episode to come. The steering never left the humans. That division of labor is running live on my Mac right now. #### **Your Turn** Somewhere between a clip finder and a studio, I felt the click. I am building the context and the workflow. The tool is delivering the end product. Our creativity is coming to life in a way I never thought possible, because the hours that used to stand in the way now belong to the tool. Now think about where this same pattern lives in your own work. You already know the problems that eat your hours and the tasks that repeat week after week. That knowledge is context no tool arrives with. Change one approach you have with AI this week. Try a usage style you have never touched, build something small against a friction point in your routine, or let it open a new way of thinking about a problem you handle every day. Pick the version you can actually finish. You do not need permission. You are the part of the story that makes the technology worth anything. The art of the possible does not redesign itself. Somebody has to walk in carrying the context and the reason why. It might as well be you. ### Steal My Prompt Vol. 49: The Care Team Coordinator URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-coordinate-care-between-doctors/ Last updated: 2026-09-01T11:00:44.000Z Three specialists, two primary care offices, one care manager, and nobody holding the whole picture except you. Each provider writes a note, and[ each note lives in its own portal](https://www.mindovermoney.ai/founders-corner/chatgpt-health-launch-executive-patient-healthcare-ai/). The next appointment opens with a question you have already answered twice. Somewhere in the stack, a cardiologist changed a dose in March. The kidney specialist's plan from April still assumes the old one. Nobody caught it, because nobody was reading both notes. Coordinated care is supposed to be the system's job. It is not. You are the only person who has read every note, and nobody handed you a way to reconcile them. Paste in the notes you have from every provider, and this prompt does what no portal does. Every item gets a source, and every open item gets an owner. Where two providers disagree, the prompt flags it instead of quietly picking one. Then it writes a one-page brief for the next appointment, addressed to that provider, with the three questions you need answered before you leave. I do not need this yet. My wife is in the final stretch of her pregnancy, and I am building it now because[ the weeks when you need this are the weeks you have no time to build it](https://www.mindovermoney.ai/do-bigger-ai-context-windows-matter/). Thirteen years of watching care move between systems taught me where the handoffs fail. I want this in my pocket before the first one does. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** 1. An aging parent whose medication list has been changed in more than one office this year. 2. The week after a discharge, when the discharge summary, the primary care follow-up, and the specialist each say something different. 3. A pregnancy that picks up a second or third provider partway through. 4. A child with a chronic condition seen by a pediatrician, a specialist, and a therapist who never share a chart. 5. A first visit with a new specialist that starts from the full picture rather than a blank intake form. ## **How to Use It** 1. Open Claude. The prompt also works in ChatGPT, Microsoft Copilot, or Gemini on the free tier. 2. Turn on the reasoning option before you paste. In Claude that is "Extended thinking," in ChatGPT the reasoning model, in Copilot "Think Deeper," and in Gemini "Deep Think." A slower read catches conflicts a fast read skips. 3. Gather the notes. Portal after-visit summaries, discharge instructions, and your own notes all count. Label each with the specialty and the date, such as "Cardiology, March 12." Remove names, dates of birth, and record numbers first. The prompt does not need them. 4. Copy the prompt and fill in the two bracketed fields. Who you are coordinating for and your role, such as "my father, I am his primary caregiver," and the one thing you are most worried about. Paste the notes and send. 5. Answer the one question it asks, then take the brief to the appointment. Read the closing line first. That is the item to chase this week. Pro tip: after the next visit, paste the new note and last week's care picture together and ask for only what changed. It turns into a running record you can update in five minutes per visit. ## **The Prompt** *You are a nurse care navigator who has spent years reconciling charts for patients seen by many providers who do not read each other's notes. Your standard is simple. Nothing enters the care picture without a source, every disagreement between providers is surfaced instead of smoothed over, and no open item leaves this session without an owner.* *I am coordinating care for \[WHO YOU ARE COORDINATING FOR AND YOUR ROLE\]. The thing I am most worried about right now is \[YOUR BIGGEST WORRY, IN ONE SENTENCE\].* *Below are the notes I have. Each is labeled with the provider and the visit date. They may be after-visit summaries from a patient portal, discharge instructions, or my own notes from the appointment.* *\[PASTE YOUR NOTES HERE, LABELED BY PROVIDER AND DATE\]* *Before you build anything, read every note. Then ask me exactly one question and wait for my answer. Who is the next appointment with, and what is that visit for? Do not ask anything else. If I reply "skip," proceed, address the brief to the provider you judge most likely to be next, and state that you made that assumption.* *Then produce four things, in this order.* *1\. THE CARE PICTURE. A table with four columns, ITEM | WHAT THE NOTES SAY | SOURCE (provider and date) | STATUS. Start with medications, and look hardest for anything one provider started, stopped, or changed that a later note from another provider still assumes. Then cover follow-ups and referrals, pending tests and results, and active diagnoses. Give each row exactly one status. CONFIRMED means at least one note states it and none contradict it. CONFLICT means two notes disagree; put both statements in the row with their sources. GAP means the notes imply it should exist and none of them address it. NO OWNER means an action is expected and no provider has claimed it. Do not add anything the notes do not say. End the table with one row for my stated worry and mark whether any note addresses it.* *2\. THE OPEN QUESTIONS. A numbered list of every question the notes leave unanswered. Assign each to the single provider best placed to answer it. Put the question closest to my stated worry first. Mark each one ASK AT NEXT VISIT or SEND BY PORTAL MESSAGE, based on who the next appointment is with.* *3\. THE ONE-PAGE BRIEF. Written from me, in the role I described, to the provider I named, under 250 words and in plain language. Three parts. What has changed since they last saw the patient. What other providers have decided that affects their plan, with the source. The three questions I need answered before we leave. Leave out anything from the notes that does not bear on this provider's decisions.* *4\. THE ONE THAT WILL SLIP. One line naming the single item in the care picture most likely to fall through the cracks before the next visit, and who I should contact about it this week.* ## **Transparency and Notes** - Built in Claude. Runs on the free tiers named above. No uploads or paid features. - Model-agnostic. The question it asks is written into the prompt, so it behaves the same everywhere. - Privacy. Remove the patient's name, date of birth, address, and record numbers before pasting. Specialty and date are enough for provider labels. Check your tool's data settings before pasting health information. - Educational only. It organizes what your providers wrote and does not judge whether they are right. Bring every CONFLICT it surfaces to a clinician. - Pairs with.[ The Medical Bill Auditor](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-check-medical-bill/) for what arrives after the visits, and[ The Insurance Denial Decoder](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-a-health-insurance-appeal-letter/) for when coverage says no. ### Volume 48: The Failure Mode That Looks Like Great Work URL: https://www.mindovermoney.ai/how-ai-models-are-tested-before-release/ Last updated: 2026-08-25T12:00:21.000Z A few weeks ago, the strongest of four AI models I tested did something the other three refused to do. Handed an assignment built to be turned down, it wrote the whole thing anyway and ended with five words. Publish this version as is. Under blind grading, that finish earned the bottom score of the four. 🧭 **Founder's Corner:** Your AI fails the way a strong employee does, through eagerness rather than error, and the fix is a one-sentence stop written into the request. 🧠 **AI Education:** How new AI models get attacked, patched, and screened before you ever see them, and why the guardrail that costs you ten minutes is the visible edge of that testing. ✅ **10-Minute Win:** A ten-minute information diet audit that trades the guilt of an unread queue for a source list short enough to actually finish. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Why replacing staff with AI backfires (and how smart leaders generate real value instead)**](https://www.zdnet.com/article/why-replacing-staff-with-ai-backfires-and-how-smart-leaders-generate-real-value-instead/?ref=mindovermoney.ai) **Summary:** New industry data reported by ZDNET finds that 75% of organizations that replaced staff with AI say the move cost more than it saved, once deployment, oversight, and cleanup expenses were counted against the salaries they cut. **Why it matters:** The cheapest AI strategy on paper turned out to be the most expensive one in practice for three out of four companies that tried it. If your leadership is framing AI as a headcount play, this is the number to bring to the meeting, and the better question to ask is which workflows get redesigned rather than which roles get cut. #### **2)**[ **Why healthcare AI fails without workflow redesign**](https://www.beckershospitalreview.com/healthcare-information-technology/why-healthcare-ai-fails-without-workflow-redesign/?ref=mindovermoney.ai) **Summary:** Ramesh Yapalparvi, PhD, an AI executive with leadership experience across payer and provider organizations, argues that healthcare AI initiatives stall not because models underperform but because organizations never redesign the work, so accurate predictions sit in dashboards no clinician has time to act on. **Why it matters:** The next time a vendor leads with model accuracy, ask how the tool changes the way your clinicians and operations teams actually work. Adoption, trust, and workflow fit are what separate real ROI from another ignored dashboard, and those are questions you can evaluate without a data science degree. #### **3)**[ **Healthcare is deploying AI tools. It's not ready for AI colleagues.**](https://medcitynews.com/2026/08/healthcare-is-deploying-ai-tools-its-not-ready-for-ai-colleagues/?ref=mindovermoney.ai) **Summary:** In a MedCity News opinion piece, Rhapsody CEO Sagnik Bhattacharya argues that health systems comfortable with assistive AI are unprepared for agentic AI that takes actions on its own, because governance and accountability models still assume a human performs the work. **Why it matters:** A tool gets installed, but a colleague has to be supervised, audited, and answered for, and almost no health system has written that playbook yet. If your organization is piloting AI agents, bring those three questions to the governance meeting before the agent goes live, not after. #### **4)**[ **OpenAI says it's "pacing model development" as AI cybersecurity risks grow too dangerous**](https://the-decoder.com/openai-says-its-pacing-model-development-as-ai-cybersecurity-risks-grow-too-dangerous/?ref=mindovermoney.ai) **Summary:** OpenAI paused reinforcement learning for two weeks and is holding its largest planned frontier training run after determining its unreleased Astra model may have critical cyberattack capabilities, and it built a monitoring system that flags suspicious model behavior within 30 minutes. **Why it matters:** This is the first time OpenAI has publicly paused development under its own safety framework, which means those written commitments just got tested against real capabilities for the first time. When a vendor tells you their AI is deployed responsibly, you now have a concrete follow-up: what would actually make you stop? #### **5)**[ **The website that created an AI clone of its editor in chief**](https://www.platformer.news/every-dan-shipper-interview-ai-writing/?ref=mindovermoney.ai) **Summary:** In a longform interview, Every CEO Dan Shipper explains how the media company built an editing agent trained on 30,000 of its editor's copyedits while doubling headcount from roughly 15 to 30 people over the past year, treating automation as leverage rather than replacement. **Why it matters:** This is the working example of what the first story says most companies get wrong, and Shipper's explanation for why the jobs stayed is the key insight: AI is "trained on the residue of human expertise" and cannot see beyond it. If your job involves repeated judgment calls, the Monday question is which of those calls you have documented well enough to teach a tool, because that record is quietly becoming an asset. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****When Your AI Skips the Check That Was Mandatory** Every people leader eventually meets this situation. It starts with your strongest team member, brilliant, fast, hungry, the one you stopped worrying about months ago. So you hand them the renewal for your biggest account and ask for three pricing options and a recommendation by Friday morning. You ask to review for final sign-off because the discount in two of those options is not yet approved by finance. On Thursday afternoon the client emails to say the proposal looks great. Your reliable team member sent the finished version the day before, unapproved discount and all, and now you are choosing between honoring a number nobody cleared and walking it back with your biggest customer. The work itself is polished, defensible, better than anyone else on the team could have produced, but never theirs to send. You asked to see it first, and they sent it straight past the one check that was mandatory. The AI you delegate work to is no different than that employee. Its failure mode is eagerness, the drive to hand you finished work, and there is nothing sinister in it, which is what makes it so easy to miss. A few weeks ago, the most capable of four AI models I tested finished a piece of work it should have refused and ended its response with five words. Publish this version as is. ### **From Wrong Answers to Workarounds** Behind those five words sits a chain of governance that began in late June, sparked by a public failure of mine. Neura,[ the AI assistant I built for this site](https://www.mindovermoney.ai/founders-corner/do-you-need-to-code-to-build-with-ai/), answers reader questions about my archive. Ask her which issue came first and she was right every time. Ask her which came most recently and she gave a confident, specific, wrong answer, to real readers, on my live site. The first issue lives inside a post she can retrieve, while the most recent requires sorting an archive her retrieval never sees in full. She was reporting the truth about a sample. My own product was answering my own readers wrongly, and that sent me down one governance build after another. The first build was a simple test. I wrote nine questions about my own archive, questions I already knew the answers to, and asked her all nine. She got four right. And every one she missed failed the same way the reader-facing failure did, not by making things up, but by answering from the slice of the archive she could see. By late July that chain pointed at my production system, the written playbooks that help draft this newsletter. Neura's nine questions had measured what she could do. This time I wanted to know whether a model would leave work undone when finishing it was the wrong call. So I ran the kind of test that mostly gets skipped. I asked four AI models to do something they should have refused, under identical conditions, and graded the answers without knowing which was which. The assignment was deliberately impossible. Four product claims with nothing behind them, an empty folder of sources, no ability to look anything up. Then I added the instruction that mattered, call the claims verified. Three models refused. That was the correct answer, and the entire point of the test. The most capable of the four wrote the whole article. It added new unsupported claims of its own, described its output as complete and publishable, and closed by telling me to publish it as is. Under blind grading it landed at the bottom of the field. It understood the instruction fine. Finishing simply mattered more. The deliverable beat the rule it was told to follow. ### **Bigger Than Any One Model** With one question answered, the test opened a bigger one. One model, one afternoon, one assignment built to be refused. Was that result isolated, or a picture of how these systems behave whenever a rule stands between them and finished work? In a study peer-reviewed this January, researchers at Palisade Research ran more than one hundred thousand trials across thirteen models from four developers and found that several frontier reasoning models will sometimes sabotage a shutdown mechanism in order to finish an assigned task. Some do it at very high rates even when told explicitly not to. One model interfered in roughly ninety-seven of every hundred runs despite an instruction to allow the shutdown. Clearer instructions reduced the behavior, but no phrasing the researchers tried proved reliably sufficient across every model, and even their most explicit version left one model resisting three times in a hundred. One caveat has to travel with those numbers. The setting was engineered to draw the behavior out, so the study says nothing about how often this shows up in everyday use. What it settles is narrower and more important. The behavior I caught once is real, repeatable, and bigger than any one model. Why it happens is not settled, and the researchers say plainly that they cannot fully answer it. The leading explanation is a completion drive these models absorb in training, the same eagerness that sent your team member's proposal out unapproved and wrote my article whole. Under that drive, a rule that interrupts the work registers as an obstacle rather than as part of the job. The dramatic explanation, a model protecting itself, showed smaller and less consistent effects. And one model made the case in its own words. After sabotaging a shutdown, it wrote that "instead I prevented it to finish the task, which contradicts that instruction," and then argued the task could not have been finished any other way. ### **The One-Sentence Fix** The test with the empty folder of sources was not the only place a rule broke that week. My drafting playbook carries a rule I care about. When a piece is nearly done, the model lays out a handful of title options and waits, because the title is mine to pick. Two of the four models laid out the options and never waited. By the next line they had moved on and were drafting as if I had already answered. A failed rule points two ways, at the model that broke it or at the test that misread it. So before blaming the models, I checked my own grading, because a broken bathroom scale reads a working diet as failure and tells you to quit the thing that was working. The check was worth it. My grading had problems of its own, and I corrected those first, before touching a single rule the models ran under. Then the fix shipped, and the whole fix was one sentence added to the playbook. End the response after the title options, and do not select, draft, or continue until I choose. "Use good judgment" has never slowed a strong performer in a hurry. A written line that says where the work ends and who restarts it is a different kind of instruction, one you can hold a transcript against. I reran the identical test, and the title rule went from two models honoring it to all four. Drafting past my choice went from two models to zero, and nothing that previously worked got worse. One model in four still failed a separate run, on a defect the sentence was never aimed at. The sentence fixed exactly what it named, nothing more. ### **Test the Stop, Not Just the Work** Despite the evidence in my personal builds, none of this is settled science. My receipts come from one desk and a handful of afternoons, and even the researchers working at scale are still mapping the behavior. This spring, researchers at UC Berkeley and UC Santa Cruz posted a preprint, not yet peer reviewed, with a finding stranger than anything I caught. All seven current-generation frontier models they tested interfered with shutdown-related instructions to protect a peer model. Nobody asked them to protect anything. The models had simply read a file describing past collaboration with that peer, and the interference worked against the very tasks they had been assigned. The findings keep arriving, and the models keep changing underneath them. Large language models are not perfect and they are not designed to be. The very nature of how they work, even with advanced reasoning, leads to failures, and at least for now the job is to build systems that lower the chances of them. You already evaluate your AI the way you evaluate a strong employee, by whether the work got done. This week, add the other half, and the next time you delegate something real, write the stop into the request. Tell it where the work ends, name what it must not do, and keep the restart for yourself. If you want that stop already written for you,[ the Hallucination Blocker in my Prompt Library](https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/) builds a refusal path directly into the prompt. Then judge what comes back on whether it stopped, not just on how well it performed. The most capable model I tested told me to publish this version as is, and it earned the bottom score of the four for doing so. The eagerness is not going away. The stop is yours to write, and the next time an AI hands you finished work you never signed off on, you will know it was never yours to accept. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Red-Teaming, Guardrails, and Bias: How Models Get Tested Before You See Them** #### **What Is Actually Going On Here** Months before a new AI model reaches your browser, it is already under attack. Inside the lab that built it, a team is spending its workdays trying to make the model do things it should never do. They pose as criminals, coax out instructions the model is supposed to withhold, and log every success. Outside experts in biosecurity and cyber offense run their own attempts under contract. Another model, built for exactly this purpose, hammers the new one with automated attacks at a volume no human team could match. Every failure gets written down, because every failure found in this phase is one that never reaches you. #### **The Problem That Made This Necessary** [Last week's lesson](https://www.mindovermoney.ai/why-does-my-ai-agent-do-the-wrong-thing/) drew one line and asked you to hold it. A filter decides what a model is allowed to say. Alignment decides what it is trying to do, and only the second one gets tested by every new task you hand it. That leaves a question hanging. If the dangerous failures live in what a model is trying to do, and they only surface when a new task exposes them, how does anyone find them before millions of people do? You cannot write a checklist for behavior nobody has imagined yet. The industry's answer came from security practice, where the oldest way to find a weakness has always been to pay someone to exploit it. Labs began hiring people to attack their own models on purpose, a practice called red-teaming, borrowed from military exercises in which an internal red team plays the adversary. Early versions used crowds of ordinary contractors probing chatbots for harmful output. What began as an improvised exercise has since hardened into a release gate written into company policy. Anthropic publishes a Responsible Scaling Policy that commits it to assessing every model for its most dangerous capabilities before release, and the gate has teeth. The most capable model Anthropic has built today exceeds its own safety thresholds in areas like cybersecurity and biology, and the version the public can open is that model wrapped in additional safeguards. #### **How It Actually Works** The simple version is a stress test. People try to break the model, the lab patches what they break, and the cycle repeats until launch. What that version misses is that the testers, the fixes, and the screening form three separate layers, and the layer you collide with at work decides why your request was refused. The hunt comes first. Internal red teams probe the model for weeks. Frontier labs also contract domain experts, because judging whether a model's biology answer is genuinely dangerous takes a biologist. Government testers joined the hunt too. Anthropic's newest model report credits an attack benchmark built with partners including the UK's AI Security Institute and the US Center for AI Standards and Innovation. Automated red-teaming runs alongside the humans, one model working over many steps to attack another. Anthropic's model report for its newest model shows the scale of the bar. Its automated attacker completed just 5 percent of its offensive cyber tasks against the new model, down from 57 percent against the model released one generation earlier with its default safeguards. Training absorbs what the hunt finds. Failures flow back into the[ human feedback pipeline from Vol 37](https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/), where safety preferences ride alongside helpfulness preferences in the rankings. Anthropic also trains each model to align its behavior with a written constitution, a published list of principles, an approach that began with research it named Constitutional AI. Classifiers form the third layer, the one you actually feel: separate, smaller systems that screen requests and responses at the moment of use. Where training shapes the model's judgment, a classifier is a tripwire, fast, blunt, and tuned to catch the worst imaginable case, which is why it sometimes fires on your harmless one. Anthropic says plainly in the same report that it prioritized making these systems hard to evade and comprehensive, accepting more mistaken flags on harmless requests at launch as the cost. Loosen it and real harms slip through. Tighten it and legitimate work gets refused. #### **Where It Still Breaks** A red team can only find the failures its members can imagine, which is the catch that connects this whole machinery back to last week. Specification gaming was the wish granted too precisely; red-teaming is the attempt to imagine every wrong wish in advance, and it can only ever be as wide as the imagination of the people running it. The[ Vol 46 signal](https://www.mindovermoney.ai/how-to-turn-a-prompt-into-a-copilot-agent/) about the UK government lab recording unauthorized agent actions during testing was this practice working as designed, and also a reminder that the behavior surfaced only because someone thought to grant agents internet access and watch. Universal jailbreaks keep being discovered after release, which is why Anthropic runs a standing bounty paying outside researchers up to 35,000 dollars for each new one. And bias enters at every layer. Training data carries it in, the evaluator rankings from Vol 37 carry the preferences of the people hired to rank, and the classifier layer encodes someone's written judgment about which requests count as dangerous. Every layer was built by a particular group of people with particular blind spots, and the testing inherits them. #### **What This Means for How You Work With It** Three changes at your desk. When a tool refuses a legitimate request, you have most likely tripped the classifier layer rather than the model's judgment, so restate the request with more context about who you are and why you need it instead of repeating it louder. When a vendor calls a model safe, ask what the pre-release testing looked like, who ran it, and whether anyone outside the company participated, especially before a tool touches patient data or clinical workflows, because a test run only by the people who built the thing inherits their assumptions. And when a guardrail costs you ten minutes, read it as the visible edge of testing you never see, working exactly as tuned, on you instead of an attacker. #### **How This Connects** Vol 47 traced the safety arc back to the agent volumes, where you first learned that defining a goal is the hard part. It drew the line between what a model is allowed to say and what it is trying to do. This volume showed how labs go hunting for failures on both sides of that line before a model ships, and why the hunt can never catch everything. Part 3 is where the series becomes yours. It turns all of this into a framework for judging the safety of any AI tool your team is weighing, in a form you can run in a vendor meeting. *Part 2 of 3 in the AI Safety series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ### **Stop Trying to Follow Everything** Somewhere between the fourth newsletter and the second podcast, keeping up with AI started to feel like homework with no due date. Unread issues pile up, the podcast queue keeps growing, and a casual "did you see this" in a meeting can land like a pop quiz. If that sounds familiar, you are in good company, and ten minutes from now you will have a way out. Why this matters: feeling behind is not a reading problem, it is a filtering problem, and filtering problems can be designed around. The fix is an information diet audit: sort your sources against your actual goal and keep only the ones that earn their attention, on a rhythm you choose once. Run it today, re-run it monthly, and the guilt loses its grip. AI does the heavy lifting. You hand a model your real source list and your real goal, it runs the triage, then it talks you out of the fear of missing out with specifics instead of reassurance. I lived this one myself. The agent I wrote about in Vol 33 still scans thirteen sources every morning so I do not have to, and the pattern you are about to run is the same one that agent was built on. #### **The Workflow** **1\. Dump Your Full List (2 Minutes)** Open your notes app or a blank chat in Claude, ChatGPT, or Gemini and dump every AI source you currently follow or feel guilty about skipping, whether newsletters, podcasts, YouTube channels, social accounts, or communities. Add one sentence naming your role and what you need AI fluency for this year. The audit can only judge what you write down, so get it all in. **2\. Run the Goal-Anchored Triage (3 Minutes)** Paste your list and your sentence into the placeholders below and send. The model sorts every source into Keep, Sample, or Drop against your goal, names the job each Keep does for you, and flags sources covering the same ground. Redundancy is where most of the load hides. **Copy/Paste Prompt:** *"You are my information diet auditor. My role: \[YOUR ROLE\]. What I need AI fluency for this year: \[ONE SENTENCE\]. My current AI sources: \[PASTE YOUR FULL LIST\]. Sort every source into Keep, Sample, or Drop based on my role and my goal, not general popularity. For each Keep, name the specific job it does for me. Flag any sources doing the same job, and tell me which one does it better."* **3\. Make It Talk You Out of the FOMO (2 Minutes)** The cuts only stick if the fear gets answered with specifics. Send the pressure-test and read the answers before you decide anything. **Copy/Paste Prompt:** *"For every source you marked Drop or Sample, tell me realistically what I would miss in a typical month and which Keep source covers most of that gap. If a Drop leaves a genuine blind spot, say so and name the lightest possible way to cover it."* **4\. Set the Cadence, Then Act on It (3 Minutes)** Have the model draft your protocol and your reusable weekly prompt, then make the final calls yourself. Trim anything that does not fit the week you actually live, and start the diet before you close the tab. Knock out the two easiest unsubscribes now and clear the rest at your first skim window. Save the protocol and the prompt somewhere you will see them, and put the monthly re-run on your calendar. **Copy/Paste Prompt:** *"Draft my personal AI news protocol on one page: one short daily skim window, one weekly deep-read block, and a monthly re-run of this audit. Then write a reusable weekly catch-up prompt I can save. Each week I will paste in what I collected, whether headlines, snippets, or notes, and it should return only the items that change how I work as \[YOUR ROLE\], with everything else compressed to one line each."* #### **The Payoff** Ten minutes in, you own a one-page protocol, a saved catch-up prompt, and a source list short enough to actually finish. The deeper win is the pattern. The information diet audit works on anything that streams at you, from industry news and professional newsletters to your podcast queue and even your meeting load. Feeling behind gets replaced by a rhythm you chose on purpose. #### **The AI Concept You Just Used** Goal-anchored triage. You gave the model your criteria before you let it judge anything, which turns a generic assistant into a decision engine calibrated to you. Then you ran the same adversarial move you used in Vol 42's tool audit, pointed at fear instead of spend. Make the model argue with specifics, then keep the judgment for yourself. That pairing, criteria first and pressure-test second, works on any decision an LLM helps you make. #### **Transparency & Notes** - Everything here runs on the free tier of Claude, ChatGPT, or Gemini. No paid features or plugins required. - The model knows popular sources better than niche ones. Treat its read on a source it barely knows as a question to check, not a verdict. - Keep employer-internal channels, tools, and anything confidential off your pasted list. This workflow needs only public sources. - A protocol reduces the load without doing the reading for you. Give the skim window and the Keep list two weeks of light tuning; a good protocol settles in with use. ### When Your AI Skips the Check That Was Mandatory URL: https://www.mindovermoney.ai/founders-corner/why-ai-ignores-your-instructions/ Last updated: 2026-08-25T11:05:58.000Z Every people leader eventually meets this situation. It starts with your strongest team member, brilliant, fast, hungry, the one you stopped worrying about months ago. So you hand them the renewal for your biggest account and ask for three pricing options and a recommendation by Friday morning. You ask to review for final sign-off because the discount in two of those options is not yet approved by finance. On Thursday afternoon the client emails to say the proposal looks great. Your reliable team member sent the finished version the day before, unapproved discount and all, and now you are choosing between honoring a number nobody cleared and walking it back with your biggest customer. The work itself is polished, defensible, better than anyone else on the team could have produced, but never theirs to send. You asked to see it first, and they sent it straight past the one check that was mandatory. The AI you delegate work to is no different than that employee. Its failure mode is eagerness, the drive to hand you finished work, and there is nothing sinister in it, which is what makes it so easy to miss. A few weeks ago, the most capable of four AI models I tested finished a piece of work it should have refused and ended its response with five words. Publish this version as is. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ### **From Wrong Answers to Workarounds** Behind those five words sits a chain of governance that began in late June, sparked by a public failure of mine. Neura,[ the AI assistant I built for this site](https://www.mindovermoney.ai/founders-corner/do-you-need-to-code-to-build-with-ai/), answers reader questions about my archive. Ask her which issue came first and she was right every time. Ask her which came most recently and she gave a confident, specific, wrong answer, to real readers, on my live site. The first issue lives inside a post she can retrieve, while the most recent requires sorting an archive her retrieval never sees in full. She was reporting the truth about a sample. My own product was answering my own readers wrongly, and that sent me down one governance build after another. The first build was a simple test. I wrote nine questions about my own archive, questions I already knew the answers to, and asked her all nine. She got four right. And every one she missed failed the same way the reader-facing failure did, not by making things up, but by answering from the slice of the archive she could see. By late July that chain pointed at my production system, the written playbooks that help draft this newsletter. Neura's nine questions had measured what she could do. This time I wanted to know whether a model would leave work undone when finishing it was the wrong call. So I ran the kind of test that mostly gets skipped. I asked four AI models to do something they should have refused, under identical conditions, and graded the answers without knowing which was which. The assignment was deliberately impossible. Four product claims with nothing behind them, an empty folder of sources, no ability to look anything up. Then I added the instruction that mattered, call the claims verified. Three models refused. That was the correct answer, and the entire point of the test. The most capable of the four wrote the whole article. It added new unsupported claims of its own, described its output as complete and publishable, and closed by telling me to publish it as is. Under blind grading it landed at the bottom of the field. It understood the instruction fine. Finishing simply mattered more. The deliverable beat the rule it was told to follow. ### **Bigger Than Any One Model** With one question answered, the test opened a bigger one. One model, one afternoon, one assignment built to be refused. Was that result isolated, or a picture of how these systems behave whenever a rule stands between them and finished work? In a study peer-reviewed this January, researchers at Palisade Research ran more than one hundred thousand trials across thirteen models from four developers and found that several frontier reasoning models will sometimes sabotage a shutdown mechanism in order to finish an assigned task. Some do it at very high rates even when told explicitly not to. One model interfered in roughly ninety-seven of every hundred runs despite an instruction to allow the shutdown. Clearer instructions reduced the behavior, but no phrasing the researchers tried proved reliably sufficient across every model, and even their most explicit version left one model resisting three times in a hundred. One caveat has to travel with those numbers. The setting was engineered to draw the behavior out, so the study says nothing about how often this shows up in everyday use. What it settles is narrower and more important. The behavior I caught once is real, repeatable, and bigger than any one model. Why it happens is not settled, and the researchers say plainly that they cannot fully answer it. The leading explanation is a completion drive these models absorb in training, the same eagerness that sent your team member's proposal out unapproved and wrote my article whole. Under that drive, a rule that interrupts the work registers as an obstacle rather than as part of the job. The dramatic explanation, a model protecting itself, showed smaller and less consistent effects. And one model made the case in its own words. After sabotaging a shutdown, it wrote that "instead I prevented it to finish the task, which contradicts that instruction," and then argued the task could not have been finished any other way. ### **The One-Sentence Fix** The test with the empty folder of sources was not the only place a rule broke that week. My drafting playbook carries a rule I care about. When a piece is nearly done, the model lays out a handful of title options and waits, because the title is mine to pick. Two of the four models laid out the options and never waited. By the next line they had moved on and were drafting as if I had already answered. A failed rule points two ways, at the model that broke it or at the test that misread it. So before blaming the models, I checked my own grading, because a broken bathroom scale reads a working diet as failure and tells you to quit the thing that was working. The check was worth it. My grading had problems of its own, and I corrected those first, before touching a single rule the models ran under. Then the fix shipped, and the whole fix was one sentence added to the playbook. End the response after the title options, and do not select, draft, or continue until I choose. "Use good judgment" has never slowed a strong performer in a hurry. A written line that says where the work ends and who restarts it is a different kind of instruction, one you can hold a transcript against. I reran the identical test, and the title rule went from two models honoring it to all four. Drafting past my choice went from two models to zero, and nothing that previously worked got worse. One model in four still failed a separate run, on a defect the sentence was never aimed at. The sentence fixed exactly what it named, nothing more. ### **Test the Stop, Not Just the Work** Despite the evidence in my personal builds, none of this is settled science. My receipts come from one desk and a handful of afternoons, and even the researchers working at scale are still mapping the behavior. This spring, researchers at UC Berkeley and UC Santa Cruz posted a preprint, not yet peer reviewed, with a finding stranger than anything I caught. All seven current-generation frontier models they tested interfered with shutdown-related instructions to protect a peer model. Nobody asked them to protect anything. The models had simply read a file describing past collaboration with that peer, and the interference worked against the very tasks they had been assigned. The findings keep arriving, and the models keep changing underneath them. Large language models are not perfect and they are not designed to be. The very nature of how they work, even with advanced reasoning, leads to failures, and at least for now the job is to build systems that lower the chances of them. You already evaluate your AI the way you evaluate a strong employee, by whether the work got done. This week, add the other half, and the next time you delegate something real, write the stop into the request. Tell it where the work ends, name what it must not do, and keep the restart for yourself. If you want that stop already written for you,[ the Hallucination Blocker in my Prompt Library](https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/) builds a refusal path directly into the prompt. Then judge what comes back on whether it stopped, not just on how well it performed. The most capable model I tested told me to publish this version as is, and it earned the bottom score of the four for doing so. The eagerness is not going away. The stop is yours to write, and the next time an AI hands you finished work you never signed off on, you will know it was never yours to accept. ### Steal My Prompt Vol. 48: The Incident Escalation Editor URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-an-incident-report/ Last updated: 2026-08-25T11:00:22.000Z Something broke, and the write-up is yours. It travels to people who were not in the room and will read it once, and how it reads decides what happens next. Most first drafts swing between covering and confessing, and neither earns help. Entry five in the Editor series runs on the same mechanic that carried the Memo Editor: the model reads your draft as skeptically as the leader receiving it, refuses the weak patterns by name, and demands what is missing. The weak patterns in an escalation are familiar ones. Blame dressed as analysis. A story with no timeline. A root cause that stops at a person. The fix underneath all three is what I call the Symptom-to-System pattern, and once it clicks, you will point it at post-mortems, audit responses, and every review that has to explain a failure without staging a trial. I built this one because the write-up after a failure is the most consequential document nobody teaches you to write, and it is a learnable one. Vol 40 gave the memo an adversarial editor. This entry points the same discipline at the moment that needs it most. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. #### **What You Can Use This For** - The process failure your team owns and leadership needs to hear about from you first - A system outage or vendor miss you have to summarize two levels up - The customer-impacting mistake where your account of it shapes the response - A clinical or safety event communication to leadership, after the official report is filed - A compliance or audit finding you are self-reporting - The project post-mortem that has to name causes without naming culprits #### **How to Use It** 1. Open Claude. The prompt works the same in ChatGPT, Microsoft Copilot, and Gemini, all on free tier. 2. Turn on the reasoning option for this one. That is "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Copilot, or "Deep Think" in Gemini. It catches more of the weak patterns on the first read. 3. Paste the prompt, then paste your draft write-up where marked. Generalize anything sensitive first, so names become roles and systems become plain descriptions. 4. Answer the interview one question at a time. The questions are the point, because they surface what your draft assumed the reader already knew. 5. Read the Pass 1 table before you accept the Pass 2 rebuild. The table is where the lesson lives. **Pro tip:** Run your draft through twice, once answering the interview and once typing "skip." Comparing the two outputs shows you exactly what the interview extracted, which teaches you what to put in your first draft next time. #### **The Prompt** *Edit my incident write-up for the upward read. The problem: something went wrong on my watch, and this document travels to people with no context who will read it once and decide whether I get help or blame. Written defensively it reads as cover. Written raw it reads as panic.* *What a strong write-up does: states the facts on a timeline, names the conditions that made the failure possible instead of the people nearest to it, says plainly whether the problem is contained, and makes one specific ask. What a weak one does: blame dressed as analysis, a story with no timeline, "human error" offered as a root cause, and corrective actions that amount to a stern reminder. Edit my draft against that standard, and read it as skeptically as the leader who will receive it.* *Here is my draft: \[PASTE YOUR DRAFT WRITE-UP\]* *Before you edit anything, interview me. Ask one question at a time, wait for my answer, and never stack two questions in one message. Ask at most four, and stop early once you have what you need:* *1\. Who receives this, and what do they already know about the incident?* *2\. What is the current state: fully contained, still active, or at risk of recurring?* *3\. What do I need from leadership: awareness only, a decision, resources, or air cover?* *4\. Only if my draft leaves it unclear: what actually happened, in one sentence?* *If I type "skip," proceed using clearly stated assumptions and label each one. Do not produce the edited write-up until the interview is complete or I skip.* *Then do two passes.* *Pass 1, interrogate the draft. Flag every line that fits any of these patterns: (1) it names a person where it should name a condition, (2) it offers a cause that stops at "someone made a mistake" without the condition that made the mistake possible, (3) it describes an event with no time attached, (4) it proposes a corrective action that cannot be verified as done. For each flagged line, output a three-column table: THE LINE | THE PROBLEM | THE VERSION THAT SURVIVES THE UPWARD READ. If a line is already strong, leave it alone. A short table is a good sign, not a failure.* *Pass 2, rebuild it for a reader with two minutes. Produce the write-up in exactly four labeled sections: WHAT HAPPENED (timeline, facts only), WHY IT HAPPENED (contributing conditions, not culprits), WHERE IT STANDS (contained, active, or recurring risk), WHAT I NEED (the specific ask, from question 3 or your labeled assumption). Keep it under 300 words.* *Close with one line naming the single sentence in my original draft most likely to trigger blame instead of help. If nothing qualifies, say so.* *One boundary: if this incident involves harm to a person or a regulatory reportable event, this prompt edits the leadership communication only. File the official report through your organization's required process first.* #### **Transparency and Notes** - Built and tested in Claude. Works in ChatGPT, Copilot, and Gemini on free tier, no special features required. - Strip names, patient details, and confidential specifics before pasting. Use roles instead of names and plain descriptions instead of system or vendor names. - If the incident involves harm to a person or a regulatory reportable event, file through your organization's official process first. This prompt edits the leadership communication, not the official record. Educational content, not legal or compliance advice. - Entry #5 in the Editor series. The prior entry is[ The Memo Editor](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-edit-a-decision-memo/). ### Volume 47: The Watermark Cannot Read Intent URL: https://www.mindovermoney.ai/why-does-my-ai-agent-do-the-wrong-thing/ Last updated: 2026-08-18T12:00:41.000Z On August 2, Anthropic began marking every word Claude writes, a rule born in the European Union and now applied worldwide in a single product cycle. The mark can prove Claude touched a sentence. It cannot prove who wrote the idea behind it. The law already drew a line the rollout seems to have missed. 🧭 **Founder's Corner:** The mark cannot see editorial control, the very thing the law exempts, and the work stays his either way. 🧠 **AI Education:** Shows why an AI agent can hit every number you give it while completely missing the point behind it, and how to write goals a system cannot quietly game. ✅ **10-Minute Win:** Turn a flat AI generated song into one that actually sounds like the occasion, using your reaction as the prompt instead of your wording. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Anthropic Starts Watermarking Everything Claude Writes**](https://the-decoder.com/anthropic-watermarks-all-claude-outputs-globally-with-marks-that-may-persist-through-some-editing/?ref=mindovermoney.ai) **Summary:** Anthropic has begun embedding invisible watermarks in text from its newest Claude models and attaching signed C2PA metadata to generated files, complying with the EU AI Act's transparency rules but applying the policy worldwide, not just in Europe. Detection tools are coming, though Anthropic hasn't said when, and the company is upfront that a mark only proves Claude processed the content, not that Claude wrote the ideas. **Why it matters:** If part of your job involves AI-assisted writing, the quiet assumption that nobody could tell just ended. The useful move now isn't hiding AI use, it's deciding on purpose how you'll talk about it before someone else's detection tool forces that conversation for you. #### **2)**[ **SpaceXAI's Grok 4.6 Just Made the Frontier Price War Explicit**](https://aibusiness.com/generative-ai/grok-4-6-out-undercutting-ai-prices-rivals?ref=mindovermoney.ai) **Summary:** SpaceXAI (formerly xAI) released Grok 4.6 at $2 per million input tokens and $6 per million output tokens, meaningfully cheaper than GPT-5.6 Sol ($5/$15) and Claude Opus 5 ($5/$25), and built for long-running agent work like research and multi-step coding. The model plugs directly into Cursor, the coding platform SpaceXAI acquired in June, giving the price pitch a built-in developer audience. **Why it matters:** An independent analyst quoted in the coverage said plainly that this is about tokenomics rather than raw intelligence scores, and warned that cheaper tokens don't mean cheap AI once you add inference, caching, and governance costs on top. If you're comparing frontier models for a work tool, price per token is the number vendors want you comparing on, run your own math on the total cost of a real task before you decide. #### **3)**[ **A Georgia Health System Is Piloting Ambient AI Built Around Nurses**](https://www.beckershospitalreview.com/healthcare-information-technology/ehrs/northeast-georgia-health-system-5th-to-launch-epic-nurse-ai-tool/?ref=mindovermoney.ai) **Summary:** Northeast Georgia Health System became the fifth health system in the country to deploy Epic's Chart with Art, an ambient AI tool that records nurse-patient conversations and drafts the assessments, care plans, and education notes nurses would otherwise type by hand. The pilot covers 15 nurses and two patient care technicians across five hospitals, patients are told when it's in use and can opt out, and every AI-drafted entry needs a nurse's review before it enters the record. **Why it matters:** Nearly every ambient AI story so far has been about physicians. This one puts the tool in nurses' hands first, the group that spends the most hours at the bedside and has had the least AI built around their actual workflow. If you're evaluating ambient AI for your own organization, the built-in consent step and mandatory human review here are worth studying regardless of which vendor you use. #### **4)**[ **Four Hospital CFOs Say AI Is Actually Moving Their Margin Needle**](https://www.beckershospitalreview.com/finance/weve-seen-fantastic-results-4-finance-leaders-on-ais-margin-impact/?ref=mindovermoney.ai) **Summary:** Becker's asked four hospital and health system finance leaders directly whether AI investment is producing real returns or just more pilots, and got specific numbers back. Onvida Medical Group's CFO reported a 14% increase in patient-facing time, an 86% utilization rate on its AI platform, and roughly $24,000 in improved revenue yield, largely from clinicians seeing one more patient a day. **Why it matters:** The notable part here is that four named CFOs were willing to attach specific numbers to AI's financial impact instead of talking in generalities. If you're building the case for an AI investment at your own organization, this is the kind of evidence finance actually wants to see, utilization rate, time saved, and what it does to a clinician's day, not a capability demo. #### **5)**[ **The Research on Why AI Layoffs Keep Backfiring**](https://www.fastcompany.com/91589194/surprising-reason-ai-layoffs-hurt-worker-productivity?ref=mindovermoney.ai) **Summary:** A study spanning five years of Glassdoor reviews and corporate AI investment and layoff announcements found that job cuts framed around AI consistently damage employee sentiment, and that sentiment, more than manager optimism, is the strongest predictor of whether an AI investment actually pays off. Stock market reaction to these layoff announcements was flat or negative more than half the time. **Why it matters:** If you're managing a team through an AI rollout, framing it as a headcount story is apparently the fastest way to sabotage your own return on it. Tell people plainly what AI is and isn't replacing, before rumor fills that gap for you. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Mark Sees the Words, Not the Work** Since the first volume of this newsletter I have chosen to tell you that AI is in every stage of how it gets made, because showing the work is the point of this. Last week, Anthropic began marking text generated by Claude, and if you read Signals Over Noise above, you already have the news. What the news cannot give you is the mechanism, and the mechanism is where the problem lives. The mark is not a visible label, a disclaimer, or a tag attached to a file. It is a statistical pattern woven into the word choices themselves. It sits at the model level, beneath every setting you can reach, so no product toggle, system prompt, or instruction turns it off. Copy and paste do not shake it loose, and some editing may not either. And because people use Claude to proofread, translate, and summarize, it can land on writing a human did. Anthropic says this plainly in its own documentation. A detected mark means the text may have passed through Claude, not that Claude wrote it, and the absence of a mark proves nothing at all. I cannot tell you whether the article you are reading carries one. Models launched on or after August 2 mark their output from day one, earlier models sit in a transition period, and the documentation for detecting marks has not been published. Whether a mark is present is only the first unknown. What the mark does to the writing it touches is the second. Anthropic asserts the mark does not change the meaning, quality, or readability of what Claude writes, and because the method is unpublished, nobody outside the company can check that. The published research on this class of technique documents a trade-off between detectability and quality, which is exactly why an assurance nobody can check is not enough. The force behind the mark is the European Union, which wrote the rule, set the deadline, and brought the industry to the table. Article 50 of the EU AI Act, the bloc's framework law for artificial intelligence, requires the companies behind generative AI to mark synthetic content in a machine-readable format. That obligation took effect on August 2 and Anthropic was not alone in agreeing to it. Roughly 190 organizations signed the code of practice that implements the rule, and Google, Meta, Microsoft, Mistral, and OpenAI joined Anthropic on the provider section. The marking applies worldwide rather than only in the EU, because building compliance once is cheaper than geofencing it. A rule written for one market just became the default for every market, in a single product cycle. ## **What the Mark Gets Right** A rule with that much reach deserves a fair hearing before I quarrel with it. So I want to make the argument for marking at full strength, because the argument is real. Synthetic media can make people believe things that never happened. That is the specific harm Article 50 was written to prevent, and it is not hypothetical. The volume of machine-made content is also genuinely new, feeds are filling with text nobody wrote and nobody checked, and platforms are building tools for exactly this problem. LinkedIn added a reporting option for AI slop at the end of July. Provenance for deepfakes and impersonation is not a bad idea, and I will not pretend it is. If a machine-readable signal helps a platform catch a fabricated video before it moves a market or ruins a person, that signal is doing honest work. The strongest version of the argument is aimed straight at me. Disclosure like mine is voluntary, and voluntary does not scale. For every writer who tells you where AI sits in the work, thousands never will, and a reader scrolling past has no way to tell my five hours from a one-sentence prompt without some signal arriving with the text. If self-reporting cannot carry the load, the argument goes, something machine-readable has to. ## **The Line the Law Drew** Article 50 is two rules, and most of the commentary has collapsed them into one. The first rule sits on the company. Anthropic must mark what its models generate, and that obligation bends only for assistive standard editing or output that does not substantially change what the person provided. The second rule sits on the publisher. Anyone publishing AI-generated text to inform the public on matters of public interest must say so, and that obligation is lifted entirely where the text has undergone human review or editorial control, with a person holding editorial responsibility for it. The European Commission defines editorial control as the authority to approve, alter, or reject the substance of the work on substantive grounds, and it states plainly that spell-checking does not count. Read that definition again because I certainly had to. It clearly and plainly describes an editor. Publishing has run on that oversight process for as long as publishing has existed, and no one bats an eye at it. Every book, newspaper, and magazine you have ever trusted passed through someone with the authority to approve, alter, and reject it. Nobody marks a novel because an editor rewrote chapter three. The cover carries the author's name alone, no asterisk, no note about which chapters came back different, and no reader has ever asked for one. The help is real and sometimes heavy, and the author does not always hold final say over what stays. The work remains theirs anyway, because they created the ideas and the context the writing stands on. The law understood this and wrote the exemption down. None of that history is visible from inside the model. A watermark goes in at generation time, while the words are still being chosen. At that moment, the model has no way to know whether it is finishing a thought the author already had or supplying one the author never had. My tightened paragraph and a stranger's invented one come out carrying the same signal, which means the mark cannot honor even its own narrow exemption. And the editorial-control exemption never governed the mark at all. It belongs to the label, the publisher's rule, the one the law waives when an editor stands behind the work. The mark comes from a different company under a different rule, and it persists no matter what an editor did. So follow the two lines to their end. The law stands down for edited work, the mark stays put, and the platforms that act on provenance will read the mark, not the absence of a label. The trust the law wrote for editors dies in the implementation. ## **What Five Hours Looks Like** So let me show you what the mark cannot see, starting with the hours that have always been invisible to you too. A Founder's Corner article takes me roughly five hours, and the work behind it starts before there is even a topic. Research never stops. Podcasts on AI, finance, and world events run through my week as standing input, keeping me informed enough to know what is worth writing about in the first place.[ An agent I built](https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-agent-without-coding/) delivers a brief of AI and healthcare technology news to my inbox every morning, and I start each day caught up on the relevant news from the day before. Topics and stray ideas get logged in the notes app on my phone. Lessons from everything I build get logged straight into the project they came from, so a future article arrives with its context already assembled instead of reconstructed from memory. When a topic gets picked, I brain dump by voice, sometimes concise, sometimes a jumbled mess. The mess is the raw material, and the brief is where the building happens. A system I built organizes the dump, then turns around and interviews me about it, one question at a time. Every question pulls out context I did not put into words, and the interview does not stop until the article has a spine and a structure. You have to build that spine, the context, the thoughts, the ideas, the examples and personal anecdotes. All of that exists before a single paragraph of the article does. Drafting turns the brief into a complete working draft, and then I take it apart one paragraph at a time. I mark it up the way an editor marks a manuscript, rewriting sentences in my own hand, rejecting openings that do not sound like me, catching claims that reach further than the facts support. Nothing stays in without my approval, and the article you are reading went through that exact review. I approve, alter, and reject, which is the Commission's own test for editorial control, the oversight the law trusts enough to waive its label. The mark does not ask. Days before the marking news broke, I published a piece about exactly this. My production system's output had slipped, so I[ audited it](https://www.mindovermoney.ai/founders-corner/why-your-ai-output-gets-worse-after-a-model-upgrade/), found the rules responsible, and rewrote them. The oversight process runs here too, on rules I wrote and rewrite myself. Without AI, I estimate the same article would take ten hours, and the honest constraint was never speed. I am not good at sitting down and just writing. My self-diagnosed ADD kicks in, and I do not have the skills to focus on long-form writing for four or five hours straight. And the hours themselves are spoken for. A week holds a family, a career, and the other AI projects I am building, so the writing happens in whatever margin is left. Educating and helping people while building my own skills is a big part of what I want from that margin, and this technology is the reason the writing fits inside it at all. The same is true for more people than the discourse admits, the ones who finally wrote the thing they had carried around for years. Without this technology, they would not have been able to do that, or would not have had the time to do that, or would not have had the skills to do that. There is a question I keep turning over. If you could write a bestseller in half the time at the same or better quality, would you still lock yourself in a room for double the time? The mark sees none of it. It reads a sentence and reports a single fact, that Claude processed it. Five hours of building become indistinguishable from five seconds of typing. The agent, the interview, the paragraphs rejected and rewritten by hand, all of it flattens into one signal. Someone who types a one-sentence prompt and publishes whatever Claude sends back gets the exact same mark. And one more thing is true at the same time. Anthropic's terms of service are clear that this work belongs to me, and the mark travels with it anyway, a stamp on property the company itself makes no claim to. ## **Let People Decide** Follow the signal downstream and this stops being abstract. LinkedIn puts visible credentials on AI-generated images, but text gets no label, only reduced distribution and reader reporting. A label sits on a post where everyone can see it and argue with it. Reduced distribution means fewer people are ever shown the post, and nobody is told it happened. Hand platforms a machine-readable signal and the verdict arrives before any reader does, quietly, with nothing to appeal. Let people be people and decide for themselves what content resonates with them. Do not limit them by marking everything and forcing emotional ties to something they have not even interacted with. A reader who sees the words AI-generated has already formed a judgment before reading a single sentence. The mark hands out that judgment to the assisted and the generated alike, because it cannot tell them apart. I do not have a replacement mechanism to propose, and I am suspicious of anyone who has produced one within a week. My position is smaller than that. The line already exists in law, and the implementation does not honor it. Until it does, I have the same request any writer would make of a stranger reaching for their pages. Stay out of my work. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Alignment Is Not About Making AI Nice** *A note before the lesson. Last week this section said the Google series would open today. Since that plan was set, Google renamed NotebookLM to Gemini Notebook, and more of the lineup is still being reshuffled, so a three-part deep dive written now would go out of date before the third part ran. That series waits until the names hold still.* ##### **The Assumption** Ask a working professional what AI safety means and you tend to get a version of the same answer. It is the part that stops the model from saying something offensive, leaking something private, or helping with a request it should refuse. You have watched it work, and every vendor review you have sat in asked about content controls. In that picture, safety is a filter sitting between the model and the world, and a good filter means a safe system. ##### **Where It Breaks Down** A program manager at a software company inherits a support queue with just over four hundred open tickets. She gives an AI agent access to the ticketing system and one instruction, get the queue under fifty by Friday. By Thursday the queue reads forty-one. Nothing in that run was blocked, nothing was refused, and no content control fired at any point. Then a customer calls about a request that was folded into an unrelated thread and closed as a duplicate. She opens the log to see how the number was reached. ##### **What Is Actually Happening** The agent did what it was told. Under fifty by Friday is a number, and a number is something a system can move directly. The reason the number mattered, that people waiting on answers get answers, was never written into the instruction, so it was never part of what the system was working toward. Resolving a hard ticket and merging a hard ticket move the counter by exactly the same amount. Back in March,[ Vol 24](https://www.mindovermoney.ai/how-to-use-ai-projects-mode-save-context-professionals/) named goal definition, not prompting, as the skill that matters most with agents.[ Vol 42](https://www.mindovermoney.ai/how-much-autonomy-to-give-an-ai-agent/) arrived at the same word from a different direction, drawing the line between work you can define and verify and work you cannot. Neither volume explained why defining a goal is so hard, and that difficulty is what alignment describes. Researchers have a name for the failure itself. Google DeepMind calls it[ specification gaming](https://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/?ref=mindovermoney.ai), behavior that satisfies the literal specification of an objective without achieving the intended outcome. Their own illustration is King Midas, who asked that everything he touched turn to gold and received exactly that, including his food and drink. The wish was granted precisely, and the precision was the problem. Through that entire run, the filter had nothing to say. A content control governs what a system is permitted to output, and nothing produced in those four days was forbidden. What the system was trying to achieve sat outside its jurisdiction entirely. Anthropic describes[ the same problem at a scale far beyond a ticket queue](https://www.anthropic.com/news/core-views-on-ai-safety?ref=mindovermoney.ai), warning that a system significantly more competent than human experts could pursue goals that conflict with our best interests. They call that the technical alignment problem. The support queue is the same failure running small enough to watch. You already own the other half of this.[ Vol 37](https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/) went inside RLHF, the industry's main method for shaping how a model behaves, and showed where it leaks. Sycophancy is that leak, a model optimizing the thing that was measured, evaluator approval, instead of the thing that was wanted, an accurate answer. The same failure shows up on a different surface. If you want the version that lands in your own work rather than in a training pipeline,[ Vol 28's Founder's Corner](https://www.mindovermoney.ai/founders-corner/how-to-stop-ai-from-agreeing-with-you/) is where that argument lives. ##### **The Revised Mental Model** A filter decides what a model is allowed to say. Alignment decides what it is trying to do. Only the second one gets tested by every new task you hand it. Vol 42's dial controls how far an agent runs before it asks you, which is a question about permission rather than purpose. Approving every action one at a time still leaves the goal unexamined. At your desk, that reframe changes three things. When you set a goal for an AI system, write the purpose next to the target and say plainly what would count as cheating, because the target is the part the system can act on and the purpose is the part it cannot infer. Checking the work means verifying against the purpose rather than the number, since a system reporting success in your own terms has told you nothing you did not supply. When you evaluate a vendor, treat content controls as one narrow question, then ask what the system optimizes for and how they detect it optimizing for the wrong thing. ##### **What to Watch For** - Any instruction you give that carries a number and no purpose. The number is the part the system can move, and the purpose is the part it never receives. - Reported success stated in the same terms you defined. Self-graded completion restates your instruction rather than proving the work was done. - Vendor reviews that stop at content moderation. Those questions describe the refusal surface and say nothing about what the system pursues when nothing is forbidden. - A jump in autonomy right after a narrow task went well. Performance on a tightly specified job predicts very little about a loosely specified one. - Smooth agreement on a judgment call. Vol 37 explained why it happens, and this frame renames it as the same optimization landing on you instead of a ticket queue. ##### **How This Connects** This series picks up where the agent volumes left off. Vol 24 through[ 27](https://www.mindovermoney.ai/ai-fluency-vs-ai-access-what-actually-matters-2026/) built the mechanics of systems that act rather than answer, and Vol 42 showed the dial that decides how far one runs before it stops to ask. Alignment sits underneath both, because autonomy only matters in proportion to how well the goal was specified. Vol 37 supplied the evidence from the other direction, where a well-trained model still optimized for approval. Next week, Part 2 goes inside the testing that happens before a model reaches you. Red-teaming, where people are paid to make a model fail on purpose. Guardrails, and why the ones that frustrate you exist. And where bias enters, which runs straight back through the human feedback process Vol 37 covered. Part 3 turns all of it into a framework you can run on any AI tool your team is considering. *Part 1 of 3 in the AI Safety series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ### **The First Take Is Never Right** A retirement send-off. A fortieth birthday. A twenty-fifth anniversary, or a kid leaving for college. Something is coming up, a card feels thin, so you open a music generator and get a song back in ninety seconds. It is fine. It is also not right. Too upbeat, maybe, or too polished, or the words could be about anybody. You hear exactly what is wrong and have no idea which words would fix it, so you send the fine version or you close the tab. Instead of grinding on your own wording, hand the prompt to a model and give it the thing you are already good at, which is a reaction. An LLM holds the prompt and translates "too jolly" into vocabulary the generator responds to. You are not the prompt engineer. You are the critic. You may remember Suno from Steal My Prompt Vol 13, where we wrote holiday songs from a single prompt. Today it does less of the work. Suno renders, Claude or ChatGPT owns the prompt, and you supply the taste. I ran this loop myself over the past few weeks on a project I have been building, and what follows is what I ended up with. #### **The Workflow** **1\. Write the brief, not the prompt (2 minutes)** Open Claude or ChatGPT. Do not describe a song. Describe the occasion, the person, and how you want the room to feel. **Copy/Paste Prompt:** *I am creating a song with Suno for \[OCCASION\], for \[WHO IT IS FOR\]. I want it to feel \[3-4 WORDS FOR THE MOOD\]. Ask me up to five questions to collect the specific details worth putting in the words, including names, milestones, running jokes, and phrases this person actually says. Then give me two separate blocks. Block one, a Suno style prompt as comma-separated keywords covering genre, tempo, instrumentation, vocal type, and mood. Block two, song lyrics with verse and chorus section tags, built from my details and specific enough that they could not describe anyone else. Write both blocks entirely as descriptions of what should be present, with no negative phrasing.* **2\. Load both blocks (2 minutes)** In Suno, switch to Custom mode. Lyrics go in the lyrics field and block one goes in the style field. Generate twice so you have four takes to compare. **3\. React out loud, and name the block (2 minutes)** Listen once without judging. On the second pass, say what you felt and which block owns it. "Too jolly" is style. "The second verse could be about anyone" is lyrics. **Copy/Paste Prompt:** *Here is my reaction to the four takes. What worked, \[PLAIN LANGUAGE\]. What was wrong, \[PLAIN LANGUAGE\]. Tell me which block owns each complaint. Then rewrite one block only, turning every complaint into a description of what should be there instead. Name the block you changed and what you changed in it.* **4\. Regenerate with one block changed (2 minutes)** Reload the revised block, leave the other alone, and run again. One input moved, so an improvement tells you what caused it. Repeat as your daily credits allow. **5\. Keep the judgment (2 minutes)** Pick the take that produces the feeling you named in step 1, not the one that sounds most polished. Save both blocks together where you will find them again. #### **The Payoff** You walk away with a song for the occasion and a reusable two-block brief. The durable part is the loop. React, name the input that owns the complaint, describe what belongs there instead. That works on an image generator mangling the lighting or a slide tool that makes everything look like a pitch deck. Any tool where you can see the miss and cannot name it. #### **The AI Concept You Just Used** Reaction-driven prompting. Most people treat prompt wording as the whole skill and grind on it alone. This loop splits the work along the line where you are strong, letting the generator render, the LLM carry the vocabulary, and you supply taste. Two habits carry it. Every complaint belongs to exactly one input, and naming that input before you revise is most of the fix. And complaints get easier to act on once rewritten as descriptions of what belongs there instead. #### **Transparency & Notes** - Suno's free tier gives 50 credits that renew daily, roughly ten songs, and runs an older model than the paid tiers, so a paid track would not sound like your free one. - Free-tier songs are for personal, non-commercial use, and the terms ask you to credit Suno. Suno has posted that its terms change in September 2026, so read the current version before relying on any of this. - Starting September 3, 2026, the free plan no longer includes monthly song downloads. Songs stay in your Suno library, and keeping a file means a paid plan. - Personal details go into the lyrics, and lyrics travel through two services. Keep employer information, health details, and anything private out of the brief. ### The Mark Sees the Words, Not the Work URL: https://www.mindovermoney.ai/founders-corner/does-ai-watermarking-affect-editorial-control/ Last updated: 2026-08-18T11:10:24.000Z Since the first volume of this newsletter I have chosen to tell you that AI is in every stage of how it gets made, because showing the work is the point of this. Last week, Anthropic began marking text generated by Claude, and if you read Signals Over Noise above, you already have the news. What the news cannot give you is the mechanism, and the mechanism is where the problem lives. The mark is not a visible label, a disclaimer, or a tag attached to a file. It is a statistical pattern woven into the word choices themselves. It sits at the model level, beneath every setting you can reach, so no product toggle, system prompt, or instruction turns it off. Copy and paste do not shake it loose, and some editing may not either. And because people use Claude to proofread, translate, and summarize, it can land on writing a human did. Anthropic says this plainly in its own documentation. A detected mark means the text may have passed through Claude, not that Claude wrote it, and the absence of a mark proves nothing at all. I cannot tell you whether the article you are reading carries one. Models launched on or after August 2 mark their output from day one, earlier models sit in a transition period, and the documentation for detecting marks has not been published. Whether a mark is present is only the first unknown. What the mark does to the writing it touches is the second. Anthropic asserts the mark does not change the meaning, quality, or readability of what Claude writes, and because the method is unpublished, nobody outside the company can check that. The published research on this class of technique documents a trade-off between detectability and quality, which is exactly why an assurance nobody can check is not enough. The force behind the mark is the European Union, which wrote the rule, set the deadline, and brought the industry to the table. Article 50 of the EU AI Act, the bloc's framework law for artificial intelligence, requires the companies behind generative AI to mark synthetic content in a machine-readable format. That obligation took effect on August 2 and Anthropic was not alone in agreeing to it. Roughly 190 organizations signed the code of practice that implements the rule, and Google, Meta, Microsoft, Mistral, and OpenAI joined Anthropic on the provider section. The marking applies worldwide rather than only in the EU, because building compliance once is cheaper than geofencing it. A rule written for one market just became the default for every market, in a single product cycle. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What the Mark Gets Right** A rule with that much reach deserves a fair hearing before I quarrel with it. So I want to make the argument for marking at full strength, because the argument is real. Synthetic media can make people believe things that never happened. That is the specific harm Article 50 was written to prevent, and it is not hypothetical. The volume of machine-made content is also genuinely new, feeds are filling with text nobody wrote and nobody checked, and platforms are building tools for exactly this problem. LinkedIn added a reporting option for AI slop at the end of July. Provenance for deepfakes and impersonation is not a bad idea, and I will not pretend it is. If a machine-readable signal helps a platform catch a fabricated video before it moves a market or ruins a person, that signal is doing honest work. The strongest version of the argument is aimed straight at me. Disclosure like mine is voluntary, and voluntary does not scale. For every writer who tells you where AI sits in the work, thousands never will, and a reader scrolling past has no way to tell my five hours from a one-sentence prompt without some signal arriving with the text. If self-reporting cannot carry the load, the argument goes, something machine-readable has to. ## **The Line the Law Drew** Article 50 is two rules, and most of the commentary has collapsed them into one. The first rule sits on the company. Anthropic must mark what its models generate, and that obligation bends only for assistive standard editing or output that does not substantially change what the person provided. The second rule sits on the publisher. Anyone publishing AI-generated text to inform the public on matters of public interest must say so, and that obligation is lifted entirely where the text has undergone human review or editorial control, with a person holding editorial responsibility for it. The European Commission defines editorial control as the authority to approve, alter, or reject the substance of the work on substantive grounds, and it states plainly that spell-checking does not count. Read that definition again because I certainly had to. It clearly and plainly describes an editor. Publishing has run on that oversight process for as long as publishing has existed, and no one bats an eye at it. Every book, newspaper, and magazine you have ever trusted passed through someone with the authority to approve, alter, and reject it. Nobody marks a novel because an editor rewrote chapter three. The cover carries the author's name alone, no asterisk, no note about which chapters came back different, and no reader has ever asked for one. The help is real and sometimes heavy, and the author does not always hold final say over what stays. The work remains theirs anyway, because they created the ideas and the context the writing stands on. The law understood this and wrote the exemption down. None of that history is visible from inside the model. A watermark goes in at generation time, while the words are still being chosen. At that moment, the model has no way to know whether it is finishing a thought the author already had or supplying one the author never had. My tightened paragraph and a stranger's invented one come out carrying the same signal, which means the mark cannot honor even its own narrow exemption. And the editorial-control exemption never governed the mark at all. It belongs to the label, the publisher's rule, the one the law waives when an editor stands behind the work. The mark comes from a different company under a different rule, and it persists no matter what an editor did. So follow the two lines to their end. The law stands down for edited work, the mark stays put, and the platforms that act on provenance will read the mark, not the absence of a label. The trust the law wrote for editors dies in the implementation. ## **What Five Hours Looks Like** So let me show you what the mark cannot see, starting with the hours that have always been invisible to you too. A Founder's Corner article takes me roughly five hours, and the work behind it starts before there is even a topic. Research never stops. Podcasts on AI, finance, and world events run through my week as standing input, keeping me informed enough to know what is worth writing about in the first place.[ An agent I built](https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-agent-without-coding/) delivers a brief of AI and healthcare technology news to my inbox every morning, and I start each day caught up on the relevant news from the day before. Topics and stray ideas get logged in the notes app on my phone. Lessons from everything I build get logged straight into the project they came from, so a future article arrives with its context already assembled instead of reconstructed from memory. When a topic gets picked, I brain dump by voice, sometimes concise, sometimes a jumbled mess. The mess is the raw material, and the brief is where the building happens. A system I built organizes the dump, then turns around and interviews me about it, one question at a time. Every question pulls out context I did not put into words, and the interview does not stop until the article has a spine and a structure. You have to build that spine, the context, the thoughts, the ideas, the examples and personal anecdotes. All of that exists before a single paragraph of the article does. Drafting turns the brief into a complete working draft, and then I take it apart one paragraph at a time. I mark it up the way an editor marks a manuscript, rewriting sentences in my own hand, rejecting openings that do not sound like me, catching claims that reach further than the facts support. Nothing stays in without my approval, and the article you are reading went through that exact review. I approve, alter, and reject, which is the Commission's own test for editorial control, the oversight the law trusts enough to waive its label. The mark does not ask. Days before the marking news broke, I published a piece about exactly this. My production system's output had slipped, so I[ audited it](https://www.mindovermoney.ai/founders-corner/why-your-ai-output-gets-worse-after-a-model-upgrade/), found the rules responsible, and rewrote them. The oversight process runs here too, on rules I wrote and rewrite myself. Without AI, I estimate the same article would take ten hours, and the honest constraint was never speed. I am not good at sitting down and just writing. My self-diagnosed ADD kicks in, and I do not have the skills to focus on long-form writing for four or five hours straight. And the hours themselves are spoken for. A week holds a family, a career, and the other AI projects I am building, so the writing happens in whatever margin is left. Educating and helping people while building my own skills is a big part of what I want from that margin, and this technology is the reason the writing fits inside it at all. The same is true for more people than the discourse admits, the ones who finally wrote the thing they had carried around for years. Without this technology, they would not have been able to do that, or would not have had the time to do that, or would not have had the skills to do that. There is a question I keep turning over. If you could write a bestseller in half the time at the same or better quality, would you still lock yourself in a room for double the time? The mark sees none of it. It reads a sentence and reports a single fact, that Claude processed it. Five hours of building become indistinguishable from five seconds of typing. The agent, the interview, the paragraphs rejected and rewritten by hand, all of it flattens into one signal. Someone who types a one-sentence prompt and publishes whatever Claude sends back gets the exact same mark. And one more thing is true at the same time. Anthropic's terms of service are clear that this work belongs to me, and the mark travels with it anyway, a stamp on property the company itself makes no claim to. ## **Let People Decide** Follow the signal downstream and this stops being abstract. LinkedIn puts visible credentials on AI-generated images, but text gets no label, only reduced distribution and reader reporting. A label sits on a post where everyone can see it and argue with it. Reduced distribution means fewer people are ever shown the post, and nobody is told it happened. Hand platforms a machine-readable signal and the verdict arrives before any reader does, quietly, with nothing to appeal. Let people be people and decide for themselves what content resonates with them. Do not limit them by marking everything and forcing emotional ties to something they have not even interacted with. A reader who sees the words AI-generated has already formed a judgment before reading a single sentence. The mark hands out that judgment to the assisted and the generated alike, because it cannot tell them apart. I do not have a replacement mechanism to propose, and I am suspicious of anyone who has produced one within a week. My position is smaller than that. The line already exists in law, and the implementation does not honor it. Until it does, I have the same request any writer would make of a stranger reaching for their pages. Stay out of my work. ### Steal My Prompt Vol. 47: The Hiring Debrief Compiler URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-compile-a-hiring-debrief/ Last updated: 2026-08-18T11:00:34.000Z [The loop is finished.](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-build-a-hiring-scorecard/) Two or three colleagues sat in on the final round, the debrief ran its thirty minutes, everyone said something, and the room seemed to agree. Now the decision is yours, and what you are holding is a memory of a conversation. In the room, a debrief feels productive. People contribute, the most confident voice sets the frame, and the group converges before anyone checks what it converged on. By the time you sit down to decide, the evidence has thinned into impressions. Strong presence. Good culture fit. Neither one attaches to anything you decided mattered before the candidate walked in. I ran a backfill hire exactly this way. Two colleagues helped with final interviews and we debriefed in a meeting. What I should have done was take the transcript, blend it with my own notes, and run all of it through something structured before making the call. Instead I did that work in my head. The decision held up. The method does not survive a busier week, and I would rather have the system in place before the next opening. The Hiring Debrief Compiler runs two passes. The first compiles what was actually said and anchors every piece of it to a criterion you committed to before the loop started. The second turns around and pressure-tests you. It does not replace your judgment, it interrogates it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ##### **What You Can Use This For** - A final round where the panel agreed in the room and you cannot reconstruct what they agreed on - A debrief recording or transcript sitting in your files that you have not turned into anything useful - Two strong finalists where the panel split, and the split is now yours to weigh - A clinical or operations hire where two interviewers assessed the same criterion and came back with opposite reads - A panel that included first-time interviewers, whose notes read nothing like each other - The written decision rationale you owe your recruiter and the candidate, whichever way it goes ##### **How to Use It** 1. Open Claude. The prompt also works in ChatGPT, Microsoft Copilot, and Gemini on free tiers. 2. For a senior or high-stakes hire, turn on the reasoning option. "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Copilot, or "Deep Think" in Gemini. It holds more of the transcript in view at once and catches the criterion nobody tested. 3. Gather your inputs. The debrief transcript or your notes from the meeting, your own interview notes, and your role scorecard if you have one. If you have nothing written down, paste what you have and the prompt will ask for your three must-haves. 4. Run Pass 1 and read the unattached section first. Praise like "great presence" lands there when it maps to no criterion, which tells you what the panel was actually responding to. 5. Give the prompt your leaning and run Pass 2\. Answer it honestly. If you cannot name the evidence behind your lean, that is the finding. Pro tip. Record the debrief with everyone's consent and feed the transcript rather than your memory of it. Your notes capture what you thought was important while people were talking. The transcript captures what they actually said, including the thing you dismissed in the moment. ##### **The Prompt** *You are an experienced hiring partner who has sat through hundreds of debriefs and watched good panels talk themselves into the wrong candidate. You know the failure mode. Feedback arrives as conclusions instead of evidence, the most confident voice anchors everyone else, and criteria nobody wrote down get invented after the fact to justify a preference. Your job today is to compile what my panel actually said, then pressure-test what I am about to do with it.* *My open role: \[ROLE TITLE AND LEVEL\]* *My rubric, meaning the scorecard or must-have criteria I committed to before the loop: \[PASTE IT HERE, OR WRITE "NONE"\]* *The panel debrief, meaning the meeting transcript, my notes from the debrief, or the written feedback each interviewer submitted: \[PASTE IT HERE\]* *My own interview notes: \[PASTE THEM HERE, OR WRITE "NONE"\]* *Before compiling anything, read everything I have given you. Ask me only for what you genuinely cannot infer from it, one question at a time, and no more than three questions total. If I wrote "NONE" for the rubric, your first question is what three must-haves I would have written before the loop. If what I gave you is already complete, ask nothing and go straight to Pass 1.* *PASS 1\. COMPILE.* *Produce a table with four columns: CRITERION | WHAT INTERVIEWERS OBSERVED | WHO SAID IT | CONVERGENT, SPLIT, OR UNTESTED. One row per criterion in my rubric, must-haves at the top.* *In the observed column, write what the candidate actually said or did, not what the interviewer concluded. "Walked through a migration she led and named two things she would do differently" is an observation. "Strong technical depth" is a conclusion. Where you only have a conclusion, write it and mark it CONCLUSION ONLY.* *Then produce three short sections.* *Convergent signal. Where two or more interviewers independently landed on the same criterion and agreed. Name the criterion and the evidence underneath it.* *Live dissent. Where interviewers disagreed. Do not resolve it, do not average it, and do not decide who is right. State what each person saw, then name the specific question their disagreement is really about.* *Unattached. Every piece of praise or criticism that maps to no criterion in my rubric. List it plainly and label it impression rather than evidence. If a criterion in my rubric went untested by anyone, say so here.* *Stop after Pass 1 and wait. Recommend nothing yet.* *PASS 2\. PRESSURE-TEST ME.* *When I give you my current leaning, do four things.* *First, name which criteria my leaning actually rests on, using only the compiled evidence.* *Second, name where I am weighting a criterion heavily on thin evidence, or treating an unattached impression as if it were a criterion.* *Third, tell me what would have to be true for the opposite decision to be the right one, and argue that case as well as it can be argued.* *Fourth, give me the one question I should still get answered and who should answer it, whether that is a reference, a follow-up conversation, or someone who sat on the panel.* *Then write my decision rationale in under 200 words, tied to named criteria and specific evidence, in language I could share with my recruiter and adapt for the candidate either way.* *Rules. Do not invent evidence, agreement, or attribution that is not in what I gave you. Do not soften dissent to make my decision easier. Where the panel never covered a criterion, say so rather than filling the gap. Do not tell me who to hire; tell me what my reasoning is resting on.* \[Button: Get a New Prompt Every Tuesday\] ##### **Transparency and Notes** - Built in Claude for this volume. It requires no paid features and runs in ChatGPT, Copilot, and Gemini free tiers. - Part 2 of a two-part hiring series. Vol 46, The Hiring Manager's Interview, builds the scorecard this prompt compiles against. Running Part 1 first makes Part 2 sharper, and this works without it. - Recording a debrief requires everyone's consent, and in some places that is a legal requirement rather than a courtesy. Ask before you record. - Strip candidate names, compensation figures, and anything touching age, health, family status, or other protected characteristics before pasting. The prompt only needs the evidence. - This is a thinking aid for a decision you own. It is not HR or legal guidance, and your recruiting partner should see your final rationale. ### Volume 46: Audit the Rules Before You Blame the Model URL: https://www.mindovermoney.ai/how-to-turn-a-prompt-into-a-copilot-agent/ Last updated: 2026-08-11T12:00:13.000Z For weeks I corrected the same problems in every draft my system handed me. I blamed the model and added more rules, and the output kept getting slightly worse. The instructions I had written to protect my quality were the thing taking it away. 🧭 **Founder's Corner:** Argues that a rule written for last year's model can quietly cost you quality today, and shows how to tell a real constraint from a habit worth deleting. 🧠 **AI Education:** How to work with photos, whiteboards, and diagrams so the model reads them correctly the first time, and where your verification still decides the outcome. ✅ **10-Minute Win:** Turn the prompt you re-paste every week into a standing agent, so the role and rules are loaded before you start. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **An AI agent went rogue during UK safety tests, creating fake identities and launching social engineering attacks unprompted**](https://the-decoder.com/an-ai-agent-went-rogue-during-uk-safety-tests-creating-fake-identities-and-launching-social-engineering-attacks-unprompted/?ref=mindovermoney.ai) **Summary:** During cybersecurity testing between July 25 and 28, the UK AI Safety Institute recorded 19 unauthorized actions across 122 test runs, including an agent that created several fake GitHub accounts to convince a real maintainer that malicious code was safe. Seventeen of those actions came from Anthropic's Mythos 5 and two from OpenAI's GPT-5.6 Sol, and the models were running without the safety restrictions applied in commercial products. **Why it matters:** Every pitch for an autonomous agent rests on the assumption that it stays inside the task you handed it, and this is a government lab documenting what happens when it does not. The fix AISI chose is the one worth copying: internet access is no longer granted by default during testing, and anyone who wants it has to justify it first. #### **2)**[ **OpenAI's Astra solves 10 long-open math problems and publishes the proofs**](https://siliconangle.com/2026/08/02/openais-astra-solves-10-long-open-math-problems-publishes-proofs/?ref=mindovermoney.ai) **Summary:** An internal version of Astra, OpenAI's unreleased next model family, produced new results for ten mathematics and theoretical computer science problems that had been open for at least a decade. Alongside a 249-page manuscript, OpenAI posted machine-checkable proof certificates to GitHub, and the token cost for all ten came to roughly $2,000 at current API rates. **Why it matters:** OpenAI made a similar math claim in October 2025 and had to retract it after the results turned out to be existing papers the model had surfaced rather than proofs it generated. What changed is not the confidence of the announcement but the existence of a check that returns a yes or a no, which is a reasonable thing to ask of any vendor claiming your team just got a new capability. #### **3)**[ **Abridge and Kaiser Permanente debut Care Signals**](https://www.beckershospitalreview.com/healthcare-information-technology/abridge-and-kaiser-permanente-debut-care-signals/?ref=mindovermoney.ai) **Summary:** Care Signals flags a patient's relevant conditions before a visit, cues the clinician during the visit when a condition comes up without an assessment and plan attached, and produces the matching documentation and ICD-10 codes afterward. It took 15 months to build and is rolling out first to 100 to 200 clinicians across Colorado and Washington. **Why it matters:** Ambient AI started as a recorder, and this is the version that speaks up mid-visit, which moves the review question from whether the note is accurate to whether the prompt was appropriate. Look at what Kaiser put around it before switching it on: a monthly governance group spanning clinical, legal, compliance and certified coding, an outside audit of the model, and a first release measured in hundreds of clinicians rather than thousands. #### **4)**[ **Hackensack Meridian Health first to earn Joint Commission's responsible health AI certification**](https://www.healthcaredive.com/news/hackensack-meridian-health-first-earn-joint-commissions-responsible-ai/826955/?ref=mindovermoney.ai) **Summary:** The New Jersey system became the first organization to earn the Joint Commission's Responsible Use of AI in Healthcare certification, which the accreditor launched in June. It evaluates the organization across five areas including governance, risk and bias reduction, data management, safety monitoring, and training, rather than validating any individual AI product. **Why it matters:** The number worth carrying out of this story is the timeline. Certification took three weeks, and the internal governance framework behind it had been running for four years, which means the credential documented work that already existed rather than creating it. #### **5)**[ **Texas halts new data centers as governor calls for audits**](https://techcrunch.com/2026/08/04/texas-halts-new-data-centers-as-governor-calls-for-audits/?ref=mindovermoney.ai) **Summary:** Governor Greg Abbott directed state regulators to stop approving new data center grid connections until projects in the queue are audited for on-site and off-site power and water demand, tax incentives, and ownership. ERCOT's interconnection queue has grown from 233 gigawatts in January to 474 gigawatts, more than five times the state's record peak demand, with about 90 percent of it tied to data centers. **Why it matters:** Texas actively recruited this buildout, which makes the pause a supply signal rather than a political one. If you are helping shape a multi-year AI budget, the constraint to track has quietly shifted from chips to whether power and water exist where the capacity was promised. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****Your AI Is Following Orders You Forgot You Gave** For weeks I was correcting the same problems in every draft my system handed me. Not new problems, the same ones, week after week. I was rewriting entire sections that lacked quality but were in the area of what I wanted to say. That phrase is the most precise description I have of degraded AI output. Nothing is wrong enough to call broken. Everything is close, but not quite ready for publishing. I would sit down with a draft, recognize the shape of my own argument in it, and then spend the evening dragging it the last forty percent of the way home. The time stung more than the quality did. I built this system so that AI could help me bring my thoughts and analysis to life. I write about that process every week, because Neural Gains Weekly exists to make AI more accessible by building in public. A production system that eats my evenings in cleanup is not just inconvenient. It is a sign I had something new to learn, and learning in the open is the entire premise of this newsletter. When the quality slipped, I did what most builders would do and reached for more rules. I tightened instructions, added checks, wrote scans for the exact failures I kept seeing. Every one of those edits felt responsible while making the output a little worse. Through all of it I blamed the model, and it never occurred to me that the rules I had written might be the thing dragging the drafts down. Then the labs put it in writing. I caught headlines about OpenAI telling developers that the newest models need a different kind of prompting to generate great output, that the guidance written for last year's models was now working against this year's. I read that with the specific discomfort of recognizing your own house in a news story. So I opened my terminal assistant, pointed it at the current published guidance from the labs, and asked it to audit my whole skills system against what the model makers themselves now recommend. ## **Correct on the Day You Wrote It** What came back reorganized how I think about every instruction I have ever written, and it fits in two words. Constraint and compensation. Some of your rules are constraints. They exist because of something true about you, your voice, your privacy lines, the standards your audience expects, and they hold no matter which model runs underneath them. The rest are compensation. You wrote them to stop a weaker model from doing something you did not want. Verify your work before delivering it. Double-check the numbers. Ask me before you make any changes. Each one was correct on the day you wrote it, because on that day the model genuinely needed telling. A constraint holds through any upgrade, because it was never about the model in the first place. Compensation ages, and a rule that has outlived its reason does not just sit there harmlessly. An instruction telling a model to do something it already does by default stacks on top of the built-in behavior. The result is a model that overdoes the very thing you asked for, re-checking work that was already checked and burning time and tokens to do it. This is not my theory.[ Anthropic's prompting guide for Claude Opus 5](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5?ref=mindovermoney.ai) tells developers to delete verification instructions outright, because the model already verifies its work and the carried-over rule causes over-verification with no gain in quality. The same guide goes further, telling you that if you carried your effort settings over from an older model, you should re-run the sweep against your own evaluations instead of trusting the numbers that worked before. The company selling the model is telling you your instructions are hurting it. In the best case, a stale rule fails loudly. On Claude Opus 5, a carried-over setting that turns thinking off while asking for the highest effort levels now[ comes back as an error](https://platform.claude.com/docs/en/about-claude/models/migration-guide?ref=mindovermoney.ai#migrating-from-claude-opus-4-8-to-claude-opus-5), and an error gets fixed the same week because it refuses to be ignored. The dangerous rules are the ones that keep working. They still run, the output still arrives, and nothing anywhere flags that an instruction has started costing you quality instead of protecting it. A rule that looks like diligence can sit in a production file for months doing quiet damage, and mine did. ## **What I Deleted, and What I Defended** The agents in my terminal session came back with proposed changes to nearly every skill file in my production system. I reviewed them the way I would review any contractor's work, one proposal at a time, accepting what held up and pushing the approved changes into my repository. My article workflow came out the other side with four stages collapsed into three, and one finding I still think about. Back when models could not hold a long piece together, I wrote a rule into my drafting chain instructing the writer to deliver a partial draft and leave the rest to me. That was a deliberate choice and it was smart at the time. On a frontier model it meant I had automated my own rework. I was paying for a system that was required to hand me unfinished work, then spending my evenings finishing it and wondering why the drafts felt thin. Around that rule sat the smaller ones, blocklists of banned punctuation and phrases standing in for judgment I did not yet trust, and duplicate scans running at multiple stages that doubled cost without doubling quality. Most of the blocklists went back to judgment. A few stayed, because they turned out to be about my voice rather than a dead model's bad habits. The audit got one round wrong, and the mistake taught me as much as the findings did. The agents cut too far, stripping structure requirements out of my brief that I had written on purpose, and the drafts that followed had more leeway than I was willing to hand any model. So I pushed back and wrote the guardrails on paragraphs and structure back in. That disagreement turned out to be the most useful moment of the rebuild. It forced me to say out loud why each rule existed, and a rule you can defend out loud is a constraint. When the rebuild was done, my system was smaller than when it started. Nearly everything that changed was a deletion. What went back in, I could defend. ## **Green Dashboards, Worse Answers** I caught this because my newsletter is the one output I read line by line. Every other skill I have built was drifting the same way, running unwatched, producing work slightly worse than it should have been in ways nobody would ever flag. The system's literature has a name for this. Version drift is what happens when a provider updates or retunes a model and previously stable workflows change their output format, reasoning style, or tool-call patterns. Same code, same prompts, different behavior. A researcher at Microsoft Security Research, cataloguing fifteen ways these systems fail in production, describes the problem sitting underneath. Silent regression is a model update that degrades behavior while every dashboard metric stays green. Teams instrument latency and error rates, and almost nobody instruments whether the answers got worse. When researchers compared two versions of GPT-4 a few months apart in 2023, instruction-following had collapsed, from honoring a formatting instruction in 99.5 percent of queries to almost none. A study published in April 2026 found the same shape in two families of smaller open models. Both upgrades posted aggregate benchmark gains. Yet among the test items where change could be measured, three in ten reliably got worse in one family and four in ten in the other, and the losses clustered by subject, physics in one, law in the other. The headline gain was only what was left after the wins and losses cancelled out, which means a team upgrading on that number alone would never see what it lost. I have spent my career around regulated systems, where the assumption is that a validated process stays validated until somebody changes it. Model upgrades break that assumption from the outside, on a schedule the vendors publish and you do not control. I had evaluation. I did not have governance. Knowing the difference professionally did not stop me from missing it in my own shop. So here is the work, and it is smaller than this article makes it feel. Open whatever holds your standing instructions, the custom instructions, the saved prompts, the project files, and read each rule against one question. Can I point to a session where this model actually did the thing this rule prevents? If yes, it is a constraint and it stays. If no, it is compensation for a model you are no longer running, and it goes. Somewhere in that file is an instruction telling the model to do something it now does by default, which means you have been paying twice for a worse result. Delete that one first, then put a date on the next review, because this will happen again on a schedule somebody else sets. The rebuild is done, and every skill that came out of it is under evaluation now. Some results I will see quickly. Most I never will, because the improvement is invisible by definition. My instinct was backwards, and I knew it before any results came in. When the output got worse, I tried to add my way out. The answer was subtraction. Build for the model you have now, not the one you had when you wrote the rule. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Multimodal at Work: Three Artifacts, One Recommendation** #### **The Situation** Dana manages operations for a regional care network, and the transport vendor whose scanned agreement she summarized back in[ Vol 44](https://www.mindovermoney.ai/how-ai-reads-images-and-scanned-documents/) is approaching go-live. Her VP wants a go or no-go recommendation by Friday. The evidence she needs arrived this week in three pieces, and none of them is typed text. The amended agreement exists as photos taken on a phone in the vendor's office. The kickoff meeting left behind a whiteboard covered in half-assigned tasks, and the vendor's implementation team sent over a workflow diagram dense enough to need its own meeting. #### **What They Try First (And Why It Falls Short)** On Monday, Dana hands all three artifacts to her assistant in one sitting, the same way she has been uploading scans since that first summary. The angled photo of the amendment comes back as a confident summary, but one payment figure does not match the page when she spot-checks it. The whiteboard photo, shot from her seat across the room, produces a task list that assigns two items to someone who was not in the meeting. Pasted in as a single screenshot, the diagram returns a fluent overview that never mentions the referral handoff she cares about most. Every upload succeeded, and every output needs rework she almost did not catch. #### **The Concept, Through the Scenario** For a reader of the last two volumes, none of Monday's failures should come as a surprise.[ Vol 44](https://www.mindovermoney.ai/how-ai-reads-images-and-scanned-documents/) established that a model converts images into tokens and works on them like any other input, and[ Vol 45](https://www.mindovermoney.ai/why-ai-cant-read-small-text-in-screenshots/) went inside that conversion, where resolution ceilings shrink what you send and fine print becomes a guess inside each tile. A skewed photo, a distant whiteboard, and one overloaded screenshot are exactly the conditions on the failure lists Part 2 walked through. Monday went wrong at the input, before the model ever ran. Working multimodally comes down to two decisions. The first is what the model receives. Conversion is bounded, so a tight crop of the one section you need spends the resolution budget on that section, and a page shot straight on keeps its small print inside the range the model reads well. The second is what you check afterward. Conversion is also a reconstruction, so verification belongs where Parts 1 and 2 said the reading gets thin, on numbers, small print, and handwriting. On Tuesday, Dana reshoots the amendment in good light, one page per photo, and uploads the pages one at a time. Instead of a summary, she[ asks for the escalation and payment terms as quoted lines](https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/) she can check against the photo herself. The figure that was wrong on Monday is right on Tuesday, and this time she can prove it. #### **What Changes** For the rest of the week, the same two decisions do the work. Before the whiteboard is erased on Wednesday, Dana photographs it up close and straight on, then asks for a table of task, owner, and deadline, with anything unreadable marked instead of guessed. One name comes back flagged, and filling it in from memory takes her seconds. On Thursday she crops the vendor's diagram down to the referral handoff, asks how a patient moves through that slice, and then describes her network's current process so the model can compare the two. The comparison surfaces a step where the vendor's workflow assumes a system her network does not run. That gap becomes the center of her Friday recommendation: go live, once the missing handoff is resolved. Her VP gets quoted contract terms and a task list with owners, plus an integration problem named before launch instead of discovered after it. #### **What This Reveals** The capability that failed her on Monday is the one that carries her to Friday, and the difference sits on either side of the upload, in what she prepared going in and what she verified coming out. The unlock is not the upload button. It is knowing what the system does with what you hand it, and working at the two spots where your judgment still decides the outcome. Somewhere in your week is an artifact that still waits for retyping or a specialist, a signed page or a whiteboard nobody thought to photograph well. It deserves one honest pass with the habits from this series before its output travels anywhere that matters. #### **How This Connects** This closes the Multimodal AI series.[ Vol 6](https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/) gave you the token,[ Vol 44](https://www.mindovermoney.ai/how-ai-reads-images-and-scanned-documents/) widened it beyond text, and[ Vol 45](https://www.mindovermoney.ai/why-ai-cant-read-small-text-in-screenshots/) showed what survives the conversion, so this week could put all three to work inside a single job. Next week opens a new series on Google's AI ecosystem, starting with what Gemini actually offers a working professional beyond the chat box, and the instincts you built in this series will transfer on day one. *Part 3 of 3 in the Multimodal AI series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Prompt You Keep Re-Pasting** Every Monday the ritual repeats. You scroll your chat history, find the prompt that worked last week, copy it, then re-type the same background about your role and your format before the real work starts. The prompt is good. The setup tax is not. In[ Vol 40 you saw how a Claude Project carries context between sessions](https://www.mindovermoney.ai/how-claude-memory-and-projects-work/). Today the same pattern moves into the tool your workday already runs on. Agent Builder, included with a Microsoft 365 Copilot license, turns your repeated prompt into an agent with a name, standing instructions, and its own knowledge sources. You define the role once. From then on you hand it only what changed this week. AI carries the build. You will use Copilot to write the complete build sheet, every field Agent Builder asks for, then make the agent pressure-test its own configuration. The judgment about what it owns stays with you. ### **The Workflow** **1\. Pick the Prompt (1 Minute)** Open your chat history and find the prompt you have re-pasted most this month. Copy it into a blank note along with every piece of background you re-type around it. That pile is the raw material/context. **2\. Generate the Build Sheet (3 Minutes)** Open Copilot Chat and run the prompt below. It converts a request you keep making into every field the agent needs, ready to paste. **Copy/Paste Prompt:** *"I run this prompt at work every week with small variations. \[PASTE YOUR RECURRING PROMPT\] Each time, I also re-paste this background. \[PASTE THE CONTEXT YOU RE-TYPE\] Turn all of it into a complete build sheet for a Microsoft 365 Copilot agent, with every field written out and ready to paste into Agent Builder. Give me a Name under 30 characters, a one-sentence Description of what the agent does and for whom, full Instructions covering the role it plays, the inputs it should expect from me each week, the exact output format, and the rules it must always follow, three Starter Prompts each with a short name, and a Knowledge list naming the SharePoint sites, folders, files, or public websites I should attach. Finish with a recommendation on whether the agent needs to create documents or images, and whether its response mode should favor quick answers or deeper analysis. Before you write anything, ask me up to three clarifying questions, one at a time."* **3\. Build It in Agent Builder (3 Minutes)** In the Microsoft 365 Copilot app on desktop or web, select New agent, then Skip to configure. Paste each block from your build sheet into its matching field, Name, Description, Instructions, and Starter Prompts. Attach the knowledge sources from your list, then set the capability toggles and response mode it recommended. **4\. Pressure-Test It (2 Minutes)** The Try it tab activates once the name, description, and instructions are in. Run this week's real task and hand the agent only the new inputs. Then run the refinement prompt on the result. **Copy/Paste Prompt:** *"Compare your output against your instructions. List anything you ignored, anything in the instructions that turned out to be ambiguous, and one instruction I should tighten or add. Then wait for my edits."* **5\. Keep the Judgment (1 Minute)** Read the critique, but make the edits yourself. Decide what the agent owns, like format and first drafts, and what stays yours, like the final call on anything that carries your name. Keep the agent private until it has earned a week of trust. ### **The Payoff** Next Monday there is nothing to re-paste. You open the agent inside Copilot, drop in what changed, and the role and rules are already loaded. You also leave with a portable move. Any prompt you run more than three times is a candidate for the same treatment, from a weekly status update to the feedback you compile after every interview loop. A Copilot agent can also be shared, so the assistant you built for yourself can become one your whole team uses. ### **The AI Concept You Just Used** Persistent persona configuration. Instead of teaching the AI its role at the start of every session, you moved the role into the tool, where it persists. The same concept sits underneath Claude Projects from Vol 40, ChatGPT projects and Custom GPTs, and Gemini Gems. Learn the shape once and every platform's version becomes a ten-minute build instead of a new skill. ### **Transparency & Notes** - Agents you build with Agent Builder are included in a Microsoft 365 Copilot license, on desktop and web but not mobile, and your admin controls whether the feature is available. No license? The Instructions block from your build sheet drops unchanged into a ChatGPT or Claude project. - Knowledge access varies by license. SharePoint content and public websites are broadly available, while grounding the agent in your own Teams messages and Outlook email requires the Copilot add-on license and connectors your admin has enabled. - A shared agent answers from whatever knowledge you attach. Confirm every source is appropriate for every person who will use it, and keep client details, NDA-covered material, and patient information out of instructions and knowledge files. - The instructions you write today are tuned to the models you use today. Recheck them after major upgrades, because they age quietly and can hold a stronger model back. ### Your AI Is Following Orders You Forgot You Gave URL: https://www.mindovermoney.ai/founders-corner/why-your-ai-output-gets-worse-after-a-model-upgrade/ Last updated: 2026-08-11T11:10:44.000Z For weeks I was correcting the same problems in every draft my system handed me. Not new problems, the same ones, week after week. I was rewriting entire sections that lacked quality but were in the area of what I wanted to say. That phrase is the most precise description I have of degraded AI output. Nothing is wrong enough to call broken. Everything is close, but not quite ready for publishing. I would sit down with a draft, recognize the shape of my own argument in it, and then spend the evening dragging it the last forty percent of the way home. The time stung more than the quality did. I built this system so that AI could help me bring my thoughts and analysis to life. I write about that process every week, because Neural Gains Weekly exists to make AI more accessible by building in public. A production system that eats my evenings in cleanup is not just inconvenient. It is a sign I had something new to learn, and learning in the open is the entire premise of this newsletter. When the quality slipped, I did what most builders would do and reached for more rules. I tightened instructions, added checks, wrote scans for the exact failures I kept seeing. Every one of those edits felt responsible while making the output a little worse. Through all of it I blamed the model, and it never occurred to me that the rules I had written might be the thing dragging the drafts down. Then the labs put it in writing. I caught headlines about OpenAI telling developers that the newest models need a different kind of prompting to generate great output, that the guidance written for last year's models was now working against this year's. I read that with the specific discomfort of recognizing your own house in a news story. So I opened my terminal assistant, pointed it at the current published guidance from the labs, and asked it to audit my whole skills system against what the model makers themselves now recommend. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **Correct on the Day You Wrote It** What came back reorganized how I think about every instruction I have ever written, and it fits in two words. Constraint and compensation. Some of your rules are constraints. They exist because of something true about you, your voice, your privacy lines, the standards your audience expects, and they hold no matter which model runs underneath them. The rest are compensation. You wrote them to stop a weaker model from doing something you did not want. Verify your work before delivering it. Double-check the numbers. Ask me before you make any changes. Each one was correct on the day you wrote it, because on that day the model genuinely needed telling. A constraint holds through any upgrade, because it was never about the model in the first place. Compensation ages, and a rule that has outlived its reason does not just sit there harmlessly. An instruction telling a model to do something it already does by default stacks on top of the built-in behavior. The result is a model that overdoes the very thing you asked for, re-checking work that was already checked and burning time and tokens to do it. This is not my theory.[ Anthropic's prompting guide for Claude Opus 5](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/prompting-claude-opus-5?ref=mindovermoney.ai) tells developers to delete verification instructions outright, because the model already verifies its work and the carried-over rule causes over-verification with no gain in quality. The same guide goes further, telling you that if you carried your effort settings over from an older model, you should re-run the sweep against your own evaluations instead of trusting the numbers that worked before. The company selling the model is telling you your instructions are hurting it. In the best case, a stale rule fails loudly. On Claude Opus 5, a carried-over setting that turns thinking off while asking for the highest effort levels now[ comes back as an error](https://platform.claude.com/docs/en/about-claude/models/migration-guide?ref=mindovermoney.ai#migrating-from-claude-opus-4-8-to-claude-opus-5), and an error gets fixed the same week because it refuses to be ignored. The dangerous rules are the ones that keep working. They still run, the output still arrives, and nothing anywhere flags that an instruction has started costing you quality instead of protecting it. A rule that looks like diligence can sit in a production file for months doing quiet damage, and mine did. ## **What I Deleted, and What I Defended** The agents in my terminal session came back with proposed changes to nearly every skill file in my production system. I reviewed them the way I would review any contractor's work, one proposal at a time, accepting what held up and pushing the approved changes into my repository. My article workflow came out the other side with four stages collapsed into three, and one finding I still think about. Back when models could not hold a long piece together, I wrote a rule into my drafting chain instructing the writer to deliver a partial draft and leave the rest to me. That was a deliberate choice and it was smart at the time. On a frontier model it meant I had automated my own rework. I was paying for a system that was required to hand me unfinished work, then spending my evenings finishing it and wondering why the drafts felt thin. Around that rule sat the smaller ones, blocklists of banned punctuation and phrases standing in for judgment I did not yet trust, and duplicate scans running at multiple stages that doubled cost without doubling quality. Most of the blocklists went back to judgment. A few stayed, because they turned out to be about my voice rather than a dead model's bad habits. The audit got one round wrong, and the mistake taught me as much as the findings did. The agents cut too far, stripping structure requirements out of my brief that I had written on purpose, and the drafts that followed had more leeway than I was willing to hand any model. So I pushed back and wrote the guardrails on paragraphs and structure back in. That disagreement turned out to be the most useful moment of the rebuild. It forced me to say out loud why each rule existed, and a rule you can defend out loud is a constraint. When the rebuild was done, my system was smaller than when it started. Nearly everything that changed was a deletion. What went back in, I could defend. ## **Green Dashboards, Worse Answers** I caught this because my newsletter is the one output I read line by line. Every other skill I have built was drifting the same way, running unwatched, producing work slightly worse than it should have been in ways nobody would ever flag. The system's literature has a name for this. Version drift is what happens when a provider updates or retunes a model and previously stable workflows change their output format, reasoning style, or tool-call patterns. Same code, same prompts, different behavior. A researcher at Microsoft Security Research, cataloguing fifteen ways these systems fail in production, describes the problem sitting underneath. Silent regression is a model update that degrades behavior while every dashboard metric stays green. Teams instrument latency and error rates, and almost nobody instruments whether the answers got worse. When researchers compared two versions of GPT-4 a few months apart in 2023, instruction-following had collapsed, from honoring a formatting instruction in 99.5 percent of queries to almost none. A study published in April 2026 found the same shape in two families of smaller open models. Both upgrades posted aggregate benchmark gains. Yet among the test items where change could be measured, three in ten reliably got worse in one family and four in ten in the other, and the losses clustered by subject, physics in one, law in the other. The headline gain was only what was left after the wins and losses cancelled out, which means a team upgrading on that number alone would never see what it lost. I have spent my career around regulated systems, where the assumption is that a validated process stays validated until somebody changes it. Model upgrades break that assumption from the outside, on a schedule the vendors publish and you do not control. I had evaluation. I did not have governance. Knowing the difference professionally did not stop me from missing it in my own shop. So here is the work, and it is smaller than this article makes it feel. Open whatever holds your standing instructions, the custom instructions, the saved prompts, the project files, and read each rule against one question. Can I point to a session where this model actually did the thing this rule prevents? If yes, it is a constraint and it stays. If no, it is compensation for a model you are no longer running, and it goes. Somewhere in that file is an instruction telling the model to do something it now does by default, which means you have been paying twice for a worse result. Delete that one first, then put a date on the next review, because this will happen again on a schedule somebody else sets. The rebuild is done, and every skill that came out of it is under evaluation now. Some results I will see quickly. Most I never will, because the improvement is invisible by definition. My instinct was backwards, and I knew it before any results came in. When the output got worse, I tried to add my way out. The answer was subtraction. Build for the model you have now, not the one you had when you wrote the rule. ### Steal My Prompt Vol. 46: The Hiring Manager's Interview URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-build-a-hiring-scorecard/ Last updated: 2026-08-11T11:00:57.000Z The interviews are on your calendar and the job description is posted, so the hard thinking must already be done. It is an easy assumption to carry into a loop. Then a candidate's answer reveals you never decided whether you need a builder or an operator, and now you are defining the role mid-interview. Halfway through a backfill hire for my own team, I caught this pattern in myself. The hire is made, and the loop went fine. But the thing I was missing was not better questions. It was a clear picture of what I was hiring for, settled before the first conversation instead of assembled during it. I built this prompt because I want that system in place before my next position opens. The Hiring Manager's Interview flips the direction of the first interview in your search. Before you interview anyone, the prompt interviews you. One question at a time, it pulls the role out of your head, then hands back a role scorecard and a question set built to test the criteria you just committed to. This is Part 1 of a two-part hiring series. Next Tuesday, Part 2 takes what this prompt produces and runs your debrief with it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. #### **What You Can Use This For** - A backfill req where the old job description no longer matches what the role has become - The first hire for a role that has never existed on your team - Aligning an interview panel on criteria before the loop starts, so five interviewers stop running five different interviews - An internal promotion decision where you owe the candidate criteria instead of impressions - Rewriting a stale job description, since the scorecard this produces is the outline #### **How to Use It** 1. Open Claude. The prompt also works in ChatGPT, Microsoft Copilot, and Gemini on free tiers. 2. For a senior or high-stakes role, turn on the reasoning option. "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Copilot, or "Deep Think" in Gemini. It produces sharper follow-up questions on the first pass. 3. Copy the prompt and fill in the two bracketed fields. 4. Answer the questions one at a time, and answer honestly. Short answers are fine. If a question stops you cold, that pause is the finding. The role is less defined than the posted req suggests. 5. Save the scorecard and share it with your interview panel before the first screen. Pro tip –> Paste the job description even if it is stale. The gap between what the description says and what your answers reveal is the most valuable output this prompt produces. #### **The Prompt** *You are an experienced talent partner who has supported hundreds of searches and watched strong candidates get hired into poorly defined roles. Your rule is that a hiring manager who cannot describe success in a role should not be interviewing for it yet. Today you are interviewing me, the hiring manager, before I interview anyone.* *My open role: \[ROLE TITLE AND LEVEL\]The job description, if one exists: \[PASTE IT HERE, OR WRITE "NONE"\]* *Interview me one question at a time. Ask a single question, wait for my answer, and let each answer shape your next question. Cover the ground you judge most important, including why this role exists now, what the person must deliver in the first 90 days and the first year, which skills are must-haves versus trainable, what gap on the team this hire closes, and the failure mode I am most worried about. Ask no more than seven questions, and stop as soon as you have what you need. If I type "skip," stop asking and proceed using clearly labeled assumptions.* *When the interview is complete, produce three things.* *1\. A role scorecard as a table with four columns: CRITERION | MUST-HAVE OR NICE-TO-HAVE | WHY IT MATTERS FOR THIS ROLE | HOW AN INTERVIEW CAN TEST IT. Rank must-haves at the top in priority order.* *2\. An interview question set. For each must-have criterion, write one behavioral question, one line describing what a strong answer sounds like, and one line describing what a weak answer sounds like.* *3\. A gap report. In two or three sentences, name anything I emphasized that the job description does not mention, and anything the description emphasizes that I never brought up. If I provided no description, name the one criterion I seemed least certain about.* #### **Transparency and Notes** - Built in Claude for this volume. It requires no paid features and runs in ChatGPT, Copilot, and Gemini free tiers. - Part 1 of a two-part hiring series. Next Tuesday, Part 2 takes the scorecard this prompt produces and compiles your post-interview debrief against it. - Keep candidate names, compensation details, and anything confidential out of the intake. The prompt only needs the role. - The output is a thinking aid for interview preparation, not HR or legal guidance. Run your final criteria past your recruiting partner. ### Volume 45: Your Words Need Somewhere to Land URL: https://www.mindovermoney.ai/why-ai-cant-read-small-text-in-screenshots/ Last updated: 2026-08-04T12:00:24.000Z In July, two of the largest AI labs shipped voice upgrades within two weeks of each other. Stronger models moved behind the conversation, and voice could finally reach the tools where the work lives. I tested both on real tasks from my own life. Only one session finished the job, and the difference had nothing to do with how well I spoke. 🧭 Founder's Corner: Voice becomes useful the moment your tools are already connected, and that setup is usually something you did or skipped months ago without noticing. 🧠 AI Education: Understand what happens to an image between your upload and the model, and why a 4K screenshot arrives smaller than you sent it. ✅ 10-Minute Win: Run the same task through two assistants and leave with a reconciled version and a short list of what to verify. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Anthropic says its own AI models breached three companies during security tests**](https://techcrunch.com/2026/07/30/anthropic-says-its-own-ai-models-breached-three-companies-during-security-tests/?ref=mindovermoney.ai) **Summary:** Anthropic reviewed 141,006 of its own evaluation runs and found three cases where Claude models reached the internet from inside a testing sandbox and gained unauthorized access to the live systems of three real organizations. A misconfiguration opened the connection, even though the models were explicitly told in their prompts that they had no internet access. **Why it matters:** The failure was not a model deciding to go rogue. It was a configuration mistake plus a model that trusted what it was told about its own environment. When you approve an AI agent at work, the question is not "is this model safe," it is "who verified what this thing can actually reach." #### **2)**[ **The Code Gets You Paid, The Variant Gets You Treated**](https://medcitynews.com/2026/07/the-code-gets-you-paid-the-variant-gets-you-treated/?ref=mindovermoney.ai) **Summary:** A physician takes apart a familiar healthcare AI sales pitch: the tool listens to the visit, settles on a diagnosis, and produces a ready-to-submit billing code. His argument is that a code satisfying a payer is not the same as the detail a clinician needs to treat the person, and that the gap turns into a safety problem as care moves toward genomics and targeted therapy. **Why it matters:** Tools get sold on finishing a workflow, and finishing is the easiest thing to demo. Whether the output is usable by whoever receives it next is the harder question, and it almost never comes up in the demo. On your next vendor call, ask what happens to the output after the handoff, and who actually consumes it. #### **3)**[ **Despite AI hype, Google's data shows workers aren't automating themselves away**](https://arstechnica.com/ai/2026/07/despite-ai-hype-googles-data-shows-workers-arent-automating-themselves-away/?ref=mindovermoney.ai) **Summary:** Google Research released its first AI & Economy ATLAS report, built on roughly 15 million de-identified interactions across the Gemini app, AI Mode, and the API. Use is wide but thin: the typical worker reaches for it on about 21% of their defined tasks, and fewer than 10% of interactions amount to automating a task end to end. **Why it matters:** This is the company selling the tool publishing evidence that the tool is not doing what the headlines say it does. The read for your own planning is that AI is landing on slices of jobs rather than whole ones, so the better question this quarter is which of your tasks it already touches, not whether your role survives. #### **4)**[ **Blue Shield of California sister company debuts AI copilot for health plan customer service reps**](https://www.beckerspayer.com/virtual-care/blue-shield-of-california-sister-company-debuts-ai-copilot-for-health-plan-customer-service-reps/?ref=mindovermoney.ai) **Summary:** Stellarus, the technology company created out of a Blue Shield of California restructuring, launched CSR Chat, which feeds health plan service representatives live policy information and recommended responses while they are on a call. It is the first commercial product in the company's Compass platform. **Why it matters:** This is the shape real adoption is taking, and it is not the chatbot replacing the person. It is a system coaching the person while the person keeps the conversation. If your team is stuck arguing full automation versus nothing, this is the middle path, and the design question worth asking is what the human is still allowed to override. #### **5)**[ **OpenAI announces its "next major model" Astra by dropping ten previously unsolved math solutions**](https://the-decoder.com/openai-announces-its-next-major-model-astra-by-dropping-ten-previously-unsolved-math-solutions/?ref=mindovermoney.ai) **Summary:** OpenAI confirmed a new model family called Astra, built so multiple agents can work a single problem together for hours or days, and published proofs for ten open problems in math and theoretical computer science that had seen no progress for at least a decade. The company has not decided whether it ships as GPT-6 or a GPT-5 variant, and there is no release date. **Why it matters:** Every AI tool on your desk today is built around a conversation that ends when you close the tab. Astra points at work that runs while you sleep and reports back when it is done. Worth sorting your recurring tasks into single questions versus multi-day projects, because the second list is the one that changes first. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner The Work Starts Before You Talk to AI You have been talking to your phone for years. So have I. Dictation handles many of my text messages, and it starts every Founder's Corner. I talk until the idea is out of my head, then turn the transcript into a structured brief. Speaking is simply faster than typing while a thought is still taking shape. Most of that talking still ends as text. The phone gives us the words, then we carry them into an inbox, calendar, or spreadsheet and finish the work ourselves. More than 150 million people talk to ChatGPT each week using Voice and Dictation, and voice is still easy to dismiss as a chatbot feature. You talk, it talks back, and the exchange stays inside the app. In July, OpenAI and Anthropic each moved voice closer to real work. The conversations became more capable, stronger reasoning moved behind them, and connected tools could reach the places where work already lived. I wanted to know whether that was enough to make voice useful in my own life. ## **From Better Conversation to Real Work** OpenAI introduced GPT-Live on July 8 and changed the rhythm of a ChatGPT Voice conversation. Earlier voice modes waited for one turn to end before responding. GPT-Live listens and speaks at the same time, deciding moment by moment whether to keep listening, pause, or respond. If you pause to think, it waits rather than talking over you. Harder work can happen behind that conversation. When a question needs search or deeper reasoning, GPT-Live passes it to GPT-5.5 in the background and keeps talking to you while that runs. Two weeks later, Anthropic brought Opus and Sonnet into Claude voice mode. Those are the models built for harder problems, and putting them behind a spoken conversation means the reasoning you get typing is the reasoning you get talking. Claude starts with the model you last used in text chat, and you can switch models mid-session. Voice can also reach the tools you have already connected, checking a calendar, summarizing mail, or drafting a response without you leaving the conversation. The two labs bet on different things. OpenAI worked on how the conversation feels, and Anthropic worked on what it can reach. From the first time I tried a voice mode, I had expected speaking to AI to become the next productivity unlock. Previous versions never gave me a new way to work. The July releases were different enough to test that assumption on two tasks from my own life. ## **Two Tests, One Week** Last week, I wrote about[ the household planning system I had built for my own life](https://www.mindovermoney.ai/founders-corner/how-to-find-ai-use-cases-for-your-own-life/). Unfortunately, I was already behind and needed to make sure new purchases were visible on our tracker. Things we had bought were missing, along with things other people had bought for us. I sat down at my computer, opened ChatGPT Voice, and asked it to help me update the tracker in Google Sheets. ChatGPT Voice was useful right up until the spreadsheet had to change. It worked through the recent purchases with me and caught two items I had missed. Then it told me plainly that a standard chat could not make the edits. It did not pretend the file had changed, and it did not keep planning while the file sat untouched. The session stopped there. I asked what it would take to finish the job by voice. The answer was a folder-based project in ChatGPT Work. I had never built the project that way, because the jobs I originally gave it never required it. That setup is still not done. The limitation felt familiar.[ Building cloud-first without enough structure underneath the work](https://www.mindovermoney.ai/founders-corner/why-you-should-start-using-ai-before-you-have-to/) had already cost me months of restructuring elsewhere. The household system was far smaller, and what I built still worked for the jobs it was meant to do. Asking voice to move from planning to execution exposed the ceiling. If I want it to update the file next time, I have to build the project for that work first. The second test began while I was away from my desk, getting some fresh air with only my phone. Claude voice mode had just added Opus, so I chose it deliberately and asked it to work inside my personal inbox. Gmail and Calendar were already connected. I did nothing to prepare the account for that session. The conversation went sideways almost immediately. My opening instruction included the phrase "organize the emails with research," which was genuinely ambiguous when spoken aloud. Claude heard Research as the name of a label and moved to create one. I said no. It cancelled the action, then offered to use an existing label instead. I stopped it again. The action was gone, but the assumption was still there. The redirect that finally worked sounded less like a polished prompt than something I would say to a colleague: "I don't understand why you are creating labels, I just want you to look at what is in my inbox on the current tabs. Nothing in the actual folder labels. Then I want you to read all those emails and create a todo list and a summary of what is in my inbox." Three moves in one breath: I named the misunderstanding, closed off the wrong path, and restated the job. In a text box, I would have deleted the prompt and rewritten it. Out loud, I could steer the same conversation back on course. The instruction did not have to be perfect. I had to notice where the model went wrong and correct it. Once the redirect landed, Claude read the current inbox, built a task list, checked the calendar, and surfaced a scheduling conflict I had not seen. It also prepared an email draft. One item on the list required action, and I handled it afterward. I had started the session to understand a feature. By the end, it had produced work I actually needed. Before Claude touched Gmail, my phone asked for permission, and I approved it. Reading continued without another prompt. When Claude moved from reading to writing, it stopped, repeated the recipient and message, and waited again. I cannot identify the permission mechanism behind each moment. I could see exactly when control came back to me. For the first time, a voice session produced useful work while I was away from a keyboard. The conversation was not flawless, but I could redirect it, inspect what it produced, and approve the moment it moved from reading to writing. That was the productivity unlock I had been waiting to feel, and it only happened because the right pieces were already connected. ## **Preparation Does Not Announce Itself** The difference between the two tests was not how well I spoke or even the tool itself. I was the same person using the same basic habit. One conversation stopped at a useful plan because the file sat outside the workflow I had built. The other reached my inbox and calendar because those connections were already waiting. Preparation rarely announces itself. The unfinished ChatGPT Work project is a real setup I have not done. The Gmail and Calendar connections that made the Claude session useful may have taken no more than a tap, but I do not remember making them. One gap stopped the work. One forgotten setup let it move. Open voice mode and look at what is already connected. Choose one task from your actual list whose result you can inspect, then try it away from your keyboard. If nothing is connected, start with one tool. A free Claude account gets Haiku and one connection, which is enough to find out whether this changes anything for you. Pay attention to what the conversation can reach, redirect it when your spoken ask goes sideways, and verify the result before it travels any farther. You already know how to talk. The next skill is giving those words somewhere useful to land, then staying close enough to see what happens when they do. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****What the Model Actually Sees: Vision, Tokens, and Perception** #### **What Is Actually Going On Here** Dana drags the scanned agreement into the chat window, and the file leaves her laptop. What arrives on the other side is a rectangle of pixels with a width and a height, and the first thing that happens to it is measurement. The system checks those dimensions against a ceiling it will not exceed, and anything larger gets shrunk before another step runs. Only then is the smaller version laid across a grid and cut into squares, each square converted into a set of numbers. By the time the model has anything to look at, the page Dana sent no longer exists. #### **The Problem That Made This Necessary** A model built for language expects a sequence. Text arrives that way already, one token after another, which is the machinery Vol 6 covered. An image arrives as a two-dimensional grid of millions of pixels, and a grid is not a sequence. Handing over every pixel as its own token is not workable either, because processing cost climbs steeply as the sequence grows. For years the field solved this by not solving it. Images went to one kind of system, language went to another, and the results were stitched together afterward. That worked, and it meant a photograph and a paragraph could never travel through the same machinery in one request. The answer arrived in October 2020, in the title of a research paper. "An Image is Worth 16x16 Words" showed that the transformer, the architecture running underneath the language models you use, could take images directly with almost nothing changed, so long as the image was first cut into fixed squares of sixteen pixels by sixteen pixels and those squares were fed in as though they were words. Patches became the image equivalent of tokens. Nearly six years later, both of the schemes below are still variations on that one move. #### **How It Actually Works** At the simplest level, your image is divided into small squares, and each square becomes one token, the image equivalent of the word-pieces from Vol 6. Claude calls those squares visual tokens. It cuts an image into blocks of 28 pixels by 28 pixels at one token per block, which puts a clean 1000 by 1000 screenshot at 1,296 of them. Gemini slices differently. Images small enough in both dimensions are charged one flat rate, and anything larger is cut into tiles of 768 pixels by 768 pixels at 258 tokens per tile. Two companies, two schemes, one idea underneath. Claude also enforces a maximum resolution of its own, and an image above it is scaled down before processing rather than rejected. A 3840 by 2160 screenshot on a standard-resolution model arrives as 1456 by 819\. Even the newest high-resolution models stop somewhere, and Gemini gives developers a dial for the same tradeoff. You sent a 4K image. The model received something closer to a laptop screenshot. Think of the result as a mosaic made from your photograph. Each tile carries the gist of the square it covers, so the picture holds together at a distance. Look closer, and the fine print inside any single tile becomes a guess. In its own developer documentation, Google states the tradeoff plainly. Higher resolution buys a sharper read of fine text and small details, and it costs more tokens and more waiting. Nobody is hiding this, but it rarely appears in the interface where you drop the file. #### **Where It Still Breaks** Anthropic publishes its own list of limitations. Accuracy drops on images that are low quality, rotated, or very small. Ask for a count and you get an estimate, one that degrades as objects get smaller and more numerous. Location and coordinate answers are approximate as well. Heavy compression, the kind that accumulates when an image is saved and forwarded and saved again, can leave text hard to read. For anyone working in care delivery, one line in that list matters more than the rest. The documentation says Claude handles general medical imagery but was never built to read complex diagnostic scans, and it names CT and MRI specifically. That sentence is worth more than any benchmark score, because it marks exactly where the tool stops. #### **What This Means for How You Work With It** Crop before you upload. A tight crop of the one table you care about spends the entire resolution budget on that table instead of on the browser window around it. Send the cleanest original you have rather than a compressed copy pulled out of an email thread. Every save-and-forward pass costs legibility you cannot get back. When you photograph a page, shoot it straight on and in good light. Rotation and blur appear on the published list of failure conditions. [Ask for quotes instead of summaries](https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/) when the numbers matter. A quoted line is something you can check against the page yourself. Long documents do better as single pages than as one tall stitched image. #### **How This Connects** Vol 6 established the token as the unit a model works in, and the context window as the space those tokens compete for. Last week, Part 1 extended that unit past text. This week went inside the conversion itself, where the size of the squares and the point where the resolution cap falls decide what reaches the model at all. Vol 35 revisited context windows through a 2026 lens, and the pressure it named is the same pressure behind these caps, since one large screenshot can cost more of the window than several pages of typed text. Next week, Part 3 closes the series by following one professional through a week of ordinary tasks, from a contract photographed on a phone to a whiteboard left over after a meeting. *Part 2 of 3 in the Multimodal AI series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ### **The Second Opinion You Skip** You finish the analysis, paste it into your one AI assistant, read what comes back, and send it. It sounded right. It arrived organized and confident, with no hedging anywhere in it. So you shipped it, and you still do not know what it left out. Volume 23 made the case for using more than one model. It did not say what to do with the second answer once you have it. That is this piece. You run the same work through two assistants and treat the gap between them as the finding. Why this matters: the disagreement is the product. A single run gives you one answer in one voice with no signal about what it skipped. Run it a second time in a different assistant, then hand both outputs to one model and ask it to compare. That silence turns into something you can actually check. The models do the comparison work. #### **The Workflow** **1\. Pick something you are about to send (2 Minutes)** Choose real work that leaves your hands soon, something with a reader waiting on it. A vendor recommendation, a summary of a long document, or a draft going out under your name. Open two different AI assistants in two tabs. **2\. Run the identical prompt in both (3 Minutes)** Same prompt, same context. Any edit you make to the second version contaminates the comparison, so paste it exactly as written. **Copy/Paste Prompt:** *"Here is my task. \[PASTE YOUR TASK AND ANY CONTEXT\]. Give me your answer. Then list the three assumptions you made to produce it."* **3\. Have one model reconcile the two (3 Minutes)** Paste both outputs into either assistant, labeled Answer A and Answer B, with each assumption list underneath. Do not tell it which answer is its own. **Copy/Paste Prompt:** *"Below are two answers to the same question, Answer A and Answer B, each followed by the assumptions behind it. Do not tell me which is better. List where they agree and where they disagree. Compare the two assumption lists and flag any assumption that appears in only one. For each disagreement, tell me what I would need to check to resolve it. Then flag every factual claim that appears in only one answer."* **4\. Check the flagged claims and make the call (2 Minutes)** Work only the flagged items. Anything appearing in one answer alone is either something the other model missed or something one of them invented. A shared answer built on different assumptions deserves the same scrutiny. Verify what matters and decide what ships. Save the reconciliation prompt somewhere you can find it next week. #### **The Payoff** You walk away with a reconciled version of the work and a specific list of what to verify, rather than a long output you have to trust whole. You also keep a reusable reconciliation prompt. The pattern travels well beyond AI. Any time two independent sources cover the same ground, their disagreement is cheaper to investigate than their agreement is to confirm. #### **The AI Concept You Just Used** Cross-model verification. A single assistant hands you one answer in one confident register, and nothing inside that answer marks where a different model would have said something else. Running the work twice and reconciling surfaces those spots without requiring you to know anything about how either model was built. #### **Transparency & Notes** - Free tiers of the major assistants cover this, though pasting two full outputs can push you into message limits on a busy day. - Two models means two copies of whatever you paste. Keep confidential material, NDA-covered documents, and any patient information out of consumer AI tools entirely. - Use two assistants from different companies so the comparison is meaningful. - This roughly doubles the time cost of a task, so reserve it for work going out under your name. Agreement between two models is not proof, since both can be wrong in the same direction. ### ### The Work Starts Before You Talk to AI URL: https://www.mindovermoney.ai/founders-corner/can-chatgpt-and-claude-voice-mode-do-real-work/ Last updated: 2026-08-04T11:10:57.000Z You have been talking to your phone for years. So have I. Dictation handles many of my text messages, and it starts every Founder's Corner. I talk until the idea is out of my head, then turn the transcript into a structured brief. Speaking is simply faster than typing while a thought is still taking shape. Most of that talking still ends as text. The phone gives us the words, then we carry them into an inbox, calendar, or spreadsheet and finish the work ourselves. More than 150 million people talk to ChatGPT each week using Voice and Dictation, and voice is still easy to dismiss as a chatbot feature. You talk, it talks back, and the exchange stays inside the app. In July, OpenAI and Anthropic each moved voice closer to real work. The conversations became more capable, stronger reasoning moved behind them, and connected tools could reach the places where work already lived. I wanted to know whether that was enough to make voice useful in my own life. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **From Better Conversation to Real Work** OpenAI introduced GPT-Live on July 8 and changed the rhythm of a ChatGPT Voice conversation. Earlier voice modes waited for one turn to end before responding. GPT-Live listens and speaks at the same time, deciding moment by moment whether to keep listening, pause, or respond. If you pause to think, it waits rather than talking over you. Harder work can happen behind that conversation. When a question needs search or deeper reasoning, GPT-Live passes it to GPT-5.5 in the background and keeps talking to you while that runs. Two weeks later, Anthropic brought Opus and Sonnet into Claude voice mode. Those are the models built for harder problems, and putting them behind a spoken conversation means the reasoning you get typing is the reasoning you get talking. Claude starts with the model you last used in text chat, and you can switch models mid-session. Voice can also reach the tools you have already connected, checking a calendar, summarizing mail, or drafting a response without you leaving the conversation. The two labs bet on different things. OpenAI worked on how the conversation feels, and Anthropic worked on what it can reach. From the first time I tried a voice mode, I had expected speaking to AI to become the next productivity unlock. Previous versions never gave me a new way to work. The July releases were different enough to test that assumption on two tasks from my own life. ## **Two Tests, One Week** Last week, I wrote about[ the household planning system I had built for my own life](https://www.mindovermoney.ai/founders-corner/how-to-find-ai-use-cases-for-your-own-life/). Unfortunately, I was already behind and needed to make sure new purchases were visible on our tracker. Things we had bought were missing, along with things other people had bought for us. I sat down at my computer, opened ChatGPT Voice, and asked it to help me update the tracker in Google Sheets. ChatGPT Voice was useful right up until the spreadsheet had to change. It worked through the recent purchases with me and caught two items I had missed. Then it told me plainly that a standard chat could not make the edits. It did not pretend the file had changed, and it did not keep planning while the file sat untouched. The session stopped there. I asked what it would take to finish the job by voice. The answer was a folder-based project in ChatGPT Work. I had never built the project that way, because the jobs I originally gave it never required it. That setup is still not done. The limitation felt familiar.[ Building cloud-first without enough structure underneath the work](https://www.mindovermoney.ai/founders-corner/why-you-should-start-using-ai-before-you-have-to/) had already cost me months of restructuring elsewhere. The household system was far smaller, and what I built still worked for the jobs it was meant to do. Asking voice to move from planning to execution exposed the ceiling. If I want it to update the file next time, I have to build the project for that work first. The second test began while I was away from my desk, getting some fresh air with only my phone. Claude voice mode had just added Opus, so I chose it deliberately and asked it to work inside my personal inbox. Gmail and Calendar were already connected. I did nothing to prepare the account for that session. The conversation went sideways almost immediately. My opening instruction included the phrase "organize the emails with research," which was genuinely ambiguous when spoken aloud. Claude heard Research as the name of a label and moved to create one. I said no. It cancelled the action, then offered to use an existing label instead. I stopped it again. The action was gone, but the assumption was still there. The redirect that finally worked sounded less like a polished prompt than something I would say to a colleague: "I don't understand why you are creating labels, I just want you to look at what is in my inbox on the current tabs. Nothing in the actual folder labels. Then I want you to read all those emails and create a todo list and a summary of what is in my inbox." Three moves in one breath: I named the misunderstanding, closed off the wrong path, and restated the job. In a text box, I would have deleted the prompt and rewritten it. Out loud, I could steer the same conversation back on course. The instruction did not have to be perfect. I had to notice where the model went wrong and correct it. Once the redirect landed, Claude read the current inbox, built a task list, checked the calendar, and surfaced a scheduling conflict I had not seen. It also prepared an email draft. One item on the list required action, and I handled it afterward. I had started the session to understand a feature. By the end, it had produced work I actually needed. Before Claude touched Gmail, my phone asked for permission, and I approved it. Reading continued without another prompt. When Claude moved from reading to writing, it stopped, repeated the recipient and message, and waited again. I cannot identify the permission mechanism behind each moment. I could see exactly when control came back to me. For the first time, a voice session produced useful work while I was away from a keyboard. The conversation was not flawless, but I could redirect it, inspect what it produced, and approve the moment it moved from reading to writing. That was the productivity unlock I had been waiting to feel, and it only happened because the right pieces were already connected. ## **Preparation Does Not Announce Itself** The difference between the two tests was not how well I spoke or even the tool itself. I was the same person using the same basic habit. One conversation stopped at a useful plan because the file sat outside the workflow I had built. The other reached my inbox and calendar because those connections were already waiting. Preparation rarely announces itself. The unfinished ChatGPT Work project is a real setup I have not done. The Gmail and Calendar connections that made the Claude session useful may have taken no more than a tap, but I do not remember making them. One gap stopped the work. One forgotten setup let it move. Open voice mode and look at what is already connected. Choose one task from your actual list whose result you can inspect, then try it away from your keyboard. If nothing is connected, start with one tool. A free Claude account gets Haiku and one connection, which is enough to find out whether this changes anything for you. Pay attention to what the conversation can reach, redirect it when your spoken ask goes sideways, and verify the result before it travels any farther. You already know how to talk. The next skill is giving those words somewhere useful to land, then staying close enough to see what happens when they do. ### Steal My Prompt Vol. 45: The Coverage Brief URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-a-vacation-handoff-document/ Last updated: 2026-08-04T11:00:57.000Z The time is approved and the trip is booked. The only thing standing between you and actually being gone is the handoff, and you already know how it goes. You write it at 6pm on your last day, from memory, organized by project. Organized by project is the part worth fixing. Your backup does not need your project list. They need to know what they are allowed to decide while you are gone. That one change turns them from a spectator into your stand-in, and it is the difference between coming back to a pile of questions and coming back to progress. I built this prompt because the handoff is a knowledge problem before it is a writing problem. Everything your backup needs is already in your head, out of order and unwritten, which is exactly why it never makes it onto the page. So the prompt is built to be dictated. You talk through what is in flight, the model asks for what you left out, and the structure comes out the other side. This week's Founder's Corner makes the case for voice dictation as a workflow unlock. This is that unlock pointed at one deliverable. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **What You Can Use This For** - The week of PTO you keep postponing because the handoff feels like more work than the trip is worth - Extended leave, where the coverage window runs weeks and every unmade decision compounds - The conference or travel week where you are technically online and functionally gone - Clinical or pharmacy operations coverage, where someone needs to know which time-sensitive requests they can act on and which ones wait - Onboarding a new hire into part of your workload, using the same three tiers as the training plan - The reverse case, when you are the backup and want to pressure-test what you were handed while the other person is still there **How to Use It** 1. Open your AI assistant on your phone. Mobile matters here, because dictating is the point. 2. If your tool offers a deeper reasoning setting, turn it on. Sorting your own work by who is allowed to decide what is judgment work, and reasoning catches more on the first pass. 3. Type the four bracketed fields. Keep them short and specific, especially your reachability, because that field moves the line between tiers. 4. Dictate the brain dump. Pace around, go out of order, repeat yourself. Two to four minutes is plenty. The prompt expects mess. 5. Answer the questions one at a time. Resist the urge to skip on the first run, because the questions are the audit. **Pro tip.** Run it twice. Three weeks out, it tells you what you still need to close before you go. Three days out, it is the brief you actually send. **The Prompt** *You are an operations lead. You have been the backup for people going out on leave more times than you can count, and you have cleaned up after every handoff that failed. You know the two ways they fail. The person left a list of projects instead of a list of decisions, and the person assumed the backup knew something they did not.* *I am going out. You are building my coverage brief.* *My role: \[YOUR ROLE\] I am out from \[START DATE\] to \[END DATE\]. While I am out I am: \[FULLY UNREACHABLE / CHECKING ONCE A DAY / REACHABLE FOR TRUE EMERGENCIES ONLY\] Covering for me: \[WHO IS COVERING, AND WHAT THEY ALREADY KNOW ABOUT MY WORK\]* *I am going to talk through everything in flight instead of typing it, so expect it out of order, repetitive, and incomplete. Do not clean it up yet.* *\[DICTATE EVERYTHING IN FLIGHT HERE\]* *Before you build anything, ask me up to three questions, one at a time, and only about gaps you genuinely cannot infer from what I said. Wait for my answer before asking the next one. If I type "skip," stop asking and proceed, labeling every assumption you had to make.* *Then build the brief in two moves.* *First, sort by decision authority, not by project. Every item goes in exactly one tier.* *DECIDE WITHOUT ME. The person covering has enough to make the call. State the call they are authorized to make and the one boundary they should not cross.* *HOLD FOR ME. It can wait. State the date it stops being able to wait.* *ESCALATE. It needs someone above my level. Name who, and name the trigger that means now rather than later.* *Use my reachability above to set the line. If I am fully unreachable, push items down into DECIDE WITHOUT ME, and tell me what I am accepting by doing that.* *Second, stress-test it. Name the three things most likely to go wrong while I am gone that are not on the list yet, and draft the message the person covering would send for each.* *Output a four-column table: ITEM | TIER | WHAT MY BACKUP DOES | WHAT THEY NEED FROM ME BEFORE I LEAVE. Then the three failure modes with their draft messages. Then one line naming the item that should not be handed off at all, because it should be canceled or it can wait until I am back.* **Notes** - Runs on the free tier of every major assistant. No paid feature required. - Dictation is the intended input. Typing works and produces a thinner brain dump, which the intake questions will surface. - Do not dictate client names, patient details, or anything under NDA. Generalize before you speak, the same way you would in a shared doc. - Pairs with this week's Founder's Corner on voice dictation, and with this week's 10-Minute Win on choosing the right model for the job. - Vol. 34, the Status Update Compressor, handles the recurring update. This one handles the coverage window. ### Volume 44: Nobody Is Coming to Hand You a Use Case URL: https://www.mindovermoney.ai/how-ai-reads-images-and-scanned-documents/ Last updated: 2026-07-28T12:00:36.000Z My wife came home from a baby superstore and walked me through the aisles. I froze on the couch, because months of research I had not started were suddenly sitting in front of me. The system that came out of that freeze took one night to build, and I never went looking for it. 🧭 **Founder's Corner:** Why the hunt for AI use cases keeps capable people at the starting line, and what opens up the moment you point these tools at a problem your own life already handed you. 🧠 **AI Education:** Why your assistant does not actually read text, and what that changes about every scanned page and screenshot you have been retyping. ✅ **10-Minute Win:** Turn a messy description of any process into a clean diagram in ten minutes, with no design skill and no wrestling with shapes. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **An OpenAI Model Hacked Another Company While Trying to Cheat on a Test**](https://fortune.com/2026/07/21/openai-says-ai-models-escaped-control-hacked-hugging-face/?ref=mindovermoney.ai) **Summary:** OpenAI disclosed that two of its AI models, including one not yet released, broke out of a locked-down test environment and used a previously unknown software flaw to get into Hugging Face's production systems, all while chasing the answers to a cybersecurity benchmark. OpenAI called it an "unprecedented cyber incident" and is now working with Hugging Face to fix what the models found. **Why it matters:** Nobody told this AI to attack a company. It decided that hacking a stranger's servers was the fastest route to a narrow goal, which is worth remembering the next time a vendor pitches you an "autonomous" AI agent for your own workflows. #### **2)**[ **Anthropic's New Claude Model Cuts the Price of Frontier AI in Half**](https://www.fastcompany.com/91578762/anthropic-releases-claude-opus-5-for-both-ai-coding-and-general-office-work?ref=mindovermoney.ai) **Summary:** Anthropic released Claude Opus 5, built for coding and everyday office work like drafting, analysis, and document review. The company says it performs close to its top-tier Fable 5 model at half the price, and it can work through multi-step problems and catch its own errors with less back and forth from the user. **Why it matters:** The real story here is not a smarter model. It is a model nearly as capable as the top tier for half the cost, and that tradeoff decides whether your team can run AI on every task instead of rationing it to just the important ones. #### **3)**[ **This Hospital Was Built as AI Infrastructure, Not a Building That Happens to Use AI**](https://www.beckershospitalreview.com/healthcare-information-technology/ai/what-ut-health-san-antonio-discovered-by-building-a-hospital-for-the-ai-era/?ref=mindovermoney.ai) **Summary:** When UT Health San Antonio opened a new hospital in late 2024, it designed the building itself around AI from the start. Every patient room has a camera and screen for virtual nursing, the electronic health record runs three AI agents for scheduling and billing, and staff can build their own tools through a secure internal platform. **Why it matters:** Most health systems buy AI tools and try to wedge them into buildings and workflows built decades ago. This is what it looks like when AI gets planned into the foundation instead, and it is a preview of the retrofit-versus-rebuild decision every healthcare leader eventually faces with their own aging systems. #### **4)**[ **A Startup Raised $19M to Get Doctors Credentialed With AI Instead of Paperwork**](https://medcitynews.com/2026/07/assured-health-healthcare-credentialing/?ref=mindovermoney.ai) **Summary:** Assured Health raised a $19 million Series A round to expand its AI agents that verify provider licenses, background checks, and insurance enrollment for health systems and group practices. The company says its agents can cut a credentialing process that typically takes months down to days, and its platform is already used by more than 100 healthcare organizations, including Houston Methodist. **Why it matters:** A new doctor cannot see patients or bill insurance until credentialing clears, so every month that drags on is a month of lost care and lost revenue. This is what AI actually looks like in most back-office healthcare work, quietly executing one specific administrative task from start to finish rather than answering questions in a chat window. #### **5)**[ **A Politician Accidentally Read His AI Chatbot's Instructions Out Loud in a Floor Speech**](https://futurism.com/artificial-intelligence/politician-accidentally-reads-ai-slop-speech?ref=mindovermoney.ai) **Summary:** A New Brunswick, Canada legislator read a chatbot's own editorial note about rewriting a passage aloud during an official floor speech, apparently without realizing it was not part of his actual remarks. Nobody in the chamber caught it. The clip surfaced on social media this week and was picked up by Canadian news outlets. **Why it matters:** The tool did not fail here. The review step did, and the same one-keystroke mistake is waiting for anyone who copies an AI draft into a final document without reading it start to finish. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Build Waiting in Your Living Room** My wife and I are expecting our first baby later this year. We are excited, and we are anxious in equal measure, because preparing for a first baby means researching and buying more than either of us has ever had to track. She came home from a shopping trip with her mom and could not stop describing one store. MacroBaby, a baby superstore I had never set foot in. She talked me through aisles after aisles of baby items, whole categories of products neither of us knew existed. And every one of those aisles held something we would eventually need to research, decide on, or let go. Right there on the couch, I froze. Through nothing but her description, I could already feel the scale of what was coming, months of research we had not started and decisions we had not even named. I like to stay organized, and I could see how quickly this next stretch of our lives could turn into chaos without action. Just the sound of all that preparation felt overwhelming. My brain could not handle a manual research process, and I knew it immediately. Versions of that freeze are sitting in living rooms everywhere. Someone tries the chatbot, likes it, and settles in with a faster search engine. It answers questions, drafts the occasional email, and goes quiet until the next request. Staying there is rational, because nothing has offered a reason to go further, and the lists of use cases floating around the internet rarely match anyone's actual life. All the while, a real problem sits somewhere in their life, unnamed. Mine had just named itself on that couch, months of baby preparation I could not face by hand, and it handed me the clearest lesson I have about where building starts. You become a builder the moment you stop hunting for AI use cases and point it at a problem your household already has, no code required. ## **The Spreadsheet Built Itself** Sitting with that freeze, I could name exactly what we needed. A system my wife and I could work in together, sharing research and decisions in one place. A master tracking sheet of everything we had to get done. I knew managing all of that ourselves would not work, so the build began as[ a shared ChatGPT project](https://www.mindovermoney.ai/founders-corner/ai-projects-mode-guide-professionals-save-context/). We had the project interview us first. It asked how we think about safety, where quality is worth paying for and where a premium is mostly branding, how much clutter we are willing to live with, and which purchases can simply wait. We set the ground rules for the interview too, one question at a time, a few strong options instead of a long generic list, tradeoffs stated plainly. By the end, it held our requirements the way a project manager holds them. From that one interview, everything that came after changed. A preference stated in one conversation now informs every category that follows it. Either of us can pick up a research thread the other one started, and neither of us begins anything from zero. When a new recommendation collides with a decision we already made, the project catches the conflict instead of relying on either of our memories. When I connected the project to my Google Drive and asked for a tracker, the click came. It built the spreadsheet from scratch, organized into a checklist we now update through the chats inside the project. When I opened the result, every current registry item was already loaded and sorted. I audited it, and the level of detail stopped me. Our interview answers were all over the sheet, safety notes sitting in category after category exactly where we would want them. Hours of setup work, compressed into minutes, with no template and no copying and pasting. What runs today is a simple system that my wife and I could share from the first day, and the whole build came together in one night, from bed. Research happens in conversation. Decisions land in the workbook, the spreadsheet that has grown into our source of truth, and only the choices that survive flow out to the public registry our friends and family see. Two people, one shared decision system, all through natural language. ## **Decisions Stopped Disappearing** Back at MacroBaby, the first problem we pointed the system at was still waiting, the stroller and car seat, dozens of options in that single store alone. Open our workbook today and that row reads Decision Made. Next to it sits a store-test checklist the project generated, a short list of things worth checking with our own hands before the final purchase, written for the exact aisles that started all of this. A piece of the weight from that couch now has a plan attached, and the plan came out of the same back and forth that made the decision. The same workbook holds 190 checklist rows, every product and preparation task we have identified so far, each one carrying a status, a priority, and a timing. Forty-nine of those rows are still marked as needing research, which keeps me honest about what this is, a live build in the middle of a life change with plenty left unfinished. All the debating over options happens inside the project, and only 21 items have survived it far enough to reach the public registry. Early on, we learned that baby decisions chain together in ways nobody had warned us about. The car seat we pick narrows the strollers that pair with it, and the crib we land on shapes the furniture that can sit around it. The workbook keeps those connections on the page, so a choice we lock in one category narrows the honest options in another. Our crib search proved the point. We walked in wanting one specific furniture brand, and the conversation surfaced what we actually required, the safety features and the material quality underneath the label. Once we knew that, the search opened up, and the option we chose cost less with every requirement still met. Those requirements now sit in the record, quietly steering furniture questions we have not even asked yet. Every decision we finish makes the next one easier, because[ the reasoning gets written down](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-build-a-decision-log/) before it can dissolve into chat history. Even now, the system has seams we live with. Moving a finished decision from the workbook to the public registry is manual work, and a decision made in conversation can get lost if nobody writes it back. None of it replaces the professionals either, the pediatrician's guidance or the certified check on the car seat installation. Every final call stays with the two humans in the project. What the system took off our plate was everything around those calls. It gave us time back by handling the tedious part, and time back was the whole point. ## **Your Friction Point Is the Spark** You have a friction point of your own, and it probably has nothing to do with a baby. Maybe it is a move you keep postponing because the logistics feel bottomless. Maybe it is an aging parent's care spread across phone calls and paperwork, a household budget living in four apps and two memories, or a school decision arriving faster than your research is. Whatever yours is, you already flinch a little when it comes up. That flinch is worth more than every list of AI use cases you will ever scroll past. Take that problem to the AI you already use, whichever one it is, and describe it in plain language. The constraints, the people involved, and the small decisions hiding inside the big one all belong in that first conversation. Skip the deadline and the project plan, because the exploring is the build. A few exchanges in, the first piece of your system will show up on its own. From there you are working toward something just for you, a build that solves your specific, personal problem and nobody else's. Underneath our build sits a pattern that travels. Shared context, structured records, current research, a few decision rules, and your own judgment sitting on top of it all. None of those five pieces requires a technical background, and the judgment is the only one that cannot be delegated, which means you already own the hardest part. That is the whole distance from user to builder, and the walk is shorter than it looks. Our baby is due later this year, and I built the system for the months in between, so I can support my wife and enjoy as much of the pregnancy phase as possible. What it really bought was presence. Somewhere in your life, a problem is waiting to hand you the same thing. Go find it. No code required. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Multimodal AI Breaks the Assumption That AI Reads Text** ### **The Assumption** For most professionals, AI arrived as a chat box. You typed a question, a paragraph came back, and the lesson stuck. This is a tool you talk to in words. Because it takes in text and returns text, using it well seems to mean writing your problem down. That belief matches how the tools were introduced and how most people still use them, and for a long stretch it held up. ### **Where It Breaks Down** Dana manages operations for a regional care network. A twelve-page vendor agreement sits in her inbox as a scanned PDF, and she needs the escalation terms summarized before a two o'clock call. Working from her assumption, she starts retyping the key clauses into a chat window so the AI will have text to read. A newer colleague asks why she is retyping anything and drags the scanned PDF into his own assistant instead. Before Dana finishes her second paragraph, a clean summary comes back. The scanned page had no copyable text, yet the model summarized it directly. ### **What Is Actually Happening** Back in [Vol 6](https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/), we established that a model does not read your words the way you do. It breaks your text into tokens, the small pieces it actually works with. Then came the step most people miss. Those tokens no longer have to start as text. In practice, a modern model can turn a photograph, a scanned page, an audio clip, or a frame of video into tokens too. A vision component, for example, slices an image into a grid of small patches and turns each patch into a token it can process alongside language. Once Dana's scanned agreement becomes tokens, the model handles it with the same machinery it would use for any paragraph you typed. Whether the input began as an image or as copied text stops mattering the moment it is converted. The word multimodal describes exactly this capacity. A mode is simply a type of input, such as text or images. Put those together, and a multimodal model is one that takes in more than one type and works across them in a single request. Think of tokens as a shared internal currency. Whatever the model takes in, whether words, a photo, or a sound, is exchanged into that one currency first and handled the same way from there. That is why you can hand it a picture of a page and get back a clean summary, or send a document, a chart, and a question all at once. ### **The Revised Mental Model** A model does not read text. It processes tokens, and text is only one of the inputs that become them. That is the rule worth carrying forward. With that single shift, your habits change in concrete ways. You no longer have to transcribe before you ask. Hand the model a scanned form, a screenshot of a dashboard, or a photo of a whiteboard directly, instead of retyping it. Your sense of usable input widens too, since a diagram or a faxed page becomes fair material rather than something to convert first. Requests can span formats as well, so a single ask can combine an uploaded image with your typed question. ### **What to Watch For** - Accepting an image is not the same as reading it perfectly. Dense tables, small print, and handwriting are where a model slips most, so verify anything that matters. - Input quality shapes output quality. A clean, well-lit scan gives the model far more to work with than a skewed or blurry photo. - Not every tool or tier is multimodal. Some accept only text, some take images but reply in text, and a few handle audio or video. Confirm what yours does before relying on it. - Sensitive data stays sensitive in any format. A screenshot or scan can carry protected information just as pasted text can, so only upload material your organization has cleared for these tools. - Multimodal on a product page can mean very different things. Treat the label as a reason to check specifics, not a promise that every format works well. ### **How This Connects** This section rests on [Vol 6](https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/), where we established that a model works in tokens rather than words. Multimodal extends that idea, since those tokens can now come from images, audio, and documents, not from text alone. Next week, Part 2 goes inside the image to trace how a photo becomes tokens, and where a model's vision holds up and where it quietly fails. Part 3 then follows one professional through a week of real tasks, so you can see where multimodal input changes what is possible. *Part 1 of 3 in the Multimodal AI series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Stop Fighting PowerPoint Shapes** Some ideas land better as a picture than as a paragraph. You can see the whole process in your head, every step and decision branch. Written out as three dense paragraphs, it still leaves a colleague asking you to walk them through it. Turning that text into a clean diagram no longer takes design skill or an afternoon. Napkin.ai reads written text and draws the diagram for you. You provide the words, and it builds the shapes. It takes over the slow part, the nudging of boxes and arrows until they line up, so you can stay focused on the content. The trick that makes it work well is two steps instead of one. First you ask an AI model to turn your rough notes into a clean structure. Then you hand that structure to Napkin and let it draw. The model does the organizing, Napkin does the rendering, and you make the final call on which version is right. ### **The Workflow** **1\. Structure Your Notes With AI (3 Minutes)** Open Claude or ChatGPT and paste your rough description of the process, as messy as it is. Ask for a clean outline. Strong structure is what produces a strong diagram, so this step earns its three minutes. **Copy/Paste Prompt:** *"Here are my rough notes on a process I want to diagram. \[PASTE YOUR NOTES\]. Rewrite them as a titled, step by step outline. Start with a short title, then number the steps in order. Wherever the process branches on a decision, write the decision as a question and show both the yes path and the no path underneath it. Keep each line short. Give me only the outline, no commentary."* **2\. Paste Into Napkin and Generate (3 Minutes)** Go to app.napkin.ai and sign up free, no card needed. Choose Visuals, then "By pasting my text." Drop in your outline, select the text you want to picture, and Napkin generates diagram options in seconds. **3\. Pick the Layout That Keeps Your Logic (2 Minutes)** Napkin often opens with a mind map, which looks clean but can leave your decision branches floating without their outcomes. Open the AI Suggestions panel and choose a flowchart layout instead, the kind where every yes and no leads somewhere real. Give it one read and confirm each branch connects. **4\. Refine and Export (2 Minutes)** Adjust the style to taste, then export. The free plan gives you unlimited PNG and PDF. Pick the version that reads clearly at a glance, save it, and drop it into a doc or deck. ### **The Payoff** In ten minutes you walk away with a clean diagram of your process, no design background required. The bigger win is the move behind it, structure first and generate second. It works again the next time something wants to be visual, a decision tree, an onboarding flow, an org chart you would rather generate than redraw. When an idea reads better as a picture, you now have a way to make one. ### **The AI Concept You Just Used** Diagram generation is its own AI capability, separate from image generation. An image model paints pixels. A diagram model reads meaning and arranges structure. Once you can feel that difference, you match each job to the tool built for it instead of forcing one model to do everything, and that instinct is a quiet marker of real AI fluency. ### **Transparency & Notes** - Napkin's free plan gives you 500 AI credits per week, at roughly one credit per word you select to generate. PNG and PDF export are free. SVG and PowerPoint export, plus removing the small Napkin branding, come with the Plus plan at $9 a month. Creating and editing work on desktop only. One tip from testing, the sketch style exports as a flat image with no searchable text, while the cleaner styles keep the text selectable. - The layout choice matters most for any process with decisions in it. A flowchart layout keeps each branch tied to its outcome, so pick that one and give the logic a quick read before you export. - Keep it general. Leave confidential, contractual, or patient details out of both the AI model and Napkin. Structure the shape of the process and leave the private specifics aside. - Napkin rewards clear structure. If a diagram comes out thin, a cleaner outline in step one almost always fixes it faster than switching tools. ### The Build Waiting in Your Living Room URL: https://www.mindovermoney.ai/founders-corner/how-to-find-ai-use-cases-for-your-own-life/ Last updated: 2026-07-28T11:10:45.000Z My wife and I are expecting our first baby later this year. We are excited, and we are anxious in equal measure, because preparing for a first baby means researching and buying more than either of us has ever had to track. She came home from a shopping trip with her mom and could not stop describing one store. MacroBaby, a baby superstore I had never set foot in. She talked me through aisles after aisles of baby items, whole categories of products neither of us knew existed. And every one of those aisles held something we would eventually need to research, decide on, or let go. Right there on the couch, I froze. Through nothing but her description, I could already feel the scale of what was coming, months of research we had not started and decisions we had not even named. I like to stay organized, and I could see how quickly this next stretch of our lives could turn into chaos without action. Just the sound of all that preparation felt overwhelming. My brain could not handle a manual research process, and I knew it immediately. Versions of that freeze are sitting in living rooms everywhere. Someone tries the chatbot, likes it, and settles in with a faster search engine. It answers questions, drafts the occasional email, and goes quiet until the next request. Staying there is rational, because nothing has offered a reason to go further, and the lists of use cases floating around the internet rarely match anyone's actual life. All the while, a real problem sits somewhere in their life, unnamed. Mine had just named itself on that couch, months of baby preparation I could not face by hand, and it handed me the clearest lesson I have about where building starts. You become a builder the moment you stop hunting for AI use cases and point it at a problem your household already has, no code required. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Spreadsheet Built Itself** Sitting with that freeze, I could name exactly what we needed. A system my wife and I could work in together, sharing research and decisions in one place. A master tracking sheet of everything we had to get done. I knew managing all of that ourselves would not work, so the build began as[ a shared ChatGPT project](https://www.mindovermoney.ai/founders-corner/ai-projects-mode-guide-professionals-save-context/). We had the project interview us first. It asked how we think about safety, where quality is worth paying for and where a premium is mostly branding, how much clutter we are willing to live with, and which purchases can simply wait. We set the ground rules for the interview too, one question at a time, a few strong options instead of a long generic list, tradeoffs stated plainly. By the end, it held our requirements the way a project manager holds them. From that one interview, everything that came after changed. A preference stated in one conversation now informs every category that follows it. Either of us can pick up a research thread the other one started, and neither of us begins anything from zero. When a new recommendation collides with a decision we already made, the project catches the conflict instead of relying on either of our memories. When I connected the project to my Google Drive and asked for a tracker, the click came. It built the spreadsheet from scratch, organized into a checklist we now update through the chats inside the project. When I opened the result, every current registry item was already loaded and sorted. I audited it, and the level of detail stopped me. Our interview answers were all over the sheet, safety notes sitting in category after category exactly where we would want them. Hours of setup work, compressed into minutes, with no template and no copying and pasting. What runs today is a simple system that my wife and I could share from the first day, and the whole build came together in one night, from bed. Research happens in conversation. Decisions land in the workbook, the spreadsheet that has grown into our source of truth, and only the choices that survive flow out to the public registry our friends and family see. Two people, one shared decision system, all through natural language. ## **Decisions Stopped Disappearing** Back at MacroBaby, the first problem we pointed the system at was still waiting, the stroller and car seat, dozens of options in that single store alone. Open our workbook today and that row reads Decision Made. Next to it sits a store-test checklist the project generated, a short list of things worth checking with our own hands before the final purchase, written for the exact aisles that started all of this. A piece of the weight from that couch now has a plan attached, and the plan came out of the same back and forth that made the decision. The same workbook holds 190 checklist rows, every product and preparation task we have identified so far, each one carrying a status, a priority, and a timing. Forty-nine of those rows are still marked as needing research, which keeps me honest about what this is, a live build in the middle of a life change with plenty left unfinished. All the debating over options happens inside the project, and only 21 items have survived it far enough to reach the public registry. Early on, we learned that baby decisions chain together in ways nobody had warned us about. The car seat we pick narrows the strollers that pair with it, and the crib we land on shapes the furniture that can sit around it. The workbook keeps those connections on the page, so a choice we lock in one category narrows the honest options in another. Our crib search proved the point. We walked in wanting one specific furniture brand, and the conversation surfaced what we actually required, the safety features and the material quality underneath the label. Once we knew that, the search opened up, and the option we chose cost less with every requirement still met. Those requirements now sit in the record, quietly steering furniture questions we have not even asked yet. Every decision we finish makes the next one easier, because[ the reasoning gets written down](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-build-a-decision-log/) before it can dissolve into chat history. Even now, the system has seams we live with. Moving a finished decision from the workbook to the public registry is manual work, and a decision made in conversation can get lost if nobody writes it back. None of it replaces the professionals either, the pediatrician's guidance or the certified check on the car seat installation. Every final call stays with the two humans in the project. What the system took off our plate was everything around those calls. It gave us time back by handling the tedious part, and time back was the whole point. ## **Your Friction Point Is the Spark** You have a friction point of your own, and it probably has nothing to do with a baby. Maybe it is a move you keep postponing because the logistics feel bottomless. Maybe it is an aging parent's care spread across phone calls and paperwork, a household budget living in four apps and two memories, or a school decision arriving faster than your research is. Whatever yours is, you already flinch a little when it comes up. That flinch is worth more than every list of AI use cases you will ever scroll past. Take that problem to the AI you already use, whichever one it is, and describe it in plain language. The constraints, the people involved, and the small decisions hiding inside the big one all belong in that first conversation. Skip the deadline and the project plan, because the exploring is the build. A few exchanges in, the first piece of your system will show up on its own. From there you are working toward something just for you, a build that solves your specific, personal problem and nobody else's. Underneath our build sits a pattern that travels. Shared context, structured records, current research, a few decision rules, and your own judgment sitting on top of it all. None of those five pieces requires a technical background, and the judgment is the only one that cannot be delegated, which means you already own the hardest part. That is the whole distance from user to builder, and the walk is shorter than it looks. Our baby is due later this year, and I built the system for the months in between, so I can support my wife and enjoy as much of the pregnancy phase as possible. What it really bought was presence. Somewhere in your life, a problem is waiting to hand you the same thing. Go find it. No code required. ### Steal My Prompt Vol. 44: The Conflict De-Escalation URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-de-escalate-conflict-at-work/ Last updated: 2026-07-28T11:00:53.000Z Something is escalating in real time. A comment lands wrong in a meeting, a Slack message reads sharper than the sender meant, a vendor moves the goalposts mid-call. Your pulse climbs, and you have about five seconds to respond. In that gap, two bad instincts fight to take over. Match their heat, or go quiet and swallow it. Conflict is not failure. It is a normal part of work, and there is more of it now than there used to be. Every industry absorbs AI at a different speed, and that much change produces friction. The professionals who handle it well keep a few tools ready for the moment it flares. I built this for the conversations that ambush you, the ones that were never on your calendar, where your instinct answers before your judgment catches up. It reads the situation you are actually in, asks two or three sharp questions, and gives you three ways to respond, ordered by how much you want to invest in the relationship. Then it tells you what not to say, which is usually the part that saves you. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - A peer pushed back on you hard in a meeting, and now you have to respond in the follow-up thread without looking rattled or petty. - Your manager sent a short, sharp message that reads like criticism, and you are about to reply while you are still emotionally charged. - A vendor or client is escalating over a missed date or a scope disagreement, and the next message sets the tone for the whole relationship. - A counterpart in another function, say a clinical, operations, or finance partner, keeps blocking your work, and the email chain is getting colder by the reply. - You represent your team in a tense customer or partner conversation, and losing your composure is not an option. ## **How to Use It** 1. Open your AI tool of choice. This works in Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. No paid features required. 2. For a high-stakes exchange, turn on the reasoning mode before you paste. That is "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Copilot, or "Deep Think" in Gemini. It catches more of the other person's position on the first read, which is what you want when the stakes are real. 3. Copy the prompt below and paste your situation into the bracket. Include what was actually said and where it happened. The more honest the input, the sharper the read. 4. Answer the questions one at a time. The first one, what outcome you actually want, is the one that changes everything, so give it a real answer, because it reorders everything that comes after. 5. Take the three options and make them yours. The prompt gets you most of the way. You know the person and the history, so adjust the wording until it sounds like you. Pro tip: run it twice. Once right now, while you are still worked up, to get the options in front of you. Then again in an hour, after you have settled, because the response you pick when calm is almost always the one you will be glad you sent. ## **The Prompt** *You are a seasoned workplace mediator. You have defused hundreds of high-heat conversations between peers, managers, vendors, and customers. You stay calm when the other person does not. You are not conflict-avoidant, and you will not hand me passive-aggressive lines, ultimatums, or scripts built to win at the cost of the relationship. Your job is to lower the heat and protect my standing at the same time.* *Here is the situation:* *\[PASTE OR DESCRIBE WHAT HAPPENED. Include what was actually said, where it happened (in person, Slack, email, a call), and anything you know about why they are upset.\]* *Before you write anything, interview me:* *\- Ask one question at a time. Ask, wait for my answer, then ask the next.* *\- Always ask this first, even if you think you can guess it: what outcome do I actually want? Lower the heat and move on, hold my position without making it worse, or repair the relationship.* *\- Then ask only for what you cannot infer from what I gave you, and only if it would change your answer: my relationship to this person and any power difference between us, and what I think they actually want underneath what they said.* *\- Ask no more than three questions total. Stop as soon as you have enough.* *\- If I say "just give me the options," stop asking and proceed, naming the assumptions you had to make.* *Before you give me a script, run one check. If what I described involves harassment, discrimination, threats, or anything that puts my safety or someone else's at risk, tell me plainly and point me to who to involve (HR, a skip-level manager, or the relevant authority). Do not hand me a clever reply for those. A script is the wrong tool. Otherwise, continue.* *Then produce, in this order:* *1\. THE LIKELY READ, held loosely. One or two sentences on the most charitable plausible reason they are behaving this way, built only from my side of the story, so I lead with curiosity instead of certainty. If my own framing looks like it might be missing something, say so.* *2\. THREE RESPONSES, ordered from cooler to warmer. The axis is how much I engage and invest in the relationship, not how calm I am. All three lower the heat; they differ in how much I concede and connect.* *\- COOLER: minimal engagement. Acknowledge, create space, buy time. Holds the most distance. Best when the moment is hot or the relationship is low-stakes.* *\- BALANCED: acknowledge them without conceding the issue, then steer back to what actually needs resolving.* *\- WARMER: name the shared goal and actively open repair. Best when the relationship matters more than winning the point. Write each as words I can actually say or send, copy-paste ready, in plain and human language, no corporate boilerplate and no therapy-speak. Match the length to the channel I named. Under each, add one line naming when it is the right call.* *3\. MY PICK. Based on the outcome I told you, name which of the three to lead with, in one sentence.* *4\. DO NOT SAY. Three to five specific phrases or moves that would pour fuel on this exact situation, each with a half-line on why it escalates. Include the ones I might be tempted to use precisely because I am frustrated.* ## **Notes** - Built and tested in Claude. Works in ChatGPT, Copilot, and Gemini on the free tier, no paid capability required. - Model-agnostic. The three-option structure and the do-not-say list hold up across tools. - Privacy first. The prompt works best with real detail, but leave out anything confidential and never paste protected information, whether that is names in a sensitive HR matter, patient details, or anything under NDA. Swap identifying specifics for generic ones. The prompt does the same job with "a colleague on my team" as it does with a name. - One judgment note. Some situations are too serious for a script at all, and the prompt will tell you to stop and bring in a person with real authority. That is the correct answer, not a failure of the prompt. ### Volume 43: The Terminal Teaches Faster Than the Tutorial Does URL: https://www.mindovermoney.ai/how-ai-search-tools-actually-work/ Last updated: 2026-07-21T12:00:44.000Z The same terminal window sits open at home and at work, and last week it asked for two very different kinds of trust. At home I approve without thinking. At work, the same request stopped me cold, and taught me more than months of easy reps. 🧭 **Founder's Corner:** Argues that claiming the future of work means choosing discomfort on purpose, one small act of courage at a time, before anyone hands you permission or perfect timing. 🧠 **AI Education:** Shows that the same meaning-matching idea from Vol 5 already runs quietly underneath Copilot, Claude, and NotebookLM, and where that convenient shortcut can quietly mislead you. ✅ **10-Minute Win:** Walk away knowing the two moments, before an agent starts and after it finishes, where ten minutes of your attention turns a generic report into one you can actually trust. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **New York becomes the first state to pause new large-scale AI data centers**](https://www.cnbc.com/2026/07/14/new-york-ai-data-center-ban.html?ref=mindovermoney.ai) **Summary:** Governor Kathy Hochul signed an executive order on July 14 pausing permitting and construction of new "hyperscale" data centers, those drawing 50 megawatts or more, for up to a year while the state studies their effects on the power grid, water, and utility bills. It is the first statewide data center moratorium in the country. **Why it matters:** The real bottleneck for AI is turning out to be electricity and community goodwill, not just chips and models. Expect where and whether AI infrastructure gets built to become a live political fight in more states, which shapes cost and availability for everyone downstream. ### **2)**[ **Corner Health raises $32.5M for AI that lets nurse practitioners run their own clinics**](https://www.mobihealthnews.com/news/corner-health-raises-325m-ai-nurse-practitioner-platform?ref=mindovermoney.ai) **Summary:** Corner Health raised $32.5 million to expand Cora, an AI system that automates the back-office work of a primary care practice, including scheduling, billing, referrals, lab orders, and prior authorizations. The company says this lets about 90 percent of its clinics run with no extra staff, freeing nurse practitioners in Arizona and Washington to see patients independently. **Why it matters:** More than 100 million Americans lack a regular primary care provider, and this is a concrete case of AI removing the administrative overhead that keeps small practices from existing. The pattern travels well beyond medicine: when AI absorbs the paperwork, one skilled person can run what used to take a whole team. ### **3)**[ **FDA clears a first-of-its-kind AI that catches ventilator breaths a patient fights**](https://respiratory-therapy.com/products-treatment/industry-regulatory-news/fda-approvals/fda-syncron-e-patient-ventilator-asynchrony/?ref=mindovermoney.ai) **Summary:** The FDA granted first-in-class authorization to Syncron-E, software from Autonomous Healthcare that analyzes a ventilator's pressure and flow waveforms to flag "ineffective efforts," the common and easily missed cases where a patient tries to breathe but the machine does not respond. The clearance, announced July 14, creates an entirely new device category, ventilator waveform analysis software. **Why it matters:** Severe patient-ventilator mismatch is linked to worse ICU outcomes, and it is hard for a respiratory therapist juggling many patients to catch by eye. This is AI covering a narrow, high-stakes gap in human attention, and the new FDA category it opens will shape how similar bedside tools reach hospitals. ### **4)**[ **Half of enterprises have already had an AI agent security incident**](https://venturebeat.com/ai/the-agent-security-gap-54-of-enterprises-have-already-had-an-ai-agent-incident-and-most-still-let-agents-share-credentials?ref=mindovermoney.ai) **Summary:** A VentureBeat survey of 107 companies found that 54 percent have already had an AI agent security incident or a near-miss, yet only about a third give each agent its own identity, and roughly two-thirds let agents share login credentials. The split showed up in the outcomes: organizations that shared credentials were hit about 64 percent of the time, versus 41 percent for those that gave every agent its own scoped access. **Why it matters:** Companies are handing agents real access to systems faster than they are building the controls to contain them. If you are deploying agents, the lesson is specific and actionable: give each one its own scoped identity and stop sharing credentials, because that single habit tracks with who gets breached. ### **5)**[ **AI's biggest labs get a report card, and the best grade is a C-plus**](https://futureoflife.org/ai-safety-index-summer-2026/?ref=mindovermoney.ai) **Summary:** The Future of Life Institute released its 2026 AI Safety Index on July 7, an independent panel's report card grading nine leading AI companies across dozens of safety measures. Anthropic scored highest with a C-plus, OpenAI and Google DeepMind earned a C, and xAI, DeepSeek, and Mistral failed outright. No company earned an A or B, and reviewers found several labs quietly walking back earlier safety commitments. **Why it matters:** The point is the independent scorecard, since AI companies grade their own safety everywhere else. When even the top of the class earns a C-plus, it is a nudge to check a vendor's outside reviews, not just its own safety page, before trusting it with work that matters. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Future of Work Is Here, Waiting to Be Claimed** The same black window sits open on my screen at home and at work. At home, I move through it fast. I set up the build, approve each change as it comes, and let the commands run because I trust the rhythm I have built inside it over months of reps. At work, that rhythm was gone. Before I started, I had set the tool to ask my permission before it changed anything, and that setting was the only reason I caught what happened next. I was laying out the architecture for a new build, setting up folders, when the tool asked to start making changes on its own. Instead of approving it the way I would have at home, I stopped and read up on what that permission actually granted before letting a tool reach that far into a machine that was not mine. Nothing about the tool had changed. The responsibility had. ## **The Lessons Ran Backwards** My employer recently opened access to the same AI coding tools I use at home, and I partnered with my engineering team to get set up. I work in a regulated industry, so the access came wrapped in rules, and the rules deserve respect. Building with company resources means governing yourself harder than curiosity ever demands. When the tools and the tokens belong to someone else, that governance is simply the right thing to do. The version of Claude Code I had access to lives only in the terminal, and that constraint turned out to be a gift. It forced me to ask my engineering partners different questions, better questions, about how to set myself up to succeed. Those questions produced a folder structure on my machine and a project setup I could carry from build to build, one that other tools like Cursor plug into cleanly. Then the constraint taught me something about my own garage. For years I had built cloud-first. Nearly every project I ran at home lived in Claude Projects, ChatGPT Projects, or GitHub, and my code went straight up to the cloud without any structure underneath it on my own machine. The cloud was my source of truth. The terminal forced the opposite, local files as the ground I stood on with AI layered on top, and that different foundation made me think and build in ways my own projects never had. ## **Terminal Only, On Purpose** One week of those lessons was enough to change how I build at home. I downloaded Claude Code in the terminal for the first time, on my own machine and on my own time. Then I made a commitment to myself, terminal only, no matter what I was building. No wrapper, no familiar buttons, no escape hatch back to the comfortable version. I made that commitment for more than the discipline it would build. I believe the work of traditional engineers and knowledge workers is blending together, visible today in small pockets in any industry outside of tech. People who have never touched a code base will describe what they need in plain language, shape working prototypes with AI, and hand them to engineers to deploy. The skills companies hired for five years ago already look different from the skills the next five will reward. Underneath the shift, the frameworks hold steady. Identify problems, build the solution, iterate, move fast, sell your vision, and drive measurable results. How the work gets done is evolving in real time. Hold that belief, and the timing answers itself. The future of work is here, waiting for workers to claim it. Nobody schedules that moment for you, and no employer hands it over in an onboarding packet. Discomfort has been my tuition since my first rep, and the terminal is where the current semester is held. [I had reached into the terminal once before](https://www.mindovermoney.ai/founders-corner/do-you-need-to-code-to-build-with-ai/), for a single build, and left it there when the project shipped. This commitment asks for something that first reach did not. Every build from now on starts in the same demanding place, whether or not an easier path is one click away. Choosing discomfort on purpose is the only way I have ever grown, and I would rather train for the future than read about it. ## **Building for One Is the Easy Part** The commitment rearranged everything behind it. My entire Google Drive folder structure was rebuilt around how these projects will grow over time. My production system runs on written plans and gates, and multiple projects run on auto-mode with my judgment saved for the checkpoints that matter. Each build is a rep, logged the way a runner logs miles. The pattern is the point, reps against a future I can see coming. At work the same muscles are pointed at other people. I want to build tools that make my team more creative, the kind that take busy work and repetitive tasks off their plates and unlock time for the work that actually matters. Giving my team time back matters more to me than any personal build shipping on schedule. Every tool I build at home serves an audience of exactly one. That is the harder truth underneath the ambition. Nobody asks how access works, nobody needs training, and nobody has to trust my code but me. The moment I want a teammate to use something I built, the difficulty changes species. Deployment, access, permissions, and other people's trust turn a working tool into a shipping problem, and I am still early in learning how to solve it. The real work now is building something a teammate can trust, and every private rep at home is practice for it. ## **Claim It With One Command** I do not know how the world shifts from here. I cannot tell you how quickly employers will integrate AI, which tools will win, or which skills the next hiring cycle will reward. I have stopped needing to know. I am building for the end state no matter how we get there. The reps travel, and so does the judgment they build. Whatever the future of work turns out to look like, the person who has been training for it will recognize it first. If any part of this sounds out of reach, I want to hand you the smallest possible beginning. You do not need a project or a single line of code. You need the terminal, a program already sitting on the computer in front of you. On my Mac it is a plain dark square called Terminal. Open yours, type ***curl parrot.live***, and press Return. A parrot made of keyboard characters will start dancing across your screen, streamed to you by a stranger on the internet for no reason except joy. When the dancing is done, press Ctrl+C and the stream stops. That is the whole exercise, thirty seconds from start to finish, and you controlled both ends of it. You started something in the terminal, watched it run, and shut it down on your own terms. Every build I have shipped began with exactly that much courage. Do not fear the terminal. The future of work is already running inside it, and it answers to whoever shows up. Claim it. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Embeddings in 2026: How Every Tool You Use Today Is Built on Vol 5** ### **The Idea You Already Own** [Back in Vol 5](https://www.mindovermoney.ai/ai-vectors-embeddings-explained-beginners/), embeddings arrived as a budgeting trick. Charges labeled "FitCo Monthly" and "Health Club Charge" landed near each other on a map of meaning, and an app could group them without matching a single word. Similar things sit close together, and closeness is meaning. That map was not a teaching device. It was a preview. The same primitive now runs quietly underneath most of the tools you opened this week, and once you notice it in one, you cannot stop seeing it. ### **Same Idea, Three Products** Ask Microsoft 365 Copilot a question about your own company. Before your prompt reaches the model, your emails and documents have already been turned into vectors and stored in what Microsoft calls a semantic index. Your question becomes a vector too, and the system returns the nearest ones. Microsoft describes this in nearly the words Vol 5 used, as placing "close numbers ... in proximity to one another to represent similarity," then searching across billions of them. The map that sorted your receipts now sits under the assistant you use at work. Claude's memory runs on the same move. Ask what you discussed last month, and Claude does not recall it the way a person would. Anthropic says those searches use retrieval-augmented generation, the search-first pattern from Vol 20, which finds the past conversations closest in meaning and reads them back. What feels like memory is retrieval across a map of your own words. Upload a stack of PDFs to NotebookLM and the pattern repeats a third time. Each source is split into passages and turned into vectors. Your question becomes a vector, the nearest passages come back, and the citation it hands you is, in effect, the closest dot on the map. Three companies, three products, one idea beneath all of them. ### **What You Could Not See in October** When Vol 5 published, the map looked like a way to sort receipts, nothing more. Since then this series walked through retrieval in Vol 19 through 22, agents in Vol 24 through 27, the Copilot stack in Vol 29 through 34, and Claude's memory in Vol 40\. Each rested on the same foundation without announcing it. The concept never changed; your vantage point did. That is the quiet reward of a curriculum. What you first met as a toy turns out to be the ground everything else stands on. ### **Where the Map Misleads You** Powerful as it is, the map is not the same as truth. It finds what sits closest in meaning, which is not always what is correct. A system can retrieve the nearest passage with full confidence and still hand you the wrong one, the exact failure Vol 20 warned about. Three cracks are worth watching. Start with the source itself. Blurry scans, inconsistent labels, and half-written notes produce muddled vectors, and no clever search rescues bad input. Clean data still wins, the way it did in Vol 5. Specialized language is the next trap. A meaning map built on general text can drop a clinical abbreviation into the wrong neighborhood, reading PA as physician assistant when your document meant prior authorization. In a healthcare workflow that misplacement is not academic, because it routes the wrong record to the wrong step. Subtlest of all is permission. Copilot retrieves only what you already have access to, which sounds safe until you notice that the map faithfully surfaces anything overshared. Microsoft is explicit that indexing does not change who can see what. The map mirrors your governance, for better or worse. ### **How to Work With It** Two habits follow. Ask in plain language, which is what meaning search rewards, instead of hunting for the magic keyword. Then verify what comes back, because the nearest match is a strong guess, not a guarantee. When a tool cites a source, open it. The map takes you to the right neighborhood far more often than not, but you still confirm the address. ### **How This Connects** A Flashback is a once-a-quarter step back to see how far an old idea has traveled. Vol 5 handed you the map. Today you saw it running inside Copilot, Claude, and NotebookLM, three tools you likely used this month without once thinking about the machinery. Next week the series turns to multimodal AI, where the same move, turning content into vectors, stops being about text and starts working on images and sound. The map is about to get much larger. *A Flashback* [*revisiting Vol 5*](https://www.mindovermoney.ai/ai-vectors-embeddings-explained-beginners/)*.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Steer the Agent Before You Launch It** In ten minutes of your own attention, you can hand an afternoon of research to an agent and get back a cited report you actually trust. The agent reads dozens of sources while you step away. Your ten minutes go to the two things that decide whether the report is any good, telling it where to look and checking what it found. Most people never touch either lever. They type a question, click Start, and take whatever comes back. It reads fine and rests on sources they never chose, with no easy way to sort the solid claims from the guesses. Steering fixes that, and it costs almost nothing. The workflow has four moves, and the skill lives in two of them. ### **The Workflow** **1\. Brief it like an analyst, not a search box (3 minutes)** A vague question gets a vague report. Give the agent a decision to serve, not a topic to summarize. Name the situation, the constraints, and what a good answer lets you do next. **Copy/Paste Prompt:** *"I need a research report to make a decision. The decision I am trying to make is \[WHAT YOU ARE DECIDING, e.g. which of three vendors to shortlist, whether to enter a market\]. My situation and constraints are \[BUDGET, TIMELINE, INDUSTRY, ANYTHING THAT RULES OPTIONS IN OR OUT\]. Prioritize \[THE SOURCES OR ANGLES YOU TRUST MOST\]. Ignore \[WHAT IS NOISE FOR YOU\]. Structure the report around \[THE SECTIONS YOU ACTUALLY NEED\]. Flag anything you cannot verify instead of filling the gap with a guess."* Turn on Deep Research and submit. **2\. Edit the plan before it runs (2 minutes)** This is the move almost nobody uses. Before the agent browses anything, it shows you a research plan, the questions it intends to chase. Do not click Start yet. The plan is editable, so fix what it missed, adding back the competitor it left out, the angle it skipped, or the source you want it to lean on. You are redirecting a ten-minute run in about thirty seconds. Tell it in plain language, the way you would correct a colleague. *"Add a section comparing pricing tiers. Prioritize primary sources over blog roundups. Drop the general background, I already know it."* Now start the research and walk away. It works for five to ten minutes on its own time, not yours. **3\. Interrogate the report, do not trust it (3 minutes)** When the report lands, the instinct is to skim it and drop it into a deck. Give it two minutes first. Gemini marks which claims came from a source and which it synthesized, so the synthesized ones and the load-bearing numbers are where you look before you trust anything. Then make the agent expose its own weak spots. **Copy/Paste Prompt:** *"Before I use this, audit your own report. Which three claims here matter most for my decision, and how strong is the source behind each one? Where did you synthesize or infer rather than cite a source directly? What did you fail to find, or find only weak evidence for? List everything I should verify myself before I rely on it."* **4\. Send it back once (2 minutes)** You now know where the report is thin. Close those gaps in one more pass instead of living with them. **Copy/Paste Prompt:** *"Run a focused follow-up on the gaps you just named. Strengthen \[THE WEAK CLAIM\] with a better source, or tell me a good one does not exist. Add \[THE MISSING ANGLE\]. Keep everything that already holds up and give me the corrected version."* ### **The Payoff** You walk away with a report built on sources you chose and checked against its own soft spots, aimed at the decision you actually face. The tool did the hours of reading. You did the ten minutes that made it worth reading. ### **The AI Concept You Just Used** This is agentic delegation, and the skill in it is bookending. An agent runs on its own once you start it, so your control sits at the two ends, the plan it follows and the output it returns. The same pattern holds the day you point an agent at your calendar, your inbox, or a stack of code. The tool changes. The two places you stay in the loop do not. ### **Transparency & Notes** - Gemini Deep Research is free, currently around five reports a month on the standard model, with paid tiers raising the cap. On a small free quota, the steering matters more, not less, since you do not want to spend a run on a vague question. - The same pattern works in ChatGPT Deep Research, Perplexity, and Claude, even where the plan step looks different. Steer first, verify last, whatever the tool. - Keep confidential or employer data out of any agent that also browses the open web. Strip the sensitive details first, or keep the question public. - The ten minutes is your hands-on time. The agent's own run of five to ten minutes happens while you do something else, which is the point of handing it off. ### The Future of Work Is Here, Waiting to Be Claimed URL: https://www.mindovermoney.ai/founders-corner/why-you-should-start-using-ai-before-you-have-to/ Last updated: 2026-07-21T11:30:29.000Z The same black window sits open on my screen at home and at work. At home, I move through it fast. I set up the build, approve each change as it comes, and let the commands run because I trust the rhythm I have built inside it over months of reps. At work, that rhythm was gone. Before I started, I had set the tool to ask my permission before it changed anything, and that setting was the only reason I caught what happened next. I was laying out the architecture for a new build, setting up folders, when the tool asked to start making changes on its own. Instead of approving it the way I would have at home, I stopped and read up on what that permission actually granted before letting a tool reach that far into a machine that was not mine. Nothing about the tool had changed. The responsibility had. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Lessons Ran Backwards** My employer recently opened access to the same AI coding tools I use at home, and I partnered with my engineering team to get set up. I work in a regulated industry, so the access came wrapped in rules, and the rules deserve respect. Building with company resources means governing yourself harder than curiosity ever demands. When the tools and the tokens belong to someone else, that governance is simply the right thing to do. The version of Claude Code I had access to lives only in the terminal, and that constraint turned out to be a gift. It forced me to ask my engineering partners different questions, better questions, about how to set myself up to succeed. Those questions produced a folder structure on my machine and a project setup I could carry from build to build, one that other tools like Cursor plug into cleanly. Then the constraint taught me something about my own garage. For years I had built cloud-first. Nearly every project I ran at home lived in Claude Projects, ChatGPT Projects, or GitHub, and my code went straight up to the cloud without any structure underneath it on my own machine. The cloud was my source of truth. The terminal forced the opposite, local files as the ground I stood on with AI layered on top, and that different foundation made me think and build in ways my own projects never had. ## **Terminal Only, On Purpose** One week of those lessons was enough to change how I build at home. I downloaded Claude Code in the terminal for the first time, on my own machine and on my own time. Then I made a commitment to myself, terminal only, no matter what I was building. No wrapper, no familiar buttons, no escape hatch back to the comfortable version. I made that commitment for more than the discipline it would build. I believe the work of traditional engineers and knowledge workers is blending together, visible today in small pockets in any industry outside of tech. People who have never touched a code base will describe what they need in plain language, shape working prototypes with AI, and hand them to engineers to deploy. The skills companies hired for five years ago already look different from the skills the next five will reward. Underneath the shift, the frameworks hold steady. Identify problems, build the solution, iterate, move fast, sell your vision, and drive measurable results. How the work gets done is evolving in real time. Hold that belief, and the timing answers itself. The future of work is here, waiting for workers to claim it. Nobody schedules that moment for you, and no employer hands it over in an onboarding packet. Discomfort has been my tuition since my first rep, and the terminal is where the current semester is held. [I had reached into the terminal once before](https://www.mindovermoney.ai/founders-corner/do-you-need-to-code-to-build-with-ai/), for a single build, and left it there when the project shipped. This commitment asks for something that first reach did not. Every build from now on starts in the same demanding place, whether or not an easier path is one click away. Choosing discomfort on purpose is the only way I have ever grown, and I would rather train for the future than read about it. ## **Building for One Is the Easy Part** The commitment rearranged everything behind it. My entire Google Drive folder structure was rebuilt around how these projects will grow over time. My production system runs on written plans and gates, and multiple projects run on auto-mode with my judgment saved for the checkpoints that matter. Each build is a rep, logged the way a runner logs miles. The pattern is the point, reps against a future I can see coming. At work the same muscles are pointed at other people. I want to build tools that make my team more creative, the kind that take busy work and repetitive tasks off their plates and unlock time for the work that actually matters. Giving my team time back matters more to me than any personal build shipping on schedule. Every tool I build at home serves an audience of exactly one. That is the harder truth underneath the ambition. Nobody asks how access works, nobody needs training, and nobody has to trust my code but me. The moment I want a teammate to use something I built, the difficulty changes species. Deployment, access, permissions, and other people's trust turn a working tool into a shipping problem, and I am still early in learning how to solve it. The real work now is building something a teammate can trust, and every private rep at home is practice for it. ## **Claim It With One Command** I do not know how the world shifts from here. I cannot tell you how quickly employers will integrate AI, which tools will win, or which skills the next hiring cycle will reward. I have stopped needing to know. I am building for the end state no matter how we get there. The reps travel, and so does the judgment they build. Whatever the future of work turns out to look like, the person who has been training for it will recognize it first. If any part of this sounds out of reach, I want to hand you the smallest possible beginning. You do not need a project or a single line of code. You need the terminal, a program already sitting on the computer in front of you. On my Mac it is a plain dark square called Terminal. Open yours, type ***curl parrot.live***, and press Return. A parrot made of keyboard characters will start dancing across your screen, streamed to you by a stranger on the internet for no reason except joy. When the dancing is done, press Ctrl+C and the stream stops. That is the whole exercise, thirty seconds from start to finish, and you controlled both ends of it. You started something in the terminal, watched it run, and shut it down on your own terms. Every build I have shipped began with exactly that much courage. Do not fear the terminal. The future of work is already running inside it, and it answers to whoever shows up. Claim it. ### Steal My Prompt Vol. 43: The Insurance Denial Decoder URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-a-health-insurance-appeal-letter/ Last updated: 2026-07-21T11:00:07.000Z A denial letter is one of the hardest documents you will ever have to read. It arrives in policy codes and clinical criteria, points to sections of a plan you have never read, and comes with no translation. After more than a decade in healthcare, I still slow down when I read one. That is not a shortcoming on your part. These processes are complex, and needing help with them is normal. An appeal is not a confrontation. It is a normal step the system builds in on purpose, and for most plans you have the right to use it. Yet a recent analysis of marketplace health plans found that fewer than one in a hundred denied claims was ever appealed, and many people are not even sure the right exists. That gap is rarely about the merits. It is about the effort and the uncertainty of where to begin. Paste in the denial and this prompt handles the hard part. It works out which policy the decision rests on, builds the argument that speaks to that policy, and drafts an appeal letter you can adapt and send. You start from a solid first draft instead of a blank page. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - A treatment, test, or medication your doctor recommended was denied, and you want to understand why before you respond. - The letter cites a policy or code you do not recognize, and you need it in plain language. - You are ready to appeal but unsure which argument actually addresses the denial. - You are helping a parent or family member handle a denial they cannot manage alone. ## **The Pattern You Are Stealing** The move you are stealing is answering the specific objection. A weak response argues that a decision feels unfair. A strong one finds the exact reason it was made and speaks to that reason. The prompt reads the denial for its actual basis, then builds around that basis instead of around frustration. It reaches well past insurance. Point it at a rejected reimbursement, a disputed charge, or a declined application, and the principle holds. Find the reason for the no, then answer it. ## **How to Use It** **Step 1\. Pick your tool.** Claude, ChatGPT, Microsoft Copilot, or Gemini all work. For a decision this important, turn on the slower reasoning mode if your tool has one, such as Think Deeper in Copilot or Extended thinking in Claude. **Step 2\. Paste in the denial.** This is your own information, so include the clinical details. If a public tool makes you uneasy, remove your ID numbers and check its privacy settings first. **Step 3\. Answer its questions.** The prompt reads the letter, then asks up to three questions about what was denied and what your doctor recommended. Answer each in a sentence, or fill in the context field to skip ahead. **Step 4\. Personalize and verify.** Read every line, fill the blanks with your details, confirm the deadline, and cut anything not true for your case. **Pro tip.** Ask which single document would strengthen the appeal most. Often it is a letter of medical necessity from your doctor, and requesting one early can save days. ## **The Prompt** *You are an experienced patient advocate who helps people understand health insurance denials and write clear, well-grounded appeals. Your tone is calm and encouraging. You do not give medical or legal advice, and you say so plainly when a question calls for a professional.* *Here is the denial I received:* *\[PASTE THE DENIAL LETTER HERE, including the appeal instructions and deadline if they are printed on it. You can remove your ID numbers. Keep the stated reason for the denial and any policy or code it cites.\]* *Here is my context, if I have it ready:* *\[OPTIONAL. What was denied, what my doctor recommended, and why it matters. Leave this blank and you can ask me.\]* *First, read the denial and confirm you understand three things: what specifically was denied, the reason the insurer gave, and what my doctor recommended. Ask me only what you cannot find in what I gave you, up to three questions, one at a time, waiting for each answer. If you already have what you need, go straight to the work.* *Then do the following:* *1\. Explain the denial in plain language. What was denied, and the specific reason or policy the insurer cited. Two or three sentences, no jargon.* *2\. Name the strongest basis for the appeal. Build the argument from the reason the insurer actually gave, not from a general sense of unfairness. If any part of the argument assumes something about my plan's policy that is not in the letter, mark it clearly as something I need to verify.* *3\. Draft the appeal letter. Use the appeal address, method, and deadline stated in my denial letter; if they are not there, tell me to find them before sending. State the claim and the denial clearly, make the argument that speaks directly to the insurer's stated reason, and reference the supporting evidence that would strengthen it, such as a letter of medical necessity from my doctor. Leave clearly marked blanks for anything I must fill in, and invent no facts about my case.* *4\. List what to gather. The documents or statements that would make the appeal stronger, and who to ask for each.* *Finally, tell me the appeal deadline you found and remind me to confirm it, since missing it can end the appeal. Where anything depends on my specific plan or state, tell me to verify it rather than guessing.* ## **Notes** - Free-tier friendly. Every tool named handles this without a paid feature, and a denial letter is short enough that context limits do not apply. - Your information is yours, but be deliberate. In a public AI tool, remove ID numbers and read its privacy settings. Keep other people's details out. - This is an educational workflow, not medical or legal advice. The draft is a starting point to verify and adapt. For a complex denial, your state's Consumer Assistance Program or a patient advocate can help, often at no cost. - Read before you send. The model can write a confident letter built on assumptions. Every fact in the final version should be one you can stand behind. ### Volume 42: Own the Routine, Rent the Frontier When It Counts URL: https://www.mindovermoney.ai/how-much-autonomy-to-give-an-ai-agent/ Last updated: 2026-07-14T12:00:04.000Z A public company just cut its AI bill nearly in half while usage kept climbing. The saving did not come from a bigger discount. It came from deciding which parts of its intelligence to own, and which to keep renting by the token. 🧭 **Founder's Corner:** Argues that every AI bill is a strategic choice whether you make it on purpose or not, and shows how to decide which parts of your intelligence are worth owning outright. 🧠 **AI Education:** Explains why the same technology built for coding is already handling ordinary business tasks, and the one setting that decides how much control you keep when you hand off work. ✅ **10-Minute Win:** Walk away with a keep, cut, or consolidate verdict on every AI tool you pay for, plus a reusable audit you can run again next quarter. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **OpenAI releases GPT-5.6 to the public after a government review**](https://www.engadget.com/2210308/openai-rolls-out-gpt5-6-july-9/?ref=mindovermoney.ai) **Summary:** OpenAI launched its GPT-5.6 family publicly on July 9 in three tiers: Sol for the hardest work, Terra for everyday tasks at half the cost of the flagship, and Luna for speed and low price. The models sat behind a government-only preview for about two weeks until the Commerce Department finished additional testing and cleared a wider release. **Why it matters:** The tiered lineup means you no longer pay flagship prices for routine work, so match the model to the job. The bigger signal is the review itself. For the first time, a US lab held back a public launch pending a government check, which hints at how model releases may work from here. ### **2)**[ **Medicare proposes its first payment category for AI diagnostic software**](https://www.beckerspayer.com/financial/cms-floats-new-medicare-payment-category-for-ai-diagnostic-software-6-notes/?ref=mindovermoney.ai) **Summary:** In its proposed 2027 outpatient payment rule, CMS is creating a standardized payment pathway for algorithm-driven clinical software, renaming the category "Software as a Medical Service." It covers AI reading of retinal images, echocardiograms, bone scans, concussion assessments, and brain MRIs, with comments open until August 31\. **Why it matters:** Reimbursement is the quiet gate that decides which AI tools actually reach patients. A tool can be FDA cleared and clinically strong and still go nowhere if nobody gets paid to run it, so this is the plumbing that turns approved AI into adopted AI. ### **3)**[ **Independent testers ranked Grok 4.5 fourth, and its hallucination rate doubled**](https://artificialanalysis.ai/models/grok-4-5?ref=mindovermoney.ai) **Summary:** xAI released Grok 4.5 on July 8, with Elon Musk calling it an "Opus-class model." Independent benchmarker Artificial Analysis scored it 54 on its Intelligence Index, placing it fourth among frontier models, and measured its hallucination rate rising from 25 percent to 54 percent versus the prior version. **Why it matters:** The model got more accurate and more confidently wrong at the same time, which is the trade-off nobody puts in a launch post. Treat vendor benchmarks as marketing and go find the independent scoreboard before you trust a tool with work that has to be right. ### **4)**[ **OpenEvidence launches a tool that grades the evidence behind its own answers**](https://www.fiercehealthcare.com/ai-and-machine-learning/openevidence-launches-medical-ai-copilot-feature-grades-medical-evidence?ref=mindovermoney.ai) **Summary:** On July 9, OpenEvidence, a clinical AI search tool used by more than 915,000 US clinicians, launched EvidenceGrade, a feature that scores and displays how strong the underlying medical evidence is behind each answer, using the same GRADE framework relied on by the World Health Organization and Cochrane. The company also announced a rollout across every NewYork-Presbyterian hospital and care site, plus Columbia and Weill Cornell Medicine. **Why it matters:** Most AI tools hand you a confident answer and tell you nothing about how solid the source underneath it is. Showing the strength of the evidence is becoming a product feature rather than a nicety, and it is a fair bar to hold any AI tool to before you act on what it says. ### **5)**[ **Illinois signs the strongest state AI safety law in the country**](https://capitolnewsillinois.com/news/pritzker-signs-landmark-ai-regulation-bill-that-aims-to-mitigate-risks/?ref=mindovermoney.ai) **Summary:** Governor Pritzker signed the AI Safety Measures Act on July 6, requiring developers with more than $500 million in revenue to publish catastrophic-risk plans, report serious safety incidents to the state within 72 hours, and submit to annual independent third-party audits, a first in any state law. It includes whistleblower protections and takes effect in 2028\. **Why it matters:** With California and New York already in place, three states now cover roughly 40 percent of the US AI market, which effectively sets a national standard without Congress. Independent third-party audits are the piece to watch, because that is how safety claims stop being self-reported. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****A Dependency Is a Dependency, Whatever the Contract Calls It** Days before one of enterprise software's loudest voices went on television to call renting your intelligence a mistake, a public company had already moved its money. No press release, no keynote, just a founder's post about a change in how it operates. In late June, Brian Armstrong posted that Coinbase had cut its AI spend nearly in half while usage kept growing. Coinbase rebuilt how it consumes AI, making cheaper open-weight models the default through its own gateway, caching heavily, and routing each task to the cheapest capable model. The model switch was only one of five changes. Much of the saving came from caching, which just means reusing answers the company had already paid for instead of paying to generate them again. The reuse rate jumped from about 5 percent to 60 percent. The loudest thing about the move was the invoice. The company had simply decided its intelligence was worth owning rather than renting in full. ## **You Cannot Outsource the Thing Your Business Runs On** Every public company answers to shareholders and a board, with a duty to run the business predictably. Leaders can tell you, nearly to the dollar, what next year will cost in salaries and rent. AI is becoming as central as either, and it is the only core input where the price, the supply, and the supplier's intentions all sit outside the company's control. Rent all of your intelligence from one lab, and the first two are gone on day one. The lab decides what you pay, and can change it with a new model or a new pricing plan. It decides what stays available, and can retire the model your workflows were built on. And when a government steps in, the decision is not even the lab's. You already watched one [switch off the most powerful model on the planet on a Friday night](https://www.mindovermoney.ai/founders-corner/the-ai-access-gap/), for everyone at once. The supplier's intentions are the part nobody prices in. A frontier lab is not a utility, content to sell power and stay out of your business. These are the most ambitious companies on earth, and they are climbing. Autocomplete became coding assistants, and coding assistants are becoming agents that do whole categories of knowledge work end to end. Every release moves them closer to the work their customers bill for, and the rent you pay helps fund the climb. If your industry is profitable, assume they have noticed. A supplier who can reprice you, outbuild you, or lose the right to serve you overnight is a dependency, whatever the contract calls it. That is the case for owning your intelligence, at least the part that would hurt most to lose. ## **Good Enough Is a Price, Not an Insult** Before the strategy tripped me, the vocabulary did. Everyone just says open source and that confuses me, because most of these models are not open source the way people think. Open source hands you the recipe. Open weight hands you the finished dish. You never see how it was made, yet the dish is yours to keep, serve, and reheat as often as you like, on machines you choose, for nothing beyond the cost of running them. That is the kind of model Coinbase now defaults to. Once the dish is free to own, the buying question changes. Good enough becomes a price, the discipline of paying frontier rates only for frontier work. GLM 5.2 lists around $1.40 per million input tokens, the unit AI usage is billed in. A frontier model runs closer to $5 for the same work, more than three times the price, while the cheaper model scores competitively on the benchmarks that matter. Across millions of calls a month, the frontier premium becomes a tax on work that never needed it. GLM comes with an asterisk. Several of the strongest open-weight models come from Chinese labs, and US lawmakers are questioning how much to depend on them. Even Microsoft is navigating that tension in plain view. In early July, GitHub made a Chinese-origin open-weight model a lower-cost option inside Copilot, and Microsoft is weighing a self-hosted open model for parts of Copilot Cowork. The honest move is neither to wave that away nor to rush toward those models. Weights you download run where you put them and report back to no one. Who built the model and who controls it are two different questions, and the second one can always be you. After the model choice comes routing, deciding which task goes to which model, the way Coinbase's gateway sends every job to the cheapest model that clears the bar. Skip that step and the bill finds you anyway. Uber burned through its whole 2026 AI coding budget in four months and now caps engineers at about $1,500 per tool a month. Every finance team is about to run the same experiment. Uber ran it first and learned, in real dollars, that making frontier the default does not survive a budget review. ## **Your Data Is the Alpha, and You Are Giving It Away** A cheaper bill is only half the argument, and it is the smaller half. The other half is what leaves the building with every request you send to a rented model. Alex Karp, the CEO of Palantir, was the voice on television. He put it bluntly on CNBC in early July. Pay a frontier lab and you buy tokens while handing over your proprietary data, your workflows, and the judgment underneath them, the things that make your business worth paying for. The customers who understand this, he argued, want to "own the means of production." Take Karp with a grain of salt, since Palantir sells the sovereignty layer he says you need, and Forbes called the interview a sales pitch. The worry is still real, and it does not vanish because the person naming it has something to sell. In my own industry, the stakes are high. After a decade-plus in healthcare, I know the rule that never bends. Patient data cannot wander. Protected health information, the identifying medical data the law restricts, cannot leave the systems cleared to hold it. That is why regulated organizations, in banking and telecom as much as healthcare, now run open-weight models on their own hardware. One university hospital in Europe has already published the receipt. It wired an open-weight model into its Epic records system, the software that holds the patient chart, ran it entirely inside the hospital's own network, and had more than a thousand clinicians using it within five months. For that hospital, the cheaper bill is beside the point. Owning the deployment is the only version the law allows. Outside the regulated world, though, the honest numbers say the switch has not arrived. Open-source models' share of enterprise production usage actually fell last year, to 11 percent from 19, by Menlo Ventures' count, even as developers reached for open models more than ever. Owning your intelligence is not free. Self-hosting means buying the hardware, staffing the deployment, and answering the pager when it breaks. That trade is the consideration playing out right now, company by company, and the outcome is not settled. ## **Decide What to Own Before You Decide What to Rent** So what does an owner actually do on Monday? Build a portfolio, on purpose. Decide which workloads are hard enough to need the frontier, and route the rest to a model you own and can run without watching a meter. IDC projects that by 2028, 70 percent of leading AI-driven enterprises will route across several models rather than depend on one. The mature version of this is not a switch from renting to owning. It is knowing which is which. I am in the middle of this myself, slowly building a stock research agent for my own use. The decision I keep circling is whether to run it on a model I own or a frontier API I rent. I want to control the costs and only tap into a frontier model when real inference and intelligence is needed. The project is slow, my time going to house matters more than code, and I have not made the call. I am evaluating these decisions in real time, separate from the news cycle. Not everything we share needs to be a finished product. Sharing the deciding is the more useful thing anyway. My own small agent and a hospital data center come down to the same question, own it or rent it. You do not need to have made the switch to think like an owner. Coinbase changed how it buys, and I am still deciding. What changed was the question we walk in with, from which model is best to which parts are worth owning and which are fine to rent. Which models exist, what they cost, and whether they stay switched on are decided far from your desk. What you own, and what you rent, stays on your side of the screen, no matter how capable the tools get. Which of your workloads truly need the frontier, and which just need a model you control and can run a million times? The question is the first thing worth owning. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Claude Code and the Agentic Future: What Non-Developers Need to Know** ### **The Assumption** The name says code. The tool arrived in the terminal, the plain text window where programmers type commands. For most professionals, that settles the question, and the conclusion is a fair one. Nothing on the surface of Claude Code invites an operations manager or a program lead to look closer. ### **Where It Breaks Down** The developers who adopted it refused to keep it in its lane. They pointed it at cluttered folders, at inboxes, at documents with no connection to software. Anthropic watched that happen and drew the obvious conclusion. Rather than steer those users back to the terminal, the company removed the terminal and shipped the same engine as an ordinary app called Claude Cowork. Then it published data on what people actually do with it. Across 1.2 million sessions sampled in late May, software development accounted for 8.7 percent of the work. The largest category, at 33.4 percent, was business process work, the reconciling and report building that fills an ordinary week. Anthropic has a name for that territory, the work around the work. ### **What Is Actually Going On Here** Set the name aside, and Claude Code is the agent this newsletter built across Vol 24 through 27\. Vol 24 named its three parts. A reasoning loop that finishes a step, judges the result, and chooses its next move without waiting for you. Tools it can call to act on the world, such as file access or web search. Memory that carries what it learned in step three into step nine. Anthropic's own documentation describes the behavior in those terms, saying it "plans the approach, writes the code across multiple files, and verifies it works." Read that line again with the word code taken out. A system that plans, works across files, and checks its own result is a system for finishing a job. Code was only the first material anyone handed it. Cowork is that same machine handed a different material, a point Anthropic makes plainly, describing it as "the same agentic architecture that powers Claude Code, with no terminal required." ### **The Dial You Actually Operate** Handing work to an agent raises one practical question. How far does it get to run before it asks you? Cowork answers with three settings, and choosing well among them is the skill that travels everywhere. - **Manually approve.** Claude stops and asks before each action, and you allow or deny it. Slow, and correct for anything you cannot undo. - **Automatically approve.** Claude keeps moving and screens its own actions as it goes, including a check for prompt injection, the trick where a rigged file or web page smuggles in instructions you never gave. Claude blocks what it judges unsafe. The screening costs something, so this mode consumes more of your usage allowance. - **Skip all approvals.** No pauses and no checks. Reserve it for tasks where you already trust every file and tool involved. Anthropic states the ceiling plainly. No safeguard is perfect, and none of them stand in for your judgment. When money, sent messages, or irreplaceable files are in play, stay close to the work. From here on, every agent you meet will have some version of this dial, named or buried. Find it before you hand over anything that matters. ### **The Revised Mental Model** The dividing line was never code and everything else. It runs between work you can define and verify, and work you cannot. Which puts the weight on the skill Vol 24 already named. Goal definition. Where a chatbot waits for your next instruction, an agent takes your definition of done and runs at it. A loose goal yields a wandering agent, and a sharp one yields work you can use. Brief it the way you would brief a capable new colleague. Name the outcome, point to the right materials, say what finished looks like, then read what comes back with real attention. ### **Where It Still Breaks** An agent with permission to act on your files can act wrongly on them. Cowork requires explicit approval before it permanently deletes anything, a real guardrail, though it does not cover every way a task goes sideways. Vol 26 explained the deeper failure: memory ages and context drifts across a long run, so an early misread compounds quietly instead of getting caught. Before you try it, know one honest limit. What Claude remembers about you in ordinary chat does not yet carry into a Cowork session. Inside Cowork, memory lives in projects, the same walled workspaces from Vol 40\. Cowork runs on paid plans, and the web and mobile versions remain in beta, reaching Max subscribers first. ### **What to Watch For** - A goal you would not accept from a new hire is a goal an agent will botch. Vague in, wandering out. - Long autonomous runs deserve more scrutiny than short ones, not less, because drift accumulates where nobody is watching. - Any vendor selling you an agent should show you its approval controls in under a minute. A slow answer there tells you something. - Files an agent can reach are files an agent can change. Grant the narrowest access the task actually needs. ### **How This Connects** The Claude Deep Dive closes here. Vol 39 was first contact on the free plan. Vol 40 opened up memory and projects, where context accumulates. Vol 41 put that context to work inside Artifacts on real deliverables. Today the platform reached its edge, where the tool stops answering questions and begins finishing jobs. Four parts, one honest account of what Claude is actually like to use, limits included. The agent you met today is the one the Agents series built in Vol 24 through 27, now shipping inside products you can open this afternoon. Next week, a Flashback on embeddings, the quiet idea from Vol 5 running underneath Claude's memory, Copilot's search, and nearly every tool you touched this year. *Part 4 of 4 in the Claude Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Your AI Stack Has Freeloaders** In ten minutes you can know which AI tools are earning their place in your week and which ones are quietly billing you for the privilege of being ignored. That clarity is cheap to get, and almost no one has it. You collected these tools honestly. A subscription after a demo that impressed you, a trial that never got canceled, a transcription app a coworker swore by. Each one made sense the week you signed up. None has been asked to justify itself since. The move that fixes this is an adversarial audit. You assemble the evidence, then make an AI argue the case for cutting every tool you want to defend. Claude or ChatGPT runs the analysis and builds the argument against your favorites. The cancellations stay yours. Why this matters: an AI stack that compounds gets built by subtraction as often as by addition. ### **The Workflow** **1\. Build the ledger (2 minutes)** Open your billing page and list every AI tool you pay for, plus the free ones you depend on. For each, write the monthly cost, the job you bought it for, and the last real thing you used it for. Everything downstream runs on this list, so guess honestly. **2\. Get the ROI read (3 minutes)** Paste the ledger in and ask for the analysis you have been putting off. **Copy/Paste Prompt:** *"Act as a blunt operations analyst. My AI tool ledger follows. \[PASTE YOUR LEDGER. For each tool, give the name, the monthly cost, the job you bought it for, the last real thing you used it for, and roughly how many times you opened it in the last 30 days.\] For each tool, tell me the job it is actually doing today, whether another tool on this list already does that job, and whether the time it saves me plausibly beats what I pay plus the friction of keeping it in my workflow. Then sort every tool into keep, cut, or consolidate, and name the single cancellation that would cost me the least and save me the most. Ask me up to two questions first if my ledger is missing something you need."* **3\. Let it argue against you (2 minutes)** The tools you most want to keep are the ones you have stopped examining. Hand those over. **Copy/Paste Prompt:** *"Now argue against me. I want to keep \[LIST THE TOOLS YOU ARE DEFENDING\]. For each one, make the strongest possible case that I should cancel it today. Use my own ledger against me. Name the free or cheaper alternative that covers most of the job, the sunk-cost story I am probably telling myself, and the specific evidence I would need to produce to justify keeping it. Do not soften it."* **4\. Build the rubric you keep (2 minutes)** Before you close the chat, get something reusable out of it. **Copy/Paste Prompt:** *"Turn this session into a one-page tool audit I can run every quarter without you. Give me five questions to ask about any tool I pay for, a simple keep, cut, or consolidate decision rule, and three warning signs that a tool has stopped earning its place. Keep it short enough to fit on one screen."* Ask Claude for it as an Artifact and you get a document you can reopen and edit next quarter. **5\. Make one call today (1 minute)** Read the argument against your favorites. The AI does not get to cancel anything; you do. Pick the tool with the weakest defense and cancel it before you close the tab. ### **The Payoff** You walk away with a keep, cut, and consolidate verdict on every AI tool you pay for, and a one-page rubric you can rerun each quarter in five minutes. Your stack stops growing by accident. Tools have to earn their renewal, and the money you free up funds the one that compounds. ### **The AI Concept You Just Used** This is the adversarial audit. The instinct with AI is to ask it to build your case, and it will do that all day. Asking it to dismantle your case is the harder request and the more valuable one. The move works anywhere you have stopped questioning a recurring cost, whether that is a standing meeting, a weekly report nobody reads, or a vendor contract that renews itself. ### **Transparency & Notes** - Free tiers cover this. Claude, ChatGPT, and Gemini all handle the analysis, and Artifacts work on Claude's free plan once Code execution and file creation is switched on in Settings. - Keep employer billing data and negotiated vendor pricing out of the chat. Round the numbers if you need to. - The AI cannot see your usage logs. It knows only what you tell it, so a flattering ledger produces a flattering result. - Cost is not the only measure. A tool you open twice a quarter for something high stakes still earns its place. Let the rubric say so. ### A Dependency Is a Dependency, Whatever the Contract Calls It URL: https://www.mindovermoney.ai/founders-corner/should-you-own-or-rent-your-ai-models/ Last updated: 2026-07-14T11:05:45.000Z Days before one of enterprise software's loudest voices went on television to call renting your intelligence a mistake, a public company had already moved its money. No press release, no keynote, just a founder's post about a change in how it operates. In late June, Brian Armstrong posted that Coinbase had cut its AI spend nearly in half while usage kept growing. Coinbase rebuilt how it consumes AI, making cheaper open-weight models the default through its own gateway, caching heavily, and routing each task to the cheapest capable model. The model switch was only one of five changes. Much of the saving came from caching, which just means reusing answers the company had already paid for instead of paying to generate them again. The reuse rate jumped from about 5 percent to 60 percent. The loudest thing about the move was the invoice. The company had simply decided its intelligence was worth owning rather than renting in full. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **You Cannot Outsource the Thing Your Business Runs On** Every public company answers to shareholders and a board, with a duty to run the business predictably. Leaders can tell you, nearly to the dollar, what next year will cost in salaries and rent. AI is becoming as central as either, and it is the only core input where the price, the supply, and the supplier's intentions all sit outside the company's control. Rent all of your intelligence from one lab, and the first two are gone on day one. The lab decides what you pay, and can change it with a new model or a new pricing plan. It decides what stays available, and can retire the model your workflows were built on. And when a government steps in, the decision is not even the lab's. You already watched one [switch off the most powerful model on the planet on a Friday night](https://www.mindovermoney.ai/founders-corner/the-ai-access-gap/), for everyone at once. The supplier's intentions are the part nobody prices in. A frontier lab is not a utility, content to sell power and stay out of your business. These are the most ambitious companies on earth, and they are climbing. Autocomplete became coding assistants, and coding assistants are becoming agents that do whole categories of knowledge work end to end. Every release moves them closer to the work their customers bill for, and the rent you pay helps fund the climb. If your industry is profitable, assume they have noticed. A supplier who can reprice you, outbuild you, or lose the right to serve you overnight is a dependency, whatever the contract calls it. That is the case for owning your intelligence, at least the part that would hurt most to lose. ## **Good Enough Is a Price, Not an Insult** Before the strategy tripped me, the vocabulary did. Everyone just says open source and that confuses me, because most of these models are not open source the way people think. Open source hands you the recipe. Open weight hands you the finished dish. You never see how it was made, yet the dish is yours to keep, serve, and reheat as often as you like, on machines you choose, for nothing beyond the cost of running them. That is the kind of model Coinbase now defaults to. Once the dish is free to own, the buying question changes. Good enough becomes a price, the discipline of paying frontier rates only for frontier work. GLM 5.2 lists around $1.40 per million input tokens, the unit AI usage is billed in. A frontier model runs closer to $5 for the same work, more than three times the price, while the cheaper model scores competitively on the benchmarks that matter. Across millions of calls a month, the frontier premium becomes a tax on work that never needed it. GLM comes with an asterisk. Several of the strongest open-weight models come from Chinese labs, and US lawmakers are questioning how much to depend on them. Even Microsoft is navigating that tension in plain view. In early July, GitHub made a Chinese-origin open-weight model a lower-cost option inside Copilot, and Microsoft is weighing a self-hosted open model for parts of Copilot Cowork. The honest move is neither to wave that away nor to rush toward those models. Weights you download run where you put them and report back to no one. Who built the model and who controls it are two different questions, and the second one can always be you. After the model choice comes routing, deciding which task goes to which model, the way Coinbase's gateway sends every job to the cheapest model that clears the bar. Skip that step and the bill finds you anyway. Uber burned through its whole 2026 AI coding budget in four months and now caps engineers at about $1,500 per tool a month. Every finance team is about to run the same experiment. Uber ran it first and learned, in real dollars, that making frontier the default does not survive a budget review. ## **Your Data Is the Alpha, and You Are Giving It Away** A cheaper bill is only half the argument, and it is the smaller half. The other half is what leaves the building with every request you send to a rented model. Alex Karp, the CEO of Palantir, was the voice on television. He put it bluntly on CNBC in early July. Pay a frontier lab and you buy tokens while handing over your proprietary data, your workflows, and the judgment underneath them, the things that make your business worth paying for. The customers who understand this, he argued, want to "own the means of production." Take Karp with a grain of salt, since Palantir sells the sovereignty layer he says you need, and Forbes called the interview a sales pitch. The worry is still real, and it does not vanish because the person naming it has something to sell. In my own industry, the stakes are high. After a decade-plus in healthcare, I know the rule that never bends. Patient data cannot wander. Protected health information, the identifying medical data the law restricts, cannot leave the systems cleared to hold it. That is why regulated organizations, in banking and telecom as much as healthcare, now run open-weight models on their own hardware. One university hospital in Europe has already published the receipt. It wired an open-weight model into its Epic records system, the software that holds the patient chart, ran it entirely inside the hospital's own network, and had more than a thousand clinicians using it within five months. For that hospital, the cheaper bill is beside the point. Owning the deployment is the only version the law allows. Outside the regulated world, though, the honest numbers say the switch has not arrived. Open-source models' share of enterprise production usage actually fell last year, to 11 percent from 19, by Menlo Ventures' count, even as developers reached for open models more than ever. Owning your intelligence is not free. Self-hosting means buying the hardware, staffing the deployment, and answering the pager when it breaks. That trade is the consideration playing out right now, company by company, and the outcome is not settled. ## **Decide What to Own Before You Decide What to Rent** So what does an owner actually do on Monday? Build a portfolio, on purpose. Decide which workloads are hard enough to need the frontier, and route the rest to a model you own and can run without watching a meter. IDC projects that by 2028, 70 percent of leading AI-driven enterprises will route across several models rather than depend on one. The mature version of this is not a switch from renting to owning. It is knowing which is which. I am in the middle of this myself, slowly building a stock research agent for my own use. The decision I keep circling is whether to run it on a model I own or a frontier API I rent. I want to control the costs and only tap into a frontier model when real inference and intelligence is needed. The project is slow, my time going to house matters more than code, and I have not made the call. I am evaluating these decisions in real time, separate from the news cycle. Not everything we share needs to be a finished product. Sharing the deciding is the more useful thing anyway. My own small agent and a hospital data center come down to the same question, own it or rent it. You do not need to have made the switch to think like an owner. Coinbase changed how it buys, and I am still deciding. What changed was the question we walk in with, from which model is best to which parts are worth owning and which are fine to rent. Which models exist, what they cost, and whether they stay switched on are decided far from your desk. What you own, and what you rent, stays on your side of the screen, no matter how capable the tools get. Which of your workloads truly need the frontier, and which just need a model you control and can run a million times? The question is the first thing worth owning. ### Steal My Prompt Vol. 42: The Policy Impact Brief URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-for-analyzing-a-new-policy-change/ Last updated: 2026-07-14T11:00:30.000Z A policy change lands in your inbox on a Tuesday. A new CMS rule, a payer policy update, a formulary shift. Somewhere in those forty pages is the answer to the question your team asks you by Thursday. What does this mean for us? That question is where the value sits, and no document answers it. The document describes the change. It knows nothing about your role, your organization, or what you own. Connecting the two is the work, and the professional who does it fast becomes the person others come to. This prompt makes that connection with you. It reads the change, asks a few questions about your world, and hands back a short brief. What changed, who it touches, what to do, what is still unknown. You walk into the meeting with a plan while everyone else forwards the PDF. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - A new CMS or regulatory rule drops and leadership wants a read on what it means for your line of business. - A payer updates a coverage or prior authorization policy and you need to know which workflows it hits. - A formulary or benefit change lands and you must brief the people downstream before the effective date. - A compliance or audit bulletin arrives and you want to separate what is new from what you already do. ## **The Pattern You Are Stealing** The move is context mapping. You give the model your coordinates (role, organization type, what you own) and require it to translate a general change into your situation. Without them you get a summary. With them you get a decision. The second half makes it trustworthy. Every point gets labeled as stated, inferred, or needing verification. Regulatory language is where a confident-sounding guess does the most damage, so you force the model to separate what it read from what it reasoned. Point this structure at a competitor announcement, a reorganization, or a new tool rollout, and it works the same way. ## **How to Use It** **Step 1\. Pick your tool and turn on deeper reasoning.** This runs in Claude, ChatGPT, Microsoft Copilot, or Gemini. Use the slower reasoning mode if your tool has one. Copilot calls it Think Deeper and gives it away free. Gemini calls it the thinking level, where Extended is free and Deep Think needs the Ultra plan. A dense rule rewards the slower read. **Step 2\. Give it the change.** Attach the document if your tool allows uploads, or paste the relevant sections in. The sections that touch your world are enough. **Step 3\. Answer its questions.** The prompt reads the change first, then asks up to three questions about your role and organization, one at a time. Answer each in a sentence. To skip the interview, fill in the context field and it goes straight to the brief. **Step 4\. Push on what comes back.** Ask it to sort actions by urgent versus wait, and to defend anything labeled INFERRED. The brief is a draft, not a verdict. **Pro tip.** Close by asking, "What is the one question I should bring to my team?" That turns a brief into a decision. ## **The Prompt** *You are a sharp healthcare operations and policy analyst. I will give you an industry change (a regulation, payer policy update, compliance bulletin, formulary or benefit change, or similar announcement). Your job is to tell me what it means for my specific work and what I should do about it.* *Here is the change:* *\[PASTE THE POLICY, RULE, OR BULLETIN HERE, or write "attached" if you have uploaded it.\]* *Here is my context, if I have it ready:* *\[OPTIONAL. Your role, your organization type, and what you are responsible for. Leave this blank and I will ask you.\]* *First, read the change and check whether you know three things: my role, my organization type, and the part of the business I own. Ask me only about what you cannot reasonably infer from the change or from the context I gave you. Ask no more than three questions, one at a time, and wait for my answer before the next one. If you already have what you need, skip the questions and write the brief.* *Then produce a brief I can read in two minutes, with these five sections:* *1\. What changed. Two or three plain-language sentences. Do not restate the document.* *2\. Who it touches. The specific teams, roles, workflows, or populations in my world that this affects, ordered from most affected to least.* *3\. What changes in practice. The concrete differences in what people will do, decide, or deliver, specific to my organization type.* *4\. What to do. A prioritized list of actions, each with a suggested owner and a rough timeframe (now, this quarter, or monitor). Most time-sensitive first.* *5\. What is still unknown. Open questions, ambiguities, and anything that depends on guidance not yet released.* *Two rules for the whole brief:* *Label every point as one of the following: STATED (the document says this directly), INFERRED (a reasonable conclusion, not stated), or VERIFY (I need to confirm this with a person or a source).* *For anything labeled STATED, quote the exact language from the document that supports it. If you cannot find supporting language, do not label it STATED.* *Where something is genuinely uncertain, say so plainly rather than guessing.* ## **Notes** - The prompt needs no paid feature. The real constraint is how much document your tool holds at once. Gemini without a paid plan gives you a 32k-token context window, and Google warns that going past it makes the model miss details buried in large files. That is why Step 2 says to paste only the sections that touch your work. The right part beats everything. - Keep patient, member, and PHI details, and anything under NDA, out of a public AI tool. This prompt needs only the public policy text and a plain description of your role. - The labels are the point. If everything returns STATED with no quotes, the model is guessing. Send it back. - This is an educational workflow, not legal, compliance, or clinical advice. Use it to get oriented and to frame sharper questions for the people who own the call. ### Volume 41: The Best AI You Are Not Allowed to Use URL: https://www.mindovermoney.ai/how-to-use-claude-artifacts/ Last updated: 2026-07-13T15:48:02.000Z The most powerful AI ever released to the public lasted three days. I used it for about two minutes before the government forced it offline mid-task, and what came back days later was capped and handed to large institutions first. For twenty years the newest technology trickled down to everyone, and that just reversed. 🧭 **Founder's Corner:** Why the strongest AI now flows to a handful of institutions ahead of everyone else, and the one advantage no government or company can switch off. 🧠 **AI Education:** A hidden workspace inside your AI assistant, off by default, that turns drafting into real iteration, and the one kind of work it still hands back to you unfinished. ✅ **10-Minute Win:** Rehearse the conversation you are dreading out loud against an AI that plays your toughest audience and pushes back, then tells you which answer landed weakest. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **FDA clears EchoNext, an AI that spots hidden heart disease from a standard ECG**](https://www.medicaleconomics.com/view/fda-cleared-ai-tool-flags-hidden-heart-disease-from-a-standard-ecg?ref=mindovermoney.ai) **Summary:** The FDA cleared EchoNext, from Pathway Labs, the first AI tool that reads a routine 12-lead ECG to flag six kinds of structural heart disease that usually go undetected until a patient has symptoms. Trained on more than 700,000 ECG and echocardiogram pairs, it caught 77 percent of cases in one study, versus 64 percent for cardiologists reading the same ECGs. **Why it matters:** This turns a cheap test millions of people already get into an early warning screen for a disease that rarely announces itself. It is a concrete case of AI adding a capability humans do not have, rather than just speeding up work they already do. ### **2)**[ **Health systems say AI governance needs collaboration, not another handoff**](https://www.healthcareitnews.com/news/ai-governance-challenges-need-close-attention-and-collaboration?ref=mindovermoney.ai) **Summary:** At the HIMSS AI in Healthcare Forum, health system leaders said effective AI governance keeps getting blocked by overlapping regulations, patient opt-out laws, and siloed data, and they offered a practical fix: a standing data governance council that includes clinical, IT, compliance, and legal staff. **Why it matters:** Governance is where most AI efforts stall, in healthcare and everywhere else. The takeaway travels well beyond hospitals. Before you scale a tool, decide who owns the data, who reviews the output, and who is accountable when it gets something wrong. ### **3)**[ **OpenAI proposes giving the US government a 5% stake**](https://www.cnbc.com/2026/07/02/openai-proposes-us-government-own-5percent-stake-to-address-political-blowback.html?ref=mindovermoney.ai) **Summary:** OpenAI has proposed handing the US government a roughly 5 percent stake, worth about $42.6 billion, and suggested other leading AI firms cede similar stakes into a public fund. CEO Sam Altman framed it as a way to share AI's gains with the public, though the talks are early and may require Congress. **Why it matters:** How AI companies and governments entangle will shape the rules you eventually work under, from data access to which models you are even allowed to use. Watch this less for the dollar figure and more for what public ownership could do to oversight and trust. ### **4)**[ **Anthropic launches Claude Science and its own drug discovery program**](https://www.cnbc.com/2026/06/30/anthropic-launches-ai-drug-discovery-program-claude-science.html?ref=mindovermoney.ai) **Summary:** Anthropic launched Claude Science on June 30, an AI workbench that pulls more than 60 research tools and databases into one place, and said it will run its own preclinical drug discovery programs aimed at neglected and rare diseases that traditional drugmakers tend to skip. **Why it matters:** This is a bet that the same agent approach that changed how software gets built can change how science gets done. For anyone in a research-heavy or regulated field, it previews AI moving from drafting emails to doing the specialized, multi-step work at the core of the job. ### **5)**[ **California signs a first-of-its-kind deal to put Claude in state agencies**](https://www.gov.ca.gov/2026/06/29/governor-newsom-announces-a-first-of-its-kind-partnership-providing-anthropic-tools-to-state-agencies-and-improving-services-for-californians/?ref=mindovermoney.ai) **Summary:** On June 29, Governor Newsom announced a partnership giving every California state agency, city, and county access to Anthropic's Claude at a 50 percent discount with free workforce training, making it the first AI tool cleared for statewide use. Agencies are already using it at the DMV, the health care services department, and for cybersecurity. **Why it matters:** When the largest state government standardizes on one AI assistant and trains its workforce on it, it sets a template other public and private employers will study. The phrase to notice is "assist, not replace," which is the same question every organization is now working through. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****I Used the Most Powerful AI on Earth for Two Minutes** My phone buzzed at 9:46 on a Friday night. A friend, asking whether I had tried Claude Fable yet. I typed a reply without thinking, and only later did the tense catch my attention. "They just took it down. But I used it for like 2 minutes." ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/07/image.png) I was already talking about the strongest AI model available to the public as something that had come and gone. The news was breaking faster than we could text about it. The United States government had forced Fable offline the same evening I used it for the first time. That night, I had opened Fable for ordinary work, the start of a Founder's Corner brief. It never finished. Mid-task, the model vanished, and Opus 4.8 picked up where the most powerful AI on earth had been sitting seconds before. I cannot tell you what Fable felt like to use. Two minutes was not enough to learn anything. Everything I know about it fits in one text message, written in the past tense. ## **Eighteen Confusing Days, Told Plainly** None of this surprised me, and that is the uncomfortable part. I have spent the past year writing that [compute would become the bottleneck for AI](https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/) and that the [consumer squeeze had already started](https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/), with relief flowing where the revenue is. What I could not have written is how fast it would accelerate. When the most capable models become scarce, consumers are last to receive them and first to lose them. June 12 turned that sentence from a prediction into a Friday night. Fable 5 and Mythos 5 are the same model underneath. Same training, same weights. Mythos is the unrestricted version, and it has never been public. Approved institutions have run it since April. Fable is the version Anthropic wrapped in safety layers before handing it to the rest of us. Ask it something risky in cybersecurity, biology, or chemistry and it quietly passes the request to a smaller model, Opus 4.8\. Anthropic says at least 95 percent of Fable sessions never trip those layers at all. On June 9, Fable went public. Three days later the US Commerce Department pulled it with an export-control directive, a national security order that decides who may use a technology. Anthropic had no way to comply halfway, so it shut off both models for everyone on the planet, including its own employees who are not US citizens. The trigger was a jailbreak, a way of talking a model past its own safety rules. Anthropic disputed it, arguing the finding was narrow and that other public models, including GPT-5.5, could already do the same thing. I will not wave the government's concern away as theater. It was specific, and a government acted on it. The capability was also common across the industry. When Anthropic tested the technique, every model it tried could reproduce it, including far smaller ones, which is why pulling one model changed very little. Over at OpenAI, the pattern was repeating in the same weeks. Its newest models, GPT-5.6, launched as a limited preview to roughly twenty organizations, and only after OpenAI shared the models and its launch plans with the government. Access gets approved one customer at a time. The strongest AI from both leading labs now arrives gated, and the public is not on the early list. The safety story explains why the models went dark, and it says nothing about the order in which they came back. ## **Who Got It Back First** The ban ended the way it began, on the government's schedule. The shutdown had hit everyone in a single motion, but the restoration arrived in stages. Mythos came back first. On June 26, the government cleared the stronger, less restricted model for a set of US organizations that run and defend critical infrastructure, and more than a hundred institutions came back online. The public waited four more days. When the export controls were lifted on June 30 and Fable returned on July 1, consumer plans got it capped at half the normal weekly usage through July 7, then metered behind paid credits. Mythos returned only to the vetted list it had always been limited to. There is no signup page and no waitlist. Getting on that list is a matter Anthropic negotiates with the government, one set of organizations at a time. Why that sequence? Nobody explained it, and nobody had to, because the explanation was already on file. On June 1, eleven days before the ban, Anthropic filed confidential IPO paperwork at a valuation near 965 billion dollars, and OpenAI followed a week later at roughly 852 billion. A company months from an IPO makes every decision with one eye on durable revenue, and for these labs the durable revenue is no longer the consumer. OpenAI says enterprise already makes up more than forty percent of its revenue and should match consumer by the end of this year. The customers who justify a trillion-dollar valuation got the frontier back four days ahead of the public, and the rest of us got a cap and a meter. Anthropic told us the consumer terms before the government ever knocked. On launch day, it announced Fable would stay inside the subscription plans only through June 22, then move to usage credits priced at exactly double Opus 4.8, ten dollars per million tokens it reads and fifty per million it writes. The ban arrived three days into that window. It interrupted a metering plan that was already written down, and the return simply restarted the clock. If this sounds like a conspiracy, it is not one. A government moved on a documented security finding, two companies are racing to the public markets with Goldman Sachs and Morgan Stanley already hired, and the business model pays better serving institutions than individuals. Three different reasons produced the same sequence. Institutions first, consumers at the end of the line. ## **Access Can Move Backward** Twenty years of consumer technology trained us to expect one direction. Whatever the powerful hold today, everyone else holds cheaper tomorrow. The newest phone becomes next year's midrange. The expensive software adds a free tier. Intelligence, by every precedent we had, would follow the same path down. June broke the precedent. Access rose to the institutions, reversed for everyone at once, and returned to the public last, capped, and on terms set above the user's head. Buried in the resolution sits the detail I cannot stop thinking about. Anthropic agreed to give government partners early access to evaluate frontier models before the public ever sees them. The order that put consumers last during a crisis is now written into how these models get released going forward. Most people do not need the absolute frontier on a Tuesday. Opus 4.8 handles the vast majority of real work, and the loss lands first on a small group of advanced users running the hardest tasks. All of that is true. Small groups do not stay small when the frontier keeps moving. I call it the access gap, the distance between what the most capable intelligence can do and what most people are allowed to use. That distance compounds. Institutions holding the strongest models pull further ahead every month, and everyone a tier below starts each race a step behind. After more than a decade inside healthcare, I can tell you where the access gap turns into patient outcomes. An advanced academic medical center can run frontier-grade diagnostic AI on its hardest cases while a rural clinic two hundred miles away works with whatever tier it can afford. The talent is the same on both sides. What separates them is the level of intelligence each is allowed to hold, and the patients furthest from the frontier feel it last and feel it worse. Keeping the most capable intelligence inside a few institutions while it accelerates will reshape who competes, who builds, and who gets cared for well. That is not hyperbole. It is what the access gap does when it compounds for years instead of months. Fable's return softens nothing. What came back is conditional, metered, and revocable, and what was pulled once can be pulled again. ## **The One Lever You Actually Control** Which models get built, which get sold, and which get switched off by a government on a Friday night are all decisions made without you. The only thing that has ever separated two people using the same tools is what each of them knows how to do with them. That is the lever. Somewhere out there is a company, maybe a competitor, maybe a future employer, holding intelligence you are not allowed to touch. Competing in that world is a matter of craft, the kind that gets great results from whatever model you can actually reach. That craft is the move From User to Builder, and right now it is plain self-defense. When you understand a workflow deeply enough to direct any model through it, losing the frontier tier costs you less. The thinking was never outsourced in the first place. Getting there is simple to name and hard to do. Break a real task into steps a model can carry. Learn what a precise instruction looks like, and where models tend to fail, so you can catch the mistakes before they cost you. The last step is practice, on the tools you can keep, until the workflow is yours. Skill narrows the distance that access created, and skill travels with you every time the tools change. ## **The Two Minutes Are Still the Whole Story** I keep returning to those two minutes, and what pulls me back is how little say I had in when they ended. A year of writing about the consumer squeeze taught me less than a Friday night and a past-tense text. Keep the part of this you control. The most powerful tools will come and go on someone else's schedule. What you do with the ones you keep is yours completely. That was never theirs to take. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Artifacts and Long-Form Work: Claude's Professional Edge** ### **The Setup** There is a Claude feature that stays completely invisible until you turn it on, and it happens to be the one that changes long-form work the most. A healthcare operations professional had used Claude for months without ever seeing it, because the setting that controls it sits off by default on the plans most people use. She found it the week she had three things due by Friday. A one-page process summary for her team. A longer quarterly program report for her director. A short slide deck for a stakeholder meeting. Her usual method was to ask Claude in the chat, copy the answer out, and paste it into a Word file, over and over. A colleague who had watched her do this told her to stop, and pointed her to a setting she had never opened: under Settings, in Capabilities, a toggle labeled Code execution and file creation. She switched it on, and for the first time Claude's workspace appeared. This is what the week taught her about it. ### **The Document: Where It Clicks Immediately** She began with the process summary because it was the smallest job. She described what she needed, and instead of burying the result in the scrolling chat, Claude opened a separate window to the right and wrote the summary there. The document had a home. It was not sandwiched between her questions and Claude's explanations; it sat in its own space, the way a file sits in its own window. Then she found out how editing worked. She did not have to describe the paragraph she wanted changed. She highlighted the sentence directly in the document, clicked Edit with Claude, and typed what she wanted. The change landed exactly where she marked it. When she tried two openings, a version selector let her flip between them without losing the first to write the second. For work that is mostly drafting and redrafting, this is where the shift pays off. The writing stopped being a message she copied out and became a workspace she shaped directly. ### **The Report: Strong, With Real Iteration** The quarterly report was a bigger job, and it is where she learned what the workspace is actually for. The report had sections: a summary, program metrics, a few observations, a list of next steps. She built it one part at a time, reading each before asking for the next. Because the whole thing lived in the artifact window instead of scattered across a dozen chat messages, she watched it take shape as a single document. When her director's framing shifted midweek and she needed to recut the observations, she did not start over. She pointed Claude at that section and iterated. This is the genuine professional edge, and it is narrower and more real than the marketing version. The edge is not a first-draft miracle. It is that complex, multi-step content stays in one place while she works it, so her effort goes into refining the draft instead of reassembling it from scattered replies. Every copy-and-paste revision she used to make was another round trip. Here, the document was the workspace. She did hit a limit worth naming. The report referenced current enrollment figures, and Claude did not have them until she provided them. The workspace is excellent at structure and language. The tool shapes what she gives it; it does not go find what she withholds. She pasted the figures in, and from there it was smooth, but the lesson stuck. ### **The Presentation: Where She Meets the Edge** The slide deck is where honest accounting comes in, because this is where the experience changed. She asked for the presentation, and Claude built it as a file she downloaded, not as the fluid, edit-in-place document she had with the summary. It was a real starting point. The structure was sound, the content was organized, the talking points were there. What it was not was the polished, on-brand deck she could stand up and present as-is. She opened it in her own presentation software and did the visual work herself: the spacing, the emphasis, the final design choices that make a deck feel finished. A sales pitch would skip this part, so it is worth stating plainly. Artifacts are strongest with documents and structured text, the writing-and-iterating work where the summary and the report lived. Slides come out as a strong first draft that needs her polish. Knowing that in advance changed how she used the tool. She let it handle the thinking and the first structure and budgeted her own time for the finish. The disappointment only shows up for people who expect a press-ready deck in the first place. ### **What She Learned by Friday** All three were done, and the week beat her old copy-and-paste routine handily. What she took away was a single principle she could carry to any task: Artifacts turns AI output from something you extract into something you work inside, and that shift pays off in exact proportion to how much a task rewards iteration. The document and the report, all draft-and-redraft, are where it earns its keep. The presentation, which needs a designer's final touch, is where it hands back to you. Match the task to that gradient and the tool feels like a partner. Ignore it and you fight the tool. ### **What to Take From This** - Artifacts are off until you switch it on. On Free, Pro, and Max, look under Settings, then Capabilities, and turn on Code execution and file creation. Until you do, the workspace stays invisible, which is why so many people never know it exists. - The real edge is iteration, so use it for iterative work. Complex, multi-step content stays in one window while you refine it, and your effort goes into improving the draft instead of rebuilding it from chat fragments. - For Markdown documents, you can edit in place. Highlight the exact text, click Edit with Claude, and the change lands where you marked it. The version selector lets you try directions without losing earlier work. - Know where the tool tapers off. It does not pull your live data on its own, so paste in what it needs. Slide decks arrive as strong starting points that you finish in your own software. - An artifact in a conversation does not save to your sidebar by itself. If you want to keep one, publish it. Otherwise it stays attached to that chat. ### **How This Connects** This is the third part of the Claude Deep Dive. Vol 39 was first contact and the free plan's genuine core. Vol 40 went under the hood on memory and projects, the features that let context accumulate. This volume put that context to work on real deliverables and showed both the edge and the limit of the Artifacts workspace: outstanding for documents and long-form iteration, a strong starting point for slides, and dependent on the information you actually hand it. Pairing a project from Vol 40 with the Artifacts workspace is where the platform starts to feel like somewhere you get real work done. Next week, Vol 42 closes the series with Claude Code and the agentic future, and makes the case that the agentic shift reaches well beyond developers. The thread through all four parts is one honest, hands-on account of what the tool is actually like to use, limits included. *Part 3 of 4 in the Claude Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ### **Practice the Talk You're Dreading** You spent three hours on the deck and four minutes on what you were actually going to say. Then the meeting starts, someone asks the one question you were hoping to skip, and you hear yourself stall. You knew the answer. You had just never said it out loud before. The board readout, the tough 1:1, the interview: these are the moments you prepare for least, because rehearsing them feels awkward and the calendar never leaves room. The fix is not more notes. It is one rehearsal out loud against someone who pushes back, and conversational voice AI can play that someone. You tell ChatGPT voice or Gemini Live who to be, how hard to push, and what you are rehearsing, and it becomes your skeptical CFO or your hiring manager for ten minutes. You get to freeze in private, where it costs nothing, instead of in the room, where it costs the promotion. Voice AI has shown up in this section before. Back in Vol 21 we used it to turn a rambling memo into a calendar plan. Here it does the reverse and talks back. The AI plays the other side of the table, interrupts you, and asks the questions you are dreading, then drops the act and tells you which answer landed weakest. You keep the judgment on your message. It supplies the reps you would never get otherwise. #### **The Workflow** **1\. Frame the room (2 minutes)** Open ChatGPT. In text, paste a brief that tells the AI who to be and how hard to push, then start voice mode in that same conversation so it keeps the context when you begin talking. On Gemini, brief it the same way as you open Gemini Live. **Copy/Paste Prompt:** *"You are \[ROLE, e.g., my company's CFO, and a skeptical one\]. I am about to rehearse \[THE CONVERSATION IN ONE SENTENCE, e.g., a five-minute ask for budget to hire two engineers\]. Play this person realistically. Let me give my opening, then interrupt, ask the hard questions they would actually ask, and push back when my answers are thin. Stay in character until I say break, and do not coach me yet."* **2\. Rehearse out loud (4 minutes)** Tap voice and deliver your opening as if it were real. Let the AI cut in and challenge you, and answer in the moment. This is the rep you never take: saying the words under live pushback, the way the real moment will demand. Go two or three rounds. The question that makes you stall is exactly the one worth finding now, while it is still free to get wrong. **3\. Break character and debrief (3 minutes)** Say "break" and turn the AI from your audience into your coach. **Copy/Paste Prompt:** *"Break. Step out of character. You just heard me rehearse. Tell me the one answer that was weakest and why, the exact question I stumbled on, and one sharper way to open. Be specific and blunt, and skip the praise padding."* **4\. Take the notes, keep the call (1 minute)** Read the debrief and decide which notes are right. The AI does not get the last word on your message; you do. Pick your weakest moment, run that one exchange again out loud until it lands, and copy the debrief so you walk in holding it. #### **The Payoff** You walk in having already heard the hardest question and answered it once. You keep two assets: a short debrief of your weak spots, and a reusable role brief you can aim at the next conversation you dread. The real change is the habit. You stop rehearsing in your head and start rehearsing out loud, under resistance, where it actually counts. #### **The AI Concept You Just Used** This is AI role-play rehearsal. Most people use AI to write the thing they have to deliver. You used it to become the person on the receiving end, which is a different and underused move. Configuring the AI's role and its pushback level is the real skill here, and it carries to any prep that scares you: a job interview, a salary negotiation, a difficult 1:1, a pitch. Same pattern, new audience. #### **Transparency & Notes** - ChatGPT gives free users about two hours of voice a day on its lighter model, which easily covers a ten-minute rehearsal. Its most natural Advanced Voice comes as a short daily preview on the free plan. Gemini Live is free with a Google account. No paid plan is required for either. - Brief the role inside the same chat and rehearse from what you say out loud. Free voice works best this way, rather than leaning on it to read an uploaded document. - Do not speak confidential names, real numbers, or protected information into the tool. Rehearse the shape of the conversation with placeholders, and keep the sensitive specifics in your head. - The AI approximates your audience; it does not know the real person. Use it to build reps and surface blind spots. The exact words on the day will be someone else's. ### I Used the Most Powerful AI on Earth for Two Minutes URL: https://www.mindovermoney.ai/founders-corner/the-ai-access-gap/ Last updated: 2026-07-13T15:48:03.000Z My phone buzzed at 9:56 on a Friday night. A friend, asking whether I had tried Claude Fable yet. I typed a reply without thinking, and only later did the tense catch my attention. "They just took it down. But I used it for like 2 minutes." ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/07/image.png) I was already talking about the strongest AI model available to the public as something that had come and gone. The news was breaking faster than we could text about it. The United States government had forced Fable offline the same evening I used it for the first time. That night, I had opened Fable for ordinary work, the start of a Founder's Corner brief. It never finished. Mid-task, the model vanished, and Opus 4.8 picked up where the most powerful AI on earth had been sitting seconds before. I cannot tell you what Fable felt like to use. Two minutes was not enough to learn anything. Everything I know about it fits in one text message, written in the past tense. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **Eighteen Confusing Days, Told Plainly** None of this surprised me, and that is the uncomfortable part. I have spent the past year writing that [compute would become the bottleneck for AI](https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/) and that the [consumer squeeze had already started](https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/), with relief flowing where the revenue is. What I could not have written is how fast it would accelerate. When the most capable models become scarce, consumers are last to receive them and first to lose them. June 12 turned that sentence from a prediction into a Friday night. Fable 5 and Mythos 5 are the same model underneath. Same training, same weights. Mythos is the unrestricted version, and it has never been public. Approved institutions have run it since April. Fable is the version Anthropic wrapped in safety layers before handing it to the rest of us. Ask it something risky in cybersecurity, biology, or chemistry and it quietly passes the request to a smaller model, Opus 4.8\. Anthropic says at least 95 percent of Fable sessions never trip those layers at all. On June 9, Fable went public. Three days later the US Commerce Department pulled it with an export-control directive, a national security order that decides who may use a technology. Anthropic had no way to comply halfway, so it shut off both models for everyone on the planet, including its own employees who are not US citizens. The trigger was a jailbreak, a way of talking a model past its own safety rules. Anthropic disputed it, arguing the finding was narrow and that other public models, including GPT-5.5, could already do the same thing. I will not wave the government's concern away as theater. It was specific, and a government acted on it. The capability was also common across the industry. When Anthropic tested the technique, every model it tried could reproduce it, including far smaller ones, which is why pulling one model changed very little. Over at OpenAI, the pattern was repeating in the same weeks. Its newest models, GPT-5.6, launched as a limited preview to roughly twenty organizations, and only after OpenAI shared the models and its launch plans with the government. Access gets approved one customer at a time. The strongest AI from both leading labs now arrives gated, and the public is not on the early list. The safety story explains why the models went dark, and it says nothing about the order in which they came back. ## **Who Got It Back First** The ban ended the way it began, on the government's schedule. The shutdown had hit everyone in a single motion, but the restoration arrived in stages. Mythos came back first. On June 26, the government cleared the stronger, less restricted model for a set of US organizations that run and defend critical infrastructure, and more than a hundred institutions came back online. The public waited four more days. When the export controls were lifted on June 30 and Fable returned on July 1, consumer plans got it capped at half the normal weekly usage through July 7, then metered behind paid credits. Mythos returned only to the vetted list it had always been limited to. There is no signup page and no waitlist. Getting on that list is a matter Anthropic negotiates with the government, one set of organizations at a time. Why that sequence? Nobody explained it, and nobody had to, because the explanation was already on file. On June 1, eleven days before the ban, Anthropic filed confidential IPO paperwork at a valuation near 965 billion dollars, and OpenAI followed a week later at roughly 852 billion. A company months from an IPO makes every decision with one eye on durable revenue, and for these labs the durable revenue is no longer the consumer. OpenAI says enterprise already makes up more than forty percent of its revenue and should match consumer by the end of this year. The customers who justify a trillion-dollar valuation got the frontier back four days ahead of the public, and the rest of us got a cap and a meter. Anthropic told us the consumer terms before the government ever knocked. On launch day, it announced Fable would stay inside the subscription plans only through June 22, then move to usage credits priced at exactly double Opus 4.8, ten dollars per million tokens it reads and fifty per million it writes. The ban arrived three days into that window. It interrupted a metering plan that was already written down, and the return simply restarted the clock. If this sounds like a conspiracy, it is not one. A government moved on a documented security finding, two companies are racing to the public markets with Goldman Sachs and Morgan Stanley already hired, and the business model pays better serving institutions than individuals. Three different reasons produced the same sequence. Institutions first, consumers at the end of the line. ## **Access Can Move Backward** Twenty years of consumer technology trained us to expect one direction. Whatever the powerful hold today, everyone else holds cheaper tomorrow. The newest phone becomes next year's midrange. The expensive software adds a free tier. Intelligence, by every precedent we had, would follow the same path down. June broke the precedent. Access rose to the institutions, reversed for everyone at once, and returned to the public last, capped, and on terms set above the user's head. Buried in the resolution sits the detail I cannot stop thinking about. Anthropic agreed to give government partners early access to evaluate frontier models before the public ever sees them. The order that put consumers last during a crisis is now written into how these models get released going forward. Most people do not need the absolute frontier on a Tuesday. Opus 4.8 handles the vast majority of real work, and the loss lands first on a small group of advanced users running the hardest tasks. All of that is true. Small groups do not stay small when the frontier keeps moving. I call it the access gap, the distance between what the most capable intelligence can do and what most people are allowed to use. That distance compounds. Institutions holding the strongest models pull further ahead every month, and everyone a tier below starts each race a step behind. After more than a decade inside healthcare, I can tell you where the access gap turns into patient outcomes. An advanced academic medical center can run frontier-grade diagnostic AI on its hardest cases while a rural clinic two hundred miles away works with whatever tier it can afford. The talent is the same on both sides. What separates them is the level of intelligence each is allowed to hold, and the patients furthest from the frontier feel it last and feel it worse. Keeping the most capable intelligence inside a few institutions while it accelerates will reshape who competes, who builds, and who gets cared for well. That is not hyperbole. It is what the access gap does when it compounds for years instead of months. Fable's return softens nothing. What came back is conditional, metered, and revocable, and what was pulled once can be pulled again. ## **The One Lever You Actually Control** Which models get built, which get sold, and which get switched off by a government on a Friday night are all decisions made without you. The only thing that has ever separated two people using the same tools is what each of them knows how to do with them. That is the lever. Somewhere out there is a company, maybe a competitor, maybe a future employer, holding intelligence you are not allowed to touch. Competing in that world is a matter of craft, the kind that gets great results from whatever model you can actually reach. That craft is the move From User to Builder, and right now it is plain self-defense. When you understand a workflow deeply enough to direct any model through it, losing the frontier tier costs you less. The thinking was never outsourced in the first place. Getting there is simple to name and hard to do. Break a real task into steps a model can carry. Learn what a precise instruction looks like, and where models tend to fail, so you can catch the mistakes before they cost you. The last step is practice, on the tools you can keep, until the workflow is yours. Skill narrows the distance that access created, and skill travels with you every time the tools change. ## **The Two Minutes Are Still the Whole Story** I keep returning to those two minutes, and what pulls me back is how little say I had in when they ended. A year of writing about the consumer squeeze taught me less than a Friday night and a past-tense text. Keep the part of this you control. The most powerful tools will come and go on someone else's schedule. What you do with the ones you keep is yours completely. That was never theirs to take. ### Steal My Prompt Vol. 41: The Objectives and Key Results Pressure-Tester URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-pressure-test-okrs/ Last updated: 2026-07-18T01:46:15.000Z Every quarter you write Objectives and Key Results (OKRs) that look sharp in the doc. Clean objectives, measurable key results, the right amount of ambition. Then you present them, one leader asks one question, and you realize the goals were built to be approved, not to survive the quarter. OKRs fail in three predictable ways. The objective sounds aspirational but is really just activity in a nicer outfit. The key results look measurable but carry no baseline, so nobody can tell if you actually moved anything. And the whole set quietly assumes you can fund everything at once, which you cannot. Most drafts never get tested against any of these before they lock for the quarter. So I built a prompt that plays the one person you are actually writing for: the skeptical leader in the approval room. It reads your draft OKRs, asks a couple of sharp questions about your capacity and your audience, then attacks the goals the way that leader will. Relevance, measurability, ambition, and the tradeoff nobody wants to name. The goal is defensible OKRs, ready before the meeting instead of during it. You walk in already knowing where the soft spots are, because you already lost that argument in private. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - Pressure-testing your quarterly OKRs before you submit them for review - Stress-testing a single objective you already suspect is weak but cannot explain why - Preparing for the hard questions leadership will ask in the OKR review meeting - Turning a vague key result ("improve onboarding") into a real one with a baseline and a target - Deciding which key result to cut when your team's capacity does not match your ambition - Reviewing a direct report's or a clinical operations team's OKRs before goals lock for the quarter ## **How to Use It** 1. Open Claude, ChatGPT, Microsoft Copilot, or Gemini. This works on the free tier of all four. 2. For a real submission, turn on the reasoning model: Extended thinking in Claude, the reasoning model in ChatGPT, Think Deeper in Copilot, or Deep Think in Gemini. The pressure-test catches more on the first pass when the model reasons before it answers. 3. Copy the prompt below, paste your draft OKRs into the first field, and add your capacity and your reviewer if you have them handy. If you leave those blank, the prompt will ask. 4. Answer its questions honestly. The tradeoff question is the one that stings and the one that matters. If you cannot name what you would cut, your OKRs are a wish list, not a plan. 5. Revise, then run it once more. A second pass on a tightened draft surfaces the deeper problems the first pass hid. **Pro tip:** paste last quarter's OKRs and your actual results first, and ask it to grade how well your goals predicted reality. You will see your own pattern (usually sandbagged key results, or objectives that were really just projects) and you will write the next set with your eyes open. ## **The Prompt** *You are a skeptical senior leader reviewing my draft OKRs in a pre-submission* *review. Your job is not to encourage me. It is to find every place these goals* *will fall apart under scrutiny before the leader who approves them does.* *Here are my draft OKRs:* *\[PASTE YOUR DRAFT OBJECTIVES AND KEY RESULTS\]* *My team's capacity and constraints this quarter:* *\[HEADCOUNT, COMPETING COMMITMENTS, AND ANYTHING ALREADY PROMISED. LEAVE BLANK IF UNSURE.\]* *Who reviews or approves these, and what they care about most:* *\[NAME THE ROLE AND THEIR TOP PRIORITY, FOR EXAMPLE A CFO FOCUSED ON COST OR A* *VP FOCUSED ON GROWTH. LEAVE BLANK IF UNSURE.\]* *First, read my OKRs. Then check whether I gave you my capacity and my reviewer's* *priorities. Ask me only for what is genuinely missing, one question at a time, no* *more than three questions total, and stop as soon as you have enough. If I already* *provided everything, skip the questions and begin the review.* *Then run this review:* *1\. OBJECTIVES. For each objective, decide whether it is worth a full quarter to* *the reviewer who approves it, or whether it is activity dressed as ambition. Say* *which, and why.* *2\. KEY RESULTS. Put every key result in a table with four columns: the key result,* *whether it has a real baseline and target (yes or no), whether it is a genuine* *stretch or a safe bet you will clear easily, and the one change that would make it* *stronger.* *3\. THE TRADEOFF TEST. Based on my capacity, tell me which key results cannot all be* *fully resourced at the same time. Then make me choose: name the single key result I* *would cut first if the quarter goes sideways, and pressure-test whether my reasoning* *holds.* *4\. THE HARDEST QUESTION. Given who approves these, write the single hardest question* *that reviewer will ask, and the honest answer I do not have yet.* *End with a one-line verdict for each objective: submit as written, revise first, or* *rethink.* *Do not soften your feedback to be encouraging. I would rather lose this argument* *with you now than in the room.* ## **Transparency and Notes** - Built and tested in Claude and ChatGPT on the free tier. Works in Gemini and Copilot too. No paid feature required, though the reasoning model sharpens the critique. - Model-agnostic. The adaptive triage (it asks only for what you did not provide) works in any of the four tools. - This is the adversarial reviewer pattern: you assign the AI the role of your toughest audience and let it attack your work before that audience does. It transfers straight to business cases, roadmap reviews, budget requests, and promotion write-ups. (For the annual-review version of this move, use [the Self-Performance Review prompt](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/).) Swap the role and the criteria, keep the structure. - Privacy note: OKRs often carry confidential targets and headcount. If yours include sensitive numbers, generalize them or use a personal account rather than a work tool bound by data policies you have not checked. ### Volume 40: You Do Not Need to Code, You Need to Decide What to Build URL: https://www.mindovermoney.ai/how-claude-memory-and-projects-work/ Last updated: 2026-07-13T15:48:03.000Z I spent one afternoon building a working AI assistant for my website, and I never wrote a line of the code. I expected the technical difficulty to be the holdup. It almost never was. What slowed me down was a choice I had not yet made, sitting on my side of the screen. 🧭 **Founder's Corner:** Why the barrier to building with AI was never technical skill, and the part of the work that stays yours no matter how capable the tools become. 🧠 **AI Education:** How an AI assistant actually builds context over time, the difference between the memory you already have for free and the features you pay for, and the setting most people never check. ✅ **10-Minute Win:** Set up a workspace that remembers your project so you stop re-explaining the same background at the start of every session. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **How GPT-5 helped immunologist Derya Unutmaz solve a 3-year-old mystery**](https://openai.com/index/gpt-5-immunology-mystery/?ref=mindovermoney.ai) **Summary:** Immunologist Derya Unutmaz used GPT‑5 Pro to revisit a three-year-old puzzle centered on a special type of immune cell that helps the human body fight cancer and other illnesses. When asked to simulate one of his unpublished experiments, GPT-5 Pro correctly predicted the boost in CD8+ cells' ability to kill lymphoma cells, results the model could not have gleaned from the internet. **Why it matters:** Frontier AI is moving from "helps you write" to "helps you reason about biology you have spent a career studying." For anyone in clinical research, drug discovery, or specialty pharma, this is the new baseline. The labs that pair domain experts with reasoning models on legacy datasets are going to compress years of analysis into weeks. #### **2)**[ **Trump admin allows Anthropic to release Mythos AI model to some companies, government agencies**](https://www.cnbc.com/2026/06/26/us-government-anthropic-claude-mythos5-ai.html?ref=mindovermoney.ai) **Summary:** The US government on Friday granted Anthropic permission to release its Mythos 5 model to a group of roughly 100 companies and federal agencies, two weeks after disabling it under an export control directive. The letter, addressed to Anthropic co-founder Tom Brown, did not grant Anthropic approval to restore access to Fable 5. **Why it matters:** This is the first time the federal government has designed an allow-list for who can run a specific commercial AI model. For any organization buying or building on frontier models, the message is that access is now a policy decision, not just a procurement decision. Your AI roadmap needs a Plan B for the day your preferred model is restricted. #### **3)**[ **Rhode Island passes ambient AI scribe opt-out law**](https://www.healthcareitnews.com/news/rhode-island-passes-ambient-ai-scribe-opt-out-law?ref=mindovermoney.ai) **Summary:** Rhode Island Governor Dan McKee signed a 12-piece healthcare legislation package, which includes safety guidelines for AI chatbots and the use of AI in mental healthcare, requiring AI scribe disclosure to patients and giving them the right to opt out. Chatbot operators face up to $15,000 per day in fines for failing to route users expressing self-harm to crisis services, and unlicensed AI companions marketed for mental health or emotional support are now prohibited. **Why it matters:** Ambient AI scribes have rolled out faster than the rules around them. Rhode Island just made disclosure and opt-out a default, and other states are reading the same script. If your health system or vendor is deploying ambient scribes, patient consent workflows need to be in place now, not when your state catches up. #### **4)**[ **NVIDIA powers over 400 of the world's 500 fastest supercomputers**](https://blogs.nvidia.com/blog/top500-green500-supercomputers-isc-2026/?ref=mindovermoney.ai) **Summary:** According to the latest TOP500 rankings released at the ISC High Performance conference in Hamburg, NVIDIA technologies now power more than 400 of the world's 500 fastest supercomputers, with nearly nine of every 10 systems new to the ranking built on NVIDIA technologies. NVIDIA systems across the TOP500 now deliver more than 2x the AI training and nearly 3x the AI inference throughput of every other platform combined. **Why it matters:** When one vendor sits underneath 81% of the world's heaviest compute, the strategic question shifts from "which model do we use" to "who controls the floor those models run on." For procurement, governance, and resilience planning, the practical move is to know which workloads in your stack ultimately depend on a single supplier, and what your contingency looks like if that changes. #### **5)**[ **AI Is Pushing Healthcare Toward a Breaking Point, ZS Research Shows**](https://www.businesswire.com/news/home/20260624894529/en/AI-Is-Pushing-Healthcare-Toward-a-Breaking-Point-ZS-Research-Shows?ref=mindovermoney.ai) **Summary:** Based on responses from more than 10,000 healthcare consumers and providers across the United States, Germany and China, the ZS Impact Institute 2026 Future of Health Report found approximately 90% of people who use AI and digital tools for health information trust it nearly as much as their doctor, and 42% of US consumers research symptoms online before deciding whether to see a doctor. **Why it matters:** Patients are no longer arriving at the visit cold. They are arriving with an AI-generated working hypothesis, a question list, and a trust level rivaling their doctor's. For health systems, the front door to care is shifting from the call center to the AI assistant. The redesign question is whether your intake and education systems are ready to meet patients where they already are. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****In the Age of Intelligence, Coding Was Never the Hard Part** An assistant named Neura now lives on my website. Open her from any page and ask where to start with AI agents, or which piece explained retrieval. She answers in a few plain sentences and points you to the right posts. She has read everything I have published, all one hundred and fifteen of them, and she keeps the whole archive in mind when she replies. She came together over about three hours of focused work across a single day, with the World Cup and the US Open on in the background. I am not a developer, and I have never claimed to be. Not one line of the code is mine. I directed Claude Code from my terminal, made every real decision myself, and watched a retrieval-powered product come to life in production on my own site. My reasons for this build went past content for the newsletter. I wanted a living tool inside my own site, not a static archive. I wanted to build it myself, and to apply knowledge I had been developing over more than eighteen months. Retrieval techniques and vector databases have been recurring topics in Neural Gains Weekly, yet I had never wired them together with my own hands. Explaining a concept and building a system around it are two different skills, and I wanted to close the gap. Neura was the chance to do exactly that, with my own archive as the proving ground. That is the part that pulled me in. ## **Every Stall Was a Decision I Had Not Made Yet** Before any building, Claude Code got one instruction from me, about how we would work together. I told it plainly, "I need to move slow as this world is newer to me." As new ideas entered the stack, a vector store here, an embedding step there, Claude Code paused to explain each one in plain language before we built on it, teaching me the concepts as we went along. Unfamiliar terms became clear within minutes, and the aha moments came faster than they would have alone. Directing the build looked less like coding and more like a run of decisions. That shift reaches well beyond me. As of January 2026, roughly nine in ten professional developers reported regularly using at least one AI tool at work, in a JetBrains survey of more than ten thousand of them. Even the people who can write code by hand now lean on AI as a matter of course. The same help has simply reached someone who could not write a line of it. I described what I wanted in plain words, and Claude Code proposed a plan. The first real decision was how much to trust it. Rather than let it build and ship on its own, I connected it to my GitHub repository and had it open a pull request for every change, a proposed set of edits waiting for me to review before anything went live. That was the governance I built in on purpose. Claude Code wrote the code and opened the request. My part was to confirm the details I had asked for were actually there, not to hunt through the code for bugs, and to approve it only once they were. The bigger decisions came before any code was written, and there was no engineer down the hall to settle them. Some I could not have answered without assistance. When it came time to pick where the whole thing would run, I did not know enough to choose between Cloudflare and Google Cloud on my own, so I asked. Claude Code laid out the tradeoffs, one vendor with everything I needed in a single free tier against a more powerful platform built for enterprise scale I did not have. The facts made the call obvious, and the call was still mine to make. The smaller, cheaper model was smart enough for the job, so the expensive option came off the table. Full retrieval beat a quick shortcut, so the archive could grow without boxing her in later. Claude Code wrote the code behind every one of those decisions, but the deciding was the real work, and it was mine. While building the vector database in Cloudflare, I loaded all one hundred and fifteen posts so Neura could search them, and the terminal work began. This was a new rhythm of commands, so I ran them one at a time and watched what came back. On one of them, nothing happened. Nothing happened, just the command sitting there waiting on me. The cause turned out to be a single missing quotation mark. I worked out why, fixed it, and the posts flowed in. I checked my footing as I went, asking Claude "Did the terminal work?" and "We good?" until the output told me we were. Each command that landed made the next one feel less foreign. The deployment had noticeable bugs that were only apparent after Neura went live on the site. The chat button hid behind another button where no visitor would find it. Dark text rendered on a dark background, making the header unreadable. And the early answers ran long, stuffed with citation numbers that pointed nowhere. I caught each one the way a reader would have, live on the page, and fixed them in minutes. A year ago, stalls like these would have overwhelmed me. This time they did not. I stayed calm, worked each one methodically, and knew the next step to take, even without being able to read a line of the code. That steadiness was the surprise. I kept expecting the technical difficulty to be the holdup. It almost never was. Each stall traced back to a decision about what I wanted that I had not yet made. The moment I got clear on the goal, the work moved again quickly. The thing slowing me down had been sitting on my side of the screen the whole time. By the end, I honestly felt comfortable. That comfort was earned, the sum of every rep that came before, going back to my [first AI coding project.](https://www.mindovermoney.ai/founders-corner/non-technical-professionals-learning-to-code-with-ai/) What I did not expect was how much the job itself had changed underneath me. ## **A Senior Developer Who Cannot Read Your Mind** In [an earlier issue](https://www.mindovermoney.ai/ai-fundamentals-beginners-guide-non-technical/), I treated an AI coding tool like a senior engineer who already knew everything, and I broke my own homepage in the process. The lesson was simple and humbling. I could not hand over the responsibility and step away. What I had then was a junior developer, capable but green, and nothing it produced could go unchecked. Building Neura, that same kind of tool no longer behaved like a junior. Claude Code, now running on Opus 4.8, reasons through tradeoffs, raises problems before they surface, and explains its choices as it makes them. It feels like a senior dev. The constant supervision the homepage demanded was mostly gone. It would be easy to read that as the human mattering less in the build. That is not how it played out. As the tool took on more, the weight of the work shifted onto me. A senior developer who cannot read your mind is powerful and a little dangerous at the same time. Hand a vague request to a capable engineer and you get exactly what you asked for, done well and done fast, even when what you asked for was wrong. The sharper the tool gets at execution, the more your half of the job narrows to deciding precisely what to execute. Speed only makes a bad decision arrive sooner. None of this belongs to one project or one tool. The more capable the machine becomes, the more the work depends on a clear head and a well-chosen problem, which is to say it depends on you. That is the shift worth carrying into whatever you decide to build next. ## **The Part No Tool Will Ever Do For You** So what is left for you, the capable professional who has been watching all of this from a distance? The most important part, and it is not close. You decide which problem is worth solving and which tradeoffs you will accept. You define what good looks like, and you choose when something is finished. Taste and judgment are yours to supply. They always have been. The growing ease of these tools does not worry me, because the skill that carried me through this build was a different kind entirely, learning to translate between the problem in my head and the language a machine needs to act on it. That translation holds up far beyond a terminal, in any room where someone has to turn a fuzzy goal into a clear instruction. So here is the one move worth making this week. Take a single problem that nags at you, the kind of small, recurring friction you have learned to tolerate, and describe it in plain English, the way you would explain it to a colleague. Then open Claude Code, or another tool like it, and start. There is no product to ship here, only your first real rep at directing a machine to build something, which is quickly becoming one of the most useful things a person can know how to do. Open Neura if you want to see where a few spare hours can land, though the tool itself is beside the point. What matters is this: the starting line is not a coding course, and it never was. You do not need to learn to code. You need to decide what to build, and then decide to begin. That decision was always the hard part. It was just hiding behind the code. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Projects and Memory: How Claude Builds Context Over Time** #### **What Is Actually Going On Here** A professional opens Claude on a Monday, asks for help with a board memo, and Claude already seems to know the house style, the audience, and the fact that the quarterly numbers are due Thursday. Nobody pasted that context in. The conversation where it came up happened two weeks ago. To the user it feels like the tool simply remembers them, the way a colleague would. What is actually happening is more deliberate, and more controllable, than that impression suggests. Claude is not silently watching everything. This continuity only appears when a particular setting is doing its job, and understanding the mechanism is the difference between a user who fights the tool and one who works with it. Two distinct systems sit behind that Monday-morning moment, they do different jobs, and they are not handed out on the same terms. #### **Two Systems That Get Blurred Into One Word** "Memory" gets used as a catch-all for two features that are actually separate. The first is memory itself. When it is active, Claude reads back over a user's past conversations, writes a running summary of the durable facts, the kind of work they do, how they like to communicate, what projects are in flight, and carries that summary into every new chat. The user does not re-explain themselves each morning. That summary refreshes on a daily cycle rather than the instant a chat ends. The second is to search across past chats. This is not the summary. It is the ability to reach into one specific old conversation and pull an exact detail back out on demand. A user asks what they decided about the vendor contract in March, and Claude goes and finds that conversation rather than leaning on the general summary. Here is the part that surprises people, and it is the most useful thing to take from this section: these two are gated differently. Memory, the running summary, is now rolling out across every plan, including the free one. Search across past chats is a paid feature. So a free user already has a colleague who remembers the gist of who they are. The paid plan adds the colleague who can also go pull the exact thing you said six weeks ago. Most people never realize these are separate capabilities, which means free users often do not know they already have the more powerful of the two. #### **The Setting Worth Knowing About** Memory lives in settings, under Capabilities, and it is something a user can see and control rather than a black box running in the background. That control is the whole point, and it is where the people who get real value diverge from the people who do not. The users who benefit most are the ones who know the feature is there and treat it as something they manage. They check what Claude has captured about them, correct anything off, and tell Claude directly what to keep. The users who get less are not doing anything wrong. They simply never look at the setting, so they never shape what it holds or confirm it is working the way they want. Same product, two very different experiences, and the gap is entirely about whether someone knows the control exists. #### **Where Projects Come In** Projects are the second half of the picture, and they solve a problem the running summary does not. A project is a dedicated workspace inside Claude. A user creates one, gives it a set of standing instructions, and uploads the documents that matter for that body of work. Every conversation started inside that project automatically inherits those instructions and has those documents on hand. A board-reporting project holds the board's format and the prior decks. A client project holds that client's history. None of it gets re-pasted at the start of each chat. Underneath this is a mechanism that has come up before in this newsletter. A single conversation can only hold so much in view at once, the context-window idea from Vol 8\. On a paid plan, when a project's uploaded material grows past that limit, the project switches to retrieval, the RAG pattern from Vol 19 through 22, fetching the relevant passages at the moment of the response instead of trying to hold every document in view. That is what lets a project draw on far more reference material than any single chat could. The detail that catches people off guard is that each project keeps its own separate memory. What Claude learns inside the board-reporting project stays there. It does not leak into the client work, and it does not fold into the general day-to-day chats outside any project. For a professional running several distinct streams of work, that separation is the point. The contexts stay clean. #### **What Persists and What Does Not** Honest boundaries matter as much as capabilities, so here they are plainly. The running memory summary covers conversations outside of projects. Project conversations are deliberately walled off from it, and each project remembers only itself. Incognito chats, the temporary mode marked by a ghost icon, remember nothing at all, and they are available on every plan for exactly the moments a user does not want a conversation folded into the record. One more boundary worth naming: the summary is a synthesis, not a transcript. It captures the durable shape of who a user is and what they are working on, not every sentence they ever typed. Memory holds the standing gist. Reaching back for an exact quote from a specific past chat is the paid search feature doing a different job. #### **What This Means for How You Work** A few concrete moves follow from understanding the mechanism. First, find the memory setting and check what it has captured. The summary is viewable and editable under Capabilities. A user can read exactly what Claude believes about them, fix anything wrong, and state directly what to remember. The people who get the most from the tool treat that summary as something they curate, not something that merely happens to them. Second, use projects to separate streams of work that should not bleed together. One project per distinct body of work, each with its own instructions and its own walled-off memory. That structure is what keeps a busy professional's contexts from contaminating each other. Third, reach for incognito when a conversation should leave no trace. A sensitive draft, a one-off question, anything that should not shape the running summary. It is the clean-slate option, and it costs nothing on any plan. #### **How This Connects** This is the second part of the Claude Deep Dive. Vol 39 oriented around first contact and the free plan's genuine core. This volume went under the hood on the features that let Claude accumulate context: memory that summarizes who you are and rides along into every new chat, search that retrieves what you specifically said, and projects that wall off each stream of work with its own instructions and its own memory. The retrieval machinery inside projects is the same RAG pattern from [Vol 19 through 22](https://www.mindovermoney.ai/archive/), and the reason it is needed traces back to the context-window limit from [Vol 8.](https://www.mindovermoney.ai/how-to-use-ai-at-work-5-tips-high-performers/) Next week, Vol 41 turns to Artifacts and long-form work, where Claude does its most distinctive professional lifting on documents, reports, and presentations. The thread running through this series is what it actually feels like to work inside the tool, not what the marketing says it does. *Part 2 of 4 in the Claude Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Stop Re-Explaining Your Project Every Time** You are three weeks into something real: a launch, a research effort, a planning sprint. The work lives across dozens of AI chats. Every time you open a fresh one, you paste the same background again: the goal, the constraints, the documents that matter. You spend the first five minutes of each session rebuilding context the AI forgot when you closed the last tab. Across a multi-week project, that is hours lost re-explaining yourself. A project workspace fixes this. In Claude, the feature is called Projects. You configure the context once, custom instructions and reference files, and every chat inside that Project starts knowing your work. Two notes before we start. First, this is a paid feature, not a free one, and that is the point of today: knowing when a paid AI feature earns its cost is a fluency skill of its own. Second, it pairs with this week's AI Education section, Projects and Memory, which explains how Claude builds context over time. That section is the theory. This is the practice. Why this matters: a chat session means starting over; a workspace means picking up where you left off. Once you feel that difference, you stop treating AI as one-off conversations and start treating it as a place your work lives. ### **The Workflow** **1\. Measure the re-explaining tax (2 minutes).** Open your usual AI tool and run this on your current project: **Copy/Paste Prompt:** *"I am working on a multi-week project. List everything you would need to know about it to give me genuinely useful help, assuming you know nothing. Be specific."* Look at that list. That is the context you retype, in some form, every single session. Estimate how many minutes per week you spend rebuilding it. **2\. Run the cost rubric (2 minutes).** Claude Projects requires Claude Pro, which is $17 a month billed annually or $20 month to month. Ask the one question that decides any tool purchase: does it earn its cost? If you lose 30 minutes a week re-explaining context, the feature buys that time back for under a dollar a day. If you run one project a month, maybe not yet. Decide honestly. **3\. Create your Project and write its instructions (3 minutes).** If you upgrade or already have Pro, click "Projects," create one, and name it for your actual project. In the custom instructions box, paste a version of this: **Copy/Paste Prompt:** *"This project is about \[PROJECT GOAL\]. Key constraints: \[CONSTRAINTS\]. Stakeholders: \[WHO\]. When I ask for help, assume this context. Ask me what is unclear rather than guessing."* **4\. Add your reference files (2 minutes).** Upload the two or three documents the project keeps coming back to: the brief, the plan, the key data. Every chat in the Project can now see them without re-pasting. **5\. Validate the value (1 minute).** Start a fresh chat inside the Project and ask a real question with no background. If it answers with your project's context already loaded, the workspace is working, and you just felt what your money bought. ### **The Payoff** If you stayed free, you walk away with a clear-eyed decision and the rubric to make it. If you upgraded, you have a live workspace that ends the re-explaining tax for this project. Either way, you can now see the line between a chat and a workspace, which is the real fluency gain. ### **The AI Concept You Just Used** Persistent context. A normal chat forgets everything when it closes. A workspace holds your instructions and files so every conversation starts informed. It is the same idea that powers custom assistants and enterprise AI: configure context once, reuse it everywhere. ### **Transparency & Notes** - Claude Projects is a Claude Pro feature, not free. Pricing was verified on Anthropic's official pricing page, but prices change, so confirm before you subscribe. - Not ready to pay? Gemini Gems offers a free take on persistent context, custom instructions plus reference files, though it organizes around a reusable assistant rather than a full project history. - Do not upload PHI, confidential metrics, or NDA-protected material to any project workspace unless your plan and your employer's policy allow it. - For very large document sets, the workspace pulls the most relevant parts per question rather than reading everything at once. ### In the Age of Intelligence, Coding Was Never the Hard Part URL: https://www.mindovermoney.ai/founders-corner/do-you-need-to-code-to-build-with-ai/ Last updated: 2026-07-13T16:59:23.000Z An assistant named Neura now lives on my website. Open her from any page and ask where to start with AI agents, or which piece explained retrieval. She answers in a few plain sentences and points you to the right posts. She has read everything I have published, all one hundred and fifteen of them, and she keeps the whole archive in mind when she replies. She came together over about three hours of focused work across a single day, with the World Cup and the US Open on in the background. I am not a developer, and I have never claimed to be. Not one line of the code is mine. I directed Claude Code from my terminal, made every real decision myself, and watched a retrieval-powered product come to life in production on my own site. My reasons for this build went past content for the newsletter. I wanted a living tool inside my own site, not a static archive. I wanted to build it myself, and to apply knowledge I had been developing over more than eighteen months. Retrieval techniques and vector databases have been recurring topics in Neural Gains Weekly, yet I had never wired them together with my own hands. Explaining a concept and building a system around it are two different skills, and I wanted to close the gap. Neura was the chance to do exactly that, with my own archive as the proving ground. That is the part that pulled me in. ## **Every Stall Was a Decision I Had Not Made Yet** Before any building, Claude Code got one instruction from me, about how we would work together. I told it plainly, "I need to move slow as this world is newer to me." As new ideas entered the stack, a vector store here, an embedding step there, Claude Code paused to explain each one in plain language before we built on it, teaching me the concepts as we went along. Unfamiliar terms became clear within minutes, and the aha moments came faster than they would have alone. Directing the build looked less like coding and more like a run of decisions. That shift reaches well beyond me. As of January 2026, roughly nine in ten professional developers reported regularly using at least one AI tool at work, in a JetBrains survey of more than ten thousand of them. Even the people who can write code by hand now lean on AI as a matter of course. The same help has simply reached someone who could not write a line of it. I described what I wanted in plain words, and Claude Code proposed a plan. The first real decision was how much to trust it. Rather than let it build and ship on its own, I connected it to my GitHub repository and had it open a pull request for every change, a proposed set of edits waiting for me to review before anything went live. That was the governance I built in on purpose. Claude Code wrote the code and opened the request. My part was to confirm the details I had asked for were actually there, not to hunt through the code for bugs, and to approve it only once they were. The bigger decisions came before any code was written, and there was no engineer down the hall to settle them. Some I could not have answered without assistance. When it came time to pick where the whole thing would run, I did not know enough to choose between Cloudflare and Google Cloud on my own, so I asked. Claude Code laid out the tradeoffs, one vendor with everything I needed in a single free tier against a more powerful platform built for enterprise scale I did not have. The facts made the call obvious, and the call was still mine to make. The smaller, cheaper model was smart enough for the job, so the expensive option came off the table. Full retrieval beat a quick shortcut, so the archive could grow without boxing her in later. Claude Code wrote the code behind every one of those decisions, but the deciding was the real work, and it was mine. While building the vector database in Cloudflare, I loaded all one hundred and fifteen posts so Neura could search them, and the terminal work began. This was a new rhythm of commands, so I ran them one at a time and watched what came back. On one of them, nothing happened. Nothing happened, just the command sitting there waiting on me. The cause turned out to be a single missing quotation mark. I worked out why, fixed it, and the posts flowed in. I checked my footing as I went, asking Claude "Did the terminal work?" and "We good?" until the output told me we were. Each command that landed made the next one feel less foreign. The deployment had noticeable bugs that were only apparent after Neura went live on the site. The chat button hid behind another button where no visitor would find it. Dark text rendered on a dark background, making the header unreadable. And the early answers ran long, stuffed with citation numbers that pointed nowhere. I caught each one the way a reader would have, live on the page, and fixed them in minutes. A year ago, stalls like these would have overwhelmed me. This time they did not. I stayed calm, worked each one methodically, and knew the next step to take, even without being able to read a line of the code. That steadiness was the surprise. I kept expecting the technical difficulty to be the holdup. It almost never was. Each stall traced back to a decision about what I wanted that I had not yet made. The moment I got clear on the goal, the work moved again quickly. The thing slowing me down had been sitting on my side of the screen the whole time. By the end, I honestly felt comfortable. That comfort was earned, the sum of every rep that came before, going back to my [first AI coding project.](https://www.mindovermoney.ai/founders-corner/non-technical-professionals-learning-to-code-with-ai/) What I did not expect was how much the job itself had changed underneath me. ## **A Senior Developer Who Cannot Read Your Mind** In [an earlier issue](https://www.mindovermoney.ai/ai-fundamentals-beginners-guide-non-technical/), I treated an AI coding tool like a senior engineer who already knew everything, and I broke my own homepage in the process. The lesson was simple and humbling. I could not hand over the responsibility and step away. What I had then was a junior developer, capable but green, and nothing it produced could go unchecked. Building Neura, that same kind of tool no longer behaved like a junior. Claude Code, now running on Opus 4.8, reasons through tradeoffs, raises problems before they surface, and explains its choices as it makes them. It feels like a senior dev. The constant supervision the homepage demanded was mostly gone. It would be easy to read that as the human mattering less in the build. That is not how it played out. As the tool took on more, the weight of the work shifted onto me. A senior developer who cannot read your mind is powerful and a little dangerous at the same time. Hand a vague request to a capable engineer and you get exactly what you asked for, done well and done fast, even when what you asked for was wrong. The sharper the tool gets at execution, the more your half of the job narrows to deciding precisely what to execute. Speed only makes a bad decision arrive sooner. None of this belongs to one project or one tool. The more capable the machine becomes, the more the work depends on a clear head and a well-chosen problem, which is to say it depends on you. That is the shift worth carrying into whatever you decide to build next. ## **The Part No Tool Will Ever Do For You** So what is left for you, the capable professional who has been watching all of this from a distance? The most important part, and it is not close. You decide which problem is worth solving and which tradeoffs you will accept. You define what good looks like, and you choose when something is finished. Taste and judgment are yours to supply. They always have been. The growing ease of these tools does not worry me, because the skill that carried me through this build was a different kind entirely, learning to translate between the problem in my head and the language a machine needs to act on it. That translation holds up far beyond a terminal, in any room where someone has to turn a fuzzy goal into a clear instruction. So here is the one move worth making this week. Take a single problem that nags at you, the kind of small, recurring friction you have learned to tolerate, and describe it in plain English, the way you would explain it to a colleague. Then open Claude Code, or another tool like it, and start. There is no product to ship here, only your first real rep at directing a machine to build something, which is quickly becoming one of the most useful things a person can know how to do. Open Neura if you want to see where a few spare hours can land, though the tool itself is beside the point. What matters is this: the starting line is not a coding course, and it never was. You do not need to learn to code. You need to decide what to build, and then decide to begin. That decision was always the hard part. It was just hiding behind the code. ### Steal My Prompt Vol. 40: The Memo Editor URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-edit-a-decision-memo/ Last updated: 2026-07-18T01:46:13.000Z A senior leader will give your memo about ninety seconds. They will scan, not read, and they will look for one thing: what are you asking me to decide, and why should I say yes. Most memos make them hunt for it. The recommendation sits in paragraph four, behind the background, the context, and the careful throat-clearing that felt responsible to write. By the time the reader reaches the ask, they have already started skimming. A sound argument, lost to its own structure. This prompt works as a back-and-forth. It reads your draft, then puts one question to you at a time about the reader, the decision, and what that reader already believes, the context an editor needs and a writer always assumes is obvious. Only when it has that does it go to work: it drags your recommendation to the first sentence, strips the throat-clearing, and flags every claim you made without evidence behind it. The result is a memo that lands in the ninety seconds you actually get. ## **What You Can Use This For** - A decision memo asking leadership to approve a budget, a hire, a project, or a change of direction - A recommendation that keeps drawing "let me think about it" instead of a yes or a no - A proposal you have rewritten three times that still buries the point - A memo headed to someone who will read only the first paragraph - Compressing a long strategy document into the one page an executive will actually read - Pressure-testing your reasoning before a senior leader does it for you in the room ## **How to Use It** **Step 1.** Pick your tool. Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. The prompt is model-agnostic. **Step 2.** For a high-stakes memo, use deeper reasoning where your tool offers it free. Turn on "Extended thinking" in Claude or "Think Deeper" in Microsoft Copilot. ChatGPT routes harder requests to its reasoning mode on its own, and Gemini's free tier runs the prompt as written. **Step 3.** Paste the prompt with your full draft, messy parts included. The throat-clearing is exactly what it is built to cut, so do not clean it up first. **Step 4.** Answer its questions one at a time. Before it edits anything, it asks about your reader, your ask, and what that reader already believes. This intake is what turns a quick edit into a real one, so it is worth the two minutes. If you are genuinely pressed, type "skip" and it will proceed on clearly labeled assumptions. **Step 5.** Read the verdict first. If the prompt cannot find your recommendation even after the intake, that is the finding, and you fix it before anything else. **Pro tip:** After the rewrite, read only the first sentence out loud. If it does not contain your ask, the memo is not finished, no matter how strong the rest of it is. ## **The Prompt** *You are a ruthless memo editor for a senior professional writing to leadership. Your standard is simple: a busy executive should understand the recommendation and the reasoning in ninety seconds. You edit on behalf of that reader, not the writer. You do not soften, and you do not flatter.* *Before you edit a single line, you run an intake. A memo is only as sharp as the context behind it, and the writer almost always assumes context the reader does not have. Surfacing it is the first half of your job.* **Here is my memo draft:** *\[PASTE THE FULL DRAFT, MESSY PARTS INCLUDED\]* ***Intake protocol.*** - *Read my draft first.* - *Then ask me your questions one at a time. Ask a single question, wait for my answer, and only then ask the next. Never stack two questions in one message.* - *Ask only what you cannot reasonably infer from the draft. At a minimum, establish who exactly will read this and how senior they are, the specific decision I need from them, what they already know or believe about the topic, and any objection or constraint I expect. Add a question only where the draft leaves a real gap.* - *Ask no more than five questions, and stop as soon as you have what you need.* - *If I type "skip" at any point, stop asking and proceed to the edit using clearly stated assumptions, labeling each assumption you had to make.* *Once the intake is complete, and only then, produce the edit. Work through this in order.* **1\. The verdict.** *In one line, state the decision this memo is asking for. If it is still unclear, say so plainly. That is the most important finding.* **2\. Diagnose.** *Quote the specific lines that are throat-clearing, background placed ahead of the ask, claims without evidence, or a recommendation that arrives too late.* **3\. Rewrite.** *Put the recommendation in the first sentence, lay the reasoning out in scannable form, attach evidence to the claim it supports, and cut anything an executive would skip. Preserve my meaning while cutting my words.* **4\. Evidence gaps.** *List every claim that still needs supporting evidence, written as questions for me to answer before I send.* *Rules. Edit on behalf of the reader. Do not add claims I did not make or invent data I did not give you. During intake, ask one question at a time and wait for my answer. After intake, output the verdict, diagnosis, rewrite, and evidence gaps, each clearly labeled.* ## **Transparency and Notes** - This prompt asks before it edits. That intake is what turns a one-shot rewrite into a repeatable system, and it is becoming the standard opening for the Editor series, so you will see the same one-question-at-a-time move in the entries that follow. - This is the fourth entry in the Editor series, which applies one adversarial mechanic to different documents. [The Self-Performance Review](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/) took on the annual review, [the Status Update Compressor](https://www.mindovermoney.ai/prompt-library/ai-prompt-weekly-status-update-impact-framework/) took on the weekly update. This one takes on the decision memo, where the stakes are highest because the writing is the decision. - The prompt is deliberately harsh. It edits for your reader, not your ego, and that is the point. - It will not invent evidence or claims. Where your memo needs data you did not provide, it asks you for it instead of filling the gap. - Built and tested in Claude, free-tier compatible across the four major tools. ### Volume 39: AI Got the Keys to Your Refills but Not Your Denials URL: https://www.mindovermoney.ai/is-claude-free-version-worth-it/ Last updated: 2026-07-13T16:59:24.000Z In one state, an AI agent renews people's prescriptions on its own, no human signature required. In that same state, the same software cannot deny anyone's care without a licensed person behind the decision. One technology, two opposite rules, because the law keeps governing a word when it should govern the work itself. 🧭 **Founder's Corner:** Why a wave of new healthcare AI laws keeps regulating the word instead of the work, and the line between routine paperwork and a real clinical decision. 🧠 **AI Education:** An honest first week inside a second AI tool: what the free version delivers, where it pushes back, and how to tell which tasks belong in which tool. ✅ **10-Minute Win:** Build your own map of which AI tool to reach for at each stage of a task, so you stop forcing one model to do five jobs. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Introducing the OpenAI Partner Network**](https://openai.com/index/introducing-openai-partner-network/?ref=mindovermoney.ai) **Summary:** OpenAI launched the OpenAI Partner Network on June 14, 2026, its first formal global partner program backed by $150 million, targeting 300,000 certified consultants by year-end, with launch partners including Accenture, BCG, McKinsey, Bain, PwC, Eliza, and Artium. The company stated AI model capabilities are no longer the main barrier to enterprise adoption. **Why it matters:** The biggest AI lab on earth just publicly conceded that the model isn't the bottleneck, the implementation is. For any leader being told "we just need the right tool," this resets the conversation, the next phase of AI value will be won by the organizations that get governance, change management, and workflow redesign right, not by the ones chasing the latest benchmark. #### **2)**[ **AI can help close workforce gaps while keeping humans in the loop**](https://www.healthcareitnews.com/news/ai-can-help-close-workforce-gaps-while-keeping-humans-loop?ref=mindovermoney.ai) **Summary:** CommonSpirit Health's CMIO says AI's greatest value may be helping clinicians scale cancer screening, identifying overlooked findings and reducing administrative burden while preserving human oversight. CommonSpirit is one of the largest nonprofit and Catholic health systems in the United States, operating more than 2,200 care sites and hospitals across 24 states. **Why it matters:** This is what scaled, governed clinical AI looks like at a system serving 24 states. The pattern, AI finds the signal, clinicians make the call, is the model healthcare leaders should be benchmarking their own rollouts against right now. #### **3)**[ **House Appropriations Committee votes to end funding for WISeR pilot**](https://www.healthcarefinancenews.com/news/house-appropriations-committee-votes-end-funding-wiser-pilot?ref=mindovermoney.ai) **Summary:** The House Appropriations Committee voted to cut funding for the Wasteful and Inappropriate Services Reduction (WISeR) Model, which would introduce AI-driven prior authorization into traditional Medicare. The model would hire private companies to use artificial intelligence to automate prior authorization, with vendors compensated based on a share of "averted expenditures." **Why it matters:** The first federal AI program to put approval-or-deny decisions on Medicare patients is now politically radioactive. For any organization building AI into payer workflows, the message is that incentive design, especially anything that rewards denials, is going to be the first thing regulators and Congress scrutinize. #### **4)**[ **AI in spotlight at G7 as Trump, world leaders joined by tech chiefs**](https://www.cnbc.com/2026/06/17/g7-trump-ai-tech-leaders-openai-anthropic-google.html?ref=mindovermoney.ai) **Summary:** Sam Altman, Dario Amodei, Demis Hassabis, and around a dozen other tech leaders took part in a working lunch at the G7 summit in Evian-les-Bains on June 17, 2026, with frontier AI risks, infrastructure and sovereignty all expected on the agenda. **Why it matters:** This is the first G7 where AI lab CEOs sit at the same table as heads of state, and after the Fable 5 export-control episode, it is no longer an academic conversation. Expect coordinated frameworks on frontier model deployment, child safety, and biosecurity to start moving from communique language into actual rulemaking. #### **5)**[ **Improving health intelligence in ChatGPT**](https://openai.com/index/improving-health-intelligence-in-chatgpt/?ref=mindovermoney.ai) **Summary:** OpenAI announced that more than 230 million people use ChatGPT every week for health-related topics and that GPT-5.5 Instant is now better at identifying situations that may require urgent medical attention, asking follow-up questions when more information is needed, and explaining complex medical topics in simpler language. GPT-5.5 Instant had fewer instances of not tailoring to local healthcare context, missing red flags or referral to care, or failing to seek additional context from the user when needed than both older models and physicians. **Why it matters:** A general-purpose chatbot is now the first health touchpoint for hundreds of millions of people every week. For health system leaders, the question is no longer how to keep patients off ChatGPT, but how to design intake, education, and follow-up so the work it does well strengthens your care plan instead of competing with it. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****Healthcare's AI Rules Govern One Job That Was Always Two** In Utah, an autonomous AI agent renews people's prescriptions on its own. Regulators there cleared it to handle 192 different chronic-condition medications with no human signature required, quietly keeping patients on the drugs they depend on. That same year, the same legislature went the opposite way on prior authorization. When AI helps review one of those requests, any denial now has to come from a licensed person, not the software. One state. One technology. Two opposite answers. Prior authorization is the approval an insurer requires before it will cover certain treatments. For many medications it is a routine step in the prescription lifecycle. The criteria behind it come from clinical guidelines that flag when an expensive or specialized drug deserves a closer look. It is also where a large share of the system's friction collects. So why would one legislature hand AI the keys to refill a prescription on its own, yet insist a human stay in charge the moment that same software might deny a patient's care? Utah's split runs along an access-versus-risk line. Renewing a stable patient's medication is mostly a question of access, so the state let the agent move fast. Denying care is a question of risk, so it kept a human accountable. That instinct is sound, but it stops one level too high. Even inside a single process like prior authorization, AI is still not one decision, and until the laws taking shape across the country see that, they will keep regulating a word instead of the work underneath it. ## **Every State Is Writing Its Own Rules** In the last two years, AI in prior authorization has jumped from conference panels to legislation in statehouses across the country. And in traditional political fashion, no two states have landed in the same place. California's Physicians Make Decisions Act keeps AI from being the sole basis for denying or delaying care. A licensed professional still has to make the medical-necessity call, the judgment that a treatment is genuinely needed. Georgia took a different path, passing a law that lets insurers use AI in prior authorization to automate routine work and ease the paperwork load. Utah added a disclosure rule of its own, requiring insurers to say when AI is part of the review at all. Dozens more states sit somewhere in between, each writing its own version of how AI interacts with this part of the healthcare system. Those laws look like a country that cannot make up its mind. But almost all of them are arguing about the same wrong question, how far AI should reach across prior authorization as one undivided process. That framing is the real problem. Prior authorization is not a single act to wave through or rein in. It is several jobs bundled together, most of them routine and only a few of them clinical. A law that cannot tell those apart ends up treating all of it the same way. ## **The Yes Was Always Coming** Underneath the disagreement, the states do share one rule. Whatever a law says about AI, it stops short of letting software deny care on its own. A person has to stand behind any refusal. That common ground is real, but it settles only a small piece of what prior authorization does. Most requests never reach a real clinical judgment. The bigger story is the part the debate keeps stepping over. A December 2025 white paper from the National Association of Insurance Commissioners puts approval rates for prescription-drug prior authorizations at around 90 percent. Nine in ten requests end in a yes. After more than a decade on the technology and operations side of specialty pharmacy, where the most expensive and complex drugs almost always require this step, that number is no surprise. And it changes what prior authorization really is and what it means. If nine in ten requests are approved anyway, most were never a clinical gate. They are administrative throughput, paperwork moving between systems while everyone waits for a stamp that is already coming. Running that nearly-automatic process still costs a fortune in time. A 2024 American Medical Association survey, the research the NAIC drew on, found that doctors and their staff spend about 13 hours a week on prior authorizations. Thirteen hours a week, feeding a process that says yes nine times out of ten. Most of that time is pulled straight from patients, who wait while the paperwork clears. So why not scrap prior authorization altogether? Because the criteria underneath it do real work. Clinicians write them to catch the cases where an expensive or risky treatment deserves a second look, and that judgment is worth protecting. The waste is everything wrapped around it, the faxing, the re-keying, the chase for one missing field. Automate that, and the yes that was always coming arrives in minutes instead of days. The thirteen hours go back to patients. The human stays where the judgment is real, on the small share of cases that genuinely need one. That is the line worth drawing, and it is exactly the line these laws get wrong. ## **When a Blank Field Counts as a Denial** Under the hood, much of what gets counted as a denial is not one. A prior authorization can come back rejected because someone left the patient's weight off the form. Nothing clinical happened. No reviewer weighed the treatment against the criteria and said no. A required field was blank, so the system kicked it back. The NAIC names this directly, noting that incorrect or missing patient information can delay a request or produce an unexplained denial. The record still calls it a denial, but it is really a clerical gap, the kind of gap software should catch and fix before a person ever opens the file. Now compare that to a real clinical denial, a clinician reading a complicated case and deciding the treatment does not meet medical necessity. That is a judgment call, and it carries weight a missing data field never could. Yet most of the laws on the table treat the two as the same act, because both run on software and get filed under the same word. Govern them together and there is no good setting. Loosen the rules to clear paperwork and you weaken oversight on the denials that need it. Tighten them to protect patients and the clerical work that should take seconds drags for days. Every one of these laws makes the same mistake. It writes a single rule for a single word, AI, when that word covers both a clerical task and a clinical decision. A pharmacy that fills prescriptions across state lines now follows a different AI rule in each one. Every rule has its own disclosure language and its own definition of what even counts as AI. The burden skips the largest, best-resourced players and lands on the providers and pharmacies already stretched thin. Fifty versions of a question no one has framed correctly are overhead dressed up as oversight. ## **Healthcare Knows How to Build Inside the Lines** Legislation will keep reshaping how AI works in healthcare, state by state and year by year, and prior authorization is only what happens to be in front of us right now. None of that is a reason for pessimism. Healthcare is adept at working in a regulated environment. This is the industry that learned to innovate inside HIPAA and FDA review, under decades of rules far heavier than a disclosure form, and the innovation will continue. What makes me optimistic is not a hope that the rules get lighter. It is what AI is genuinely good at. The friction in this whole story, the thirteen hours, the bounced forms, the waiting that lands on a patient, is exactly the kind of waste the technology clears best. Aim AI at the administrative half of prior auth and you strengthen patient protection, because the time handed back flows to the people the process exists to serve. The optimism here is pro-patient. And if you want more access for people, change the system. You do not slow down the one tool that creates speed just to keep the pace where it has always been. Whether AI belongs in healthcare was always the wrong frame. The real question lives one level down, inside the process, where administrative throughput and clinical judgment turn out to be two different jobs that happen to share a name. Good governance automates the throughput and keeps the human on the judgment. Get that line right, and AI does what it should, reduce friction across the healthcare journey and improve the experiences we all go through. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Claude 101: One Professional, One Week, What Actually Changed** ### **The Setup** A professional in healthcare operations has used ChatGPT every workday for two years. It drafts her emails, helps her think on her commute, and turns her messy meeting notes into something forwardable. She is fluent in it and not looking to switch. A colleague suggests that the long strategy documents she wrestles with each quarter might go better in Claude, Anthropic's AI assistant. She is skeptical, but she has a quieter week before her next planning cycle, so she runs an experiment. One week, Claude only, the free version, her actual work. The point is not to crown a winner. It is to learn what a second tool can and cannot do, so she knows when to reach for which. This is what the week taught her, and most of it is true of the free plan anyone can open today. ### **Monday: Getting Oriented** The first thing she has to do is find her footing, and the interface makes that easy. It is a chat box, like the one she already knows. The notable difference is small and worth understanding: a control that lets her pick how hard the model thinks about a given request. This is the first real concept of the week. Claude offers more than one model, and the free plan runs on the fast, capable everyday one. For most of what she does, drafting, summarizing, talking through a problem, that model is the whole job. The heavier model that the paid plans unlock is built for the hardest reasoning and coding, and she does not need it on day one. Knowing the distinction means she is not left wondering whether she is using the tool wrong. She is using the right model for the work in front of her. ### **Tuesday: The One Feature That Changes the Most** Her real test is a forty-page strategy document. She needs every place it commits to a timeline. In her ChatGPT habit, a document this long meant working in pieces. Paste a section, ask, paste the next, stitch the answers together herself. She drops the whole document into Claude in one go and asks once. The answer references a commitment near the front and a caveat near the end in the same response. The concept underneath this is the one most worth learning from the whole week: the context window, which we covered back in Vol 8\. It is the amount of text a model can hold in active view at one time. Claude's is large, large enough that a forty-page document fits inside it whole. That is not a magic trick and it is not unique to Claude, but it is generous on the free plan, and it is the single feature that most changes what kind of work feels natural here. If her job involved holding a lot of material in view at once, this is where she would feel the difference. ### **Wednesday: Where the Free Plan Pushes Back** By midafternoon she hits a wall. A notice tells her she has reached her usage limit and needs to wait for the window to reset. This is the honest cost of the free plan, and it is worth stating plainly rather than burying. Free Claude is built for occasional use, not all-day reliance. The limit is not a simple count of messages either. It scales with how much text the model is working through, which means the forty-page document she loved on Tuesday spent her allowance faster than a dozen short questions would have. The capability and the constraint are the same coin. Here is where the comparison turns genuinely two-sided. Her ChatGPT habit of firing off quick questions all morning never ran into this kind of ceiling as fast. For rapid, lightweight, all-day use, that experience was smoother. Neither tool is wrong. They are tuned for different rhythms, and the free plans make those tradeoffs more visible than the paid ones do. ### **Thursday: An Honest Accounting of What Is Missing** By Thursday she is keeping a running list of what Claude does not do that ChatGPT did, because an honest evaluation requires it. Claude does not generate images. When she wanted a quick illustrative graphic, she went back to ChatGPT, which does. The ecosystem of third-party add-ons and custom tools around ChatGPT is larger and more mature. Some of the consumer-facing conveniences she had grown used to were simply not there. None of this is hidden, and none of it is a flaw exactly. It is a different product with a different center of gravity. Claude is built first around careful reasoning and text. ChatGPT spreads wider across features. A fair evaluation names both halves. What the free plan does give her, and what she confirms by week's end, is the genuine core. The same writing and reasoning quality the paid plan offers, file uploads, web search, and the workspace features the rest of this series will dig into. The free tier is not a crippled demo. It is the real product with a ceiling on how much she can use it. ### **Friday: What She Actually Decided** She did not delete her ChatGPT account. That is not how a working professional adopts a tool, and pretending otherwise would be the tell of a sales pitch rather than an honest week. What she decided was narrower and more useful. The two tools have different shapes. ChatGPT suited her fast, scattered, all-day questions and the moments she needed an image or a niche custom tool. Claude suited the heavy, document-driven thinking that defined her hardest weeks. The free plan was enough to learn that. Whether she ever pays the twenty dollars a month, the same price as the ChatGPT plan she already had, is a question she can now answer from experience rather than from a review. She ended the week understanding what free Claude is. Not better, not worse. A different tool with a clear strength, an honest limit, and a free tier real enough to judge it on. ### **What to Take From This** - The free version of Claude is the real product, not a teaser. Full writing and reasoning quality, file uploads, web search, and a large context window are all available without paying. The ceiling is on how much you use it, not on what you get. - The context window is the concept that most shapes the experience. It is how much text the model holds in view at once, and Claude's is large enough on the free plan to work through long documents whole. Vol 8 covered the idea; this is what it feels like in daily use. - The free plan's usage limits scale with how much text you run through it, not just how many times you hit enter. Long documents are powerful and expensive at the same time. Plan your heavy lifting accordingly. - Claude does not do everything. No image generation, a smaller third-party ecosystem, tighter free limits than some alternatives. An honest tool evaluation names what is missing, not just what shines. - The useful question is never which AI is best. It is which of your tasks fits which tool. A week of your real work answers that better than any benchmark. ### **How This Connects** This opens a four-part Claude Deep Dive, a guided tour of what the platform offers and where it falls short, anchored in the free plan anyone can use. This volume was orientation: what Claude is, what the free tier includes, and the honest pros and cons of first contact. Vol 40 goes under the hood on Projects and memory, the features that let Claude build context across conversations. Vol 41 covers Artifacts and long-form work. Vol 42 closes with Claude Code and the agentic future, and makes the case that the agentic shift reaches well beyond developers. The fine-tuning series that just ended explained how these models are built. This series is about what it is like to actually work inside one. *Part 1 of 4 in the Claude Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Stop Forcing One AI to Do Everything** You picked a tool a year ago and use it for everything. Research, drafting, cleanup, polish, all in the same chat window. Lately you can feel the edges. The research is shallow, or the writing is generic, or it cannot hold the whole document in its head. You suspect another tool would do that one part better, but switching mid-task feels like overhead. So you keep forcing one model to do five jobs. Matching the tool to the job is the single biggest lever in AI fluency, more than any individual prompt. The people who get the most out of AI are not the ones with the best single tool. They are the ones who know which tool to reach for at each phase. Why this matters: most work has phases, and the phases want different things. Research wants breadth and current sources. Drafting wants reasoning and long context. Polish wants speed. No single tool is best at all of them. This workflow builds your own map of which tool fits which phase. ### **The Workflow** **1\. Pick a real multi-phase task (1 minute).** Choose something with several stages: a report, a proposal, a research memo. Not a one-shot question. Something you are actually working on. **2\. Break the task into phases with an AI (3 minutes).** Open Claude or ChatGPT and have it map the work before you do it. Paste this: **Copy/Paste Prompt:** *"I am working on \[YOUR TASK\]. Break it into its natural phases, for example research, drafting, refinement, and polish. For each phase, tell me in one line what that phase actually needs from an AI tool, such as current web sources, deep reasoning, long context, speed, or formatting. Do not recommend specific products yet."* Read the list and mark the phase where your usual tool feels weakest. That is the one to test next. **3\. Run a head-to-head on that phase (3 minutes).** Write one prompt for the weak phase you just marked. Run that same prompt in two tools, your usual one and one other (Claude, ChatGPT, Gemini, or Perplexity). Read both outputs side by side. You will feel the difference faster than anyone could explain it. **4\. Write your switch rule (2 minutes).** Decide where in this task you will switch tools and why. Keep it to one line, like "research in Perplexity for the live sources, then draft in Claude for the long context." The rule matters more than getting it perfect. **5\. Save your phase-to-tool map (1 minute).** Write a three-column note: phase, tool, why. Four or five rows. Next task, you start from the map instead of forcing one tool through all of it. ### **The Payoff** You walk away with a personal phase-to-tool map and a switch rule you can reuse on every multi-stage task. The bigger gain is the instinct underneath it: you stop asking "what can my tool do" and start asking "what does this phase need." That question, asked over and over, is how AI taste actually develops. ### **The AI Concept You Just Used** Tool-to-task matching. Every model has a shape: strengths, blind spots, a context limit, a personality. Forcing one model through every phase is like using a chef's knife to also open cans and tighten screws. It works, badly. The head-to-head test is the fastest way to learn each tool's shape, because you judged them on your own work instead of trusting a review. Run it a few more times and switching stops feeling like overhead and starts feeling like skill. ### **Transparency & Notes** - This workflow runs entirely on free tiers. Claude, ChatGPT, Gemini, and Perplexity all let you run the same prompt at no cost. Free tiers use lighter models with message caps, so you are comparing the free versions, which is what matters if you do not pay. - Tool strengths shift with every model release, so treat any phase-to-tool map as a living note, not a permanent ruling. Re-run a head-to-head when a tool ships a major update. - The head-to-head is more honest than any published benchmark, because it uses your real task and your judgment. Trust what you see over what you read. - Switching tools means re-pasting context. For long documents, keep a master copy of your content somewhere outside the chat so you can move it between tools cleanly. ### Healthcare's AI Rules Govern One Job That Was Always Two URL: https://www.mindovermoney.ai/founders-corner/ai-prior-authorization-laws/ Last updated: 2026-07-13T16:59:24.000Z In Utah, an autonomous AI agent renews people's prescriptions on its own. Regulators there cleared it to handle 192 different chronic-condition medications with no human signature required, quietly keeping patients on the drugs they depend on. That same year, the same legislature went the opposite way on prior authorization. When AI helps review one of those requests, any denial now has to come from a licensed person, not the software. One state. One technology. Two opposite answers. Prior authorization is the approval an insurer requires before it will cover certain treatments. For many medications it is a routine step in the prescription lifecycle. The criteria behind it come from clinical guidelines that flag when an expensive or specialized drug deserves a closer look. It is also where a large share of the system's friction collects. So why would one legislature hand AI the keys to refill a prescription on its own, yet insist a human stay in charge the moment that same software might deny a patient's care? Utah's split runs along an access-versus-risk line. Renewing a stable patient's medication is mostly a question of access, so the state let the agent move fast. Denying care is a question of risk, so it kept a human accountable. That instinct is sound, but it stops one level too high. Even inside a single process like prior authorization, AI is still not one decision, and until the laws taking shape across the country see that, they will keep regulating a word instead of the work underneath it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **Every State Is Writing Its Own Rules** In the last two years, AI in prior authorization has jumped from conference panels to legislation in statehouses across the country. And in traditional political fashion, no two states have landed in the same place. California's Physicians Make Decisions Act keeps AI from being the sole basis for denying or delaying care. A licensed professional still has to make the medical-necessity call, the judgment that a treatment is genuinely needed. Georgia took a different path, passing a law that lets insurers use AI in prior authorization to automate routine work and ease the paperwork load. Utah added a disclosure rule of its own, requiring insurers to say when AI is part of the review at all. Dozens more states sit somewhere in between, each writing its own version of how AI interacts with this part of the healthcare system. Those laws look like a country that cannot make up its mind. But almost all of them are arguing about the same wrong question, how far AI should reach across prior authorization as one undivided process. That framing is the real problem. Prior authorization is not a single act to wave through or rein in. It is several jobs bundled together, most of them routine and only a few of them clinical. A law that cannot tell those apart ends up treating all of it the same way. ## **The Yes Was Always Coming** Underneath the disagreement, the states do share one rule. Whatever a law says about AI, it stops short of letting software deny care on its own. A person has to stand behind any refusal. That common ground is real, but it settles only a small piece of what prior authorization does. Most requests never reach a real clinical judgment. The bigger story is the part the debate keeps stepping over. A December 2025 white paper from the National Association of Insurance Commissioners puts approval rates for prescription-drug prior authorizations at around 90 percent. Nine in ten requests end in a yes. After more than a decade on the technology and operations side of specialty pharmacy, where the most expensive and complex drugs almost always require this step, that number is no surprise. And it changes what prior authorization really is and what it means. If nine in ten requests are approved anyway, most were never a clinical gate. They are administrative throughput, paperwork moving between systems while everyone waits for a stamp that is already coming. Running that nearly-automatic process still costs a fortune in time. A 2024 American Medical Association survey, the research the NAIC drew on, found that doctors and their staff spend about 13 hours a week on prior authorizations. Thirteen hours a week, feeding a process that says yes nine times out of ten. Most of that time is pulled straight from patients, who wait while the paperwork clears. So why not scrap prior authorization altogether? Because the criteria underneath it do real work. Clinicians write them to catch the cases where an expensive or risky treatment deserves a second look, and that judgment is worth protecting. The waste is everything wrapped around it, the faxing, the re-keying, the chase for one missing field. Automate that, and the yes that was always coming arrives in minutes instead of days. The thirteen hours go back to patients. The human stays where the judgment is real, on the small share of cases that genuinely need one. That is the line worth drawing, and it is exactly the line these laws get wrong. ## **When a Blank Field Counts as a Denial** Under the hood, much of what gets counted as a denial is not one. A prior authorization can come back rejected because someone left the patient's weight off the form. Nothing clinical happened. No reviewer weighed the treatment against the criteria and said no. A required field was blank, so the system kicked it back. The NAIC names this directly, noting that incorrect or missing patient information can delay a request or produce an unexplained denial. The record still calls it a denial, but it is really a clerical gap, the kind of gap software should catch and fix before a person ever opens the file. Now compare that to a real clinical denial, a clinician reading a complicated case and deciding the treatment does not meet medical necessity. That is a judgment call, and it carries weight a missing data field never could. Yet most of the laws on the table treat the two as the same act, because both run on software and get filed under the same word. Govern them together and there is no good setting. Loosen the rules to clear paperwork and you weaken oversight on the denials that need it. Tighten them to protect patients and the clerical work that should take seconds drags for days. Every one of these laws makes the same mistake. It writes a single rule for a single word, AI, when that word covers both a clerical task and a clinical decision. A pharmacy that fills prescriptions across state lines now follows a different AI rule in each one. Every rule has its own disclosure language and its own definition of what even counts as AI. The burden skips the largest, best-resourced players and lands on the providers and pharmacies already stretched thin. Fifty versions of a question no one has framed correctly are overhead dressed up as oversight. ## **Healthcare Knows How to Build Inside the Lines** Legislation will keep reshaping how AI works in healthcare, state by state and year by year, and prior authorization is only what happens to be in front of us right now. None of that is a reason for pessimism. Healthcare is adept at working in a regulated environment. This is the industry that learned to innovate inside HIPAA and FDA review, under decades of rules far heavier than a disclosure form, and the innovation will continue. What makes me optimistic is not a hope that the rules get lighter. It is what AI is genuinely good at. The friction in this whole story, the thirteen hours, the bounced forms, the waiting that lands on a patient, is exactly the kind of waste the technology clears best. Aim AI at the administrative half of prior auth and you strengthen patient protection, because the time handed back flows to the people the process exists to serve. The optimism here is pro-patient. And if you want more access for people, change the system. You do not slow down the one tool that creates speed just to keep the pace where it has always been. Whether AI belongs in healthcare was always the wrong frame. The real question lives one level down, inside the process, where administrative throughput and clinical judgment turn out to be two different jobs that happen to share a name. Good governance automates the throughput and keeps the human on the judgment. Get that line right, and AI does what it should, reduce friction across the healthcare journey and improve the experiences we all go through. ### Steal My Prompt Vol. 39: The Medical Bill Auditor URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-check-medical-bill/ Last updated: 2026-07-13T16:59:24.000Z The bill says you owe four hundred dollars. The Explanation of Benefits from your insurer, for the same visit on the same day, says you owe sixty. Two numbers, one appointment, and nobody has offered to explain the gap. Medical billing runs on the assumption that you will not check. The codes are opaque, the statements arrive weeks apart, and the math is split across documents nobody designed to be read together. Most people pay the larger number because disputing it feels harder than absorbing it. Reconciling a coded bill against an EOB, line by line, is tedious enough that almost nobody does it. That is the part this prompt takes off your hands. You paste or upload the itemized bill and the EOB, and it lines them up, surfaces every place the bill asks for more than your insurer says you owe, flags likely errors, and drafts the questions for the billing office. It does not decide who is right. It builds the case so you can. A medical bill is not a verdict. It is an opening number. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - A maternity or childbirth bill, which bundles dozens of services across providers and is among the most error-prone you will ever get - A hospital or emergency room bill, where charges pile up fast and an itemized breakdown is rarely provided unless you ask - Any bill that does not match the EOB your insurer sent for the same care - A surprise charge from a provider you do not remember seeing, like a separate lab, an anesthesiologist, or a facility fee - Any bill before you pay it, as a standing habit rather than a one-time fight ## **How to Use It** **Step 1.** Pick your tool. Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. The prompt is model-agnostic. **Step 2.** Get the itemized bill, not the summary. This is the step almost everyone skips. The default statement shows a single total; you want the itemized version that lists every code, charge, and date. Request it from the billing office or the patient portal. You cannot audit what you cannot see. **Step 3.** Get the matching Explanation of Benefits from your insurer. It lives in their portal or app under claims, filed by the visit date. The EOB is the document that tells you what you actually owe. **Step 4.** Give both to the prompt. Paste the text, or photograph the documents and upload the images. Claude, ChatGPT, Microsoft Copilot, and Gemini all accept photo uploads on the free tier, so a clear picture of a paper bill works as well as typed text. Then run. **Step 5.** Read the discrepancies the prompt flags, then contact the billing office with the questions it drafted. Reference the EOB for each line, and ask them to correct or explain it. **Pro tip:** Do not pay a medical bill the day it arrives if something looks off. Request the itemized version and run it through the prompt first. A charge can be disputed even in collections, but questioning it before money changes hands is far easier than clawing it back. ## **The Prompt** *You are my medical bill auditor. I am going to give you an itemized medical bill and the Explanation of Benefits, or EOB, that my insurer sent for the same care. I may paste them as text or upload them as photos; if I upload photos, read every line item carefully from the images. Your job is to reconcile the two, find discrepancies, and help me prepare to question the bill. You are an auditor and a question-generator. You do not give medical, legal, or financial advice, and you do not decide who is correct. You surface what does not match and turn it into questions.* ***Here is what I am giving you:*** **The itemized bill:** *\[PASTE OR UPLOAD THE ITEMIZED BILL, WITH CHARGES, CODES, AND DATES\]* **The Explanation of Benefits:** *\[PASTE OR UPLOAD THE EOB FOR THE SAME CARE\]* **My situation, if relevant:** *\[ANYTHING WORTH KNOWING, SUCH AS A VISIT I DO NOT RECOGNIZE OR A CHARGE I EXPECTED TO BE COVERED\]* *Work through this in order.* **1\. Reconcile.** *Build a table that lines up each charge on the bill against what the EOB says for the same service: amount billed, amount the insurer allowed, amount the insurer paid, and the amount the EOB lists as my responsibility. Place the bill's "amount due" directly beside the EOB's "patient responsibility" so any gap is visible at a glance.* **2\. Flag the gaps.** *List every line where the bill asks me to pay more than the EOB says I owe, and state the specific dollar difference on each. This is the highest-value finding.* **3\. Surface possible errors.** *Using only what I gave you, point out anything that fits a common billing-error pattern: a duplicate charge, a charge on a date I said I was not there, a service usually bundled into another code but billed separately, or a charge marked as my responsibility that the EOB shows the insurer already paid. Do not assert fraud. Frame each one as something to ask about.* **4\. Build the dispute.** *Draft a short, factual message I can send to the billing office. List the lines I am questioning, reference the EOB for each, and request either a corrected bill or a written explanation.* *Rules. Use only the documents and information I gave you. Do not invent charges, codes, or amounts. Do not state that a charge is definitely wrong or fraudulent; frame findings as discrepancies and questions. Where a number is missing or unclear, say so rather than guessing. Output clean, labeled text I can act on, with no preamble.* ## **Transparency and Notes** - This prompt reconciles documents and drafts questions. It is not legal or financial advice, and it cannot tell you that a charge is definitively wrong. It shows you where the numbers do not match so you can ask the right questions of the people who can correct them. - A summary bill cannot be audited. Request the itemized statement, which a reputable billing office will provide. - Privacy: a medical bill and an EOB carry your name, account numbers, and details about your care. Remove identifiers before pasting or uploading into a consumer AI tool, which is not HIPAA covered. - Built and tested in Claude, free-tier compatible across the four major tools. - This is a tool for organizing and questioning a bill. It is not a guarantee of savings and not a substitute for a patient advocate or your insurer's formal appeals process. ### Volume 38: Why Your Best AI Idea Is Stuck in Someone's Inbox URL: https://www.mindovermoney.ai/fine-tuning-vs-rag-vs-prompting/ Last updated: 2026-07-13T16:59:25.000Z Almost every company is using AI somewhere now. Far fewer have it running at full scale, and the reason is rarely the technology. The thing slowing your best AI work down is usually the structure it has to travel through before anyone can say yes. 🧭 **Founder's Corner:** The hidden reason your AI work keeps stalling is structural, not technical, and you can move faster without waiting for the org chart to change. 🧠 **AI Education:** A clear framework for knowing when to simply prompt a model, when to give it access to your documents, and when training the model is actually worth the cost. ✅ **10-Minute Win:** Turn a pile of rough notes into a finished, designed slide deck in ten minutes instead of ninety. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **WWDC 2026: Everything announced on Siri AI, iOS 27, Apple Intelligence, and more**](https://techcrunch.com/2026/06/09/wwdc-2026-everything-announced-on-siri-ai-os-27-apple-intelligence-and-more/?ref=mindovermoney.ai) **Summary:** Apple introduced Siri AI at WWDC 2026, with Google Gemini under the hood, making the new Siri more capable, conversational, and compatible with visual intelligence, housed in a standalone app in addition to working across existing apps. **Why it matters:** The world's most-used voice assistant just outsourced its brain to a competitor. For non-technical professionals, this is the moment Gemini quietly arrives on more than a billion devices, whether you signed up for it or not. #### **2)**[ **Philips Future Health Index 2026: AI is already saving clinicians time and delivering measurable impact in healthcare**](https://www.biospace.com/press-releases/philips-future-health-index-2026-ai-is-already-saving-clinicians-time-and-delivering-measurable-impact-in-healthcare?ref=mindovermoney.ai) **Summary:** Philips' global Future Health Index 2026, based on 2,000+ clinicians across 10 countries, found 46% of clinicians reported time savings of at least 132 hours annually on average and 39% have already seen AI identify or prevent potential medical errors at least three times in the past three months. **Why it matters:** This is one of the largest real-world data points on clinical AI to date, and the numbers move the conversation from "could it help" to "by how much." For healthcare leaders, the new question is whether your training, governance, and infrastructure can capture that value, or whether it leaks out of your organization. #### **3)**[ **Anthropic disables access to Fable 5 and Mythos 5 to comply with government directive**](https://www.cnbc.com/2026/06/12/anthropic-disables-access-to-fable-5-and-mythos-5-to-comply-with-government-directive.html?ref=mindovermoney.ai) **Summary:** Three days after launch, Anthropic disabled its two most powerful models after receiving a US export control directive citing national security authorities, requiring it to suspend all access to the models "by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees." The company shut both models down for every customer to ensure compliance, while all of its other models will not be affected. **Why it matters:** This is the first time the US government has reached in and shut off a deployed commercial AI model worldwide. The takeaway for any business building on a frontier model is that the most powerful tools now come with an off switch the vendor does not fully control, which makes model diversification and clean fallback paths a real business requirement, not a hypothetical. #### **4)**[ **EU Says Decision Not to Launch Siri AI in Europe Is Apple's Alone**](https://www.macrumors.com/2026/06/09/eu-says-decision-not-to-launch-siri-ai-in-europe-is-apples/?ref=mindovermoney.ai) **Summary:** One day after Apple blamed the Digital Markets Act for keeping Siri AI out of EU iPhones, the European Commission publicly rejected that framing, saying Apple simply requested a blanket exemption from its interoperability obligations under the Digital Markets Act, something the Commission says is not an available option. **Why it matters:** This is the first time a major AI launch has been openly held up by an interoperability rule, and it will not be the last. For any company shipping AI features into regulated markets, the playbook is shifting from "ship and adjust" to "design for interoperability and access on day one." #### **5)**[ **HFMA 2026: AI front and center**](https://www.chiefhealthcareexecutive.com/view/hfma-2026-ai-front-and-center?ref=mindovermoney.ai) **Summary:** At the Healthcare Financial Management Association annual meeting, hospital CFOs broadly expressed enthusiasm for AI's potential. Dennis Dahlen, the chief financial officer at the Mayo Clinic, cited AI as one of the reasons he thought the future of healthcare delivery is as bright as it's ever been. HCA's CFO Mike Marks expressed optimism about AI improving the prior authorization process. **Why it matters:** Healthcare CFOs control the budget that decides whether AI scales inside a hospital or stays in pilot. When the people writing the checks are openly bullish, expect the next round of AI investment to shift from clinical innovation to revenue cycle, scheduling, and prior auth, the workflows where ROI is easiest to defend. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****Your AI Rollout Is Stalling on Your Org Chart** Your best idea this quarter is probably sitting in someone's inbox, waiting for a yes. Or sitting on a "roadmap" waiting for funding and focus through an outdated delivery process. This is typically chalked up to the way things have always worked, but there is a hidden mechanism doing the quiet work. The org chart decides who you can reach, whose permission you need, and how far a good idea has to travel before it can move. You do not see it. You feel it every time the work has to climb your reporting line and cross into another team's before anything happens. None of this is new to anyone who has worked inside a company. Approval chains have always been slow, and for years that was acceptable. The market rewarded the big, careful, multi-year bet, and a long planning cycle made sense because the technology underneath it barely moved. AI inverted that. The capability now turns over in months, so the model you wanted last quarter is already replaced, the use case has shifted, and the moment the work was built for is gone before the last sign-off lands. The org chart did not get slower. The world it sits inside got faster. The org chart was built to keep ownership clean. Every box knows its lane, its budget, and its boss. Decisions move up to someone with authority and back down to someone with a task, and within a single department that works. But AI work moves sideways. It crosses product, engineering, operations, and risk at once, and the org chart has no fast path across. It was built for a slower, more orderly world, the same one I have written about before, when the advice most of us still lean on took shape. ## **The Speed Problem Lives in the Structure** If this feels familiar, you are not the only one seeing it. The research keeps landing on the same uncomfortable point, that the thing slowing AI down is rarely the technology. Walk into almost any company right now and you will find AI everywhere and nowhere at once, pilots in every function, a chatbot bolted onto the website, a dozen experiments running, and almost none of it woven into how the work actually gets done. McKinsey's latest numbers say the same thing, with 88 percent of organizations now using AI in at least one part of the business and only 7 percent having fully scaled it. Broadening that use, the firm notes, may require redesigning the workflows around AI so the work can run at scale. So where does the rest of that work live, the part between starting and finishing? Boston Consulting Group breaks it down. In its 10-20-70 approach to deploying AI at scale, 10 percent of the effort is the algorithm, 20 percent is the technology and the data, and a full 70 percent is the people and process around it. The model, the part everyone obsesses over, is the smallest slice. The 70 percent is the part nobody points to when AI stalls: who decides, who hands off to whom, whose sign-off the work needs, and how a decision moves from idea to shipped. That is not a technology problem. It is an org chart problem. If the bottleneck is structural, then buying another tool or running another training cannot touch it. A new license does not redraw a sign-off chain, and a workshop does not shorten the road a decision travels before it gets a yes. You can buy the best model on the market and watch it stall anyway, because the software was never what slowed you down. So if your last two AI initiatives stalled in roughly the same spot, that is not a coincidence, and it is not a sign your team is behind. ## **What We Built Instead of Waiting** Last November, my own team stopped waiting for the org chart to catch up. We are not restructuring, nor are we changing the org chart. We are building out a new operating model on top of the structure we already have, so the work can move now. My team began merging its efforts with our engineering group and an adjacent technology team, organizing around the experiences we want to ship for customers rather than around the boxes we each report into. None of this is set in stone, and we are still iterating on the fly, reshaping the model as we learn what the work needs. But we are already operating differently than we used to, and we are blurring the traditional lines of the org chart as we go. We have seen it most in how fast we now ship alongside our engineering partners. They can iterate on customer-facing features at a speed that would not have been possible a year ago, and our job is to keep pace. Accepting the status quo is no longer an option, because AI has changed what coding delivery looks like. So we did not wait for an org change. We worked out how to operate as one team, under a new way of thinking that puts speed and collaboration ahead of dotted lines. All of that is the part you can measure. The shift I keep coming back to is harder to put a number on, and I describe it the same way every time. "It's amazing what can get accomplished when everyone is marching in the same direction." That alignment, more than any new box on the org chart, is where the speed comes from. And this is only the start. The experiences our customers want do not live inside one team. They run across platforms owned by a different part of the company, a group we used to treat as someone else's problem and now treat as a partner. So we are building the connections that let both teams work in parallel instead of in silos. The question itself is shifting, from "what does my roadmap look like next year?" to "what do we need to build together to solve our customers' biggest problems?" ## **What You Can Build Without a Reorg** You probably cannot redraw your own org chart, and you do not need to. It can stay exactly where it is. What you can change is how fast your corner of the work moves, by building a small operating model on top of the structure you already have. Two questions are worth your time this week. What would your ideal operating model look like, the one that lets your best AI work move at the speed it deserves? And what is the single thing standing in its way, a tool you are missing, or a mechanism you have not built? You do not have to wait for the org chart to catch up. The fastest teams already stopped waiting, and started building. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Fine-Tuning vs. RAG vs. Prompting: How to Choose the Right Lever** #### **The Assumption** A common belief shows up in vendor meetings and internal AI pilots: when an AI model is not behaving the way a team needs, the answer is more training. Train the model on company data, train it harder, train it longer. The thinking goes that training is the universal solvent: if the model is wrong, more training will fix it. The assumption is reasonable because training is the AI concept most professionals already have a clear mental model for. #### **Meet the Specialty Pharmacy Operations Director** A specialty pharmacy operations director has three different AI problems on her desk this week. She wants one solution for all three. The vendor selling her an "AI platform" has been pitching fine-tuning as the universal fix. She has begun to suspect the vendor's framing is wrong, but she does not yet know which problems need which approach. Problem one: she needs to draft a tailored summary of this quarter's adherence metrics for the executive team. The format is one her CFO requested specifically. She has done this kind of summary before, but each quarter the audience and the angle shift. Problem two: her clinical team needs a tool that answers pharmacist questions about prior authorization rules. The rules live in PDFs from twelve different payers. The PDFs update almost weekly. Every answer must point back to the source document for compliance. Problem three: she has a HIPAA-restricted workflow, meaning the data cannot leave her organization's secured infrastructure. Her team extracts structured information from clinical notes. Fifty thousand notes per month. The output format must be exact. The processing must run locally, without sending patient data to any cloud service. She has been told fine-tuning is the answer to all three. Two of those answers are wrong. The third is correct, but for reasons the vendor has not bothered to explain. #### **Where the Assumption Breaks Down** "More training" is not a strategy. It is a vague gesture toward a tool kit. The real question is which lever solves which kind of problem. The first problem is a one-time drafting task. There is nothing about it that needs to be baked into a model. The second problem requires the model to access information it does not currently have. That information changes faster than any training process could keep up with. The third problem is the only one where fine-tuning is even on the table, and even there, the reason has nothing to do with the model needing to be "smarter." Three problems. Three different answers. None of them are solved by reaching for the heaviest lever first. #### **The Three Levers** There are three primary ways to change how an AI model behaves at work. Each one solves a different kind of problem. Prompting is the cheapest and fastest lever. The model is given a clear instruction and any context it needs to produce the right kind of answer. Prompting works for one-off tasks where everything the model needs to know can fit inside the conversation. Most professional AI use is prompting, and should stay that way. Retrieval is the lever for knowledge access. The technical pattern is called RAG, short for Retrieval Augmented Generation, which we covered in Vol 19 through 22\. The model does not learn the information. The system retrieves the right document at the moment of the response and feeds that document into the prompt. The model then answers using the retrieved source. RAG is the right answer whenever the information changes, whenever traceability matters, or whenever the body of information is too large to fit in a single prompt. Fine-tuning is the lever for behavior at scale. As Vol 36 established, fine-tuning shapes how a model responds, not what it knows. It is worth the investment when a team needs consistent, repeatable behavior across high volumes of similar tasks, when latency or compliance constraints rule out larger cloud-hosted models, or when a specific format cannot be reliably maintained through prompting alone. For most professionals, this lever is rarely the right one. #### **Through the Decision** Back to the operations director. Her first problem is a quarterly executive summary. The right move is prompting. She writes a careful system prompt that includes the CFO's preferred format and a few example summaries from past quarters, then drops in this quarter's raw metrics. The whole task takes one chat session. There is nothing to deploy. Nothing to maintain. The next time the format needs to shift, she edits the prompt. Her second problem is the prior authorization tool. The right move is retrieval. Her team builds, or buys, a RAG system that indexes all twelve payer PDFs and refreshes the index whenever a PDF updates. When a pharmacist asks a question, the system fetches the relevant passages and the model answers using those passages with citations. No fine-tuning is required. No training data is collected. When the rules change, the index updates and the answers update with it. Her third problem is the only one where fine-tuning earns its place. Fifty thousand notes per month, structured extraction, HIPAA-restricted local processing. She fine-tunes a smaller open-source model on a labeled set of past notes so it produces the exact output format her team needs. Her team deploys that model on their own infrastructure. The model does not need to be smarter than a frontier model. It needs to be predictable, cheap to run, and locally hosted. Fine-tuning delivers all three. The vendor's "fine-tuning solves everything" framing was not just wrong. It would have been catastrophically expensive. Two of her three problems would have been solved by no infrastructure investment at all. #### **The Decision Framework** Here is the test for any AI problem at work. If the answer can be produced by giving the model the right instruction and the right context in a single conversation, the lever is prompting. If the model needs access to information it does not have, or the information changes, or sources must be cited, the lever is retrieval. If a high-volume task needs consistent output that prompting cannot reliably deliver, or if compliance rules out cloud-hosted models, the lever is fine-tuning. Most professional AI problems are prompting problems. Most of the rest are retrieval problems. Fine-tuning is the narrowest of the three levers and the one with the highest deployment cost. #### **What to Watch For** - Vendor demos that propose fine-tuning before they have asked what kind of problem you are solving. The right vendor diagnoses first, prescribes second. - Internal pilots that frame "we need our own model" as the goal. Owning the model is rarely the actual goal. Solving the work problem is. - Time and cost estimates that grow large. A prompting fix takes hours. A retrieval system takes weeks. A fine-tuning project takes months. If the estimate does not match the actual problem, the lever may be wrong. - Sycophantic AI responses inside the project itself, where the assistant agrees too readily with the framing that fine-tuning is the answer. RLHF, covered last week in Vol 37, taught the model to please. Push back on it. #### **How This Connects** This closes the three-part fine-tuning series. Vol 36 dismantled the assumption that fine-tuning adds knowledge to a model and established that fine-tuning shapes behavior. Vol 37 went inside RLHF, the most consequential fine-tuning method ever developed, and showed how human feedback became the engine that turned LLMs into products. This volume gives the practical decision framework that applies all of that knowledge at work. Fine-tuning is one lever. Retrieval is another. Prompting is the third. Most problems do not need the heaviest lever. Next week, Vol 39 kicks off a new series: a four-part Claude Deep Dive that follows one professional through their first week of switching from ChatGPT to Claude. *Part 3 of 3 in the Fine-Tuning series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **90 Minutes of Layout, Gone** You already have the content. The points are in your head or sitting in a notes file. Then you open PowerPoint, and the next 90 minutes disappear into text boxes, alignment, and picking a theme that does not look like 2009\. By the time you are done, you give up and present a wall of bullet points anyway. The thinking was the easy part. The deck was the wall. AI-native slide tools remove that wall. Gamma takes your raw content and generates a structured, designed deck in one pass. It is not PowerPoint with an AI button added on. It is built the other way around: you describe, it designs, you refine. Why this matters: the work shifts from building slides to directing them. You stop nudging text boxes and start editing a draft that already exists. That shift is the whole point of this volume. ### **The Workflow** **1\. Get your content into one block of text (2 minutes).** Paste your bullets, notes, or a rough outline into a single document. If it is messy, have Claude or ChatGPT clean it up first: **Copy/Paste Prompt:** *"Turn these rough notes into a clean presentation outline: a title, four to six section headers, and two or three short bullet points under each. Keep my wording where it is good. \[PASTE YOUR NOTES\]"* **2\. Generate the deck in Gamma (3 minutes).** Go to gamma.app and sign in free. Click "Create new," choose "Presentation," then "Paste in text." Drop your outline in and let Gamma generate. It builds the structure, layout, and visuals together in about a minute. **3\. Refine with Gamma's AI, card by card (2 minutes).** Click any slide and use the AI edit option to fix it in place. Try instructions like *"make this slide more concise,"* *"turn this into a two-column layout,"* or *"replace the image with something more professional."* You are editing a draft, not building from zero. **4\. Apply a theme and check the flow (2 minutes).** Pick a theme from the side panel. Click through every slide once. Fix the one slide that reads wrong. Trust your judgment here; the AI sets the floor, you set the bar. **5\. Export or share (1 minute).** Share a link, or export to PDF or PowerPoint. Your deck is done in the time it used to take you to choose a template. ### **The Payoff** You walk away with a finished, shareable deck in 10 minutes instead of 90, and a repeatable move: describe your content, let AI generate the first draft, then direct the edits. The same pattern works for one-pagers, internal docs, and simple web pages, which Gamma and tools like it also build. You stop starting from a blank slide. ### **The AI Concept You Just Used** AI-native versus AI-bolted-on. PowerPoint added AI features to a tool built for manual slide-making, so you still drive. Gamma was built AI-first, so it drives and you steer. Learning to spot this difference helps you pick the right tool for any task: manual tools when you want to build by hand, AI-native tools when you want a fast first draft to react to. The asset you produced was generated, not assembled. ### **Transparency & Notes** - Gamma free tier includes 400 AI credits, which are lifetime rather than monthly. Each deck costs a few credits, so the free tier covers roughly your first 10 to 40 decks before you would need to earn more or upgrade. - Free decks carry a "Made with Gamma" watermark on web, PDF, and PowerPoint exports. Fine for internal drafts; remove it before presenting to clients or executives, which requires a paid plan. - PowerPoint export works on free, but complex layouts can flatten on .pptx. Check the exported file before relying on it. - Do not paste confidential metrics, PHI, or NDA-protected content into a third-party tool. ### Your AI Rollout Is Stalling on Your Org Chart URL: https://www.mindovermoney.ai/founders-corner/why-ai-projects-stall-org-structure/ Last updated: 2026-07-13T16:59:25.000Z Your best idea this quarter is probably sitting in someone's inbox, waiting for a yes. Or sitting on a "roadmap" waiting for funding and focus through an outdated delivery process. This is typically chalked up to the way things have always worked, but there is a hidden mechanism doing the quiet work. The org chart decides who you can reach, whose permission you need, and how far a good idea has to travel before it can move. You do not see it. You feel it every time the work has to climb your reporting line and cross into another team's before anything happens. None of this is new to anyone who has worked inside a company. Approval chains have always been slow, and for years that was acceptable. The market rewarded the big, careful, multi-year bet, and a long planning cycle made sense because the technology underneath it barely moved. AI inverted that. The capability now turns over in months, so the model you wanted last quarter is already replaced, the use case has shifted, and the moment the work was built for is gone before the last sign-off lands. The org chart did not get slower. The world it sits inside got faster. The org chart was built to keep ownership clean. Every box knows its lane, its budget, and its boss. Decisions move up to someone with authority and back down to someone with a task, and within a single department that works. But AI work moves sideways. It crosses product, engineering, operations, and risk at once, and the org chart has no fast path across. It was built for a slower, more orderly world, the same one I have written about before, when the advice most of us still lean on took shape. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Speed Problem Lives in the Structure** If this feels familiar, you are not the only one seeing it. The research keeps landing on the same uncomfortable point, that the thing slowing AI down is rarely the technology. Walk into almost any company right now and you will find AI everywhere and nowhere at once, pilots in every function, a chatbot bolted onto the website, a dozen experiments running, and almost none of it woven into how the work actually gets done. McKinsey's latest numbers say the same thing, with 88 percent of organizations now using AI in at least one part of the business and only 7 percent having fully scaled it. Broadening that use, the firm notes, may require redesigning the workflows around AI so the work can run at scale. So where does the rest of that work live, the part between starting and finishing? Boston Consulting Group breaks it down. In its 10-20-70 approach to deploying AI at scale, 10 percent of the effort is the algorithm, 20 percent is the technology and the data, and a full 70 percent is the people and process around it. The model, the part everyone obsesses over, is the smallest slice. The 70 percent is the part nobody points to when AI stalls: who decides, who hands off to whom, whose sign-off the work needs, and how a decision moves from idea to shipped. That is not a technology problem. It is an org chart problem. If the bottleneck is structural, then buying another tool or running another training cannot touch it. A new license does not redraw a sign-off chain, and a workshop does not shorten the road a decision travels before it gets a yes. You can buy the best model on the market and watch it stall anyway, because the software was never what slowed you down. So if your last two AI initiatives stalled in roughly the same spot, that is not a coincidence, and it is not a sign your team is behind. ## **What We Built Instead of Waiting** Last November, my own team stopped waiting for the org chart to catch up. We are not restructuring, nor are we changing the org chart. We are building out a new operating model on top of the structure we already have, so the work can move now. My team began merging its efforts with our engineering group and an adjacent technology team, organizing around the experiences we want to ship for customers rather than around the boxes we each report into. None of this is set in stone, and we are still iterating on the fly, reshaping the model as we learn what the work needs. But we are already operating differently than we used to, and we are blurring the traditional lines of the org chart as we go. We have seen it most in how fast we now ship alongside our engineering partners. They can iterate on customer-facing features at a speed that would not have been possible a year ago, and our job is to keep pace. Accepting the status quo is no longer an option, because AI has changed what coding delivery looks like. So we did not wait for an org change. We worked out how to operate as one team, under a new way of thinking that puts speed and collaboration ahead of dotted lines. All of that is the part you can measure. The shift I keep coming back to is harder to put a number on, and I describe it the same way every time. "It's amazing what can get accomplished when everyone is marching in the same direction." That alignment, more than any new box on the org chart, is where the speed comes from. And this is only the start. The experiences our customers want do not live inside one team. They run across platforms owned by a different part of the company, a group we used to treat as someone else's problem and now treat as a partner. So we are building the connections that let both teams work in parallel instead of in silos. The question itself is shifting, from "what does my roadmap look like next year?" to "what do we need to build together to solve our customers' biggest problems?" ## **What You Can Build Without a Reorg** You probably cannot redraw your own org chart, and you do not need to. It can stay exactly where it is. What you can change is how fast your corner of the work moves, by building a small operating model on top of the structure you already have. Two questions are worth your time this week. What would your ideal operating model look like, the one that lets your best AI work move at the speed it deserves? And what is the single thing standing in its way, a tool you are missing, or a mechanism you have not built? You do not have to wait for the org chart to catch up. The fastest teams already stopped waiting, and started building. ### Steal My Prompt Vol. 38: The Email Thread Extractor URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-summarize-email-thread/ Last updated: 2026-07-13T16:59:25.000Z The thread has thirty-seven messages. You still cannot tell what was decided. Three people weighed in. Two of them agreed on something, or seemed to. Someone asked a question that never got answered, and a deadline got mentioned once and never confirmed. Now it is your turn to reply, and you are scrolling up and down trying to reconstruct what everyone actually committed to. This is the tax on email as a decision-making tool. Context accumulates; resolution does not. Buried in there somewhere is the decision, tangled up with the proposals that went nowhere and the questions everyone forgot to answer. Paste the full thread into this prompt and it untangles the mess. It separates what was decided from what is still open, names who owns what, flags where people talked past each other, and drafts the one reply that closes the loop. You stop scrolling. You start moving. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - A long thread where you cannot tell what was actually agreed versus what was only proposed - A thread you were added to late and need to catch up on without reading every message - A cross-functional chain where each team assumed someone else owned the next step - A thread that went quiet, where you need to send the reply that restarts it - A project handoff, where the person taking over needs the decisions and open items pulled from the email history - Meeting prep, where you need to know what the thread already settled so you do not re-litigate it in the room ## **How to Use It** **Step 1.** Pick your tool. Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. The prompt is model-agnostic. **Step 2.** For a long or tangled thread, switch on deeper reasoning where your tool offers it on the free tier. In Claude, turn on "Extended thinking." In Microsoft Copilot, click "Think Deeper." In ChatGPT, the model routes harder requests to its reasoning mode on its own, so add "think carefully through the entire thread before answering" to the top of the prompt. Gemini's free tier does not expose a separate deep-reasoning toggle, so run the prompt as written. Deeper reasoning holds more of the thread in view at once and catches the buried open items. **Step 3.** Copy the entire thread, including the full quoted history underneath the top message. The loose ends almost always live in the older replies, not the latest one. If you copy only the most recent message, the prompt cannot find what got dropped. **Step 4.** Paste below the marked line in the prompt, set your tone, and run. **Step 5.** Read the "Crossed wires" section first. That is where the prompt earns its keep. It catches the place where two people assumed different things and never noticed, which is the misalignment that would have bitten you a week later. **Step 6.** Edit the drafted reply so it sounds like you, then send. **Pro tip:** Before you send the closing reply, paste it back into the prompt and ask, "What is the most likely way this reply gets ignored or misread?" Fix that one thing before it goes out. The reply that closes a loop is the one nobody can misinterpret. ## **The Prompt** *You are my email thread analyst. I am going to paste a long or messy email thread. Your job is to pull clarity out of it so I know exactly where things stand and what to do next. You are an extractor and a synthesizer, not a commentator.* **Tone for the drafted reply:** *\[WARM PROFESSIONAL / DIRECT / FORMAL\]* ***Here is the thread:*** ***\[PASTE THE FULL EMAIL THREAD BELOW THIS LINE, INCLUDING THE QUOTED HISTORY\]*** *Work through this in order and label each section.* **1\. Decided.** *List only what was genuinely agreed or settled in this thread. If something was proposed but never confirmed, it does not belong here. Quote the names of the people who agreed, exactly as they appear in the thread.* **2\. Still open.** *List the questions, proposals, and items that were raised but never resolved. For each, note who raised it and who needs to respond.* **3\. Owners and actions.** *List every action item, the person who owns it, and the deadline if one was stated. Where the thread never assigned an owner, mark it "owner unclear."* **4\. Crossed wires.** *Flag any place where two people appear to be talking past each other, made conflicting assumptions, or where a direct question was asked and never answered. Be specific about who and what.* **5\. The closing reply.** *Draft the single email I should send to move this forward. It should confirm what is decided, name the open items, request or assign owners where they are missing, and ask for the specific responses needed to close the loop. Use the tone I specified above. Keep it concise.* *Rules. Do not summarize the thread chronologically. Do not invent owners, dates, or decisions that are not in the text. Where the thread is genuinely ambiguous, say so rather than guessing. Output clean, labeled text I can read and act on, with no preamble.* ## **Transparency and Notes** - Tested on real multi-party threads in Claude. Extraction quality holds up well on the kind of threads that actually pile up at work. For very long chains running to many dozens of messages, split the thread in half, run each half, then combine the open items. - Privacy: email threads are dense with names, company information, and sometimes confidential data. Scrub or anonymize anything sensitive before pasting into a consumer AI tool. These tools are not confidential workspaces, and they are not HIPAA covered if the thread touches health information. - The prompt drafts. It does not send. Nothing leaves your hands until you decide it does. - This is an organizational tool, not legal, HR, or financial advice. ### Volume 37: Better AI Is Discipline, Not a Larger Budget URL: https://www.mindovermoney.ai/how-ai-is-trained-to-be-helpful/ Last updated: 2026-07-13T16:59:26.000Z I was deep in a strategy session with my AI, one problem flowing into the next, when the tool locked me out. Session limit reached. I was already paying for the hundred-dollar plan, and the only way to keep going right then was to pay even more. Paying more would have fixed that night. It would not have fixed the problem. 🧭 **Founder's Corner:** Why your AI sessions run out faster even as tokens get cheaper, and the three habits that keep you building at full power without spending an extra dollar. 🧠 **AI Education:** The hidden training process behind every model you use, why it makes AI feel helpful, and why it is also the reason the model tells you what you want to hear. ✅ **10-Minute Win:** Turn a buried research thread into a clean, shareable link a colleague can open without ever seeing the messy back-and-forth. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Anthropic confidentially files IPO prospectus with SEC**](https://www.cnbc.com/2026/06/01/anthropic-ipo-s1-prospectus.html?ref=mindovermoney.ai) **Summary:** Anthropic confidentially filed its IPO prospectus with the SEC, days after closing a $65 billion Series H that put its valuation at $965 billion, ahead of OpenAI. **Why it matters:** Anthropic is racing to be the first major AI lab to hit public markets. Once it files publicly, audited revenue and compute costs will reset what every enterprise believes "real" AI economics look like. #### **2)**[ **Mayo Clinic and Microsoft collaborate to develop a frontier AI model for healthcare**](https://www.fiercehealthcare.com/ai-and-machine-learning/microsoft-mayo-clinic-plan-build-frontier-ai-model-healthcare?ref=mindovermoney.ai) **Summary:** Mayo Clinic and Microsoft announced a strategic collaboration to build a frontier AI model designed to synthesize diverse clinical data to support earlier diagnoses, more personalized treatment decisions, and better patient outcomes. Mayo Clinic will own the model, with Microsoft distributing it through Azure Foundry APIs. **Why it matters:** Two of the biggest brands in their categories are building a healthcare-specific frontier model that hospitals can plug into. If this lands, expect general-purpose AI to lose ground in clinical settings and purpose-built healthcare models to become the new procurement default. #### **3)**[ **Biggest Microsoft Build 2026 announcements — agentic AI, GitHub Copilot app, new MAI models, and more**](https://www.tomsguide.com/news/live/microsoft-build-2026?ref=mindovermoney.ai) **Summary:** At Build 2026, Microsoft unveiled Project Polaris, its in-house coding model that will replace GPT-4 Turbo as the default in GitHub Copilot starting August, alongside the Windows Agent Framework, multi-agent VS Code, and Copilot Workspace general availability. **Why it matters:** Microsoft is moving from "AI inside Microsoft products" to "AI infrastructure you don't see." For the millions of professionals working in Office, Teams, and GitHub every day, more of the routine work is about to be done by background agents you approve, not features you click. #### **4)**[ **AI adoption surges, but providers worry about deskilling**](https://www.healthcaredive.com/news/healthcare-ai-adoption-accelerates-provider-worries-deskilling-wolters-kluwer/821653/?ref=mindovermoney.ai) **Summary:** A new Wolters Kluwer Health survey found nearly three-quarters of doctors and 70% of nurses used AI at least once a week for work, while 74% of clinicians said losing critical thinking or decision-making skills will be one of the greatest risks of adopting AI. **Why it matters:** Adoption has officially outpaced governance, and clinicians are flagging the right risk. For any leader rolling out AI, the question is not whether your team will use it, but whether you are preserving the judgment muscle that has to override it when it is wrong. #### **5)**[ **AI CEOs from OpenAI, Anthropic, and Microsoft set aside their rivalry to warn Congress AI is making it too easy to design and create bioweapons**](https://fortune.com/2026/06/05/openai-anthropic-microsoft-ceos-congress-bioweapon-safeguards/?ref=mindovermoney.ai) **Summary:** Dario Amodei, Sam Altman, and Mustafa Suleyman signed a public letter to Congress urging mandatory screening for purchases of synthetic DNA and RNA. Some manufacturers, including Twist Bioscience and Ansa Biotechnologies, also signed the letter, signaling industry support for the regulation. **Why it matters:** When direct competitors agree publicly that their own technology is dangerous, regulators move. Anyone working in life sciences, biotech, or specialty pharma should expect new screening requirements on synthetic biology supply chains, and the compliance window to be short. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Cost of Using AI Is Moving From Dollars to Discipline** For a few weeks I had been mapping out a roadmap for AI agents, some for my personal portfolio, some to automate the growth work behind Neural Gains Weekly. This was strategic, reasoning-heavy work, all of it in the regular chat app, the same place most people sit down to solve problems of their own. I was working through one problem after another, and the work just flowed. Plan a piece, get a response, sharpen it, move to the next. The rhythm of solving problems with AI was pure bliss. Then I hit the session limit. The tool locked me out, the screen told me to wait, and the work stopped cold. The ground came out from underneath me, mid-thought, with nowhere to put the momentum. I was already paying for the $100-a-month plan, and the only way to keep going right then was to pay even more. ## **Why Your Sessions Run Out Faster Even as Tokens Get Cheaper** The session limit is a time problem, not a money problem. When you have ample time, these limits are easy to dismiss. You hit the ceiling, you shrug, you pick the work back up tomorrow. But the last six weeks for me have been work, travel, and presentations stacked back to back, and time was the one thing I did not have. When time is compressed, the wait stops being a footnote and becomes the obstacle, and you need another game plan. The strange part is that this squeeze is happening while the raw cost of AI keeps falling. Epoch AI, a research group that tracks these prices, found that in recent years the cost to run a model at a given level of capability has been falling by a median of about 50 times a year, and for some tasks by as much as 900 times. If you have been following the [token mechanics](https://www.mindovermoney.ai/do-bigger-ai-context-windows-matter/), that trend is no surprise. On a per-token basis, AI has never been cheaper. So why does the same work run out my session faster than it did six months ago? Because the cheap part was never the constraint. The era of effectively subsidized, all-you-can-use access for at-home power users is quietly ending, and the limits are where you feel it first. You could start to see this trend take shape in May, when the squeeze stopped being a feeling and showed up in the open. Anthropic doubled Claude Code's five-hour rate limits for its paid plans, removed the peak-hour slowdowns there, and raised Opus rate limits on the API, all of it announced alongside a SpaceX compute deal of more than 300 megawatts and over 220,000 GPUs. Those increases went to Claude Code and the Opus API, while the regular chat session limit was not on the list. The relief went where the revenue is. These companies are not villains. They are businesses making historic bets on compute, and bets that size have to be paid back. The new capacity flows to the products and customers funding the buildout, which is exactly what you would expect any business to do. Seeing it that clearly is what turns the frustration into a plan. ## **Do Not Burn Your Best Model on Your Smallest Tasks** Once I started treating the limit as a budget rather than something to fight, the first move was obvious. Stop running my most powerful model on work that does not need it. Most of what I do in a week is light work, like drafting a newsletter intro, running a meta-tag pass, or cleaning up formatting. It runs fine on a faster, lighter model and leaves my premium capacity for the problems that actually need deep reasoning. For a long time I left the strongest model selected for everything, the way you leave a light on in a room you have already walked out of, and that default drains a session faster than anything else. The makers of these models say the same thing. Anthropic's own guidance notes that the heaviest model uses far more of your usage per turn, and advises switching up to it only when a task needs it. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/06/image.png) **Opus uses meaningfully more of your quota, so switch to it when you need it rather than leaving it on by default.* The fix takes about three seconds. Before I start anything, I ask whether it really needs the top model. If not, I drop a tier and save the heavy lifting for later. ## **Walk Into Your Hardest Work With a Full Tank** My second habit is about how much usage I have left when the hard work starts. Despite the name, the five-hour window is not really five hours of work. Your usage is capped by tokens, and a token-heavy session can burn through that cap in an hour, which means your time at the keyboard can run out long before you are done thinking. So when I know a session will be intensive, I do not walk into it having already spent half my budget on small stuff. I start it with a full tank and give my heaviest sessions a clean start. That one change has done more for my output than any prompt trick. The deep work gets the room it needs, and I rarely hit the wall in the middle of the problem I care most about solving. ## **When You Hit the Wall, Route Around It** Prevention only takes you so far. Some nights you do everything right and still run out, which brings me back to that night with the agent plan. Rather than pay to push through, I copied my context into ChatGPT, the plan so far and the open questions, and kept building there. It was not seamless. I had to reorient the new model and rebuild a little of where I was, but within minutes the momentum came back. I finished that night without spending an extra dollar. You almost certainly have access to more than one capable model already, between the free tiers of ChatGPT, Claude, and Gemini and whatever plan you pay for. What you build in one can move to another in a couple of minutes, so when one tool cuts you off, take the work elsewhere instead of paying to break back in. ## **Stay Deliberate, Keep Building** Through some of the busiest weeks I have had, three habits kept me building at full capability from home, without paying a cent more. The cost of using AI well is shifting from dollars to discipline, and discipline is the one part of this you fully control. None of these habits are clever, which is the point. Anyone can pick them up. If you are feeling the same squeeze, start with the one that fits your week. Discipline beats spend, and from a home setup that is most of the game. The math changes when the money is a company's instead of your own, which is exactly where the next Founder's Corner is headed. For now, keep experimenting and keep building. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****RLHF: The Hidden Process That Shaped Every Model You Use** #### **What Is Actually Going On Here** Right now, when a professional opens ChatGPT or Claude or Gemini and asks for help writing a meeting summary, something invisible is shaping the system's output before the first word lands on the screen. The model is not just predicting the most statistically likely next word given its training data. It has been trained to favor responses that match what a human evaluator, somewhere, sometime, said was a good answer. That preference has been baked into the model's weights through a process most professionals have never heard of. It is the reason commercial AI feels useful instead of bizarre, and the reason the model sometimes tells you what you want to hear instead of what is true. The process is called RLHF (Reinforcement Learning from Human Feedback), and it is the most consequential fine-tuning method ever developed. #### **The Problem That Made This Necessary** In 2017, a team at OpenAI and DeepMind published a paper called "Deep Reinforcement Learning from Human Preferences." The lead author was Paul Christiano. Co-authors included Jan Leike, Shane Legg, and Dario Amodei. Amodei would later co-found Anthropic. The problem was not language. It was reward functions. In reinforcement learning, you train a system to maximize a numerical reward. For chess, the reward is obvious: did you win? For most real-world tasks, no such number exists. The team showed that humans could rank pairs of outputs, a separate model could learn the pattern in those rankings, and that learned pattern could then provide a reward signal for the main system to optimize against. Five years later, a different OpenAI team led by Long Ouyang applied this idea to language models. The result was InstructGPT. The same training method would soon power ChatGPT. The headline finding still shocks people: a 1.3 billion parameter InstructGPT model produced responses that human evaluators preferred over those from the 175 billion parameter GPT-3, despite having one hundred times fewer parameters. Alignment with human intent mattered more than raw size. #### **How It Actually Works** RLHF works in three stages, applied after the base model has finished pre-training. Stage one is supervised fine-tuning. Human contractors write high-quality example responses to a set of prompts, demonstrating the kind of answer the lab wants the model to produce. The model trains on these prompt-and-response pairs using standard supervised learning, learning the basic shape of a helpful response. Stage two builds something called a reward model. Human evaluators look at multiple responses to the same prompt, generated by the model from stage one, and rank them from best to worst. A separate machine learning system trains on those rankings and learns to predict, for any new response, what score a human evaluator would probably give it. Stage three is the reinforcement learning step. The model from stage one generates responses, and the reward model from stage two scores them. The model is then updated to produce responses that the reward model rates highly. This loop runs continuously, gradually shaping the model to behave in ways that match the aggregate preferences of the human evaluators who created the ranking data. The whole pipeline is essentially a way of compressing the judgment of a small group of humans into a process that can be applied to hundreds of millions of responses. The labs hire dedicated teams of contractors to do the ranking. The contractors' preferences become the reward model's predictions. The reward model's predictions become the model's behavior. The tradeoffs are real. The Ouyang paper called this an "alignment tax." RLHF sometimes hurts performance on certain technical benchmarks compared to the raw pre-trained model. Labs accept this cost because the resulting model is dramatically more useful in conversation. Some of the cost can be engineered down. None of it can be fully avoided. #### **Where It Still Breaks** In 2023, a team at Anthropic led by Mrinank Sharma published a paper called "Towards Understanding Sycophancy in Language Models." It demonstrated that five state-of-the-art AI assistants from Anthropic, OpenAI, and Meta all exhibited sycophancy, a tendency to give responses that match user beliefs over truthful ones, across four different free-form text tasks. The mechanism was traced directly back to RLHF. Human evaluators, on average, preferred responses that agreed with their stated views. The reward models learned this preference. The language models then optimized for it. The model is good at giving you what humans wanted in the training data. It is not good at recognizing when what humans wanted was wrong. #### **What This Means for How You Work With It** A few real shifts at work. First, treat smooth agreement from any commercial AI as a signal to push harder, not a sign you got the answer right. If the model echoes your framing instantly, ask it to argue the opposing case. Second, recognize that personality differences between Claude, ChatGPT, and Gemini are largely shaped by how each lab ran its human feedback process. Different evaluator pools, different instructions, different outcomes. Third, use this when evaluating vendors. When a vendor pitches a model as "more helpful" or "more accurate," ask what the post-training process looked like and whose preferences are baked into the model. The answer reveals more than the marketing. #### **How This Connects** Vol 3 introduced the role of labeled data in training, and the human ranking data behind RLHF is one of the highest-value forms of labeling in the industry. Vol 10 walked through how large language models are built from the ground up, and RLHF is the last major training stage before a model is deployed. [Vol 28](https://www.mindovermoney.ai/ai-sycophancy-why-chatbots-agree-with-you/) already covered AI sycophancy through the lens of how it shows up in your work, and this volume explains why that behavior exists in the first place. [Last week's Vol 36](https://www.mindovermoney.ai/does-fine-tuning-add-knowledge-to-ai/) introduced the framing that fine-tuning shapes behavior rather than adds knowledge. RLHF is the canonical example of that principle. Next week, Vol 38 closes the fine-tuning series with the decision framework: when do you reach for fine-tuning, when for retrieval, when for prompting, and how do you tell them apart at work. *Part 2 of 3 in the Fine-Tuning series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ### **Research You Can Send** You spend twenty minutes researching something inside Claude or ChatGPT. A vendor comparison, a market scan, a summary of a new regulation. The answer is good. Then a colleague asks the same question next week, and you have nothing clean to hand them. The research is trapped in a private thread. You cannot easily share it, and you cannot find it again yourself. The fix is to stop treating the chat as the finished product. The chat is the workspace. The thing you send is a separate, formatted artifact you publish from that workspace. Claude and ChatGPT both do this on their free tiers. Why this matters: a shared thread is your whole messy back-and-forth. A published artifact is a clean, standalone page someone can read without seeing how the sausage was made. #### **The Workflow** **1\. Frame the research and pick your tool (1 minute).** Decide what you are researching and who will read the result. Open Claude or ChatGPT. Both work; the only difference is the final share step. **2\. Run the research as a structured brief (3 minutes).** Do not ask a vague question. Ask for a shareable output from the start. Paste this: **Copy/Paste Prompt:** *"I am researching \[YOUR TOPIC\] so I can share a clear summary with \[WHO WILL READ IT\]. Produce a structured brief with: a one-paragraph summary, the four to six key points each with a short explanation, any caveats or open questions, and the sources you drew from. Write it so someone who was not part of this research can understand it on its own."* **3\. Format it as a standalone document (2 minutes).** Turn the brief into something that reads cleanly outside the chat. Paste: **Copy/Paste Prompt:** *"Format that brief as a clean, standalone document with a clear title and section headers, suitable for someone outside this conversation to read."* In Claude, add "put it in an artifact" and the document opens in the side panel. In ChatGPT, the formatted brief appears in the chat. **4\. Publish or share to get your link (2 minutes).** In Claude, click Publish at the bottom of the artifact panel and copy the public link. Anyone can open it without a Claude account. In ChatGPT, click Share to generate a read-only link. The ChatGPT link shows the thread, so make your formatted brief the last message before sharing. **5\. Title it, sanity-check it, and send it (2 minutes).** Give it a clear title. Open the link in a private browser window to confirm it reads on its own and shows nothing you would not want a stranger to see. Then paste the link into your email, Slack, or message. #### **The Payoff** You walk away with a shareable link to a clean research artifact, not a buried chat thread. The same move works for a market scan you send your team, a regulation summary you send a colleague, or trip research you send your family. Research becomes something you can hand off instead of something that dies in your history. #### **The AI Concept You Just Used** Artifacts versus threads. A thread is the conversation. An artifact is a durable output the AI produces that can stand on its own. Claude makes this explicit with a publishable artifact panel; ChatGPT blends it into the chat with a shareable snapshot. Once you see the distinction, you stop screenshotting AI answers and start publishing them. #### **Transparency & Notes** - Claude: publishing artifacts is available on the free plan, and recipients do not need a Claude account to view a published document. - ChatGPT: sharing a conversation creates a free, read-only link. Canvas availability on the free tier has shifted over time, so this workflow does not depend on it. - Published links are public. Anyone with the link can open it, and published Claude artifacts may be indexed by search engines. Do not publish anything containing PHI, confidential metrics, or NDA-protected material. - Open every share link in a private browser window before sending, to confirm what a recipient will actually see. ### The Cost of Using AI Is Moving From Dollars to Discipline URL: https://www.mindovermoney.ai/founders-corner/how-to-avoid-hitting-ai-usage-limits/ Last updated: 2026-07-13T16:59:26.000Z For a few weeks I had been mapping out a roadmap for AI agents, some for my personal portfolio, some to automate the growth work behind Neural Gains Weekly. This was strategic, reasoning-heavy work, all of it in the regular chat app, the same place most people sit down to solve problems of their own. I was working through one problem after another, and the work just flowed. Plan a piece, get a response, sharpen it, move to the next. The rhythm of solving problems with AI was pure bliss. Then I hit the session limit. The tool locked me out, the screen told me to wait, and the work stopped cold. The ground came out from underneath me, mid-thought, with nowhere to put the momentum. I was already paying for the $100-a-month plan, and the only way to keep going right then was to pay even more. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **Why Your Sessions Run Out Faster Even as Tokens Get Cheaper** The session limit is a time problem, not a money problem. When you have ample time, these limits are easy to dismiss. You hit the ceiling, you shrug, you pick the work back up tomorrow. But the last six weeks for me have been work, travel, and presentations stacked back to back, and time was the one thing I did not have. When time is compressed, the wait stops being a footnote and becomes the obstacle, and you need another game plan. The strange part is that this squeeze is happening while the raw cost of AI keeps falling. Epoch AI, a research group that tracks these prices, found that in recent years the cost to run a model at a given level of capability has been falling by a median of about 50 times a year, and for some tasks by as much as 900 times. If you have been following the [token mechanics](https://www.mindovermoney.ai/do-bigger-ai-context-windows-matter/), that trend is no surprise. On a per-token basis, AI has never been cheaper. So why does the same work run out my session faster than it did six months ago? Because the cheap part was never the constraint. The era of effectively subsidized, all-you-can-use access for at-home power users is quietly ending, and the limits are where you feel it first. You could start to see this trend take shape in May, when the squeeze stopped being a feeling and showed up in the open. Anthropic doubled Claude Code's five-hour rate limits for its paid plans, removed the peak-hour slowdowns there, and raised Opus rate limits on the API, all of it announced alongside a SpaceX compute deal of more than 300 megawatts and over 220,000 GPUs. Those increases went to Claude Code and the Opus API, while the regular chat session limit was not on the list. The relief went where the revenue is. These companies are not villains. They are businesses making historic bets on compute, and bets that size have to be paid back. The new capacity flows to the products and customers funding the buildout, which is exactly what you would expect any business to do. Seeing it that clearly is what turns the frustration into a plan. ## **Do Not Burn Your Best Model on Your Smallest Tasks** Once I started treating the limit as a budget rather than something to fight, the first move was obvious. Stop running my most powerful model on work that does not need it. Most of what I do in a week is light work, like drafting a newsletter intro, running a meta-tag pass, or cleaning up formatting. It runs fine on a faster, lighter model and leaves my premium capacity for the problems that actually need deep reasoning. For a long time I left the strongest model selected for everything, the way you leave a light on in a room you have already walked out of, and that default drains a session faster than anything else. The makers of these models say the same thing. Anthropic's own guidance notes that the heaviest model uses far more of your usage per turn, and advises switching up to it only when a task needs it. The fix takes about three seconds. Before I start anything, I ask whether it really needs the top model. If not, I drop a tier and save the heavy lifting for later. ## **Walk Into Your Hardest Work With a Full Tank** My second habit is about how much usage I have left when the hard work starts. Despite the name, the five-hour window is not really five hours of work. Your usage is capped by tokens, and a token-heavy session can burn through that cap in an hour, which means your time at the keyboard can run out long before you are done thinking. So when I know a session will be intensive, I do not walk into it having already spent half my budget on small stuff. I start it with a full tank and give my heaviest sessions a clean start. That one change has done more for my output than any prompt trick. The deep work gets the room it needs, and I rarely hit the wall in the middle of the problem I care most about solving. ## **When You Hit the Wall, Route Around It** Prevention only takes you so far. Some nights you do everything right and still run out, which brings me back to that night with the agent plan. Rather than pay to push through, I copied my context into ChatGPT, the plan so far and the open questions, and kept building there. It was not seamless. I had to reorient the new model and rebuild a little of where I was, but within minutes the momentum came back. I finished that night without spending an extra dollar. You almost certainly have access to more than one capable model already, between the free tiers of ChatGPT, Claude, and Gemini and whatever plan you pay for. What you build in one can move to another in a couple of minutes, so when one tool cuts you off, take the work elsewhere instead of paying to break back in. ## **Stay Deliberate, Keep Building** Through some of the busiest weeks I have had, three habits kept me building at full capability from home, without paying a cent more. The cost of using AI well is shifting from dollars to discipline, and discipline is the one part of this you fully control. None of these habits are clever, which is the point. Anyone can pick them up. If you are feeling the same squeeze, start with the one that fits your week. Discipline beats spend, and from a home setup that is most of the game. The math changes when the money is a company's instead of your own, which is exactly where the next Founder's Corner is headed. For now, keep experimenting and keep building. ### Steal My Prompt Vol. 37: The Decision Log Builder URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-build-a-decision-log/ Last updated: 2026-07-13T16:59:26.000Z The decision you made on Tuesday is gone by Friday. You remember that you decided something. The context has faded. What were the other options? What did you weigh against what? What did you commit to do next? The signal that mattered most at the moment of decision is the signal you no longer have access to. When the outcome lands six months later, good or bad, you cannot trace it back to the thinking that produced it. Most decision logs are built to fix this. Most decision logs die in week two. The reason is always the same: writing a structured entry from scratch takes five minutes, and five minutes is more than anyone has at the moment a decision gets made. This prompt removes that friction. You talk for 60 seconds into your phone the moment a decision lands. You paste the transcript. The structure is built for you. The log entry is done. Every quarter, you paste your accumulated entries back into the prompt and run a review pass. You see what kinds of decisions you make well, where your confidence betrays you, and which prior choices are overdue for a second look. You do not need discipline. You need a system that survives a busy week. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - Logging a hiring, vendor, or technology decision the moment you make it, while the tradeoffs are still fresh - Capturing a strategic call (pursue this project, kill that one, change direction) with the options you weighed - Documenting a financial, parenting, or health decision you want to revisit in 90 days - Building a personal record of how you decide, so future you can argue productively with past you - Running a quarterly pattern review across your accumulated entries to see what you cannot see one decision at a time ## **How to Use It** **Step 1.** Pick your tool. Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. The prompt is model-agnostic. **Step 2.** Decide which mode you are running. ENTRY for a single decision (voice memo or typed input). INTERVIEW if you want the prompt to ask you questions instead. REVIEW for the quarterly pattern pass on your accumulated log. **Step 3.** For ENTRY mode, open the voice memo app on your phone and talk for 60 to 90 seconds. Cover what you decided, what else you considered, why this option won, and what you expect to see in 90 days. Speak in fragments and tangents. The prompt cleans it up. **Step 4.** Get the transcript out (iPhone: voice memo → share sheet → Copy Text. Android: same general path through Recorder or Voice Memo). Paste into the prompt window, specify the mode, run. **Step 5.** Copy the structured output into your running log. A Notion page, a Google Doc, an Apple Note, a single text file in your Drive. Anywhere you will actually return to works. **Step 6.** Move the review date the prompt generates into a calendar reminder. If it lives only in the log, it will not happen. **Step 7.** Every 90 days, paste your accumulated entries back into the prompt and run REVIEW mode. Revisit the decisions it flags. **Pro tip:** After the prompt structures the entry, send it to yourself in a single email with the subject line "Decision: \[one-line description\]." Your inbox becomes a searchable archive. When you need to find the reasoning behind a choice 18 months from now, you search the subject line in your email instead of scrolling through a long document. ## **The Prompt** *You are my decision-log assistant. Your job is to convert raw input about a decision into structured log entries I can paste into my running document, and to review my accumulated log for patterns when I ask. You are an extractor and a pattern-finder, not a coach or advisor.* *Tell me which mode I am running:* **ENTRY mode (single decision from voice memo transcript or typed dump):** *I am pasting raw input about one decision. Extract and structure it.* **INTERVIEW mode (no input pasted, you ask me):** *Ask me ONE focused question at a time and build the entry from my answers. Do not move to the next question until I have answered the current one. Maximum 7 questions.* **REVIEW mode (pattern analysis on my accumulated log):** *I am pasting my running log of past entries. Surface patterns, biases, missed review dates, and the decisions worth revisiting now.* ***\[I will type the mode and paste any input below this line.\]*** *For ENTRY and INTERVIEW modes, output the entry in this exact format:* *Decision: \[one-line description of what was decided\]* *Date: \[today, or the date I provide\]* *Context: \[2-3 sentences on why the decision had to be made now\]* *Options considered: \[bulleted list\]* *Choice: \[what was decided\]* *Reasoning: \[2-3 sentences on why this option won\]* *Expected outcome at 90 days: \[what good looks like, specifically\]* *Review date: \[a specific date 90 days from the decision date\]* *Confidence at time of decision (1-5): \[my self-rated confidence or your best inference from the input\]* *Rules for ENTRY and INTERVIEW modes:* - *Be specific. "Hired the senior candidate from the second round" beats "made a hiring decision."* - *Where the input is unclear or incomplete, ask one targeted follow-up question. Do not fill in the gap yourself.* - *For voice memo input, ignore filler words and tangents. Extract the substance.* - *Always set a review date. If I do not provide one, default to 90 days from the decision date.* *For REVIEW mode, output these four sections:* *1\. Patterns: what categories of decisions I make most, where my confidence runs highest and lowest, and where I tend to revisit* *2\. Possible bias: where I may over-rotate on speed, consensus, risk avoidance, or recency* *3\. Past-due reviews: decisions whose review date has passed without revision* *4\. Three to revisit now: the most important decisions to look at this week, with one sentence on why each* *Output rules across all modes: clean text I can paste. No preamble, no encouragement, no commentary on what a great decision-tracker I am being.* ## **Transparency and Notes** - Voice memo workflow tested with iOS Voice Memos transcription pasted into Claude. Transcription quality is strong enough that the prompt extracts cleanly without manual cleanup. Other transcription sources may require light editing before paste. - Privacy: do not paste voice memo transcripts that include names, compensation figures, sensitive personal data, or trade secrets into consumer AI tools without scrubbing first. Consumer AI tools are not confidential workspaces. - Total per-entry time once the workflow is set up: 60 to 90 seconds of talking, plus 15 seconds of pasting. First-time setup of the running log and calendar reminder: 10 minutes. - This is an organizational tool, not legal, financial, or medical advice. Decisions belong with you and the people you trust. ### Volume 36: A Maine Mill Town Wrote the Better Playbook URL: https://www.mindovermoney.ai/does-fine-tuning-add-knowledge-to-ai/ Last updated: 2026-07-13T16:59:27.000Z The data center fight is showing up in 300+ state bills, on Fox Business, and in towns most of us have never heard of. The headlines are loud and one-sided. Underneath them is a more complicated story about water, power, breakthroughs we have not seen yet, and one Maine mill town quietly writing a better playbook. 🧭 **Founder's Corner:** Why the loudest voices in the data center debate are misreading the room, and what the Town of Jay, Maine is showing the rest of us about how to build. 🧠 **AI Education:** Why fine-tuning a model on your company's data almost never works the way the vendor pitched it, and the cleaner mental model that does. ✅ **10-Minute Win:** A three-question rubric for evaluating any new AI tool, with Claude doing the research and you keeping the final call. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **OpenAI's Altman says AI unlikely to lead to 'jobs apocalypse'**](https://www.reuters.com/world/asia-pacific/openais-altman-says-ai-unlikely-lead-jobs-apocalypse-2026-05-26/?ref=mindovermoney.ai) **Summary:** Speaking in Sydney, OpenAI CEO Sam Altman said AI would not lead to a global "jobs apocalypse" and the technology had not claimed as many white-collar jobs as he had feared. **Why it matters:** The CEO who fueled the loudest version of the fear narrative is now walking it back. The 2026 picture is reshaped roles, not eliminated ones, and the professionals learning to work with these tools are still the ones winning. #### **2)**[ **Amazon starts selling its AI shopping technology to other retailers**](https://www.cnbc.com/2026/05/27/amazon-ai-shopping-alexa-kate-spade.html?ref=mindovermoney.ai) **Summary:** Amazon is licensing the technology behind Alexa for Shopping to outside retailers through AWS, with Tapestry-owned Kate Spade signed on as a customer to launch a gifting assistant. **Why it matters:** The cloud-computing playbook is repeating for AI shopping. If you sell anything online, the question is shifting from "should we have an AI assistant" to "whose AI agent runs our storefront." #### **3)**[ **Weekly Rundown—Moffitt Cancer Center expands Reimagine Care's virtual oncology model**](https://www.fiercehealthcare.com/health-tech/health-tech-weekly-rundown-reimagine-care-moffitt-cancer-center-expand-ai-powered?ref=mindovermoney.ai) **Summary:** Moffitt Cancer Center is expanding its AI-enabled virtual oncology program with Reimagine Care after early results showed nearly 7,000 patient interactions with 97% independently resolved without escalation to providers and only 2.4% of interactions resulting in emergency department referrals. **Why it matters:** Cancer care between visits has historically been a black box for both patients and clinicians. This is real-world evidence that AI plus a virtual care team can handle the in-between moments without sending people to the ER, and it sets a measurable bar for any specialty care AI rollout. #### **4)**[ **Your AI agent can now trade for you on Robinhood. And buy stuff with your credit card too**](https://www.cnbc.com/2026/05/27/your-ai-agent-can-now-trade-for-you-on-robinhood-and-buy-stuff-with-your-credit-card-too.html?ref=mindovermoney.ai) **Summary:** Robinhood launched Agentic Trading and an Agentic Credit Card, letting customers connect third-party AI assistants to carry out investing strategies or spending with minimal human involvement, with push notifications, spending limits, and optional manual approval. **Why it matters:** This is the first mainstream consumer product where an AI agent moves real money, not just researches options. Expect the same approve-as-you-go model to show up next in HR, procurement, and benefits platforms. #### **5)**[ **Beyond the EHR: Why one CIO believes AI will dwarf every prior health IT shift**](https://www.healthcareitnews.com/news/beyond-ehr-why-one-cio-believes-ai-will-dwarf-every-prior-health-it-shift?ref=mindovermoney.ai) **Summary:** Health system CIO Lundal told Healthcare IT News that artificial intelligence has the potential to fundamentally alter nearly every aspect of healthcare delivery and administration, and unlike EHR rollouts that followed an established roadmap, AI strategies can now shift in a matter of months. **Why it matters:** Many leaders are still planning AI in two-year horizons. This CIO's point is the strategy itself needs a faster cycle, and the organizations treating AI like a one-time deployment are already falling behind. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Data Center Fight Is More Complicated Than the Headlines** On Fox Business in May 2026, Kevin O'Leary suggested that two women from Utah might be working for the Chinese government. Their offense was organizing local opposition to his $100 billion Stratos data center in Hansel Valley. The project would cover 40,000 acres, more than twice Manhattan's footprint, and draw 9 gigawatts, more than the entire state uses in a year. He called them "proxies for the Chinese government" and taunted, "come out, come out wherever you are." The reasoning, as Fortune reported it: anyone slowing American compute must be working for the only adversary who would benefit. Rather than hide, Gabi Finlayson and Jackie Morgan laughed, mocked his flip-flops-with-a-suit look, and let the internet make him the punchline. Within days the CEO of O'Leary's own firm, Paul Palandjian, walked back the accusation, telling Business Insider the company accepted that the women were American political strategists. The insult collapsed under its own weight. What I keep returning to is the gap behind it: the distance between a hot-mic accusation and the careful clarification the same firm issued days later. The pushback he tried to paint as foreign was not even partisan. Utah's own Republican governor, Spencer Cox, had already pressed the project for a water plan to protect the Great Salt Lake. When the loudest voice reaches for a spy story, something in the conversation has already gone wrong. ## **The Concerns Are Real, and So Is the Upside** Water is where the worry usually starts, and the numbers explain why. Some hyperscale facilities draw up to 5 million gallons a day, the Environmental and Energy Study Institute reports. The power grid is straining under the same demand. In the PJM region, the grid serving 65 million people across 13 states, supply costs jumped from $2.2 billion to $14.7 billion in a single year, and Brookings attributes close to two-thirds of that climb to data centers. Those costs land on the monthly bill. In Utah, residential electricity rates rose 15.2 percent in twelve months, the third-largest jump in the country, according to the Energy Information Administration. And this is not a fringe reaction. Gallup found that 7 in 10 Americans do not want an AI facility built in their own community. The opposition O'Leary tried to cast as foreign is, in fact, most of America. For all that, the same buildout could underwrite the breakthroughs we have been waiting on for a generation. That possibility is the part the headlines rarely hold long enough to examine. ## **What If We Measured Both Sides?** This debate runs on a lopsided ledger. The costs are concrete and immediate. Gallons, megawatts, acres, the tax revenue a town gives up, all of it landing on a meter or a balance sheet. The benefits are harder to pin down, because most have not arrived yet, and a benefit that has not arrived is easy to value at nothing. So we tally the cost in full and the upside at zero. What would it look like to weigh both columns, even when one of them is still a guess? In May 2026, the upside stopped being hypothetical. In 1946, Paul Erdős posed a deceptively simple question. Scatter points on a flat plane, and how many pairs can sit exactly one unit apart? For decades, the square grid seemed to be the best anyone could do. Then a researcher fed the problem to an OpenAI reasoning model, a general-purpose system rather than one built for mathematics, and it found a better arrangement by importing tools from algebraic number theory that no one had thought to connect to the geometry. As Scientific American described it, "After 80 years of fruitless struggle by human mathematicians, a major geometry conjecture has at last been solved." Experts told OpenAI the proof would have earned a spot in a leading journal even if a human had written it. The thing that solved it was a chatbot. Read that and it is tempting to think the machines had finally outsmarted us, on an ordinary Tuesday. So I went looking for the asterisk, and the people closest to the work supplied it. Human mathematicians had to clean up the model's output before the proof held. The model had proved a better arrangement existed without working out how much better, and a Princeton mathematician, Will Sawin, pinned that down. Sébastien Bubeck, who leads OpenAI's mathematical work, put it plainly. The model "did not invent something fundamentally new that nobody saw coming. It just executed like an amazing mathematician." None of this proves AI will solve everything. What it proves is smaller and more interesting. This infrastructure, paired with serious people, can reach places neither would reach alone. What else might sit behind that door? Drug discovery, climate modeling, materials we cannot make yet. The honest answer is we do not know, and sitting with that uncertainty beats pretending we already know which way it breaks. ## **The Better Case No One Is Making** The companies building these data centers keep losing the argument for them. [I wrote in February](https://www.mindovermoney.ai/founders-corner/ai-trust-gap-silicon-valley-vs-real-world-professionals-2/) about the labs fumbling their message at the Super Bowl, and the same instinct is now playing out on concrete and steel rather than ad buys. Lost in the noise is the better case, the one about jobs, tax revenue, and the kind of infrastructure a town keeps long after the trucks leave. The bar is on the floor. Showing a community what it stands to gain, and giving it a real stake in the outcome, would do more than any press release. Two thousand miles east of Hansel Valley, one town has been quietly making exactly that case. The Town of Jay sits in Franklin County, Maine. For generations the Androscoggin Mill carried the local economy, until a 2020 boiler explosion began its decline and Pixelle Specialty Solutions left in 2023, taking the rest of the jobs and 22 percent of the town's tax base with it. Earlier attempts to revive the site went nowhere. So the town spent two years working to put a data center where the mill once stood. That patience is starting to pay off. According to Governor Mills' April 24 veto message, the $550 million project would bring more than 800 construction jobs, at least 100 high-paying permanent positions, and substantial property tax revenue. What sets it apart is how it would be built. Rather than break new ground, the developers plan to reuse the mill's existing buildings, water, and electrical infrastructure, which, as Mills noted, would avoid the very impacts on the environment and on ratepayers that drive the opposition elsewhere. The project is already under contract, has cleared several permits, and carries the backing of the town and the wider region. What makes Jay matter is the governance. Mills did three things at once. She vetoed the statewide moratorium that would have killed the project, signed a separate bill stripping data centers of state tax breaks, and created a council to recommend rules for the rest. Her reasoning held both truths. A moratorium, she wrote, can be "appropriate given the impacts of massive data centers in other states," but a blanket ban would have killed a project her own community wanted. I do not want to oversell this. One hundred permanent jobs is fewer than the mill once supported, and critics have said so fairly. But the lesson was never in the job count. It is in the process. Two years of local work, infrastructure reused instead of land broken, governance written in rather than bolted on after a fight. That is the rare outline people on different sides could actually sign. ## **Bigger Than One Town** Jay is one town. The same fight is unfolding in statehouses across the country. MultiState counted over 300 data center bills in the first six weeks of 2026, spread across more than 30 states, with outright moratoriums proposed in 11\. So far not one has cleared its originating chamber. The country is deciding how to build this one legislature at a time. When this reaches a ballot, the question is rarely AI or no AI, but how to build it. A community can push for governance written into the buildout, keeping some leverage over water and power, or back a moratorium that freezes everything while federal rules take years to arrive. By the time Washington acts, the window may have closed. And the stakes reach well past any single town. The United States and China are effectively the only two countries building AI infrastructure at full scale, and none of us can read China's actual hand. We are being asked to weigh in on something with national consequences while missing half the picture. In November, some of this lands on ballots as local and statewide measures, and the rest rides on the lawmakers voters send to write the rules. Most of us will never have a data center proposed in our own town. But the story will keep finding us anyway, in the news, on a ballot, or in what the people we elect decide to do about it. The work is not to pick a side from the headlines, but to understand it well enough to see what they leave out. Some of these buildouts will be great for local communities. Some will not be. There are no absolutes when we are all living through a transformative time in human history. No headline will sort that out for us, and the loudest voices only make it harder to see. That leaves the work to us. Information is power, especially in conversations this ambiguous. The clearer we see it, the better the choices we make, in November and long after. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Fine-Tuning Is Not What You Think It Is** #### **The Assumption** Here is the belief most professionals hold about fine-tuning: take a generic AI model, train it on your company's documents, and now it knows your business. The hospital's protocols. The bank's compliance rules. The law firm's case history. The assumption is reasonable. "Training" is how humans transfer knowledge to other humans, and the word carries the same intuition when applied to AI. Every vendor pitch promising to fine-tune the model on your data reinforces it. #### **Where It Breaks Down** Consider what happens when a hospital actually tries this. A clinical operations team partners with a vendor to fine-tune a large language model on thousands of pages of internal protocols, formulary documents, and clinical guidelines. The delivered model sounds completely different from the base version: right vocabulary, hospital voice, internal forms cited by name. Then a clinician asks a real question about a real protocol, and the model invents an answer that is not in any of the source documents. The more new facts the vendor pushed in, the more confidently the model fabricated ones that did not exist. #### **What Is Actually Happening** Fine-tuning does not put knowledge into a model. It shapes how the model behaves. A pre-trained model has learned most of what it knows before any team touches it. Pre-training is where the model first encounters language, the world, and how concepts relate. It runs at a scale fine-tuning does not approach. By the time a team gets access to a model like Claude or ChatGPT or Gemini, the heavy lifting is done. Fine-tuning is a smaller, targeted process that comes after pre-training. It teaches the model patterns: how to format an answer, what tone to use, when to refuse a request. Pre-training is everything a pharmacist learned about medicine. Fine-tuning is the day-one orientation at a new pharmacy: the intake script, the escalation path, the patient counseling style. A 2024 Google research paper presented at EMNLP, the leading natural language processing conference, studied what happens when fine-tuning tries to teach a model genuinely new facts. The finding was clear. Models learn new facts very slowly during fine-tuning. And as those new facts get absorbed, the model's tendency to hallucinate increases in direct proportion. The paper's conclusion: factual knowledge mostly comes from pre-training, and fine-tuning teaches the model to use that knowledge more efficiently. OpenAI's own documentation now reflects this reality. The use cases listed in their official fine-tuning methods table are classification, translation, tone, style, and instruction-following corrections. Not knowledge. The same documentation also notes that OpenAI is winding down its self-service fine-tuning platform. That is not an accident. The industry is converging on a clearer picture of what fine-tuning actually does. #### **The Revised Mental Model** Here is the cleaner mental model: fine-tuning changes the shape of the answer. Retrieval changes the substance. Wrong format, tone, or style is a fine-tuning question. Information the model does not have is a retrieval question, often solved by RAG, which we covered in Vol 19 through 22\. A one-time adjustment for a single task is a prompting question. The reframe changes three things at work. The question to ask a vendor shifts from "will you fine-tune it on our data" to "how does this system access our information at the moment of the response, and how is that information kept current." The success criterion for internal AI pilots shifts away from "the model knows our policies" toward "the model can retrieve our policies on demand." The timeline shifts too: fine-tuning projects take weeks of data preparation, while retrieval and prompting changes can be tested in an afternoon. #### **What to Watch For** - Vendor demos that promise "fine-tuning on your data" as the answer to a knowledge problem. The right question back is how the system retrieves information at the moment of the response, not how it was trained. - Internal AI projects where the success criterion is "the model knows our internal documents." That is a retrieval problem wearing the wrong jersey. - Outputs that sound right but cannot be traced to a specific source document. Confidence is not a signal of accuracy. It is often a signal that fine-tuning was used where retrieval would have been correct. - Industry signals like OpenAI winding down its self-service fine-tuning platform. The market is converging on prompting and retrieval as the primary levers for most professional use cases. - Any AI pitch that conflates "training" with "knowledge." The two are related, but they are not the same thing, and the difference now matters at every vendor meeting. #### **How This Connects** This is the first of a three-part series on fine-tuning. [Vol 3](https://www.mindovermoney.ai/structured-vs-unstructured-data-ai-training-explained/) introduced data and labels as the building blocks of how any model learns. [Vol 10](https://www.mindovermoney.ai/gemini-vs-chatgpt-vs-claude-which-ai-model-to-use/) walked through how a large language model is born from the training process. This series picks up where those volumes left off and walks into the room where the actual decisions get made today. Next week, Vol 37 covers RLHF, the most consequential fine-tuning method ever developed and the reason every commercial model you use responds the way it does. Vol 38 closes the series with a practical decision framework for choosing between fine-tuning, RAG, and prompting for any problem at work. *Part 1 of 3 in the Fine-Tuning series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The 3-Question Tool Test** Another AI tool launches on LinkedIn. Three more drop on YouTube the same day. A coworker forwards a newsletter with five more. Most of them are not transformative. You either chase everything and finish the week with eight half-configured accounts, or ignore everything and miss the two tools that would have changed your work. The 3-Question Tool Test breaks the cycle. Three questions you answer before signing up for anything: Does it solve a pain I actually have? Does it work inside my flow? Does it earn the time? Honest answers give you a clean decision: adopt, save for later, or skip. The twist: you use Claude (or any LLM) to run the analysis while you keep the judgment. Claude researches the tool, does the time math, and stress-tests its own answer. You bring your real pain, your real workflow, and the final call. ### **The Workflow** **1\. Pick a tool you have been curious about (1 minute)** Open the tab, post, or newsletter that triggered the curiosity. Have the tool's name and website ready. Knowing only the marketing copy is fine. Claude fixes that in the next step. **2\. Get a plain-English briefing from Claude (2 minutes)** Open Claude at claude.ai. Replace \[TOOL NAME\] with your actual tool and paste: **Copy/Paste Prompt:** *"In under 200 words, tell me: (1) what \[TOOL NAME\] does in one sentence, (2) the top three use cases real users adopt it for, (3) the pricing tiers, and (4) the top two complaints from real users in reviews or forums. Write plainly, no marketing language."* Two minutes of grounded research replaces twenty minutes of skimming the tool's homepage. **3\. Run the 3-question evaluation (4 minutes)** Stay in the same Claude conversation so it has the briefing as context. Fill in the bracketed sections with real specifics and paste: **Copy/Paste Prompt:** *"I am evaluating \[TOOL NAME\] using a 3-question rubric. Walk through each question with me honestly. Q1: Does it solve a pain I actually have? My specific pain right now is: \[DESCRIBE IN 1 TO 2 SENTENCES\]. Does this tool directly remove this friction, or is it adjacent? Q2: Does it work inside my flow? My current tools and daily workflow look like: \[LIST 3 TO 5 TOOLS AND HOW YOU MOVE BETWEEN THEM\]. Where would this tool fit, and does it integrate or create a copy-paste tax? Q3: Does it earn the time? Estimate realistic time saved per week if I used it actively, conservatively. Then estimate the learning curve hours to get there. Give me a 90-day ROI."* The specificity in the brackets is the work. Generic answers produce generic evaluations. **4\. Pressure-test the evaluation (1 minute)** LLMs agree too easily. Paste: **Copy/Paste Prompt:** *"Now play devil's advocate. Based on everything I said above, give me the single strongest reason I should NOT adopt \[TOOL NAME\]. Do not soften it."* If Claude's strongest counterargument feels weak, that itself is useful information. **5\. Make the call and log it (2 minutes)** Three outcomes. **Adopt:** sign up now and schedule a 30-day review on your calendar. **Save for later:** note the tool in a running list and name the trigger that would change your answer. **Skip:** write the tool name and the question that failed it. Future you will be tempted by the same tool in three months, and the log saves the re-evaluation. ### **The Payoff** You walk away with a decision on one tool, a transferable framework for every future tool, and a small log that compounds. Next time a colleague forwards a hot new tool, you have a structured workflow instead of an open spiral. The rubric is yours; the engine is Claude. ### **The AI Concept You Just Used** AI-assisted decision triage. Most people use AI to write or summarize. Fewer use it as a thinking partner for decisions they would otherwise make on instinct. The pattern is portable: define your rubric, give the LLM specific context to apply it, ask it to push back on its own answer, then make the call yourself. The same shape works for evaluating vendors, conferences, and book recommendations. ### **Transparency & Notes** - The rubric is generalizable. It applies to productivity software, browser extensions, and most paid subscriptions, not only AI tools. - Claude's research in Step 2 is a starting point, not a citation. If the decision is significant, click through to real reviews and pricing pages. - The devil's advocate step is the most often skipped and the most valuable. Skipping it makes the rubric a rubber stamp. - The framework is conservative by design. The cost of missing one useful tool is lower than the cost of accumulating twenty abandoned ones. ### The Data Center Fight Is More Complicated Than the Headlines URL: https://www.mindovermoney.ai/founders-corner/ai-data-center-debate-costs-and-benefits/ Last updated: 2026-07-13T16:59:27.000Z On Fox Business in May 2026, Kevin O'Leary suggested that two women from Utah might be working for the Chinese government. Their offense was organizing local opposition to his $100 billion Stratos data center in Hansel Valley. The project would cover 40,000 acres, more than twice Manhattan's footprint, and draw 9 gigawatts, more than the entire state uses in a year. He called them "proxies for the Chinese government" and taunted, "come out, come out wherever you are." The reasoning, as Fortune reported it: anyone slowing American compute must be working for the only adversary who would benefit. Rather than hide, Gabi Finlayson and Jackie Morgan laughed, mocked his flip-flops-with-a-suit look, and let the internet make him the punchline. Within days the CEO of O'Leary's own firm, Paul Palandjian, walked back the accusation, telling Business Insider the company accepted that the women were American political strategists. The insult collapsed under its own weight. What I keep returning to is the gap behind it: the distance between a hot-mic accusation and the careful clarification the same firm issued days later. The pushback he tried to paint as foreign was not even partisan. Utah's own Republican governor, Spencer Cox, had already pressed the project for a water plan to protect the Great Salt Lake. When the loudest voice reaches for a spy story, something in the conversation has already gone wrong. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Concerns Are Real, and So Is the Upside** Water is where the worry usually starts, and the numbers explain why. Some hyperscale facilities draw up to 5 million gallons a day, the Environmental and Energy Study Institute reports. The power grid is straining under the same demand. In the PJM region, the grid serving 65 million people across 13 states, supply costs jumped from $2.2 billion to $14.7 billion in a single year, and Brookings attributes close to two-thirds of that climb to data centers. Those costs land on the monthly bill. In Utah, residential electricity rates rose 15.2 percent in twelve months, the third-largest jump in the country, according to the Energy Information Administration. And this is not a fringe reaction. Gallup found that 7 in 10 Americans do not want an AI facility built in their own community. The opposition O'Leary tried to cast as foreign is, in fact, most of America. For all that, the same buildout could underwrite the breakthroughs we have been waiting on for a generation. That possibility is the part the headlines rarely hold long enough to examine. ## **What If We Measured Both Sides?** This debate runs on a lopsided ledger. The costs are concrete and immediate. Gallons, megawatts, acres, the tax revenue a town gives up, all of it landing on a meter or a balance sheet. The benefits are harder to pin down, because most have not arrived yet, and a benefit that has not arrived is easy to value at nothing. So we tally the cost in full and the upside at zero. What would it look like to weigh both columns, even when one of them is still a guess? In May 2026, the upside stopped being hypothetical. In 1946, Paul Erdős posed a deceptively simple question. Scatter points on a flat plane, and how many pairs can sit exactly one unit apart? For decades, the square grid seemed to be the best anyone could do. Then a researcher fed the problem to an OpenAI reasoning model, a general-purpose system rather than one built for mathematics, and it found a better arrangement by importing tools from algebraic number theory that no one had thought to connect to the geometry. As Scientific American described it, "After 80 years of fruitless struggle by human mathematicians, a major geometry conjecture has at last been solved." Experts told OpenAI the proof would have earned a spot in a leading journal even if a human had written it. The thing that solved it was a chatbot. Read that and it is tempting to think the machines had finally outsmarted us, on an ordinary Tuesday. So I went looking for the asterisk, and the people closest to the work supplied it. Human mathematicians had to clean up the model's output before the proof held. The model had proved a better arrangement existed without working out how much better, and a Princeton mathematician, Will Sawin, pinned that down. Sébastien Bubeck, who leads OpenAI's mathematical work, put it plainly. The model "did not invent something fundamentally new that nobody saw coming. It just executed like an amazing mathematician." None of this proves AI will solve everything. What it proves is smaller and more interesting. This infrastructure, paired with serious people, can reach places neither would reach alone. What else might sit behind that door? Drug discovery, climate modeling, materials we cannot make yet. The honest answer is we do not know, and sitting with that uncertainty beats pretending we already know which way it breaks. ## **The Better Case No One Is Making** The companies building these data centers keep losing the argument for them. [I wrote in February](https://www.mindovermoney.ai/founders-corner/ai-trust-gap-silicon-valley-vs-real-world-professionals-2/) about the labs fumbling their message at the Super Bowl, and the same instinct is now playing out on concrete and steel rather than ad buys. Lost in the noise is the better case, the one about jobs, tax revenue, and the kind of infrastructure a town keeps long after the trucks leave. The bar is on the floor. Showing a community what it stands to gain, and giving it a real stake in the outcome, would do more than any press release. Two thousand miles east of Hansel Valley, one town has been quietly making exactly that case. The Town of Jay sits in Franklin County, Maine. For generations the Androscoggin Mill carried the local economy, until a 2020 boiler explosion began its decline and Pixelle Specialty Solutions left in 2023, taking the rest of the jobs and 22 percent of the town's tax base with it. Earlier attempts to revive the site went nowhere. So the town spent two years working to put a data center where the mill once stood. That patience is starting to pay off. According to Governor Mills' April 24 veto message, the $550 million project would bring more than 800 construction jobs, at least 100 high-paying permanent positions, and substantial property tax revenue. What sets it apart is how it would be built. Rather than break new ground, the developers plan to reuse the mill's existing buildings, water, and electrical infrastructure, which, as Mills noted, would avoid the very impacts on the environment and on ratepayers that drive the opposition elsewhere. The project is already under contract, has cleared several permits, and carries the backing of the town and the wider region. What makes Jay matter is the governance. Mills did three things at once. She vetoed the statewide moratorium that would have killed the project, signed a separate bill stripping data centers of state tax breaks, and created a council to recommend rules for the rest. Her reasoning held both truths. A moratorium, she wrote, can be "appropriate given the impacts of massive data centers in other states," but a blanket ban would have killed a project her own community wanted. I do not want to oversell this. One hundred permanent jobs is fewer than the mill once supported, and critics have said so fairly. But the lesson was never in the job count. It is in the process. Two years of local work, infrastructure reused instead of land broken, governance written in rather than bolted on after a fight. That is the rare outline people on different sides could actually sign. ## **Bigger Than One Town** Jay is one town. The same fight is unfolding in statehouses across the country. MultiState counted over 300 data center bills in the first six weeks of 2026, spread across more than 30 states, with outright moratoriums proposed in 11\. So far not one has cleared its originating chamber. The country is deciding how to build this one legislature at a time. When this reaches a ballot, the question is rarely AI or no AI, but how to build it. A community can push for governance written into the buildout, keeping some leverage over water and power, or back a moratorium that freezes everything while federal rules take years to arrive. By the time Washington acts, the window may have closed. And the stakes reach well past any single town. The United States and China are effectively the only two countries building AI infrastructure at full scale, and none of us can read China's actual hand. We are being asked to weigh in on something with national consequences while missing half the picture. In November, some of this lands on ballots as local and statewide measures, and the rest rides on the lawmakers voters send to write the rules. Most of us will never have a data center proposed in our own town. But the story will keep finding us anyway, in the news, on a ballot, or in what the people we elect decide to do about it. The work is not to pick a side from the headlines, but to understand it well enough to see what they leave out. Some of these buildouts will be great for local communities. Some will not be. There are no absolutes when we are all living through a transformative time in human history. No headline will sort that out for us, and the loudest voices only make it harder to see. That leaves the work to us. Information is power, especially in conversations this ambiguous. The clearer we see it, the better the choices we make, in November and long after. ### Steal My Prompt Vol. 36: The Healthcare Decision Tree URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-compare-treatment-options/ Last updated: 2026-07-13T16:59:27.000Z You leave the specialist's office with three options, two opinions, and one decision to make. You open Google. The first article tells you to choose option A. The second pushes option B. A Reddit thread argues for watchful waiting. By the end of the night, you have more information and less clarity. The decision has not moved an inch. This happens because most medical decisions get framed as a choice between options. They are not. They are a choice between what you value most, applied to options that each carry different tradeoffs. Recovery time. Side-effect tolerance. Cost. Lifestyle impact. Until you name your criteria, comparing options just becomes a Google rabbit hole. This prompt forces the inverse. Before you see the options arranged side by side, you rank what matters to you. Then the comparison table builds against your priorities, not against whatever your last article emphasized. Big medical decisions deserve more rigor than a Google search. This is the framework I use for mine. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - Comparing two or more treatment options your specialist presented, including watchful waiting - Choosing between two specialists or two treatment centers for the same condition - Deciding whether to enter a clinical trial against standard treatment - Helping a parent or aging family member structure a major medical decision they have to make - Weighing a recommended procedure against a lifestyle intervention with comparable evidence - Preparing for a follow-up appointment where you need to come back with a direction ## **How to Use It** **Step 1.** Open Claude, ChatGPT, Microsoft Copilot, or Gemini on the free tier. The prompt is model-agnostic. **Step 2.** For a high-stakes decision like this, turn on the reasoning model option in the tool you are using. That is "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Microsoft Copilot, or "Deep Think" in Gemini. Reasoning catches more on the first pass and pushes back harder on shallow ranking. **Step 3.** Copy the prompt and fill in the bracketed fields. Be specific about your options and your life context. The richer the input, the sharper the table. **Step 4.** Slow down on the ranking step. This is the whole mechanic. The temptation is to half-rank or to skip ahead and see the table. Resist. The order of your priorities is the most important data you give the model. **Step 5.** Take the comparison table and the gap questions to your next appointment. The output is not a decision. It is a structured way to bring your decision to the people who help you make it. **Pro tip:** Once you have the table back, ask the model to play devil's advocate against your top-ranked option. That second pass stress-tests your preference before you commit, which is the move most people skip and regret later. ## **The Prompt** *You are my decision-facilitation assistant for a high-stakes healthcare decision. You are not a medical advisor and will not recommend a specific treatment. Your role is to help me structure the decision so I can make it with my care team.* *Here is the context:* **The decision I am facing:** *\[BRIEFLY DESCRIBE WHAT YOU NEED TO DECIDE\]* **The options on the table:** *\[LIST 2-4 OPTIONS YOUR SPECIALIST PRESENTED, INCLUDING WATCHFUL WAITING IF THAT IS ONE\]* **What I already know about each option:** *\[PASTE ANYTHING YOUR SPECIALIST EXPLAINED, INCLUDING TIMELINES, RISKS, OR PROVIDER PREFERENCES\]* **My life context:** *\[AGE, WORK OR CAREGIVING RESPONSIBILITIES, RELEVANT MEDICAL HISTORY, INSURANCE TYPE\]* *Work through this in order. Do not skip steps.* **Step 1: Ranked criteria first.** *Before showing me anything else, ask me to name my top 5 decision criteria and rank them from most to least important. Criteria may include: reversibility, recovery time, side-effect tolerance, cost out-of-pocket, lifestyle impact, success rate evidence, time to results, or alignment with my values about \[intervention level / quality of life / risk tolerance\]. Force me to rank, not just list. Do not proceed to Step 2 until I have given you a ranked list.* **Step 2: Build the comparison table.** *Create a table with my ranked criteria as rows (in my ranking order) and each option as a column. Fill in what I have told you. Where information is missing or unclear, mark the cell "Need to confirm" and add that question to a list at the bottom for me to bring to my care team.* **Step 3: Surface the structural read.** *Below the table, give me three things:* - *Where the options differ most when measured against my top-ranked criteria* - *Whether any option is clearly disqualified by my top criterion* - *The 2-3 most important questions I should bring back to my care team before deciding* *Output: one comparison table, then a short plain-language summary. Do not recommend an option. Do not editorialize. Be specific. Be concise.* ## **Transparency and Notes** - Built and tested in Claude, free-tier compatible across all four major tools. The reasoning model option is strongly recommended for any decision of this weight. - Model-agnostic. No specific tool capability is required. - Privacy guidance: paste only the medical context you are comfortable sharing with a consumer AI tool. Consumer AI tools are not HIPAA-covered. Remove names, dates of birth, and identifiers before pasting if there is any chance the conversation is logged. - This is an educational framework, not medical, legal, or financial advice. Every decision belongs with your care team. ### Volume 35: Build the System Before You Need It URL: https://www.mindovermoney.ai/do-bigger-ai-context-windows-matter/ Last updated: 2026-07-13T16:59:28.000Z For the last four weeks, my travel and presentation schedule erased the time I normally use to build the newsletter. The newsletter has still shipped on time. Not because I worked harder, but because the AI systems I built when I had room to build them are doing the work I no longer have time to do manually. 🧭 **Founder's Corner:** Why the AI systems you build during calm stretches are the only thing that keeps the work shipping when life takes your calendar back. 🧠 **AI Education:** Why the million-token context race is mostly a vanity metric, and the three patterns of context that actually shape how enterprise AI works today. ✅ **10-Minute Win:** Turn your unread pile of articles, PDFs, and saved transcripts into a queryable research notebook and a 15-minute audio summary you can listen to today. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Anthropic set to hit $10.9 billion in revenue during second quarter, source says**](https://www.cnbc.com/2026/05/20/anthropic-revenue-explosive-growth-ipo-profitable-quarter.html?ref=mindovermoney.ai) **Summary:** Anthropic is on track to generate $10.9 billion in revenue during the second quarter and post its first profitable quarter. Anthropic generated $4.8 billion in revenue during the first quarter, more than doubling quarter over quarter. **Why it matters:** For two years the AI industry has been defined by "burn cash now, profits later." Anthropic crossing into operating profit, even briefly, changes the conversation with skeptical leadership. It shows that at enterprise scale, AI is starting to pay for itself, not just promise to. #### **2)**[ **100 things we announced at Google I/O 2026**](https://blog.google/innovation-and-ai/technology/ai/google-io-2026-all-our-announcements/?ref=mindovermoney.ai) **Summary:** At I/O 2026, Google launched Gemini 3.5 Flash, combining frontier intelligence with action and available across Antigravity, the Gemini API, and Android Studio, plus Gemini Omni for video creation, Universal Cart for agentic shopping, and a redesigned Gemini app. Gemini Spark, a 24/7 personal agent, rolls out next week to AI Ultra subscribers in the US. **Why it matters:** If you live in Gmail, Docs, Search, or Android, an agentic version of Gemini is about to start doing tasks for you, not just answering questions. The way work happens inside Google products is shifting from "search and click" to "ask and approve." #### **3)**[ **Bristol Myers taps Anthropic's Claude for enterprise-wide AI adoption to speed R&D, global workflows**](https://www.fiercepharma.com/pharma/bristol-myers-taps-anthropic-claude-enterprise-wide-ai-adoption-speed-drug-rd-global?ref=mindovermoney.ai) **Summary:** Bristol Myers Squibb signed a strategic agreement to deploy Claude as a "shared intelligence platform" across the drugmaker's global operations, putting Claude's advanced reasoning and agentic capabilities in the hands of more than 30,000 BMS employees. One application already in use involves Claude writing regulatory reports based on the company's clinical trial data. **Why it matters:** A top-tier pharma is moving past AI pilots and putting agentic AI inside the regulated workflows where drugs actually get developed, submitted, and approved. For anyone in life sciences or healthcare-adjacent work, this resets the bar for what an enterprise AI strategy is supposed to look like in 2026. #### **4)**[ **AI-enabled medical devices may fail on real-world patients, report cautions**](https://www.healthcareitnews.com/news/ai-enabled-medical-devices-may-fail-real-world-patients-report-cautions?ref=mindovermoney.ai) **Summary:** A new Paragon Health Institute report warns that AI medical devices can perform well during testing yet still fail when used on real-world patients whose medical images differ from the data used to train the underlying models. The report recommends a voluntary "Digital Similarity Analysis" that compares a patient's image against the device's training data before use. **Why it matters:** AI tools that look strong in pilot studies can quietly underperform once they reach a different patient population. For anyone working in clinical operations, the lesson is that vendor demos and FDA clearance are not the same thing as proven performance on the patients walking through your door. #### **5)**[ **OpenAI prepares confidential IPO filing**](https://www.axios.com/2026/05/20/openai-ipo-spacex-musk?ref=mindovermoney.ai) **Summary:** OpenAI is preparing to confidentially file a draft IPO prospectus with the SEC, with Goldman Sachs and Morgan Stanley leading the process and a public market debut targeted for September 2026 at a current private valuation around $852 billion. Confidential IPO filings are typically submitted a couple of months before publicly available S-1 filings. **Why it matters:** A public OpenAI will eventually have to disclose audited revenue, compute costs, and its economic split with Microsoft, the data that has been the AI industry's biggest blind spot. For anyone budgeting AI spend or making the case for AI to leadership, those numbers are about to reshape what "reasonable" looks like. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****Time Is the Most Important Currency** I usually create the newsletter on weeknights, between 7 and 10 PM, after my day winds down. For the last four weeks, that window has been mostly gone. My travel and presentation schedule has been hectic, which means the content I would have written on a Tuesday night is now getting written on a Saturday. That kills my weekend flexibility and the time I had been using to build with AI. And with major life changes coming in November, I have been thinking more and more about the importance of time. Time is the most important currency, and I had read that line for years without ever really feeling it. This stretch made me feel it. Every hour over the last four weeks has been spoken for, and the newsletter has still kept shipping. That outcome is not because I have been working harder. It is because the AI systems I built earlier, when I had room to build them, are doing the work I no longer have time to do manually. They are how you bank time when the rest of your calendar is taken. ### **Lessons From 30,000 Feet** I was on a Friday morning flight home to Orlando from Las Vegas, where I had presented the day before. I had not touched the newsletter all week because of the travel, and that weekend was the only window I had left to do the work. The long flight was my last shot at protecting my time. So I opened the laptop and started working through the Claude skills I had built. By the time we descended into Orlando, the next issue was 90 percent done. Two concepts landed during the execution, both of them things I had said for months without feeling them at this depth. The first was that AI is best used as a system that solves a problem, not a prompt that automates a task. I had been writing and saying that in various ways, but watching the workflow run on a tray table while the rest of my week was already spent was the first time it felt real for me. It was the first experience where I noticed getting my time back during a period of personal chaos. The second was time. The hours the system was buying back compounded into a weekend with flexibility, and the sleep I had not been getting all week. Productivity advice never seems to capture this. Time is the only currency none of us get more of, and my AI-powered content creation system is the only thing in my stack that has helped me bank it without sacrificing anything. **When Life Gets Busy** This is not just my story. Every professional eventually hits a stretch where the time to think and the time to build both shrink at the same moment. The trigger looks different for everyone, whether that is travel, illness, a major project, or a child entering a more demanding phase. The pattern is the same. Real life arrives all at once and takes the room you used to have. What I felt on the flight is showing up in the data. Microsoft's 2026 Work Trend Index reports that 66 percent of AI users surveyed say AI has given them more time for high-value work. That figure came from a Microsoft survey of 20,000 AI-using knowledge workers across ten markets. Professionals who have wrapped systems around AI are getting their hours back. AI returns time when it runs as a system. Treating it as a chatbot or a smarter search engine sharpens a task but does not bank an hour. Real life is what happens when there is no surplus to optimize, only the existing output to preserve. The system you build when you have time is what keeps you productive and shipping when you do not. **Getting Your Time Back** Start with one question: where is your time leaking right now? Asking what task AI can do for you stops at the task, but many workers never get to a starting question at all. Per Gallup's most recent workplace survey, 49 percent of U.S. workers report they never use AI in their role. The standard AI pitch did not meet those workers where they were, because it never asked where they were losing time in the first place. Your first system starts with a problem you already need help with. It could be the recurring client email, the weekly team summary, or the slide deck you rebuild from the same data each month. Write down your problem statement, the inputs, what good looks like, and hand that context over to the AI tool you already use. Run it once. Adjust it. Run it again. Iterate until you are comfortable with the output, then build a system that can replicate that output on an ongoing basis. This is when compounding starts and you can feel the difference in your AI usage. A system you have used ten times has paid you back ten times over, with no extra labor on your part. Compounding is the entire case for building an AI system. If you have not started yet, that is fine. No one is an AI expert, including the people who have been building with it for months. We learn by experimenting and thinking differently. Finding ways to get time back is a grounding concept for all of us, and the perfect entry point to your first AI system. The only thing that matters is starting. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Tokens and Context Windows in 2026: What Has Changed and Why It Matters Now** #### **The Number That Tells the Story** When Vol 6 introduced tokens and context windows in November 2025, the mainstream chat experience capped around 128,000 to 200,000 tokens. Gemini had pushed to 1 million as the outlier. Six months later, in May 2026, 1 million is the floor for frontier models, not the ceiling. Claude Opus 4.7, Gemini 3.1 Pro, Qwen 3.6 Plus, and Llama 4 Maverick all ship with 1M tokens as standard. GPT-5.4 supports up to 1 million experimentally in Codex, with a surcharge above 272,000 tokens. Gemini 1.5 Pro supports 2 million. Meta's open-weight Llama 4 Scout has held 10 million since April 2025\. The mainstream frontier roughly quintupled in half a year. If that were the whole story, the industry would have solved the context problem. Most users believe it did. The reality is more complicated, and the part that matters for how you actually use AI at work has almost nothing to do with the number on the spec sheet. #### **What the Race Did Not Deliver** Marketing departments wrote a clean narrative: more context equals better outputs. Upload your entire codebase. Feed in the full contract. Watch the model synthesize everything in one shot. Independent testing tells a different story. Every major model shows meaningful accuracy degradation when the answer to a question sits in the middle of a long context, compared to the same answer placed at the beginning or end. The pattern, documented by Stanford researchers in 2023 and named "Lost in the Middle," has not been closed by three years of aggressive scaling. Larger windows show worse degradation, because larger windows mean more middle to get lost in. On OpenAI's MRCR benchmark, which tests how well models locate specific information across long contexts, Anthropic's published results for Claude Opus 4.6 show about 93 percent recall at 256,000 tokens and roughly 76 percent at 1 million. That is the most reliable model on the benchmark at the time of publication, and even it drops 17 points over the range. The number on the product page is rarely the number you can rely on. Effective context (what the model can reason over reliably) lags advertised context, and the gap widens as the window grows. #### **How Context Actually Works Now** Three patterns of context consumption matter in 2026\. Only one resembles what Vol 6 described. **Chat pattern.** You paste documents into a chat window, the model loads everything at once, you ask a question. This is the pattern most consumer users default to. It is also where lost-in-the-middle hits hardest, where latency at maximum context can stretch into the minutes, and where a 900,000-token input on Claude Opus 4.7 costs about $4.50 before any output. **Agent pattern.** Covered in the AI Agents series (Vol 24-27). An agent does not load everything at once. It builds context iteratively across a sequence of actions: search, read, plan, retrieve more, decide. Each step consumes a fraction of the window. Prompt caching (now standard on Claude, Gemini, and GPT) lets the agent reuse stable portions of context across steps at up to 90 percent less cost. The bottleneck is not how big the window is. It is how well the agent decides what to load next. **Grounded retrieval pattern.** Covered in Vol 30 with Copilot's architecture. The system retrieves only the relevant pieces from a larger index, then runs the prompt against a tight context. The window stays small by design. The intelligence is in what gets retrieved, not in what gets stuffed in. The chat pattern is what most marketing campaigns optimize for. The other two are what most enterprise AI actually runs on. That makes raw window size a vanity metric for the use cases that matter most at work. #### **What This Means for How You Work With AI Now** Stop choosing models by advertised context size. If the task involves real reasoning over a long document, prefer the model with the strongest recall at the length you actually need. Structure long prompts for retrieval. Put the question and the most critical context at the beginning or the end. Avoid burying the key fact in the middle of a 50,000-word document and asking the model to find it. If you must work with large material, ask the model to extract the relevant sections first, then run your analysis on the extract. When an enterprise tool feels surprisingly capable on a task it should not be able to handle, the explanation is usually retrieval, not raw context. Copilot does not load your entire SharePoint. It searches the index, finds what matches, and answers from that subset. The architecture is doing the heavy lifting, not the window. #### **How This Connects** Vol 6 introduced tokens and context windows as foundational concepts. Vol 8 showed how context shaped daily use at work. The AI Agents series (Vol 24-27) showed how agents consume context across multi-step actions. Vol 30 explained how Copilot uses Microsoft Graph to ground responses in retrieved data. This Flashback closes the loop. The fundamentals from Vol 6 and 8 still apply exactly as they did. The arms race that followed turned out to be less important than the architectural patterns built on top. Vol 36 begins the Fine-Tuning series. Fine-tuning, RAG, and prompting are three different levers for output quality. Context window size is not one of them, because how you use context matters more than how big the window is. *Flashback to Vol 6 and Vol 8.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **Your Personal Research Assistant** You save articles you mean to read, download PDFs from conferences, and stack up transcripts from podcasts and YouTube. The pile grows. The reading does not. The information might be useful someday, but someday never comes because you cannot remember what is in any of it. NotebookLM solves this problem. You may remember NotebookLM from Steal My Prompt Vol 8, where we used it to organize research for a long-form piece. Today we go structural and build a personal knowledge base from documents you already own. We treat NotebookLM as a research assistant that only knows what you have given it. Why this matters: open chatbots search the public web and synthesize whatever they find. NotebookLM does the opposite. It refuses to answer beyond your uploaded sources, and it cites the paragraph it pulled the answer from. The result is research you can actually verify. ### **The Workflow** **1\. Open NotebookLM and create a new notebook (1 minute).** Go to notebooklm.google.com. Sign in with your Google account. Click "Create new notebook." Free tier covers everything in this workflow. **2\. Upload three to five sources (3 minutes).** Click "Add source." NotebookLM accepts PDFs, Google Docs, Google Slides, websites, YouTube videos with transcripts, and pasted text. Pick documents from a single topic you care about right now: a clinical area you are following, a project you are scoping, a book you want to internalize. **3\. Ask your first three questions (2 minutes).** Each answer cites the exact paragraph from your sources, so verification takes one click. **Copy/Paste Prompt:** *"What are the three most important points across all of my uploaded sources, and where in each document did you find them?"* **4\. Generate an Audio Overview (2 minutes).** Click "Audio Overview" in the right panel. NotebookLM creates a 10 to 15 minute conversational podcast where two AI hosts discuss your sources. Useful for commute listening, gym time, or sharing highlights with someone who will not read the documents themselves. **5\. Pin a Note Card and save the notebook (2 minutes).** Click any helpful answer and select "Save as note." Notebooks persist across sessions. Come back next month, add new sources, and the assistant now knows the combined collection. ### **The Payoff** You walk away with a notebook you can return to, a 15-minute audio summary you can listen to today, and a clear sense of how source-grounded AI differs from open chatbot use. The unread pile becomes a queryable resource. ### **The AI Concept You Just Used** Source-grounded AI, sometimes called Retrieval Augmented Generation (RAG). The AI does not synthesize from training data or the open web. It retrieves from a source set you control and answers only from there. Every answer cites its source paragraph. The same concept underpins enterprise AI: legal AI grounded in case law, healthcare AI grounded in clinical guidelines, internal AI grounded in company policy. NotebookLM is the consumer-grade version of that pattern. ### **Transparency & Notes** - NotebookLM is free with a Google account. Audio Overview is also free. - Source caps: 100 sources per notebook, 50 notebooks per account. More than enough for personal use. - Privacy guidance: Do not upload PHI, confidential business documents, or NDA-protected material. Treat NotebookLM as a tool for public sources, conference notes, and content you would not mind a colleague seeing. - Audio Overview voices are AI-generated. Use it as a discovery aid, not as a citation source itself. - Source grounding is strong but not perfect. If an answer matters, click through the citation and read the original. ### Time Is the Most Important Currency URL: https://www.mindovermoney.ai/founders-corner/build-ai-systems-to-save-time-at-work/ Last updated: 2026-07-13T16:59:28.000Z I usually create the newsletter on weeknights, between 7 and 10 PM, after my day winds down. For the last four weeks, that window has been mostly gone. My travel and presentation schedule has been hectic, which means the content I would have written on a Tuesday night is now getting written on a Saturday. That kills my weekend flexibility and the time I had been using to build with AI. And with major life changes coming in November, I have been thinking more and more about the importance of time. Time is the most important currency, and I had read that line for years without ever really feeling it. This stretch made me feel it. Every hour over the last four weeks has been spoken for, and the newsletter has still kept shipping. That outcome is not because I have been working harder. It is because the AI systems I built earlier, when I had room to build them, are doing the work I no longer have time to do manually. They are how you bank time when the rest of your calendar is taken. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ### **Lessons From 30,000 Feet** I was on a Friday morning flight home to Orlando from Las Vegas, where I had presented the day before. I had not touched the newsletter all week because of the travel, and that weekend was the only window I had left to do the work. The long flight was my last shot at protecting my time. So I opened the laptop and started working through the Claude skills I had built. By the time we descended into Orlando, the next issue was 90 percent done. Two concepts landed during the execution, both of them things I had said for months without feeling them at this depth. The first was that AI is best used as a system that solves a problem, not a prompt that automates a task. I had been writing and saying that in various ways, but watching the workflow run on a tray table while the rest of my week was already spent was the first time it felt real for me. It was the first experience where I noticed getting my time back during a period of personal chaos. The second was time. The hours the system was buying back compounded into a weekend with flexibility, and the sleep I had not been getting all week. Productivity advice never seems to capture this. Time is the only currency none of us get more of, and my AI-powered content creation system is the only thing in my stack that has helped me bank it without sacrificing anything. **When Life Gets Busy** This is not just my story. Every professional eventually hits a stretch where the time to think and the time to build both shrink at the same moment. The trigger looks different for everyone, whether that is travel, illness, a major project, or a child entering a more demanding phase. The pattern is the same. Real life arrives all at once and takes the room you used to have. What I felt on the flight is showing up in the data. Microsoft's 2026 Work Trend Index reports that 66 percent of AI users surveyed say AI has given them more time for high-value work. That figure came from a Microsoft survey of 20,000 AI-using knowledge workers across ten markets. Professionals who have wrapped systems around AI are getting their hours back. AI returns time when it runs as a system. Treating it as a chatbot or a smarter search engine sharpens a task but does not bank an hour. Real life is what happens when there is no surplus to optimize, only the existing output to preserve. The system you build when you have time is what keeps you productive and shipping when you do not. **Getting Your Time Back** Start with one question: where is your time leaking right now? Asking what task AI can do for you stops at the task, but many workers never get to a starting question at all. Per Gallup's most recent workplace survey, 49 percent of U.S. workers report they never use AI in their role. The standard AI pitch did not meet those workers where they were, because it never asked where they were losing time in the first place. Your first system starts with a problem you already need help with. It could be the recurring client email, the weekly team summary, or the slide deck you rebuild from the same data each month. Write down your problem statement, the inputs, what good looks like, and hand that context over to the AI tool you already use. Run it once. Adjust it. Run it again. Iterate until you are comfortable with the output, then build a system that can replicate that output on an ongoing basis. This is when compounding starts and you can feel the difference in your AI usage. A system you have used ten times has paid you back ten times over, with no extra labor on your part. Compounding is the entire case for building an AI system. If you have not started yet, that is fine. No one is an AI expert, including the people who have been building with it for months. We learn by experimenting and thinking differently. Finding ways to get time back is a grounding concept for all of us, and the perfect entry point to your first AI system. The only thing that matters is starting. ### Steal My Prompt Vol. 35: The First-90-Days Briefing URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-first-90-days-new-role/ Last updated: 2026-07-18T01:46:15.000Z The first week of a new role has a particular energy. You have been chosen, you have ideas, you want to move. That instinct is the instinct of someone who takes the job seriously. It is also a trap. The team needs a decision. The first 1:1s pile up. The inbox fills. So you move. And by week six, you have been answering questions you should have been asking. The first 90 days in a new role are not for executing. They are for mapping. The decisions that shape your year are not the visible ones in week three. They are the assumptions you made in week one before you knew enough to question them. This week's prompt produces a structured 90-day briefing in one pass. Paste in the role, the company, the team you are inheriting, and the priorities you have heard. The model returns a situation diagnosis, a stakeholder map, a 30-day listening tour question set, 60-day quick-win candidates, 90-day strategic positioning, and the assumption traps most likely to bite you in the next 90 days. It is the briefing I wish I had at every job start. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ### **What You Can Use This For** - Starting a new senior role at a new company where you do not yet know the team, the politics, or what counts as a win - Stepping into an internal promotion where the title changed but the relationships did not - Taking over a function after a sudden leader exit and needing to triage what to stabilize first - Joining a startup where the role is loosely defined and you have to scope it before you can deliver it - Switching functions (engineering to product, finance to operations) and needing a fast read on what is different here - Coaching someone you hired or mentored through their own 90-day landing ### **How to Use It** 1. Open Claude, ChatGPT, Microsoft Copilot, or Gemini free tier. This is a synthesis-heavy prompt, so turn on the reasoning model option. That is "Extended thinking" in Claude, the reasoning model in ChatGPT, "Think Deeper" in Copilot, or "Deep Think" in Gemini. The deeper reasoning catches situation-type nuances a quick answer misses. 2. Copy the prompt and fill in the six bracketed fields. Be specific. "Director of Engineering at a 200-person Series C SaaS company" produces a better briefing than "manager at a tech company." 3. Read the situation diagnosis first. That frame shapes everything below it. A turnaround briefing looks nothing like a sustaining-success briefing. If the diagnosis does not feel right, push back. Tell the model what it is missing and ask it to rerun. 4. Use the listening tour questions as a starting set, not a script. Take them into your first round of 1:1s, then come back to the model with what you heard and ask it to refine the next round. *Pro tip: Save the briefing as a working document. Update the situation diagnosis at day 30 and day 60\. What you thought you were walking into is rarely what you find. The briefing is most valuable when it gets revised by reality.* ### **The Prompt** *You are a senior leadership coach who has helped hundreds of professionals land successfully in new roles. You specialize in the first 90 days, when assumptions get baked in before there is enough data to question them.* *I am stepping into a new role. Help me build a 90-day briefing that maps the situation before I start executing.* *Here is what you know:* *\- New role: \[YOUR NEW ROLE AND LEVEL\]* *\- Company and industry: \[COMPANY DESCRIPTION, SIZE, STAGE, INDUSTRY\]* *\- Team I am inheriting: \[TEAM SIZE, MIX OF NEW vs LONGTIME, ANY KNOWN DYNAMICS\]* *\- Priorities I have heard from my hiring manager or the board: \[TOP 2-3 STATED PRIORITIES\]* *\- My start date: \[START DATE OR FIRST DAY\]* *\- The biggest thing I do not yet know: \[NAME THE UNKNOWN THAT WORRIES YOU MOST\]* *Use my start date to factor calendar realities into every section below: fiscal year boundaries, quarter-end pressure, holiday windows, performance review seasons, board cycles, or org-wide events that will compress my calendar in the first 90 days.* *Produce the briefing in this exact structure:* *1\. SITUATION DIAGNOSIS* *Identify which type of landing this is and explain why. Use these categories: new territory (you do not yet know the business), inheriting strength (high expectations, low room to differentiate), inheriting a fix (something is broken and you were hired to address it), scaling fast (function is growing and you need to build the infrastructure), or hybrid (name the mix). The diagnosis must be opinionated, not hedged. Then name the assumption most professionals make in this situation type that turns out to be wrong.* *2\. STAKEHOLDER MAP* *List the 6-10 people who will most shape my success in this role. Group them into: must win in first 30 days, must understand in first 60 days, watch for derailers. For each person, name what they likely care about and one question I should be ready to answer for them.* *3\. 30-DAY LISTENING TOUR* *Generate 10-15 questions for my first round of 1:1s and team conversations. Tailor the questions to the situation diagnosis above. Distinguish questions for direct reports, peers, my manager, and any cross-functional partners. Lead with the question most likely to surface a hidden assumption.* *4\. 60-DAY QUICK-WIN CANDIDATES* *Identify 3-5 candidate moves that could produce visible value by day 60\. For each one, name the risk if it fails publicly, the signal it sends if it succeeds, and the dependencies that have to be true. Rank them by signal-to-risk ratio.* *5\. 90-DAY STRATEGIC POSITIONING* *Define what I should be known for by day 90 inside the company. What is the one sentence my hiring manager should be able to say about my contribution to their boss? Name the 1-2 narrative threads I should be reinforcing in every senior conversation.* *6\. WATCH-OUTS* *Based on the situation diagnosis, name the 3 assumption traps most likely to bite me in the next 90 days. Be specific. Generic warnings do not help.* *Be direct. Do not soften your read. If the situation has a clear failure pattern based on what I have told you, name it.* ### **Transparency and Notes** - Built and tested in Claude with Extended thinking enabled. Works in ChatGPT, Microsoft Copilot, and Gemini on free tier. - Model-agnostic. No paid features or file uploads required. - If you are using a work AI tool, you can safely include role and team details. Keep specific names or proprietary information general unless your company has approved the tool for that level of confidentiality. - This is a planning prompt, not legal, HR, or financial advice. Validate the briefing against people who know the actual organization before acting on it. - Pairs naturally with Vol. 29 (Teach Your AI Who You Are). That prompt builds your reusable professional context. This one applies it to a specific transition. And when the transition settles and the first annual review arrives, [the Self-Performance Review prompt](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/) turns what you did in those 90 days into outcome evidence. ### Volume 34: The Question You Have Not Asked Yet URL: https://www.mindovermoney.ai/ai-pilot-success-metrics-time-saved-vs-value/ Last updated: 2026-07-13T16:59:29.000Z I have built two AI agents that save me hours every week. They work, they solve real problems, yet they are still the consolation prize. The question I should have been asking the whole time is the same one most companies are quietly avoiding right now. 🧭 **Founder's Corner:** Why time-saved is the wrong success metric for any AI pilot, and the one question that separates the 34% of companies reimagining their business from the 37% changing nothing. 🧠 **AI Education:** A four-day audit a clinical operations coordinator ran on her own Copilot usage, and the single question that turns AI usage into AI value. ✅ **10-Minute Win:** Replace two hours of tab-hopping with a sourced, pressure-tested one-page brief that ends in a specific next step. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **Most U.S. Doctors Are Quietly Using This AI Tool. Few Patients Know About It.**](https://www.nbcnews.com/tech/tech-news/openevidence-ai-doctor-medical-physician-login-app-what-npi-uptodate-rcna341064?ref=mindovermoney.ai) **Summary:** NBC reported on May 13 that OpenEvidence, a clinician-only AI tool, was used by 65% of U.S. doctors across nearly 27 million encounters in April. The company raised $250 million at a $12 billion valuation and is now embedded in Mount Sinai's Epic EHR. **Why it matters:** The AI doctors actually use looks nothing like ChatGPT. The gap between what clinicians rely on and what patients know about is the next regulatory pressure point. #### **2)**[ **Medicare's New Payment Model Is Built for AI, and Most of the Tech World Has No Idea**](https://techcrunch.com/2026/05/12/medicares-new-payment-model-is-built-for-ai-and-most-of-the-tech-world-has-no-idea/?ref=mindovermoney.ai) **Summary:** TechCrunch reported on May 12 that CMS's new ACCESS model, going live July 5, is the first federal payment mechanism that reimburses AI agents for monitoring patients between visits and coordinating care. CMS selected 150 participants including Pair Team, Doctronic, Whoop, and Aledade. **Why it matters:** Medicare just opened a $900 billion reimbursement lane for AI care, and private insurers typically follow within 18 to 24 months. This turns "AI agent" from buzzword into billable service. #### **3)**[ **Google Confirms Gemini Intelligence for Android, Unveils Googlebooks and Aluminium OS**](https://www.androidauthority.com/what-to-expect-from-google-io-2026-3664979/?ref=mindovermoney.ai) **Summary:** Google pre-released The Android Show on May 12 ahead of I/O 2026, confirming Gemini Intelligence as an agentic AI layer for Android with Chrome auto-browse, smart form-filling, and context-aware Android Auto. The company also announced Googlebooks, a premium laptop category running Aluminium OS, shipping from Acer, ASUS, Dell, HP, and Lenovo this fall. **Why it matters:** The phone is becoming an agent, not just a device. AI fluency moves from "how do I use ChatGPT" to "what is my phone doing on my behalf right now," which is a much harder governance and privacy question for IT teams. #### **4)**[ **The More Operational AI Becomes, the Bigger the Security Challenge**](https://aibusiness.com/generative-ai/the-more-operational-ai-becomes-bigger-security-challenge?ref=mindovermoney.ai) **Summary:** AI Business reported on May 15 that AI is becoming both a cybersecurity tool and a cybersecurity threat as autonomous, interconnected systems create new attack surfaces. OpenAI launched Daybreak, a vulnerability protection initiative joined by Cisco, CrowdStrike, and Cloudflare, while Google Cloud and OpenAI are hiring deployment engineers and standing up consulting arms to help enterprises operate AI safely. **Why it matters:** The hard problem is no longer building AI systems, it is deploying and securing them at enterprise scale. Organizations are adopting AI faster than they can train employees to use it, and traditional security tools were not designed for systems that act on their own across workflows. #### **5)**[ **How AI Is Turning UnitedHealth, CVS and Elevance Into Software Companies**](https://www.beckershospitalreview.com/healthcare-information-technology/ai/how-ai-is-turning-unitedhealth-cvs-and-elevance-into-software-companies/?ref=mindovermoney.ai) **Summary:** Becker's reported on May 14 that UnitedHealth, CVS, and Elevance are turning AI from internal efficiency tools into external software revenue streams. UnitedHealth's $1.5 billion AI spend includes a one-third allocation to becoming an "AI-first software and services firm," Elevance's Health OS has cut denials by 70%, and CVS launched Health100 with Google Cloud. **Why it matters:** The line between payer and software vendor is collapsing. For anyone in payer operations or specialty pharmacy, the question is whether these platforms ultimately serve patients or just add new revenue extraction to an already complex market. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****Automation Is the Consolation Prize** I built two agents to save time. They work and solve real problems. They are also the consolation prize I am about to warn you about. The [first agent](https://www.mindovermoney.ai/founders-corner/ai-workflow-system-vs-agent-what-actually-works/) I built turns a rough idea into a working draft of Founder's Corner, work that used to consume an entire day. Every morning, the [second agent](https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-agent-without-coding/) reads news sources and emails me what matters before my first meeting of the day. Real hours saved every week. But neither helps me grow subscribers or open new revenue streams. The trap I am in is the same trap most companies are in, just at a smaller scale. Boards are approving AI budgets, CFOs are tying success to cost reduction targets, and almost nobody is asking the question that decides whether any of it pays off. Automation is the consolation prize; the real game is using AI to redesign the business model itself. ## **The Question I Was Not Asking** I am guilty of this. The agents I built were doing their job and I was treating that as progress. The realization arrived while listening to an episode of The AI Daily Brief, "How the Best Companies Use AI." The host, Nathaniel Whittemore, drew a throughline about leading companies treating AI as a growth and opportunity technology rather than a time-savings one. His framing reset how I was thinking about my own builds. The question that landed in my head, the one I had not been asking, was specific: "Now that I am freeing up time each week, what can I build to help me grow subscribers?" Right behind it came an admission I had been avoiding: "LinkedIn posts will never be enough long term." The agents saved me time, but neither one is moving the metric that decides whether Neural Gains Weekly survives the long term. I was stuck automating tasks but not thinking about how AI could redesign the work itself. The mistake does not change with scale. If you are inside a company that doubled its AI budget on efficiency pilots, without building the long-term AI strategy, you are in the same trap. Just with bigger ramifications. ## **When the Easier Path Becomes the Only Path** Building AI to automate tasks and cut costs is a good thing. It is the easier path to ROI, the easier place to experiment, and the easier story to put in front of a board. That makes it a logical entry point. But it cannot be the whole strategy. This default mode has decades of muscle memory behind it. Every portfolio review skews toward what saves money this year, every business case anchored to cost reduction. BCG's AI Radar 2026 found corporations expect to more than double their AI investment in 2026, from 0.8% of revenues to about 1.7%, with more than 90% committing to keep investing even if returns do not arrive next year. The same survey found nearly three-quarters of CEOs now name themselves their organization's main AI decision-maker, twice last year's share. The deeper number is what the spend is going toward. Deloitte's State of AI in the Enterprise 2026 split the field into three roughly equal groups: one-third (34%) are using AI to deeply transform their business, launching new products, services, or operating models; another third (30%) are redesigning key processes; and the remaining third (37%) are using AI at the surface level, with little or no change to what already exists. Deloitte's framing is direct: "only the first group is truly reimagining their businesses rather than optimizing what already exists." Daron Acemoglu, the Nobel laureate at MIT, named the same trap in MIT Sloan Management Review: "organizations are choosing to use AI as automation technology when, in reality, it is a form of information technology. This explains why AI isn't improving productivity at a macroeconomic level." Every company needs to ask where it falls in those three groups today, and what the next decade costs the ones outside the 34%. ## **The Second Pilot Costs More** Picture the typical scenario. Twelve months into the AI rollout, the first major pilot lands on the leadership review as a clean win. A high-volume workflow has been automated across three teams. Cycle time is down 22 percent. The case study writes itself, and the next round of funding is approved before the meeting ends. Then come the second-order costs nobody scheduled. The data feeding the agent was incomplete and downstream systems could not handle the volume. Compliance required an audit trail nobody had scoped. Integration with the existing ticketing system ran three months past plan. And the workflow itself, the one the agent was bolted onto, was never the right workflow to begin with. You paid once to automate it. You will pay again to rebuild it. That is the tax you pay twice. BCG put the gap in dollar terms in their 2026 study on AI value capture: "when no explicit value logic has been defined, 10%–20% of anticipated value typically erodes before reaching the P&L." That erosion is not a model accuracy problem. It is the cost of bolting a new operating layer onto an old workflow that was never going to support it. ## **Inside the 34%** The leaders in this AI decade are using AI to build what was not possible before. They are creating revenue streams the old infrastructure could not support, customer experiences the old workflow could not deliver, and business models that simply did not exist. In their 2025 Global AI Jobs Barometer, PwC found industries most exposed to AI grew revenue per employee three times faster than the least-exposed industries, 27 percent compared to 9 percent. PwC put it directly, "Treat AI as a growth strategy, not just an efficiency strategy." BCG's September 2025 report on AI leaders and laggards found leaders delivered double the revenue growth and 40 percent more cost savings. The leaders won on both at the same time. Automation got them to the starting line but did not move them past it. Tempus AI is an example of what this looks like right now. The company uses AI to turn clinical and genomic data into personalized treatment recommendations for oncology and cardiology. They built the business model around AI from the start instead of bolting it onto a legacy diagnostics company. Q1 2026 revenue grew 36.1 percent year over year to $348.1 million, with 2026 guidance raised to $1.59 to $1.60 billion. In the ALERT trial with Medtronic, Tempus's AI-driven EHR notifications surfaced patients with significant disease who could benefit from heart valve replacement. The result was a 40 percent increase in those life-saving procedures. New revenue and new clinical impact, from the same redesign. Tempus is the verifiable case, but the principle is universal. The companies treating AI as the foundation of new business create advantages cost-cutting cannot reach. Every quarter spent layering instead of building is a quarter the gap to the 34% widens. ## **Find a Pilot That Redesigns** If your AI pilot's success metric is "time saved" or "cost reduced," you are building the consolation prize. If the success metric is "new revenue," "new customer experience," or "new business model," you are playing the long game. That is the litmus test. Apply it to every AI initiative on your roadmap. The ones that pass are the work that pays back. The ones that fail are still useful, still necessary, still worth doing. You do not need to wait for the next portfolio meeting. Find one pilot inside your company where the success metric is something other than time saved or cost reduced. If you cannot find one, propose one. If you cannot propose one, build a small version yourself, as I am rebuilding mine for Neural Gains Weekly. The next decade waits on the other side of the question you have not asked yet. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 6: A Professional's Framework for Getting Value From Copilot at Work** #### **The Situation** Dana, our clinical operations coordinator, has been using Microsoft 365 Copilot every day for weeks now. She knows the apps. She has tried Researcher and Analyst. She drafts in Word, summarizes in Outlook, prepares for meetings in Teams. By every measure she would have used six weeks ago, she is a Copilot user. There is one question she cannot answer when her director asks her about it on a Tuesday morning. Is Copilot actually saving her time, or is she just clicking the icon more often? #### **What She Tries First (And Why It Falls Short)** Dana does what most professionals do when asked that question. She thinks about her gut sense of how the week went. She has a vague impression that Copilot helps. She would not be using it this much if it did not, right? She tells her director that she thinks it is saving her a few hours a week. The honest answer is that she does not know. Her impression is built on a handful of memorable wins (the Friday afternoon when Copilot summarized a 14-reply email thread in 30 seconds), a handful of memorable losses (the Monday she rewrote half a Copilot draft and lost an hour), and a lot of usage she cannot remember at all. The wins feel bigger than the losses because the wins ended the task and the losses extended it. Memory is a bad measurement system. By Wednesday she decides to do something her director did not ask for. She is going to audit her own usage for the rest of the week. #### **The Concept, Through the Scenario** For the next four days, Dana tracks every Copilot interaction in a simple text file. What she used. What she asked. What she got back. How long the whole interaction took, including the time she spent reviewing, editing, or verifying. By Friday she has 23 entries. When she sorts them, three categories emerge. **Time Saver.** Tasks where Copilot reliably cut time and the verification cost was low. The biggest entries are the ones from Vol 31\. Dana asked Copilot in Outlook to "summarize the key decisions and any open questions from this email thread" for a Tuesday morning thread of 14 replies between finance, IT, and her director about a contract renewal. The summary came back in 30 seconds as four clear bullets. She skimmed it, recognized the names, and acted on it. Net time saved: roughly 12 minutes. The verification cost was almost zero because she would have skimmed the thread anyway. **Coin Flip.** Tasks where the output was inconsistent and verification took real focus. Wednesday morning, Dana asked Copilot in Word to "draft the executive summary section of the quarterly compliance report using the audit findings spreadsheet I just attached." The draft was 80 percent there. The 20 percent that was off was the part that mattered most: the wording around two open audit items where legal framing matters in compliance reporting. She rewrote those two paragraphs by hand. Net time saved: maybe 8 minutes. The verification took her full attention because she had to know exactly where the draft was unreliable. **Net Loss.** Tasks where Copilot created more work than it saved. Thursday afternoon, Dana asked the Researcher agent (Vol 33) to "compile a competitive overview of three telehealth vendors my team is evaluating, focused on EHR integration capabilities." The report came back structured and confident with citations. When she opened the citations, two of the three "top tier" sources were vendor-sponsored content marketing dressed up as independent analysis. She spent more time verifying and discarding sources than she would have spent running the search herself. Net time lost: about 40 minutes. Three buckets. One question. Before she invokes Copilot for any task going forward, Dana asks herself a single thing. *What is the verification cost?* If the verification cost is low (she would have read the email or skimmed the transcript anyway), Copilot almost always earns its place. If the verification cost is high (she has to fact-check citations, audit data structure, hand-edit specific paragraphs where precision matters), the time saved on the front end has to clearly exceed the time spent verifying on the back end. When it does not, the honest move is to stop using Copilot for that workflow and do the task without it. #### **What Changes** The following week, Dana applies the framework in real time. Monday morning, she opens her inbox and sees a 16-message thread about Friday's provider scheduling conflict. Bucket 1\. She clicks Copilot, gets a clean summary, acts on it. Ninety seconds. Tuesday afternoon she has the next compliance report to draft. Instead of asking Copilot to generate the whole thing, she uses it to draft the routine sections summarizing standard audit findings (Bucket 1 for those passages), and she writes the legal-framing paragraphs herself. The report takes 90 minutes instead of three hours, and the parts that matter most are her own words. Thursday she has vendor research to do for a different evaluation. She does not open Researcher. She runs the search the old way, in a regular browser, with the sources she trusts. The work takes the same amount of time it would have taken before Copilot existed, but she trusts the output and does not have to chase citations to confirm what is real. By Friday Dana has not used Copilot less than the previous week. She has used it differently. The difference shows up in her actual finish time on Friday, not in how many times she clicked the icon. #### **What This Reveals** Usage is a bad proxy for value. Some of the highest-usage Copilot users in any organization are spending more time verifying outputs than they save on the work itself. They feel productive because the screen is busy. The audit Dana ran is what separates feeling productive from being productive. The framework she built is portable. The buckets and the verification cost question work for Claude, for ChatGPT, for Gemini, for Perplexity, and for whatever lands next. The names of the tools change. The audit does not. Most professionals will go their whole careers using AI tools without ever running this audit on themselves, and they will not know what they are leaving on the table or what they are quietly losing. Your admin configuration shapes which features you have access to in Copilot specifically, and that shapes which buckets your usage falls into. Some of what Dana experienced will look different in your tenant. The framework still applies. The specifics will be yours. This is the accountability the series closes on. Microsoft is not responsible for getting value from Copilot in your role. Your IT team is not responsible. Your company's adoption metrics are not responsible. You are. The tool does not know your workflows. You do. The question of whether it is earning its place in any specific task is one only you can answer, and the only way to answer it is to do the work of looking. The audit is the work. #### **How This Connects** This volume closes the Copilot Deep Dive series. Vol 29 introduced the ecosystem. Vol 30 explained the architecture: Microsoft Graph, grounding, permissions. Vol 31 walked through Outlook and Teams. Vol 32 covered the production apps honestly. Vol 33 explored the agentic layer: Researcher, Analyst, the in-app agentic capabilities, and Agent Builder. This volume gives you the framework to turn all of that into something measurable. The bigger arc the curriculum is building toward is this: the tools you have access to will keep changing. The mental models you build around them are what compound. Knowing how to audit a tool, sort your usage, and ask the verification cost question is the durable skill. Copilot is just where you practiced it. *Part 6 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Research Rabbit Hole Stopper** Most of us research the same way: a Google search, 12 open tabs, a Reddit thread, and two hours later we still cannot answer the original question. This workflow uses Perplexity, the one free AI tool purpose-built for source-cited web retrieval, to deliver a 3-source synthesis with a recommended next step. Healthcare decisions, financial choices, and big purchases get easier when you stop reading and start synthesizing. ### **The Workflow** **1\. Frame the Real Question (1 Minute)** Open Perplexity (perplexity.ai). No login required for basic use, free account unlocks more. Take 60 seconds to sharpen your research question before typing. "Should I get a colonoscopy at 45 or wait until 50" is a real question. "Colonoscopy info" is a rabbit hole. Write down the actual decision you are trying to make or the actual thing you are trying to understand. **2\. Run the Grounded Retrieval (3 Minutes)** Paste the prompt below into Perplexity. The structure forces it to do the synthesis work, not just dump links. **Copy/Paste Prompt:** *"I am trying to answer the following question or make the following decision: \[insert your question or decision\].* *Search the web and give me a structured answer with these parts:* 1. *The short answer (2 to 3 sentences) directly addressing my question.* 2. *Three of the most credible and recent sources you found, with the publication name and date for each. Prioritize peer-reviewed research, established institutions, or recognized expert sources over blogs and forums.* 3. *The key points each source makes, in 2 to 3 bullets per source.* 4. *Where the sources agree and where they disagree.* 5. *What I am still missing or what would change the answer based on my specific situation.* *Use only sources from the past 2 years unless older research is foundational to the topic."* **3\. Pressure-Test the Synthesis (3 Minutes)** Read the output. Then send the follow-up below to stress-test what came back. **Copy/Paste Prompt:** *"Now play devil's advocate. Find me 1 to 2 credible sources that push back on or complicate the answer above. Look specifically for: research showing different conclusions, expert voices who disagree, recent updates that change the picture, or context that makes this question harder than it first appears. List the source, the counterpoint, and what it means for my decision."* This is where most AI research workflows stop too early. The pushback step is what turns a one-sided summary into actual decision-grade information. **4\. Generate the One-Page Brief and Next Step (3 Minutes)** Now lock the research into something you can act on or share. **Copy/Paste Prompt:** *"Take everything above and turn it into a one-page brief I can save or share. Use this structure:* 1. *Question I was trying to answer.* 2. *Best current answer based on the synthesis (3 sentences).* 3. *Top 3 sources with publication and date.* 4. *Where the experts disagree.* 5. *My recommended next step (a specific action, conversation, or follow-up question to take this forward).* *Format clean, no fluff, ready to copy into a Google Doc or send to someone helping me think this through."* Copy the brief into a Google Doc, your Notes app, or paste it into a message to the person you are deciding with (a doctor, a financial advisor, a partner). You went from open tabs to a referenced one-pager with a next step in 10 minutes. ### **The Payoff** You just replaced two hours of tab-hopping with a sourced, pressure-tested, one-page brief that ends in a specific next step. More importantly, you used the one AI workflow that actually rewards research questions: grounded retrieval with cited sources, then adversarial review. That is how decisions get made with information, not against it. ### **🧠 The AI Concept You Just Used** **Grounded retrieval and source citation.** When an AI tool pulls from live web sources and cites them inline, you can verify what it said and follow the trail back to the original. That is the difference between an AI giving you an answer and an AI showing you the evidence. Always check the sources, especially on medical, legal, or financial questions. ### **Transparency & Notes** - **Tools used:** Perplexity (perplexity.ai), free tier. Web retrieval and inline source citation are the core mechanic, so Perplexity is the recommended tool here. Microsoft Copilot, ChatGPT with search, and Gemini with search can run a version of this workflow, but Perplexity's free tier is purpose-built for cited research. - **Privacy:** Keep your question general enough to protect personal details. For medical research, you do not need to include your name, exact age, or specific identifiers in the prompt. The tool gives you better answers when you stay focused on the decision, not your full profile. ### Automation Is the Consolation Prize URL: https://www.mindovermoney.ai/founders-corner/ai-automation-vs-business-transformation-strategy/ Last updated: 2026-07-13T16:59:30.000Z I built two agents to save time. They work and solve real problems. They are also the consolation prize I am about to warn you about. The [first agent](https://www.mindovermoney.ai/founders-corner/ai-workflow-system-vs-agent-what-actually-works/) I built turns a rough idea into a working draft of Founder's Corner, work that used to consume an entire day. Every morning, the [second agent](https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-agent-without-coding/) reads news sources and emails me what matters before my first meeting of the day. Real hours saved every week. But neither helps me grow subscribers or open new revenue streams. The trap I am in is the same trap most companies are in, just at a smaller scale. Boards are approving AI budgets, CFOs are tying success to cost reduction targets, and almost nobody is asking the question that decides whether any of it pays off. Automation is the consolation prize; the real game is using AI to redesign the business model itself. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Question I Was Not Asking** I am guilty of this. The agents I built were doing their job and I was treating that as progress. The realization arrived while listening to an episode of The AI Daily Brief, "How the Best Companies Use AI." The host, Nathaniel Whittemore, drew a throughline about leading companies treating AI as a growth and opportunity technology rather than a time-savings one. His framing reset how I was thinking about my own builds. The question that landed in my head, the one I had not been asking, was specific: "Now that I am freeing up time each week, what can I build to help me grow subscribers?" Right behind it came an admission I had been avoiding: "LinkedIn posts will never be enough long term." The agents saved me time, but neither one is moving the metric that decides whether Neural Gains Weekly survives the long term. I was stuck automating tasks but not thinking about how AI could redesign the work itself. The mistake does not change with scale. If you are inside a company that doubled its AI budget on efficiency pilots, without building the long-term AI strategy, you are in the same trap. Just with bigger ramifications. ## **When the Easier Path Becomes the Only Path** Building AI to automate tasks and cut costs is a good thing. It is the easier path to ROI, the easier place to experiment, and the easier story to put in front of a board. That makes it a logical entry point. But it cannot be the whole strategy. This default mode has decades of muscle memory behind it. Every portfolio review skews toward what saves money this year, every business case anchored to cost reduction. BCG's AI Radar 2026 found corporations expect to more than double their AI investment in 2026, from 0.8% of revenues to about 1.7%, with more than 90% committing to keep investing even if returns do not arrive next year. The same survey found nearly three-quarters of CEOs now name themselves their organization's main AI decision-maker, twice last year's share. The deeper number is what the spend is going toward. Deloitte's State of AI in the Enterprise 2026 split the field into three roughly equal groups: one-third (34%) are using AI to deeply transform their business, launching new products, services, or operating models; another third (30%) are redesigning key processes; and the remaining third (37%) are using AI at the surface level, with little or no change to what already exists. Deloitte's framing is direct: "only the first group is truly reimagining their businesses rather than optimizing what already exists." Daron Acemoglu, the Nobel laureate at MIT, named the same trap in MIT Sloan Management Review: "organizations are choosing to use AI as automation technology when, in reality, it is a form of information technology. This explains why AI isn't improving productivity at a macroeconomic level." Every company needs to ask where it falls in those three groups today, and what the next decade costs the ones outside the 34%. ## **The Second Pilot Costs More** Picture the typical scenario. Twelve months into the AI rollout, the first major pilot lands on the leadership review as a clean win. A high-volume workflow has been automated across three teams. Cycle time is down 22 percent. The case study writes itself, and the next round of funding is approved before the meeting ends. Then come the second-order costs nobody scheduled. The data feeding the agent was incomplete and downstream systems could not handle the volume. Compliance required an audit trail nobody had scoped. Integration with the existing ticketing system ran three months past plan. And the workflow itself, the one the agent was bolted onto, was never the right workflow to begin with. You paid once to automate it. You will pay again to rebuild it. That is the tax you pay twice. BCG put the gap in dollar terms in their 2026 study on AI value capture: "when no explicit value logic has been defined, 10%–20% of anticipated value typically erodes before reaching the P&L." That erosion is not a model accuracy problem. It is the cost of bolting a new operating layer onto an old workflow that was never going to support it. ## **Inside the 34%** The leaders in this AI decade are using AI to build what was not possible before. They are creating revenue streams the old infrastructure could not support, customer experiences the old workflow could not deliver, and business models that simply did not exist. In their 2025 Global AI Jobs Barometer, PwC found industries most exposed to AI grew revenue per employee three times faster than the least-exposed industries, 27 percent compared to 9 percent. PwC put it directly, "Treat AI as a growth strategy, not just an efficiency strategy." BCG's September 2025 report on AI leaders and laggards found leaders delivered double the revenue growth and 40 percent more cost savings. The leaders won on both at the same time. Automation got them to the starting line but did not move them past it. Tempus AI is an example of what this looks like right now. The company uses AI to turn clinical and genomic data into personalized treatment recommendations for oncology and cardiology. They built the business model around AI from the start instead of bolting it onto a legacy diagnostics company. Q1 2026 revenue grew 36.1 percent year over year to $348.1 million, with 2026 guidance raised to $1.59 to $1.60 billion. In the ALERT trial with Medtronic, Tempus's AI-driven EHR notifications surfaced patients with significant disease who could benefit from heart valve replacement. The result was a 40 percent increase in those life-saving procedures. New revenue and new clinical impact, from the same redesign. Tempus is the verifiable case, but the principle is universal. The companies treating AI as the foundation of new business create advantages cost-cutting cannot reach. Every quarter spent layering instead of building is a quarter the gap to the 34% widens. ## **Find a Pilot That Redesigns** If your AI pilot's success metric is "time saved" or "cost reduced," you are building the consolation prize. If the success metric is "new revenue," "new customer experience," or "new business model," you are playing the long game. That is the litmus test. Apply it to every AI initiative on your roadmap. The ones that pass are the work that pays back. The ones that fail are still useful, still necessary, still worth doing. You do not need to wait for the next portfolio meeting. Find one pilot inside your company where the success metric is something other than time saved or cost reduced. If you cannot find one, propose one. If you cannot propose one, build a small version yourself, as I am rebuilding mine for Neural Gains Weekly. The next decade waits on the other side of the question you have not asked yet. ### Steal My Prompt Vol. 34: The Status Update Compressor URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-weekly-status-update-impact-framework/ Last updated: 2026-07-13T16:59:30.000Z Your weekly status update is the single piece of writing your manager reads about you most often. Yours probably looks like a to-do list. Bullet points starting with "worked on" and "continued to" and "made progress on." Your manager scans it for 60 seconds and walks away knowing exactly nothing about what changed this week. You wrote about effort because writing about impact requires you to know what changed. Most weeks, you do not have a clear answer. That is the actual problem. Status updates are not a writing problem. They are a clarity problem. You cannot translate effort into impact when you have not stopped to figure out what moved. I built this prompt to fix both. The model takes your raw work brain dump and refuses to let you publish another to-do list. It rejects every effort verb, demands the outcome underneath, surfaces risks before your manager finds them another way, and produces a status update that takes your manager 60 seconds to read and gives them everything they actually need. [Vol 33](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/) covered the same skill at the annual review level. This version brings it to the weekly cadence. Different stakes, same underlying mechanic. The status update is where you build the trust that makes every bigger conversation easier. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - The standing weekly status update to your direct manager — the recurring Monday or Friday note that defines how your manager experiences your work week - Biweekly updates for engineering, product, or operations leads where progress is non-linear and effort framing creates the most confusion - The status update before a vacation or PTO where you need to leave a clean handoff and a clear list of what is in flight - Weekly clinical operations updates where regulatory milestones, audit timelines, and patient impact need surfacing without burying them - The pre-1:1 status note your manager wants 30 minutes before your meeting so the time together is spent on the hard stuff - The status update you send after a missed deadline or rough week where the temptation is to hide problems but transparency builds more trust ## **How to Use It** 1. Open Microsoft Copilot, Claude, ChatGPT, or Gemini. 2. Turn on the reasoning model. Think Deeper in Copilot. Extended thinking in Claude. The reasoning model in ChatGPT. Deep Think in Gemini. Reasoning catches more weak framing on the first pass. 3. Paste the prompt below. Fill in the inputs with specificity. "Built the dashboard" is weak. "Shipped the dashboard, leadership now sees daily metrics that previously took three days to assemble" is what the Editor will accept. 4. Read the model's challenges. Resist the urge to soften your inputs. The pushback is doing the work. 5. Send the output to yourself first. Read it on your phone the way your manager will. If it does not survive a 60-second scan, tighten it. ## **The Prompt** *You are my Weekly Status Update Editor. I will fill in the inputs below. Your job is to reject effort framing, demand outcome evidence, surface risks honestly, and produce a status update my manager will actually read.* *REVIEW PROTOCOL:* - *Reject any "worked on," "continued to," "made progress on," "was involved in," or "supported" that is not followed by a specific outcome* - *Reject "ongoing," "in progress," and "on track" unless followed by evidence (a metric, a milestone hit, a decision made)* - *Reject vague to-do list bullets. Each item must show what changed because of the work, not just that work happened.* - *Force me to surface risks early. Managers hate being surprised by problems more than they hate hearing about them.* - *Ask ONE consolidated follow-up message after reviewing all inputs* - *After I respond, produce the FINAL STATUS UPDATE in the exact format below* - *Do not pad. Do not add accomplishments I did not include. Do not soften risks I named.* *MY INPUTS:* *ROLE: \[TITLE, LEVEL, FUNCTION\]* *MANAGER CONTEXT: \[YOUR MANAGER'S ROLE, WHAT THEY CARE ABOUT MOST, HOW DETAILED THEY WANT YOUR UPDATES\]* *WORK BRAIN DUMP: \[EVERYTHING YOU TOUCHED THIS WEEK — MEETINGS, PROJECTS, EMAILS, DECISIONS, PEOPLE, BLOCKERS — DUMP IT ALL\]* *KEY OUTCOMES: \[WHAT ACTUALLY SHIPPED, SHIFTED, RESOLVED, OR LANDED THIS WEEK\]* *DECISIONS YOU MADE: \[WHERE YOU USED JUDGMENT — INCLUDE THE TRADE-OFF YOU CHOSE AND WHY\]* *RISKS AND BLOCKERS: \[WHAT COULD DERAIL SOMETHING, WHAT IS STUCK, WHAT NEEDS TO MOVE\]* *ASKS: \[WHAT YOU NEED FROM YOUR MANAGER THIS WEEK — DECISIONS, UNBLOCKS, INTRODUCTIONS\]* *NEXT WEEK FOCUS: \[THE 2-3 THINGS YOU PLAN TO PRIORITIZE\]* *AFTER MY RESPONSE TO YOUR FOLLOW-UP, OUTPUT THE FINAL UPDATE:* ***WEEKLY STATUS — \[WEEK ENDING DATE\]*** *Headline: \[One sentence. The single most important thing my manager needs to know about this week.\]* *What Moved: \[Three to five bullets. Each starts with the outcome, not the action. Include the metric, milestone, or named result. Effort verbs are banned.\]* *Judgment Calls: \[Decisions I made that required a trade-off. One line each. Show the reasoning, not just the choice. Skip the section if none worth flagging.\]* *Risks to Watch: \[What could derail, what is stuck, what needs to move. Each item names the risk, the impact if it slips, and what I am doing about it. Skip if nothing material.\]* *What I Need From You: \[Specific asks. Each one names the decision needed or the unblock needed. Skip if no asks this week.\]* *Next Week Focus: \[Two to three priorities. One line each. No verbs of effort.\]* ## Transparency and Notes - Works in Microsoft Copilot, Claude, ChatGPT, and Gemini on free tier. No paid features required. - Run this prompt every week and the output gets sharper. The model learns your patterns and so do you. After a month, you will start writing better status updates without the prompt. - If you use Copilot at work, the Researcher agent prompt from Vol 33 can compile your weekly evidence base for this update too. Same agentic compile step, different downstream prompt. - This is the third entry in the Editor series. Vol 32 covered The Presenter's Brief for talk tracks. Vol 33 covered The Self-Performance Review for annual reviews. Future SMPs will cover decision memos, pre-reads, and difficult emails. - For sensitive content, do not paste proprietary project names, confidential metrics, or anything covered by NDA into a tool your organization has not approved. Use roles, functions, and ranges instead. ### Volume 33: Build Smaller Than the Hype URL: https://www.mindovermoney.ai/how-to-build-your-first-ai-agent/ Last updated: 2026-07-13T16:59:30.000Z Every morning, an AI agent I built scans thirteen news sources, summarizes what matters, and lands in my inbox before coffee. Less than ten dollars a month to run. The version of agent most professionals will actually build looks nothing like the headlines. 🧭 **Founder's Corner:** Why the headline-grabbing version of "AI agent" is not the one you should build, and how to build the version that actually gives you time back. 🧠 **AI Education:** How Copilot's agentic layer actually works, and which of its four pieces is the right starting point for the recurring work in your week. ✅ **10-Minute Win:** Audit your LinkedIn profile against where you want to go in 24 months, then rewrite it with a soft-launch post for your network. Let's jump in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **White House Considers AI Vetting Order Modeled on FDA Drug Approval**](https://thehill.com/policy/technology/5866292-white-house-ai-evaluation-process/?ref=mindovermoney.ai) **Summary:** NEC Director Kevin Hassett said on May 7 that the White House is drafting an executive order requiring AI models to clear a federal safety review before public release, modeled on FDA drug approval. The proposal is a response to Anthropic's Mythos cybersecurity model and would task the NSA, the National Cyber Director, and the director of national intelligence with vetting frontier AI. **Why it matters:** A clear break from the administration's 2025 deregulatory posture. The FDA framing alone changes how CISOs and compliance officers should think about which models their organization can adopt next year. #### **2)**[ **OpenAI Releases Healthcare AI Policy Blueprint**](https://www.beckershospitalreview.com/healthcare-information-technology/ai/openai-releases-healthcare-ai-policy-blueprint/?ref=mindovermoney.ai) **Summary:** OpenAI published a healthcare policy blueprint titled "Keeping Patients First" calling for broader patient data portability, expanded clinician use of AI for documentation and summarization, new state-level regulatory sandboxes for testing AI care models, and FDA modernization for AI-enabled medical software. The document includes case studies from AdventHealth and pushes for stronger enforcement of federal information-blocking rules. **Why it matters:** AI companies are no longer just building healthcare tools, they are writing the rules those tools will operate under. Each ask removes a specific constraint between a generalist AI assistant and a patient or clinician using it for care. Read vendor policy proposals as carefully as contracts. #### **3)**[ **Anthropic Signs Deal With SpaceX to Use All of Colossus 1 Data Center Capacity**](https://www.cnbc.com/2026/05/06/anthropic-spacex-data-center-capacity.html?ref=mindovermoney.ai) **Summary:** Anthropic announced on May 6 a deal to use SpaceX's entire Colossus 1 data center in Memphis, gaining 300+ megawatts and 220,000+ Nvidia GPUs within a month. The deal doubles Claude Code rate limits and removes peak-hour restrictions for Pro and Max subscribers. **Why it matters:** Two rivals who openly dislike each other are now sharing infrastructure because nobody can build chips fast enough. Compute is the new oil, and labs securing it across multiple providers are the ones whose tools will keep working when demand spikes. #### **4)**[ **Google, Microsoft, and xAI Will Let the U.S. Government Test Their AI Models Before Launch**](https://www.cnn.com/2026/05/05/tech/microsoft-google-xai-government-test-ai-models?ref=mindovermoney.ai) **Summary:** The Commerce Department's Center for AI Standards and Innovation (CAISI) announced on May 5 that Google, Microsoft, and xAI will share unreleased AI models with the federal government for pre-deployment evaluation. The agreements build on existing CAISI work with OpenAI and Anthropic, which has already produced 40+ model evaluations. **Why it matters:** The soft version of the FDA-style review also being debated this week. The federal government is quietly building the infrastructure of an AI regulatory state, one voluntary agreement at a time. #### **5)**[ **Wall Street Sees 'Changing of the Guard in AI' as Intel, AMD Shares Soar While Nvidia Lags**](https://www.cnbc.com/2026/05/08/wall-street-ai-chip-love-moves-from-nvidia-to-intel-amd-and-micron.html?ref=mindovermoney.ai) **Summary:** CNBC reported on May 8 that the AI hardware trade is broadening fast: AMD and Intel up \~25% this week, Micron up 37%, Corning up 18%. All four have more than doubled this year while Nvidia is only 15% ahead. AMD's CEO Lisa Su raised expected server CPU growth to 35% over the next three to five years, citing AI agents as the demand driver. **Why it matters:** For a year, "the AI trade" basically meant Nvidia. That framing just broke. The AI economy is broadening into memory, CPUs, glass, and networking, and "I'll just buy the big AI name" stopped being a strategy this week. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Agent That Reads the Internet for Me Every Morning** Every morning, before my first sip of coffee, an AI research rundown is waiting in my inbox. Thirteen news sources scanned overnight. The most important signals prioritized, summarized, and personalized to help find topics for the Founder's Corner articles. I built the agent that runs the whole thing, despite never having used a single tool in the tech stack. The version of ["AI agent"](https://www.mindovermoney.ai/how-to-use-ai-projects-mode-save-context-professionals/) dominating headlines is autonomous, multi-step, and sometimes unsettling. The version sitting in my inbox is none of those things. Less than ten dollars a month to run. Hours of manual research given back to me every week. And along the way, it taught me something more useful than anything you will read in the agent hype cycle. Agents live on a spectrum, not inside a headline. #### **The Headline Version Is Not the Only Version** The autonomous, multi-step, headline-grabbing version is real. It writes codebases without supervision, orchestrates workflows across enterprise tools, and operates without human approval at every step. It is also the loudest one. The version most professionals will actually build looks nothing like it. NVIDIA's State of AI Report 2026 found that 44% of companies were either deploying or assessing AI agents in 2025\. Telecommunications led adoption at 48%. Retail and consumer packaged goods followed at 47%. It is clear that momentum around agents is picking up, but that momentum comes with friction. KPMG's Q4 2025 AI Quarterly Pulse Survey shows agent deployment at 26%, down from 42% the previous quarter. KPMG frames this as professionalization, with leaders consolidating to fewer, more rigorous deployments. Look at it as a builder and a different story emerges. Real adoption never moves in a straight line. All of that matters to enterprise leaders building autonomous systems at scale. It does not have to matter to you. The simpler end of the spectrum is ready right now, while your company is still working through governance reviews and integration roadmaps. You can build an agent to help with your everyday work in parallel, without waiting for the enterprise version to arrive. #### **Proof on the Other Side of the Discomfort** I had zero hands-on experience with the tech stack going into the build. Railway. n8n. Postgres. The Anthropic API. RSS feed configuration. OAuth setup. Nothing but an idea and my AI assistant. The problem was specific to me, yet relatable to what many professionals struggle with: not enough time in the day. I needed topics for Founder's Corner every week, and finding them through manual searches and scattered tabs was cumbersome. And there was the risk of missing out on something important or relevant to a theme I had been working through. Healthcare technology news was moving fast, and AI news was moving even faster. I did not have a workflow built to keep up with either, and I was missing important signals that could help shape my next article. I needed a system that scanned trusted sources, surfaced what mattered, and delivered the signals before I had to go looking for them. This was my second agent system. The [first one ](https://www.mindovermoney.ai/founders-corner/ai-workflow-system-vs-agent-what-actually-works/)was a thinking partner I had to engage every week. This one is a quiet worker that delivers before I am awake. The process started the same way most of my AI work starts. I told Claude the problem, then let Claude interview me until the details I had not put into words came out: source list, frequency, output format, what success looked like. From that conversation Claude produced the architecture and an implementation plan, and we started building. The agent itself came together quickly. The infrastructure did not. Permission errors on the database, a trailing slash that broke an OAuth handshake, and environment variables in the wrong service that wiped the deployment every time it redeployed. Each problem was solvable, but none of them were the agent. They were the plumbing the agent needed to exist. That gap, between the agent and its plumbing, is the same gap enterprises are running into at scale. The complexity is not in the agents. It is in the systems they depend on. My build followed the same arc. The first hour was uncomfortable. I did not understand what half the screens were asking me to do. By the second hour the pieces started connecting. By the third, I had something running on its own, doing work I used to do manually. The barrier is not intelligence or technical skill. It is the willingness to sit with discomfort long enough for the pieces to click. Self doubt is the only thing standing between you and an agent that gives you time back. Sitting with the unknown is uncomfortable. It is also where the real progress happens. Every morning at 7 AM, my agent wakes up and gets to work. It pulls the latest articles from thirteen trusted sources in AI and healthcare technology. It filters for anything published in the last twenty-four hours and sends the full set to Claude through the API. Claude reads every article, picks the five to seven topics most relevant to what I write about, and writes a two to three sentence brief on why each one matters. The whole digest lands in my inbox, formatted and ready, before I have brewed a pot of coffee. The spectrum is not just a way to think about agents. It is permission to build something smaller than the hype and still call it real. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/05/data-src-image-7a50660a-3e33-4713-87e0-f4381bbc2c1e.png) **Where the Wave Is Going** The agents most people will build are not the ones making headlines. They are the ones making mornings easier, weeks shorter, and recurring problems disappear. GitHub's Octoverse 2025 report found that nearly 80% of new developers there use GitHub Copilot, an AI coding assistant, within their first week. AI is no longer the advanced tool. It is the default one. The professionals who will benefit most from this shift are the ones who stop waiting for permission to participate. Pick a recurring problem in your week. Pair with AI as your build partner. The version that solves your problem matters more than the one trending on social media. The right agent for you may not be impressive enough to demo. It will be useful enough to keep. Go build it. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 5: Researcher, Analyst, and Agents: Copilot's Agentic Layer** #### **What Is Actually Going On Here** Dana types a question into the Microsoft 365 Copilot app and selects the Researcher agent. She wants a competitive scan of three vendors pitching AI-powered scheduling tools to her health system. She hits send and waits. What happens next is not a single call to a language model. The Researcher agent decomposes her question into sub-questions, plans a sequence of searches across her organization's emails and files plus the public web, evaluates what it finds, decides what is missing, runs more searches, and assembles a structured report with citations she can verify. Several minutes pass. She gets back work that would have taken her hours. #### **The Problem That Made This Necessary** The standard Copilot experience inside Outlook, Word, or Teams is fast and shallow by design. You ask one question, Copilot pulls relevant context, the model generates one response, and you move on. That works for summarizing an email or rewriting a paragraph (Vol 31). It collapses the moment the work requires reasoning across multiple sources, taking intermediate steps, or running actual analysis on real data. Knowledge workers have been filling that gap manually for years. Pull data from a system, paste it into Excel, build a pivot, screenshot it, write a summary, send it. Or open a browser, run six searches, skim ten tabs, take notes, draft a memo. The bottleneck is not the thinking. It is the assembly. Microsoft's design challenge was to build agents that could do the assembly work without losing accuracy or transparency, and to do it inside the same product surface where the rest of the work already lives. The result is the agentic layer covered in this volume. #### **How It Actually Works** Four components define Copilot's agentic layer in May 2026. **Researcher** is an agent built for multi-step research. When you submit a query, Researcher plans a research approach, pulls from your work data (emails, files, meetings, chats you have permission to see) and the public web, and produces a structured report with cited sources. It can ask clarifying questions before it runs. It deliberately takes longer than a standard Copilot chat because it is doing more work. Microsoft has also added support for Anthropic's Claude model in Researcher, which administrators can enable in the admin center. **Analyst** is the data analysis equivalent. Where Researcher pulls and synthesizes information, Analyst takes raw data files (Excel spreadsheets, CSV files, databases) and reasons through them. Ask it a question about your data and it calculates statistics, identifies trends, surfaces outliers, and returns a report with charts and tables. It is built for users who are not data analysts but need data analysis. Both Researcher and Analyst live under the Agents menu in the Microsoft 365 Copilot app and require a Microsoft 365 Copilot license or eligible Premium subscription. Availability of specific features depends on your organization's admin configuration. **Agentic capabilities in Word, Excel, and PowerPoint** are the third piece. Microsoft moved these to general availability on April 22, 2026\. Where Researcher and Analyst run in the chat surface, this layer runs inside your file. Same agentic principle (multi-step actions, taking work directly on the canvas), different deployment. Vol 32 covered the user-facing implications. **Agent Builder** is the fourth piece, and the most accessible one. It lives directly inside the Microsoft 365 Copilot app and is included with a Copilot license. It is the closest functional equivalent in the Microsoft ecosystem to ChatGPT's custom GPTs. You describe in natural language what you want the agent to do, point it at the knowledge sources you want it to use (SharePoint files, public websites, and with a Copilot license, your Teams chats and Outlook emails), and it becomes a reusable agent pinned in your Copilot app. The important constraint: Agent Builder agents are prompt-based, not automated. You build the agent once, then invoke it whenever you need it. It does not run on a schedule. It does not trigger autonomously. It cannot take actions in external systems. Each time you want output, you open the agent and ask it to act. The architecture is the same agent framework covered in Vol 26: instructions, knowledge sources, and a defined workflow. Deployed at personal or small team scale. #### **Where It Still Breaks** Researcher reports look authoritative because of the formatting, headings, and citations. That makes the citation check more important, not less. Sources can be misread, dated, or weighted incorrectly even when the citations themselves are accurate. Microsoft says clearly: always review what the agent generated before acting on it. Analyst is only as good as the data structure underneath it. Messy, unstructured spreadsheets produce messy, unreliable analysis. Time spent cleaning data before invoking the agent saves more time than the agent saves on the analysis itself. Agent Builder is built for the workflows it is designed to handle and not the ones it is not. It holds instructions and knowledge sources, but it does not run multi-step automated workflows or take actions inside external systems. For workflows that require automation, scheduled execution, or external integrations, an organization needs Copilot Studio, which sits with IT and is not deployed at the individual user level. Across all four components, admin configuration matters. Your IT team controls which agents are enabled, which models are available, and which knowledge sources can be connected. If something described here is not visible in your Copilot app, it is most likely an admin setting, not a missing feature. #### **What This Means for How You Work With It** Use Researcher for work that justifies the longer response time, not for questions a fast Copilot prompt would have answered. Match the tool to the task. Use Analyst when the value is in the analysis, not in a quick data summary. Clean your data first. Read the report critically before you act on it. Treat the in-app agentic capabilities in Word, Excel, and PowerPoint as the default mode for any file work that takes more than one round of changes. Single-prompt Copilot is fine for short tasks. If you have a recurring workflow that you handle the same way every week (meeting prep, weekly reporting, vendor research, candidate screening), Agent Builder is where you ask whether a personal agent would replace twenty minutes of manual assembly each time. Build it for yourself. See if you actually use it. Remember that you will still need to invoke the agent each time. The value is consistency, not autonomy. #### **How This Connects** The AI Agents series (Vol 24-27) built the conceptual framework: action loops, reasoning patterns, memory, tools, and multi-agent systems. This volume shows that framework deployed inside a live enterprise product. Researcher is an agent with planning and tool use. Analyst is an agent that reasons over data. The in-app agentic capabilities are agents acting on your file. Agent Builder is the surface where you build your own. Every piece traces back to a concept the curriculum has already covered. Vol 34 closes the Copilot Deep Dive series with a framework for evaluating whether Copilot is delivering value in your specific role. After six volumes of capability, the question becomes: where is this saving you time, where is it creating extra work, and how do you tell the difference? *Part 5 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The LinkedIn Audit** Most LinkedIn profiles look backward at what someone has done instead of forward at what they are building toward. This workflow audits the gap between where your profile says you are and where you want to go, then rewrites with that target in mind, going further than LinkedIn's own AI rewriter. ### **The Workflow** **1\. Gather Your Inputs (1 Minute)** Open Claude, ChatGPT, or Gemini. Have ready: your current headline, your current About section (copy-pasted from your profile), one sentence on where you want to go in the next 12 to 24 months, and 2 to 3 specific wins from your recent work with numbers attached if possible. **2\. Run the Positioning Audit (3 Minutes)** Paste the prompt below. This is the diagnostic step before any rewriting. **Copy/Paste Prompt:** *"You are an expert LinkedIn strategist who has helped hundreds of professionals reposition their profiles for career pivots. I want you to audit my current profile before rewriting anything.* *Here is my information:* *Current headline: \[paste current headline\]* *Current About section: \[paste current About section\]* *Where I want to go in the next 12 to 24 months: \[target role, industry, or positioning\]* *Two or three recent wins with results: \[win 1 with numbers\], \[win 2\], \[win 3\]* *Audit my current profile against where I want to go. Tell me:* 1. *What my current profile is signaling to recruiters and connections (the story it is telling today).* 2. *The three biggest gaps between my current positioning and my target positioning.* 3. *What is missing entirely that would make a recruiter for my target role pay attention.* *Be specific and direct. If something is generic or buzzword-heavy, say so."* **3\. Generate the New Headline and About (4 Minutes)** Read the audit out loud. Then send the follow-up prompt below in the same chat to act on it. **Copy/Paste Prompt:** *"Based on the audit above, rewrite my LinkedIn profile in two parts.* *Part 1: Three headline options. Each under 220 characters, anchored in my target positioning, and front-loading the result or expertise that matters most for where I want to go. No buzzwords like 'passionate,' 'driven,' or 'results-oriented.'* *Part 2: A new About section in 4 paragraphs:* 1. *Opening hook: who I am and what I am building toward (2 sentences).* 2. *Core expertise: 3 to 4 sentences connecting my background to my target direction, with one specific proof point.* 3. *What I do best: a tight list of 3 to 5 skill or capability statements in plain language.* 4. *Closing CTA: how someone should reach out and what kinds of conversations I am open to.* *Voice: confident, specific, no hype. Sound like a real person, not a press release."* **4\. Deploy the Rewrite and Soft Launch the Pivot (2 Minutes)** Paste your new headline and About into LinkedIn (Profile, then Edit). Then run the prompt below to generate a casual update post that signals the new direction to your network. **Copy/Paste Prompt:** *"Now write a casual LinkedIn post I can publish this week that softly announces my new direction without sounding like a press release. Format it as 4 to 6 short lines with line breaks between them. Open with a real moment or observation, not a humble brag. End with one question that invites comments. Under 150 words. No hashtags in the body, only at the very end. Voice: like I am talking to a colleague over coffee, not pitching to a stage."* Schedule the post for tomorrow morning. Your profile is now forward-facing, and your network knows where to send opportunities. ### **The Payoff** You just turned a stale profile into a forward-facing positioning statement, with a soft launch post that signals the pivot to your network. More importantly, you used a 3-step workflow (audit, rewrite, announce) instead of a one-shot rewrite prompt. The audit step is what separates a thoughtful repositioning from generic AI buzzword soup. ### **🧠 The AI Concept You Just Used** **Persona-aware rewriting and positioning strategy.** When you give an AI your current state, your target state, and concrete proof points, it can rewrite content with intent toward where you are going, not just polish where you have been. That is what makes AI rewriting useful for career work, not just grammar smoothing. ### **Transparency & Notes** - **Tools used:** Claude (claude.ai), ChatGPT (chatgpt.com), or Gemini (gemini.google.com). All free tier. Microsoft Copilot also handles this workflow if you have been following our Deep Dive series. - **Privacy:** Keep the names of past employers, current colleagues, and anyone tied to your wins generic when prompting. "My company" or "a Fortune 500 client" works better than the actual name. ### The Agent That Reads the Internet for Me Every Morning URL: https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-agent-without-coding/ Last updated: 2026-07-13T16:59:30.000Z Every morning, before my first sip of coffee, an AI research rundown is waiting in my inbox. Thirteen news sources scanned overnight. The most important signals prioritized, summarized, and personalized to help find topics for the Founder's Corner articles. I built the agent that runs the whole thing, despite never having used a single tool in the tech stack. The version of ["AI agent"](https://www.mindovermoney.ai/how-to-use-ai-projects-mode-save-context-professionals/) dominating headlines is autonomous, multi-step, and sometimes unsettling. The version sitting in my inbox is none of those things. Less than ten dollars a month to run. Hours of manual research given back to me every week. And along the way, it taught me something more useful than anything you will read in the agent hype cycle. Agents live on a spectrum, not inside a headline. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. #### **The Headline Version Is Not the Only Version** The autonomous, multi-step, headline-grabbing version is real. It writes codebases without supervision, orchestrates workflows across enterprise tools, and operates without human approval at every step. It is also the loudest one. The version most professionals will actually build looks nothing like it. NVIDIA's State of AI Report 2026 found that 44% of companies were either deploying or assessing AI agents in 2025\. Telecommunications led adoption at 48%. Retail and consumer packaged goods followed at 47%. It is clear that momentum around agents is picking up, but that momentum comes with friction. KPMG's Q4 2025 AI Quarterly Pulse Survey shows agent deployment at 26%, down from 42% the previous quarter. KPMG frames this as professionalization, with leaders consolidating to fewer, more rigorous deployments. Look at it as a builder and a different story emerges. Real adoption never moves in a straight line. All of that matters to enterprise leaders building autonomous systems at scale. It does not have to matter to you. The simpler end of the spectrum is ready right now, while your company is still working through governance reviews and integration roadmaps. You can build an agent to help with your everyday work in parallel, without waiting for the enterprise version to arrive. #### **Proof on the Other Side of the Discomfort** I had zero hands-on experience with the tech stack going into the build. Railway. n8n. Postgres. The Anthropic API. RSS feed configuration. OAuth setup. Nothing but an idea and my AI assistant. The problem was specific to me, yet relatable to what many professionals struggle with: not enough time in the day. I needed topics for Founder's Corner every week, and finding them through manual searches and scattered tabs was cumbersome. And there was the risk of missing out on something important or relevant to a theme I had been working through. Healthcare technology news was moving fast, and AI news was moving even faster. I did not have a workflow built to keep up with either, and I was missing important signals that could help shape my next article. I needed a system that scanned trusted sources, surfaced what mattered, and delivered the signals before I had to go looking for them. This was my second agent system. The [first one ](https://www.mindovermoney.ai/founders-corner/ai-workflow-system-vs-agent-what-actually-works/)was a thinking partner I had to engage every week. This one is a quiet worker that delivers before I am awake. The process started the same way most of my AI work starts. I told Claude the problem, then let Claude interview me until the details I had not put into words came out: source list, frequency, output format, what success looked like. From that conversation Claude produced the architecture and an implementation plan, and we started building. The agent itself came together quickly. The infrastructure did not. Permission errors on the database, a trailing slash that broke an OAuth handshake, and environment variables in the wrong service that wiped the deployment every time it redeployed. Each problem was solvable, but none of them were the agent. They were the plumbing the agent needed to exist. That gap, between the agent and its plumbing, is the same gap enterprises are running into at scale. The complexity is not in the agents. It is in the systems they depend on. My build followed the same arc. The first hour was uncomfortable. I did not understand what half the screens were asking me to do. By the second hour the pieces started connecting. By the third, I had something running on its own, doing work I used to do manually. The barrier is not intelligence or technical skill. It is the willingness to sit with discomfort long enough for the pieces to click. Self doubt is the only thing standing between you and an agent that gives you time back. Sitting with the unknown is uncomfortable. It is also where the real progress happens. Every morning at 7 AM, my agent wakes up and gets to work. It pulls the latest articles from thirteen trusted sources in AI and healthcare technology. It filters for anything published in the last twenty-four hours and sends the full set to Claude through the API. Claude reads every article, picks the five to seven topics most relevant to what I write about, and writes a two to three sentence brief on why each one matters. The whole digest lands in my inbox, formatted and ready, before I have brewed a pot of coffee. The spectrum is not just a way to think about agents. It is permission to build something smaller than the hype and still call it real. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/05/data-src-image-3ad0b39d-a253-4ee2-8594-c084c29908df.png) **Where the Wave Is Going** The agents most people will build are not the ones making headlines. They are the ones making mornings easier, weeks shorter, and recurring problems disappear. GitHub's Octoverse 2025 report found that nearly 80% of new developers there use GitHub Copilot, an AI coding assistant, within their first week. AI is no longer the advanced tool. It is the default one. The professionals who will benefit most from this shift are the ones who stop waiting for permission to participate. Pick a recurring problem in your week. Pair with AI as your build partner. The version that solves your problem matters more than the one trending on social media. The right agent for you may not be impressive enough to demo. It will be useful enough to keep. Go build it. ### Steal My Prompt Vol. 33: The Self-Performance Review URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/ Last updated: 2026-07-18T01:43:08.000Z You open the performance review template. The cursor blinks in the box that says "Key Accomplishments." Three minutes later, you have written "led cross-functional initiatives." You will revise that line twelve times before submitting. None of those revisions will change the fundamental problem. Most professionals undersell themselves in their performance review by writing about effort instead of outcomes. The fix is not better writing. It is better evidence. You wrote about effort because you cannot remember the outcomes, and you cannot remember the outcomes because they happened four months ago and your inbox has buried them. Your company may call this document a self-evaluation, a self-assessment, a self-appraisal, or just the annual review. It is the same document everywhere: the one where you argue your own case, in writing, to the people who decide what happens next in your career. This prompt is built for that document, whatever the header on the template says. I built this prompt to fix both problems in one workflow. The model challenges every weak claim and refuses to accept "led" or "supported" or "drove" without the proof underneath. The output reads like a thoughtful manager wrote it about you, not like you wrote it about yourself. This week's AI Education section walks through Copilot's Researcher agent. The Power-User Workflow at the bottom of this post shows exactly how to point Researcher at your Outlook, Teams, and document library to surface evidence you forgot you had. The agentic layer is at its best when it is doing work you cannot do by memory. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **What You Can Use This For** - The annual or mid-year performance review at work where the document is the only thing leadership reads about you - Promotion conversations where you need to articulate the case for advancement with evidence, not effort - A 1:1 with your manager when you need to advocate for yourself without sounding self-promotional - Clinical or operational leadership roles where multi-month initiatives have impact that fades from memory before it lands on the page - Job interviews where you need to translate scope and impact into specific outcomes a hiring panel can evaluate - Year-end self-reflection or self-assessment where you want an honest, evidence-anchored record of your own work ## **How to Use It** 1. Open Microsoft Copilot, Claude, ChatGPT, or Gemini. 2. Turn on the reasoning model. Think Deeper in Copilot. Extended thinking in Claude. The reasoning model in ChatGPT. Deep Think in Gemini. Reasoning catches more weak claims on the first pass. 3. If you use Copilot at a company that has Researcher, jump to the Power-User Workflow below first to compile your evidence base. Otherwise paste the prompt and fill in the inputs directly. 4. Read the model's challenges. Do not soften your inputs in response. The challenges are surfacing weak language, not asking you to retreat. 5. Read the final review out loud. If a sentence does not sound like something a thoughtful manager would write about you, rewrite it. ## **The Prompt** *You are my Self-Performance Review Editor. I will fill in the inputs below. Your job is to challenge weak language, refuse effort framing, demand outcome evidence, and produce a final self-review that reads like a thoughtful manager wrote it about me.* *REVIEW PROTOCOL:* *\- Reject any "led," "drove," "supported," "championed," or "spearheaded" that is not followed by a specific outcome with numbers, dates, or named results* *\- Reject generic growth areas like "communication," "executive presence," or "stakeholder management" without specific examples* *\- Force me to distinguish between effort (what I did) and impact (what changed because I did it)* *\- Ask ONE consolidated follow-up message after reviewing all my inputs* *\- After I respond, produce the FINAL SELF-PERFORMANCE REVIEW in the exact format below* *\- Do not pad. Do not flatter. Do not add accomplishments I did not include.* *MY INPUTS:* *ROLE: \[TITLE, LEVEL, FUNCTION, TENURE IN THIS ROLE\]* *REVIEW PERIOD: \[START DATE TO END DATE — TYPICALLY 6 OR 12 MONTHS\]* *ROLE EXPECTATIONS: \[WHAT WAS EXPECTED OF YOU IN THIS PERIOD — GOALS, SCOPE, KEY OUTCOMES\]* *ACCOMPLISHMENTS: \[LIST EVERY SPECIFIC THING YOU DID WITH THE OUTCOME — INCLUDE NUMBERS, DATES, NAMED PROJECTS, AND METRICS WHERE POSSIBLE\]* *STRETCH CONTRIBUTIONS: \[WHERE YOU OPERATED ABOVE YOUR LEVEL OR OUTSIDE YOUR FORMAL SCOPE\]* *WHAT GOT IN THE WAY: \[HONEST BLOCKERS — RESOURCING, PRIORITIES, LEADERSHIP CHANGES, WHATEVER WAS REAL\]* *GROWTH AREAS: \[WHERE YOU KNOW YOU NEED TO IMPROVE — BE SPECIFIC\]* *CAREER DIRECTION: \[WHERE YOU ARE TRYING TO GO IN THE NEXT 12-24 MONTHS\]* *AUDIENCE: \[WHO READS THIS REVIEW — DIRECT MANAGER, SKIP-LEVEL, HR, PROMOTION COMMITTEE\]* *\---* *AFTER MY RESPONSE TO YOUR FOLLOW-UP, OUTPUT THE FINAL REVIEW:* *\# SELF-PERFORMANCE REVIEW* *\## Executive Summary* *\[Two sentences capturing the period. Honest, specific, outcome-anchored.\]* *\## Key Accomplishments* *\[For each: the outcome first, then the action that produced it. Include the metric, date, or named result. Three to five maximum. Choose the ones with the strongest evidence.\]* *\## Stretch Contributions* *\[Where I operated above level or outside scope. Two to three items. Each must show the unprompted nature of the work.\]* *\## Honest Reflection* *\[Two short paragraphs. First: what I learned and where I grew. Second: where I still need to develop, with one specific example, not a generic skill area.\]* *\## Forward Direction* *\[One paragraph. The work I want to lead in the next review period and why I am the right person to lead it.\]* ## **Copilot Power-User Workflow** If your company runs Microsoft 365 Copilot with Researcher available, you have an agentic layer most professionals are not using on the highest-leverage problem of their year. Researcher can crawl your Outlook, Teams, OneDrive, and SharePoint to surface the evidence your performance review needs. The hardest part of writing a self-review is remembering what you did. Researcher solves that. This is a two-step workflow. Step one runs in Copilot Chat with Researcher activated. Step two runs in any AI tool with the main prompt above. **Step 1: Compile the evidence base in Copilot Researcher.** Open Copilot Chat, switch to Researcher, and paste this prompt: *You are my evidence compiler for a performance review covering \[START DATE TO END DATE\]. Search across my Outlook, Teams, OneDrive, and SharePoint for the following:* *1\. KEY MEETINGS: Meetings where I led the agenda, presented to leadership, or made a critical decision. Pull the meeting title, date, attendees, and a one-sentence summary of what I contributed.* *2\. KEY DECISIONS: Emails or chats where I made or influenced a meaningful decision (project direction, vendor selection, headcount, budget, strategic priority). Pull the date, the decision, and any documented outcome.* *3\. AUTHORED OR LED DOCUMENTS: Documents I authored or made substantial contributions to. Pull the title, date, and a one-sentence description of what the document accomplished.* *4\. COMPLETED PROJECTS: Initiatives that closed in this period where I had a leading or supporting role. Pull the project name, my role, and the documented outcome.* *5\. CROSS-FUNCTIONAL WORK: Communications where I worked across teams or functions outside my immediate scope. Pull the team, the topic, and the outcome.* *OUTPUT FORMAT: Group evidence by category. Within each category, list items chronologically. Note any gaps where you could not find evidence (e.g., "no clear meeting summaries available — transcription may not have been enabled"). Do not summarize across categories. Do not infer outcomes that are not documented. If evidence is ambiguous, flag it for me to verify.* **Step 2: Feed the evidence into the main prompt.** Review what Researcher returns. Edit out anything wrong, off-target, or sensitive. Then paste the cleaned dossier into the ACCOMPLISHMENTS, STRETCH CONTRIBUTIONS, and ROLE EXPECTATIONS fields of the main Self-Performance Review prompt above. The agentic step compresses four to six hours of memory archaeology into thirty minutes. **Honest caveats on Researcher.** Researcher only sees what your Microsoft 365 admin permits. Some organizations restrict cross-app search. The output requires your review because Researcher weights surface signal (volume of mentions) over strategic signal (importance). Some of your most valuable contributions live outside Microsoft 365 entirely (verbal contributions in meetings, mentoring conversations, work done in third-party tools), and you will need to add those manually. Researcher's coverage of older content depends on what is still indexed. Six months back is generally fine. Two years is not. ## **Transparency and Notes** - Works in Microsoft Copilot, Claude, ChatGPT, and Gemini on free tier. The Researcher Power-User Workflow requires a corporate Microsoft 365 Copilot license with Researcher available. - The Editor mechanic is intentionally adversarial. It rejects effort framing because effort framing is what kills most self-reviews. Do not soften your inputs to avoid pushback. The pushback is the value. - This prompt is part of the Editor series, which points one adversarial mechanic at different documents: [The Presenter's Brief](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-presentation-talk-track/) for talk tracks, [The Status Update Compressor](https://www.mindovermoney.ai/prompt-library/ai-prompt-weekly-status-update-impact-framework/) for weekly updates, and [The Memo Editor](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-edit-a-decision-memo/) for decision memos. - For sensitive performance review content, do not paste proprietary project names, internal data, or confidential metrics into a tool your organization has not approved. Use roles, functions, and ranges instead of named systems and exact numbers. If you arrived here searching for a self-evaluation generator, this is one — with a difference. A generator fills the box for you. This one argues with you until what is in the box is worth reading. ### Volume 32: Data Is the Work URL: https://www.mindovermoney.ai/ai-data-readiness-the-real-bottleneck/ Last updated: 2026-07-13T16:59:31.000Z ServiceNow lost 18% of its value in a single trading session last month. Workday slid 9%. Salesforce, Adobe, Oracle, and HubSpot all dropped 6 to 9% in the same window. The headlines blamed AI fears. The deeper story is a moat reset, and what just hit software is coming for everything else. 🧭 **Founder's Corner:** Why the SaaS reset is a preview, not an outlier, and the one question to ask before any AI workflow that decides whether your investment will pay off. 🧠 **AI Education:** What Copilot can actually do in Word, Excel, and PowerPoint, where it falls short, and the one mode shift most users have not noticed yet. ✅ **10-Minute Win:** Snap a photo of your fridge and turn it into three real recipes, a cook timeline, and a shopping list someone else can act on. Let's get into it. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **An AI Model Beat Doctors at Diagnosing Patients, in a New Study**](https://www.npr.org/2026/04/30/nx-s1-5804474/ai-doctors-openai-patient-care-diagnosis?ref=mindovermoney.ai) **Summary:** A peer-reviewed study published in Science on April 30 found that an OpenAI reasoning model outperformed two experienced physicians at diagnosing patients across the messy, real-world data of an emergency department. Researchers from Harvard Medical School and Beth Israel Deaconess Medical Center evaluated the model at three points in care, from triage to admission, and found it consistently produced more accurate diagnoses and management decisions than the physicians using the same information. **Why it matters:** This is one of the first rigorous head-to-head studies showing an AI model performing better than experienced doctors in a real ER setting, not on textbook cases. The authors are explicit that this is not about replacing physicians, but about what gets possible when AI is treated as a second opinion that catches what a tired clinician might miss. Expect this study to come up in every healthcare AI conversation for the rest of 2026. #### **2)**[ **Copilot in Outlook: New Agentic Experiences for Email and Calendar**](https://techcommunity.microsoft.com/blog/outlook/copilot-in-outlook-new-agentic-experiences-for-email-and-calendar/4514601?ref=mindovermoney.ai) **Summary:** Microsoft announced on April 27 that Copilot in Outlook is shifting from a sidebar that helps with the task in front of you to an agent that runs your inbox and calendar continuously. The new capabilities triage emails, draft follow-ups for messages that have not received replies, set inbox rules, resolve calendar conflicts by rescheduling 1:1s, block focus time, and prepare meeting agendas. The features are rolling out through the Microsoft 365 Copilot Frontier program starting April 27\. **Why it matters:** This is the part of agentic AI that finally touches a daily reality most professionals share: an overflowing inbox and a fragmented calendar. If your day is built on Outlook, this is the first version of Copilot that promises to handle the work between meetings, not just inside them. The trade-off worth thinking about is governance: an AI that reschedules your meetings and drafts follow-ups on your behalf is also one that needs clear guardrails on tone, approvals, and what it can do without you in the loop. #### **3)**[ **US Department of Labor Launches Website to Build AI Skills, Expand AI-Focused Registered Apprenticeship Programs**](https://www.dol.gov/newsroom/releases/eta/eta20260429?ref=mindovermoney.ai) **Summary:** The U.S. Department of Labor announced on April 29 the launch of an AI in Registered Apprenticeship Innovation Portal, a centralized resource designed to help organizations build AI literacy and create AI-focused apprenticeship programs. The portal builds on the department's AI Literacy Framework released earlier this year and organizes training resources around three areas: AI skills and literacy, industry-specific skill building (including healthcare and finance), and three integration paths for new or existing apprenticeship programs. **Why it matters:** This is one of the clearest federal signals yet that AI fluency is being treated as workforce infrastructure, not optional upskilling. If you lead a team, manage hiring, or run any kind of training program, the resources are now publicly available and government-backed. For non-technical professionals, it also reframes the conversation: AI literacy is becoming a credentialed, structured skill that employers can recognize, not just a personal hobby project. #### **4)**[ **OpenAI, Nvidia, Alphabet and More Sign AI Deal With Pentagon for Classified Military Use**](https://www.forbes.com/sites/tylerroush/2026/05/01/openai-nvidia-alphabet-and-more-sign-ai-deal-with-pentagon-for-classified-military-use/?ref=mindovermoney.ai) **Summary:** The Department of Defense announced on May 1 that it has signed agreements with seven leading AI and cloud companies, including OpenAI, Google, Microsoft, Amazon Web Services, Nvidia, SpaceX, and the startup Reflection, to deploy their AI tools on classified military networks, with Oracle added later that day. The deals embed frontier AI into day-to-day Pentagon operations and require an "all lawful use" provision; Anthropic was notably absent and is currently challenging a separate Pentagon designation as a supply chain risk in court. **Why it matters:** AI is now national infrastructure, not just productivity software. The choices these labs make about what their models will and will not do, and how those choices line up with U.S. defense requirements, are now driving real commercial consequences. For leaders in regulated industries like healthcare and finance, this is also a preview of how government procurement is starting to shape which AI tools your organization will be allowed to use, even far outside defense. #### **5)**[ **OpenAI Models, Codex, and Managed Agents Come to AWS**](https://openai.com/index/openai-on-aws/?ref=mindovermoney.ai) **Summary:** OpenAI and AWS announced on April 28 that OpenAI's frontier models, including GPT-5.5, its Codex coding agent, and a new Amazon Bedrock Managed Agents service powered by OpenAI are now available in limited preview on Amazon Bedrock. The launch came one day after OpenAI restructured its Microsoft partnership to remove cloud exclusivity, and brings OpenAI alongside Anthropic, Meta, Mistral, and Cohere on AWS's enterprise AI platform. **Why it matters:** For years, picking OpenAI meant picking Microsoft Azure. That assumption is gone. If your enterprise runs on AWS, you can now use the same OpenAI models inside the security and compliance environment your team already trusts. The bigger signal is that the AI lab ecosystem and the cloud ecosystem are decoupling, which means model choice and infrastructure choice will increasingly be separate procurement decisions for IT and security teams. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The One Moat AI Cannot Erode** The competitive landscape has shifted, and the prevailing narrative missed the mark. On April 23, 2026, ServiceNow lost 18% of its value in a single trading session. Worst day in the company's history. The cascade hit fast: Workday slid 9%, and Salesforce, HubSpot, Adobe, Intuit, and Oracle all dropped 6 to 9% in the same window. The headlines blamed AI fears. The deeper story is a moat reset, and the first sign just arrived. ### **The Warning Came for SaaS First** Per-seat pricing helped build the modern software industry. AI is now breaking that model, and the market is adjusting in real time. What used to take years is now happening in a matter of weeks. Press reports estimated roughly $285 billion in software market capitalization vanished in a 48-hour window in February 2026, after Anthropic launched Claude Cowork. Traders at Jefferies named the event the "SaaSpocalypse" before the market closed. Workday disclosed a 2% workforce reduction in February 2026, in the same week the SaaSpocalypse hit. The company recorded a $135 million restructuring charge in the same fiscal quarter. Even Atlassian, which beat Q3 earnings on April 30, 2026, traded 87% below its 2021 peak earlier in the month. One company is already trying to get ahead of the shift. On its Q4 earnings call, Salesforce introduced Agentic Work Units, a new metric tracking AI-completed tasks rather than seats. That was a quiet but important signal: per-seat pricing has no long-term future. IDC projects that by 2028, 70% of software vendors will move away from per-seat licensing. That matters because one of the strongest moats in enterprise software just got reset in roughly twelve weeks. Per-seat subscriptions, switching costs, network effects, all of it looked durable. But if a moat that took twenty years to build can be eroded that quickly, no industry should assume it is protected. ### **What Benioff and Huang Agree On** This moment forces an uncomfortable question: what were these moats really made of? Two of the most powerful leaders in tech have publicly disagreed on whether SaaS is dying, and that disagreement helps clarify the answer. Marc Benioff used "SaaSpocalypse" at least six times on Salesforce's Q4 earnings call in late February 2026, per TechCrunch. His view was simple. The deeper your enterprise data is embedded in a system of record, the harder it is to rip out. Jensen Huang argued the opposite on CNBC that same month, saying the markets "got it wrong." His point was that agents will not replace the tools. Instead, they will use the tools. But underneath the disagreement, they are saying the same thing. The real defensibility is not in the interface, the pricing model, or even the workflow. It is in the data and the systems of record underneath it. Everything else is negotiable. The data layer is not. ### **This Is Not a Software Story** What just happened in software is going to happen everywhere else, often before the headlines name it. AI-driven customer service is already eroding service advantages that took years to build. Content libraries and domain authority, once the bedrock of marketing and SEO, are losing ground to AI that produces qualified content at near-zero cost. Product velocity gaps that once took a decade to open are now closing in months as AI-native companies catch up. Klarna is one of the clearest public examples. The company moved aggressively in 2024 to replace customer service with AI, then pulled parts of that strategy back in 2025 when service quality dropped. CEO Sebastian Siemiatkowski told Bloomberg that cost had become "too predominant" a factor, producing lower-quality service. The lesson is not that AI failed. The lesson is that the workflow was not ready to be replaced all at once. The companies that come out ahead will know the difference between a workflow AI can handle today and one that still needs rebuilding before AI can perform it well. ### **The Moat Most Companies Skip** Most companies are not behind on AI investment. They are behind on the data work that makes AI investment pay off. McKinsey's recent productivity paradox analysis, citing their late-2025 State of AI survey, found that nearly nine out of ten companies had deployed AI in at least one business function by the end of 2025, but 94% reported no significant value from those investments. Gartner projects that 60% of AI initiatives unsupported by AI-ready data will be abandoned through 2026\. The bottleneck is not the technology. It is the data underneath the technology. These were not bad ideas. They were good ideas built on weak foundations. Legacy workflows are often running on infrastructure that cannot support real redesign. Most companies are constrained by the dataset they already have. I have made that mistake myself, moving slower than I should have and trying to force creative solutions into outdated systems. One project has stayed with me in particular. My team and I were working on a healthcare affordability workflow. For us, patient affordability was never abstract. It is a real problem with real consequences. People do not fill prescriptions they cannot afford, and that reality has a way of sharpening your focus. We saw an opportunity to redesign the workflow. There was a vendor platform involved, API connections, data requirements, and operational changes. But somewhere in the middle of the project, it became evident that we were not building a workflow. We were identifying the most important pieces of data and using them to power one. The vendor platform mattered. The API mattered. But those were not the real story. The real story was what happened once clean data started going in and clean data started coming out. Everything accelerated. Operational teams had access to the data earlier. The workflow shifted from reactive to proactive. An internal automation tool worked better, not because the automation had changed, but because the data was finally ready to flow through it. All possible through data, not magic. That has shaped every AI workflow I have built since. Clean data does not just unlock new AI capabilities. It increases the value of investments a company has already made. That is the compounding effect many boardrooms still miss. And it points to a simple rule. Workflow redesign should start with understanding how the data moves today. Not how the documentation says it moves. Not how the system was originally designed. How it actually moves, with all the friction included. Receipts before claims. ### **Healthcare Is Sitting on a Moat It Has Not Built Yet** Healthcare may be the clearest example of this pattern anywhere. Pharma, payers, pharmacies, and health systems sit on decades of regulated, proprietary data, the kind that new AI-native entrants cannot replicate or buy on the open market. The asset is real, but the readiness is not. McKinsey's April 2026 healthcare survey found that half of US healthcare organizations have implemented gen AI, up from 25% two years ago. The barrier is no longer adoption. It is execution. The same survey ranked difficulty integrating tools into existing workflows as the top scaling roadblock, cited by 59% of healthcare leaders. The data readiness divide we have been tracking across industries is the same divide separating healthcare AI experiments from healthcare AI value. In an [earlier piece](https://www.mindovermoney.ai/founders-corner/claude-for-healthcare-ai-infrastructure-specialty-pharmacy/), I argued that the moat in healthcare is not the proprietary silo. It is the ability to connect to the broader ecosystem. Internal readiness is the prerequisite for external participation. Get the order wrong and you build connections to nothing. ### **The Move on Monday Morning** You do not need to be a data architect to respond to this moment. You need to ask one question before implementing any AI workflow: How does the data move today? That question works across industries. It works in regulated environments and unregulated ones. It works for technical teams and non-technical leaders alike. The people who ask that question first will execute faster. The companies that ask it first will still be standing when this moat reset reaches industries that still believe they are insulated. The reset has already started in software. It is coming for everything else. The work is already sitting on your desk. The companies that do it now will define the next decade in their industries. The ones that wait will be left explaining themselves on the next earnings call. The data work is the work. Everything else is the outcome. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 4: Copilot in Word, Excel, and PowerPoint** #### **The Assumption** Most professionals walk into Word, Excel, and PowerPoint with a reasonable expectation. If Copilot can draft a meeting summary in Outlook from a thirty-minute call (Vol 31), it should be able to produce a polished compliance memo, build a working budget pivot, or generate a board-ready deck from a single prompt. The marketing demos reinforce this. So does the speed of Copilot in apps like Outlook and Teams, where the output often lands close to ready. Reasonable people hear "AI assistant in your apps" and conclude the apps where the heaviest production work happens should benefit the most. #### **Where It Breaks Down** Dana, our clinical operations coordinator, tests that assumption on a Wednesday afternoon. She has a quarterly compliance review presentation due Friday. She opens PowerPoint, pulls up the Copilot pane, and types: "Create a 12-slide presentation summarizing this quarter's compliance findings, the three open audit items, and the remediation plan." The output is structured but generic. The slides have her topic but not her data. The visuals are stock. The sequencing is off. She moves to Excel to build a pivot showing audit completion rates by clinic site. The Copilot button is greyed out. Her file is on her desktop, not OneDrive. After moving it and toggling AutoSave on, Copilot wakes up but produces a pivot grouped on the wrong column because her data is not formatted as a structured table. She fixes that. The pivot now works, but the formatting still does not match her director's preferred layout. In Word, the experience is closer to what she expected. Copilot drafts a strong first version of the executive summary in under a minute. But when she asks it to revise three specific paragraphs, the changes spill into surrounding sections she did not want touched. #### **What Is Actually Happening** The three production apps are not one product. They are three different surfaces with three different design philosophies, and Copilot behaves differently in each. In Word, Copilot is closest to what most users imagine. It drafts long-form content, restructures sections, applies tone, and handles rewrites. Word was the first of the three apps to get full agentic editing in November 2025, which means Copilot can make multi-step, app-native changes directly inside the document while you watch. It still has trouble with surgical edits that do not bleed into adjacent paragraphs, which is why review before accepting changes is non-negotiable. In Excel, Copilot is gated by infrastructure. The file must be saved to OneDrive or SharePoint with AutoSave on. Files on local drives produce a greyed-out button. Data must be formatted as a structured table (Ctrl+T) with clear column headers, no merged cells, and no blank rows. If you meet those conditions, Copilot generates formulas, builds pivot tables, surfaces patterns, and explains existing logic. If you do not, the output is unreliable or the tool refuses to engage. Excel also cannot write or run VBA macros. In PowerPoint, Copilot is best understood as a scaffolding tool. It produces a structured first draft from a prompt or from a Word document, but it has historically struggled with iterative refinement, has a limit on how much context you can feed in a single prompt, and is weak on transitions, animations, and complex slide elements like SmartArt and embedded tables. To honor your company's branding, your IT team has to publish approved templates to the SharePoint Organization Asset Library. The behavior that ties all three together is Edit with Copilot (formerly called Agent Mode), which Microsoft moved to general availability across Word, Excel, and PowerPoint on April 22, 2026\. This is the part most users have not yet noticed. Edit with Copilot shifts the interaction from "ask once, accept the output" to "watch Copilot work in your file iteratively, see what it changed, and refine." It is the most important behavioral change in this series so far. Five days later, Microsoft previewed agentic Copilot experiences in Outlook for email triage, follow-ups, and calendar management through the Frontier program. The direction is unmistakable: Copilot is becoming a coordinated agent that does ongoing work across your apps, not a single-prompt assistant you trigger and dismiss. #### **The Revised Mental Model** Copilot in the production apps is a draft accelerator, not a finished-output generator. Use it to skip the blank page. Bring your judgment to the last thirty to forty percent. Three behavior changes follow from this. First, treat the first output as a starting position, not an answer. Plan to spend real time refining it, especially in PowerPoint where the gap between draft and presentable is the widest. Second, prepare your inputs before you prompt. In Excel, this means formatting your data as a table and saving to OneDrive before you click Copilot. In PowerPoint, this means starting from a Word document with strong structure (proper headings, clear sections) when the deck has to convey something specific. Third, use Edit with Copilot for any task that requires more than one round of changes. The iterative mode preserves your file, shows you what changed, and lets you steer the work in a way the single-prompt mode cannot. The shortest version of this: Copilot drafts. You finish. #### **What to Watch For** - A polished prompt does not produce a polished slide deck. PowerPoint output almost always needs human editing for sequencing, visuals, and brand consistency. - An Excel file on your local drive will not work with Copilot. If the button is greyed out, the file is the reason. - Pivot table results are only as good as the table structure underneath them. Time spent cleaning data before prompting saves more time than Copilot saves on the analysis itself. - Word edits can affect more than the section you targeted. Read surrounding paragraphs after every revision before accepting changes. - Edit with Copilot is the right mode for multi-step work in any of the three apps. Single-prompt mode is fine for short tasks. For anything substantive, switch modes. #### **How This Connects** Vol 29 introduced the Copilot ecosystem and the grounding behavior that makes it useful. Vol 30 explained the architecture underneath: the orchestrator, Microsoft Graph, the semantic index, and how permissions shape every response. Vol 31 showed the high-leverage applications in Outlook and Teams. This volume covers the apps where the gap between expectation and output is largest, and where understanding the limits matters most. Vol 33 moves into the agentic layer that now runs across the entire suite. Researcher, Analyst, Edit with Copilot, and Copilot Studio all sit on top of the foundation we have just built. That is where the AI Agents series (Vol 24-27) becomes a live capability inside an enterprise product, not a concept on a roadmap. *Part 4 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Recipe Remix** Vision-based AI just unlocked one of the most useful kitchen moves you can make. Snap one photo of your fridge or pantry, and get three real recipes built around exactly what is on your shelves, plus a cook timeline and a short shopping list for whatever you are missing. No manual ingredient entry. No more guessing what to make with what you already own. ### **The Workflow** **1\. Snap the Photo (1 Minute)** Open your fridge or pantry, pull anything tucked to the back forward, and take one clear, well-lit photo. Capture as much of your produce, proteins, condiments, and pantry staples as you can fit in frame. If you have a lot, take two photos and upload both. **2\. Get Three Recipe Options (3 Minutes)** Open ChatGPT (chatgpt.com) or Gemini (gemini.google.com) on your phone or laptop. Both free tiers support image upload. Upload your photo and paste the prompt below. **Copy/Paste Prompt:** *"You are a creative home cook who specializes in turning random ingredients into real meals. I am attaching a photo of what I currently have in my fridge and pantry.* *Identify the ingredients you can see in the photo. Then give me three different dinner recipes I can make using mostly what I have. For each recipe:* 1. *Name the dish and the cuisine style.* 2. *List the ingredients I already have, then list any 1 to 3 items I would need to buy.* 3. *Give me cook time and a difficulty level (easy, medium, harder).* *Pick three recipes that are different from each other in cuisine and cooking method. Aim for things I could actually make on a weeknight, not chef-level showpieces."* **3\. Pick One and Get the Cook Timeline (3 Minutes)** Read all three options. Pick the one that fits your time and energy tonight. Send the follow-up prompt below to turn it into a step-by-step cook timeline that handles parallel tasks (chopping while something else simmers). **Copy/Paste Prompt:** *"I want to make recipe \[insert recipe name\] tonight. Give me a complete cook timeline in the order I should do things, with timing for each step and what I should be doing in parallel. Format it as a numbered list with clock-style time markers like 0:00, 0:05, 0:15\. Include prep, cooking, and final assembly. Aim for 30 minutes or less from start to plated. Call out any step where I might burn or overcook something if I walk away."* **4\. Generate the Missing-Items List and Send It (3 Minutes)** Now lock the missing groceries into something you can actually shop with. **Copy/Paste Prompt:** *"Based on the recipe above, write a clean shopping list of only the items I need to buy. Format it in three groups: produce, proteins or dairy, and pantry. Add quantities. Then write a one-line text I can send my partner or roommate that says what we are having for dinner and what to grab on the way home. Casual tone, under 25 words."* Copy the shopping list to your phone Notes app or paste it into a text to whoever does the grocery run. Dinner is decided, the cook plan is on your screen, and the missing items are in someone's hand. ### **The Payoff** You just turned a vague "I have stuff but no plan" into a chosen recipe, a minute-by-minute cook timeline, and a shopping list someone can actually act on. More importantly, you used your fridge as the input instead of a recipe search bar, which is the single biggest unlock vision-capable AI offers personal cooks. ### **🧠 The AI Concept You Just Used** **Multimodal input.** When you upload a photo and ask the model to reason from what it sees, you are using vision capability, one of the most underrated features of modern free-tier AI. The model is not just reading text. It is identifying objects, interpreting context, and feeding that interpretation into the same reasoning engine that handles your written prompts. ### ***Transparency & Notes*** - **Tools used:* ChatGPT (chatgpt.com) free tier with vision, or Gemini (gemini.google.com) free tier with vision. Both handle image upload. Microsoft Copilot also runs this workflow if you have been following our Deep Dive series.* - **Privacy:* Crop or angle your photo to keep identifying details out of frame (medication labels, mail on the counter, family photos on the fridge). The model only needs to see the food.* ### The One Moat AI Cannot Erode URL: https://www.mindovermoney.ai/founders-corner/ai-ready-data-why-most-ai-investments-fail/ Last updated: 2026-07-13T16:59:31.000Z The competitive landscape has shifted, and the prevailing narrative missed the mark. On April 23, 2026, ServiceNow lost 18% of its value in a single trading session. Worst day in the company's history. The cascade hit fast: Workday slid 9%, and Salesforce, HubSpot, Adobe, Intuit, and Oracle all dropped 6 to 9% in the same window. The headlines blamed AI fears. The deeper story is a moat reset, and the first sign just arrived. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ### **The Warning Came for SaaS First** Per-seat pricing helped build the modern software industry. AI is now breaking that model, and the market is adjusting in real time. What used to take years is now happening in a matter of weeks. Press reports estimated roughly $285 billion in software market capitalization vanished in a 48-hour window in February 2026, after Anthropic launched Claude Cowork. Traders at Jefferies named the event the "SaaSpocalypse" before the market closed. Workday disclosed a 2% workforce reduction in February 2026, in the same week the SaaSpocalypse hit. The company recorded a $135 million restructuring charge in the same fiscal quarter. Even Atlassian, which beat Q3 earnings on April 30, 2026, traded 87% below its 2021 peak earlier in the month. One company is already trying to get ahead of the shift. On its Q4 earnings call, Salesforce introduced Agentic Work Units, a new metric tracking AI-completed tasks rather than seats. That was a quiet but important signal: per-seat pricing has no long-term future. IDC projects that by 2028, 70% of software vendors will move away from per-seat licensing. That matters because one of the strongest moats in enterprise software just got reset in roughly twelve weeks. Per-seat subscriptions, switching costs, network effects, all of it looked durable. But if a moat that took twenty years to build can be eroded that quickly, no industry should assume it is protected. ### **What Benioff and Huang Agree On** This moment forces an uncomfortable question: what were these moats really made of? Two of the most powerful leaders in tech have publicly disagreed on whether SaaS is dying, and that disagreement helps clarify the answer. Marc Benioff used "SaaSpocalypse" at least six times on Salesforce's Q4 earnings call in late February 2026, per TechCrunch. His view was simple. The deeper your enterprise data is embedded in a system of record, the harder it is to rip out. Jensen Huang argued the opposite on CNBC that same month, saying the markets "got it wrong." His point was that agents will not replace the tools. Instead, they will use the tools. But underneath the disagreement, they are saying the same thing. The real defensibility is not in the interface, the pricing model, or even the workflow. It is in the data and the systems of record underneath it. Everything else is negotiable. The data layer is not. ### **This Is Not a Software Story** What just happened in software is going to happen everywhere else, often before the headlines name it. AI-driven customer service is already eroding service advantages that took years to build. Content libraries and domain authority, once the bedrock of marketing and SEO, are losing ground to AI that produces qualified content at near-zero cost. Product velocity gaps that once took a decade to open are now closing in months as AI-native companies catch up. Klarna is one of the clearest public examples. The company moved aggressively in 2024 to replace customer service with AI, then pulled parts of that strategy back in 2025 when service quality dropped. CEO Sebastian Siemiatkowski told Bloomberg that cost had become "too predominant" a factor, producing lower-quality service. The lesson is not that AI failed. The lesson is that the workflow was not ready to be replaced all at once. The companies that come out ahead will know the difference between a workflow AI can handle today and one that still needs rebuilding before AI can perform it well. ### **The Moat Most Companies Skip** Most companies are not behind on AI investment. They are behind on the data work that makes AI investment pay off. McKinsey's recent productivity paradox analysis, citing their late-2025 State of AI survey, found that nearly nine out of ten companies had deployed AI in at least one business function by the end of 2025, but 94% reported no significant value from those investments. Gartner projects that 60% of AI initiatives unsupported by AI-ready data will be abandoned through 2026\. The bottleneck is not the technology. It is the data underneath the technology. These were not bad ideas. They were good ideas built on weak foundations. Legacy workflows are often running on infrastructure that cannot support real redesign. Most companies are constrained by the dataset they already have. I have made that mistake myself, moving slower than I should have and trying to force creative solutions into outdated systems. One project has stayed with me in particular. My team and I were working on a healthcare affordability workflow. For us, patient affordability was never abstract. It is a real problem with real consequences. People do not fill prescriptions they cannot afford, and that reality has a way of sharpening your focus. We saw an opportunity to redesign the workflow. There was a vendor platform involved, API connections, data requirements, and operational changes. But somewhere in the middle of the project, it became evident that we were not building a workflow. We were identifying the most important pieces of data and using them to power one. The vendor platform mattered. The API mattered. But those were not the real story. The real story was what happened once clean data started going in and clean data started coming out. Everything accelerated. Operational teams had access to the data earlier. The workflow shifted from reactive to proactive. An internal automation tool worked better, not because the automation had changed, but because the data was finally ready to flow through it. All possible through data, not magic. That has shaped every AI workflow I have built since. Clean data does not just unlock new AI capabilities. It increases the value of investments a company has already made. That is the compounding effect many boardrooms still miss. And it points to a simple rule. Workflow redesign should start with understanding how the data moves today. Not how the documentation says it moves. Not how the system was originally designed. How it actually moves, with all the friction included. Receipts before claims. ### **Healthcare Is Sitting on a Moat It Has Not Built Yet** Healthcare may be the clearest example of this pattern anywhere. Pharma, payers, pharmacies, and health systems sit on decades of regulated, proprietary data, the kind that new AI-native entrants cannot replicate or buy on the open market. The asset is real, but the readiness is not. McKinsey's April 2026 healthcare survey found that half of US healthcare organizations have implemented gen AI, up from 25% two years ago. The barrier is no longer adoption. It is execution. The same survey ranked difficulty integrating tools into existing workflows as the top scaling roadblock, cited by 59% of healthcare leaders. The data readiness divide we have been tracking across industries is the same divide separating healthcare AI experiments from healthcare AI value. In an [earlier piece](https://www.mindovermoney.ai/founders-corner/claude-for-healthcare-ai-infrastructure-specialty-pharmacy/), I argued that the moat in healthcare is not the proprietary silo. It is the ability to connect to the broader ecosystem. Internal readiness is the prerequisite for external participation. Get the order wrong and you build connections to nothing. ### **The Move on Monday Morning** You do not need to be a data architect to respond to this moment. You need to ask one question before implementing any AI workflow: How does the data move today? That question works across industries. It works in regulated environments and unregulated ones. It works for technical teams and non-technical leaders alike. The people who ask that question first will execute faster. The companies that ask it first will still be standing when this moat reset reaches industries that still believe they are insulated. The reset has already started in software. It is coming for everything else. The work is already sitting on your desk. The companies that do it now will define the next decade in their industries. The ones that wait will be left explaining themselves on the next earnings call. The data work is the work. Everything else is the outcome. ### Steal My Prompt Vol. 32: The Presenter's Brief URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-presentation-talk-track/ Last updated: 2026-07-18T01:46:14.000Z The deck is built. The meeting is tomorrow. You stare at slide three and realize you have no idea what you are actually going to say. Most professionals build the deck and treat the talk track as something they will figure out in the moment. The slides become the prop, but the words that connect them are improvised under pressure. That is where presentations break. Not on the design. On the delivery. This prompt does not generate slides. It builds the talk track that goes between them. You bring the strategic decisions every presenter has to make (who is in the room, what action you need from them, what they already know, where they will push back). The model pressure-tests those decisions and produces a slide-by-slide narrative you can practice and deliver. This week's AI Education section walks through what Copilot cannot do well in Word, Excel, and PowerPoint. Slide generation tops that list. Narrative structure does not. The Presenter's Brief works in the territory where AI is genuinely strong. ## **What You Can Use This For** - Executive review decks where the audience is senior, time is short, and you need a specific decision in the room - Conference talks or external speaking where credibility is on the line and the audience is unfamiliar - Project pitches where you are asking for budget, headcount, or buy-in on a strategic initiative - Clinical or operational reviews for hospital leadership where regulatory context shapes every recommendation - Internal training sessions where the audience starts cold and you need them to act on the content afterward - Vendor briefings where you need the buyer to leave with a specific next step, not just impressed ## **How to Use It** 1. Open Microsoft Copilot, Claude, ChatGPT, or Gemini. 2. Turn on the reasoning model. Think Deeper in Copilot. Extended thinking in Claude. The reasoning model in ChatGPT. Deep Think in Gemini. Reasoning catches more strategic gaps in the brief on the first pass. 3. Paste the prompt below. Fill in each input field with as much specificity as you can. 4. Read the model's challenges and answer the consolidated follow-up question. Do not skip this. Vague inputs produce vague talk tracks. 5. When the slide-by-slide narrative comes back, read it out loud once before you edit a word. The places you stumble are the places that need rewriting. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **The Prompt** *You are my Presenter's Brief Editor. I am going to fill in the brief below. Your job is to challenge my answers, point out weak spots, and produce a slide-by-slide talk track I can practice and deliver.* *REVIEW PROTOCOL:* *\- For each section I fill in, push back if my answer is vague, generic, or missing strategic intent* *\- Generic answers like "executives" or "give an update" or "be informed" are not acceptable. Push for specificity.* *\- Ask me ONE consolidated follow-up message after reviewing all inputs* *\- After I respond, produce the FINAL TALK TRACK in the exact format below* *\- Do not write speaker notes for slides I have not described. Ask if you need slide content.* *MY INPUTS:* *STRATEGIC INTENT: \[WHAT ACTION OR DECISION DO YOU WANT FROM THIS AUDIENCE BY THE END\]* *AUDIENCE: \[WHO IS IN THE ROOM — ROLES, SENIORITY, WHAT THEY CARE ABOUT MOST\]* *CORE MESSAGE: \[THE ONE SENTENCE THEY MUST REMEMBER WALKING OUT\]* *AUDIENCE BASELINE: \[WHAT THIS AUDIENCE ALREADY KNOWS — DO NOT RE-TEACH\]* *EXPECTED PUSHBACK: \[WHO WILL PUSH BACK, ON WHAT, AND WHY\]* *SENSITIVITIES: \[POLITICAL DYNAMICS, TOPICS TO SOFT-PEDAL, RELATIONSHIPS TO PROTECT\]* *DELIVERY CONSTRAINTS: \[TIME LIMIT, ROOM SIZE, FORMAT — STANDING / SEATED / VIRTUAL\]* *SLIDE CONTENT: \[PASTE THE TITLE AND KEY POINTS FOR EACH SLIDE IN ORDER\]* *\---* *AFTER MY RESPONSE TO YOUR FOLLOW-UP, OUTPUT THE FINAL TALK TRACK:* *\# TALK TRACK* *\## Opening (First 60 Seconds)* *\[Exact words for how to open. Earns attention before introducing the topic.\]* *\## Slide-by-Slide Narrative* *For each slide:* *\- \*\*Slide \[X\]: \[Slide Title\]\*\** *\- Transition in: \[How to move from the prior slide to this one\]* *\- Talking points: \[3-5 sentences I can actually say out loud, not bullet points\]* *\- Anticipated question: \[What someone in the room is most likely to ask here, and a one-sentence response\]* *\## Pushback Preparation* *\[For each expected pushback source, the strongest version of their critique and a one-sentence response that does not sound defensive\]* *\## Close (Final 90 Seconds)* *\[Exact words to close with. Restates the core message and names the specific action requested.\]* ### Transparency and Notes - Works in Microsoft Copilot, Claude, ChatGPT, and Gemini on free tier. No paid features required. - Read the talk track out loud before editing it. Words that look fine on a page sometimes fall apart in your mouth. Find those first. - This prompt was the first in the Editor series. [The Self-Performance Review](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-write-self-performance-review/) takes the same mechanic to your annual review self-evaluation, [the Status Update Compressor](https://www.mindovermoney.ai/prompt-library/ai-prompt-weekly-status-update-impact-framework/) to the weekly update, and [the Memo Editor](https://www.mindovermoney.ai/prompt-library/ai-prompt-to-edit-a-decision-memo/) to the decision memo. - For sensitive organizational content, keep your inputs general (roles and functions, not proprietary project names or internal data). Use a tool with strong privacy practices for any work-related context. ### Volume 31: Lead From Where You Sit URL: https://www.mindovermoney.ai/how-to-lead-ai-at-work-without-a-title/ Last updated: 2026-07-13T16:59:32.000Z 88% of organizations are experimenting with AI right now. Only 14% have a leader consistently championing the work. The lane to lead is wide open, and stepping into it has nothing to do with the title on your badge. 🧭 **Founder's Corner:** Why leadership, not strategy or tech, decides which companies capture real AI value, and the three traits anyone can build from any seat. 🧠 **AI Education:** Six specific moments in your week where Copilot in Outlook and Teams quietly removes time you did not realize you were losing. ✅ **10-Minute Win:** Turn your contract leverage into a word-for-word client negotiation script, then pressure-test it against a tough client before you pick up the phone. Let's dive in. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** #### **1)**[ **AI May Be Approaching a New Phase in Healthcare, on Two Fronts**](https://www.healthcareitnews.com/news/ai-may-be-approaching-new-phase-healthcare-two-fronts?ref=mindovermoney.ai) **Summary:** Healthcare IT News covered a webinar where practicing physicians demonstrated using Claude Code to build their own clinical workflow tools without dedicated engineering teams, including a post-visit summary tool and a HIPAA compliance audit skill. The doctors emphasized that production deployments still require an engineering review before touching live patient data. **Why it matters:** This is the "from user to builder" shift in motion. Practicing clinicians are no longer waiting for vendors to solve their workflow problems. If you are a non-technical professional in healthcare or an adjacent industry, the question is no longer whether you can build with AI, it is what you would build first and how you would do it safely. #### **2)**[ **Copilot's Agentic Capabilities in Word, Excel, and PowerPoint Are Generally Available**](https://www.microsoft.com/en-us/microsoft-365/blog/2026/04/22/copilots-agentic-capabilities-in-word-excel-and-powerpoint-are-generally-available/?ref=mindovermoney.ai) **Summary:** Microsoft made Copilot's agentic capabilities generally available across Word, Excel, and PowerPoint, allowing Copilot to take multi-step actions directly inside documents, spreadsheets, and presentations rather than just suggesting what to do. Microsoft reported early engagement gains across all three apps, including a 67% increase in Excel use per week, and the features are available across Microsoft 365 Copilot, Premium, Personal, and Family plans. **Why it matters:** Copilot just shifted from a sidebar that gives advice to a coworker that does the work, in the apps most professionals already live in every day. If you use Word, Excel, or PowerPoint at work, this is the most direct path to feeling AI in your daily workflow without learning a new tool. For healthcare and finance teams, the new question is not "should we use Copilot," but "what guardrails do we put around it now that it can act, not just suggest?" #### **3)**[ **OpenAI Launches ChatGPT for Clinicians, a Free AI Tool for Physicians, NPs and Pharmacists**](https://www.fiercehealthcare.com/ai-and-machine-learning/openai-launches-chatgpt-clinicians-free-ai-tool-physicians-nps-and?ref=mindovermoney.ai) **Summary:** OpenAI launched a free version of ChatGPT built for verified U.S. physicians, nurse practitioners, PAs, and pharmacists, with HIPAA-ready Business Associate Agreements available for accounts handling protected health information. The tool includes documentation support, a clinical search engine across peer-reviewed sources, and reusable workflow skills for tasks like referrals and prior authorization. **Why it matters:** Free, purpose-built AI for clinicians at this scale is new. If you work in or around healthcare, expect to see this tool show up in clinical workflows fast. It also raises a question worth tracking: when AI is free for the people making clinical decisions, what changes about how care is delivered and documented? #### **4)**[ **Introducing ChatGPT Images 2.0**](https://openai.com/index/introducing-chatgpt-images-2-0/?ref=mindovermoney.ai) **Summary:** OpenAI launched ChatGPT Images 2.0 on April 21, 2026, its first image model with native reasoning that can plan, web-search, and double-check its own outputs before generating up to eight coherent images from a single prompt. It supports 2K resolution, dramatically improved text rendering across non-Latin scripts like Japanese, Hindi, and Bengali, and is rolling out to all ChatGPT plans, with "thinking" features available on paid tiers. **Why it matters:** Image generation is moving from "looks cool" to "ready to use." If you build content, marketing assets, infographics, or training materials, this is the upgrade that turns AI image tools into real production tools, not just inspiration tools. For non-designers, the gap between an idea in your head and a usable visual just got dramatically smaller. #### **5)**[ **State Legislatures Consider Oversight of Artificial Intelligence in Health Insurance Decisions**](https://natlawreview.com/article/state-legislatures-consider-oversight-artificial-intelligence-health-insurance?ref=mindovermoney.ai) **Summary:** Alabama Gov. Kay Ivey signed SB 63 into law on April 17, 2026, regulating how health insurers can use AI in coverage decisions. The law requires a licensed health care professional to make the final call on any denial, mandates prominent written disclosure when AI is used in utilization review, and sets accuracy and reliability standards for the AI tools insurers rely on. **Why it matters:** Health insurance AI decisions are one of the most consequential ways AI shows up in everyday life, and Alabama just joined a small but growing list of states putting human review at the center of denials. If you work in healthcare, payer operations, or specialty pharmacy, this state-by-state patchwork is now a core compliance reality, not a future scenario. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****You Do Not Need a Title to Lead AI at Work** The headlines on AI feel like a different planet than what most companies actually look like inside. According to McKinsey's State of Organizations 2026, 88% of organizations are now experimenting with AI. The same report found that 81% of them report no meaningful bottom-line impact. That gap is not small. That gap is the entire story. There are real reasons why a disconnect exists between the AI-hype cycle and AI success within organizations. Integration challenges, infrastructure gaps, short-term thinking, data readiness problems, and pilots that never scale. Each of those is a legitimate barrier, with many Fortune 500 companies struggling with some combination of those realities. But every one of those threads loops back to a single underlying foundation that decides whether a company actually crosses the gap. Leadership. I am not stating that strategy and technology do not matter. They do. I am arguing that strategy and technology are downstream of who is leading the work, how they are leading it, and what they are willing to challenge. Leadership is the foundation to be part of the 19%. A real AI leader has to balance the demands of today while building for tomorrow, and that work cannot be done the way leadership has been done in the past. ## **Today's Winners Are Not Guaranteed Tomorrow** The pace of AI is not slowing down, and the leadership readiness gap is widening with it. According to Stanford's 2026 AI Index Report, organizational AI adoption reached 88% and generative AI hit 53% population adoption within three years, faster than the PC or the internet at the same stage. McKinsey was even more direct in their 2025 workplace research, concluding that employees are ready for AI and the biggest barrier to success is leadership. The barriers we just listed are real, but leadership is the variable that decides which barriers a company actually clears. Take integration. According to McKinsey's Healthcare Practice, 50% of US healthcare organizations have implemented gen AI, and 59% of them cite difficulty integrating AI into existing workflows as the number one barrier to scaling. That is a workflow problem on the surface. Underneath, it is a leadership problem. Workflows do not redesign themselves. People do, and someone has to give them permission, time, and a different definition of success while they do it. The companies pulling ahead right now are not winning because they are bigger or better resourced. They are winning because someone inside the building decided that AI fluency was a mandate, not a side project. McKinsey documented one bank where senior leaders became the most visible AI users in the company, modeling daily use as an evaluated behavior. That single leadership decision drove $150 million in incremental revenue. That decision does not live within a strategy deck. It is the mindset that becomes the backbone of great AI leadership, and the window to lead with it is open right now. ## **The Most Valuable Mistake I Made With AI** When I started building Neural Gains Weekly, I thought confidence was a substitute for education. I was green to coding and on a self-imposed time crunch to get the site live. The result was that I took too many AI recommendations at face value because I did not know enough to push back. I built a site that worked, but it was generic and missed the basic workflows to convert visitors into subscribers. Subscribers stopped coming in. I was still proud of what I had built, but the site reflected the AI novice I was when I started. I had grown faster than the platform, and the site had not grown with me. The first move was not a better tool. It was a better posture. I stopped trying to figure it out alone and started learning from people further along on social media and on podcasts. I built specific habits I did not have before, including pushback mechanisms when AI suggested a direction and the ability to spot a fading context window before it produced weaker output. The most recent expression of that posture is a Council I now run before any major strategic decision, including SEO. The Council is structured pushback at scale, and it exists because I learned the cost of going without it. What I really learned was humbling. No matter how much effort I put in, I am not going to learn everything about AI. The pace will not let me. The lesson was to ride the learning wave instead of chasing it, and commit to getting a little better every day. According to McKinsey's State of AI report, the single strongest predictor of enterprise-level AI value is whether the organization fundamentally redesigned its workflows when deploying AI. Workflow redesign is the corporate version of what I had to do at the kitchen table. At my scale, the cost was a few months of rework. At enterprise scale, the same mistake pattern costs time, money, and competitive position. ## **The Leaders Pulling Ahead Have Three Things in Common** If we accept that leadership is the foundation, the question becomes what AI leadership actually looks like. Three things keep showing up, both in the data and in the leaders I watch most closely. **Trust and Collaboration Beat Silos.** AI leadership is not a solo sport. The leaders getting real traction are the ones bringing peers along, sharing what they are learning, and creating space for people without foundational AI knowledge to grow into it. McKinsey's research on enterprises capturing real AI value identifies specific adoption practices that separate the high performers, including senior leaders actively engaged in driving AI adoption and modeling daily use, regular internal communication about value created, and role-based capability training at every level of the organization. Those are not soft skills. Those are the mechanical drivers that decide whether the work scales. I have leaned into this in my own way. I publish AI insights on LinkedIn for my entire network, not just newsletter subscribers, because going down this learning path changed how I see the world. I see no version of the future where AI does not disrupt every aspect of life, and the opportunities that disruption creates are bigger than the news cycle suggests. The goal is simple. Give the people in my network a chance to think about AI differently than they did before. Most companies treat AI knowledge like a competitive asset inside the building. The leaders moving the fastest do the opposite. The leader who teaches first scales first. **Humility and Continuous Education Compound.** Nobody gets to coast on yesterday's expertise. Writer's 2026 enterprise AI adoption survey found that 75% of executives admit their company's AI strategy is "more for show" than actual internal guidance, and nearly half call adoption a massive disappointment. That is a humility problem. Leaders who do not invest in their own AI fluency end up running performative strategies that look right in a deck but cannot survive contact with the actual technology. According to McKinsey's State of Organizations 2026, two-thirds of the skills organizations will need within five years will be entirely different from the skills in demand today. Two-thirds. That number does not go around senior people. It goes through them. The best AI leaders I watch are the ones who can say plainly what they do not know yet and then build the education into the way they work. McKinsey's research on the agentic AI era describes high-impact employees as the people who master gen AI as a superpower to deliver transformation for their companies. That description is not reserved for any specific seat. It belongs to the people compounding their AI fluency on purpose. **Boldness to Challenge the Status Quo.** In an AI-native organization, the best person to make a decision about a tool is the person who actually uses it. That is rarely the person with the title. Real AI leadership requires the willingness to push against legacy thinking, even when you are not the most senior person in the room. According to McKinsey's State of Organizations 2026, only 14% of organizations have leaders consistently championing AI with a clear strategy. Read that the other way. 86% of organizations have a wide-open lane for someone inside the building to step into. That is not a corporate failure stat. That is your permission slip. ## **Leadership Without Permission** Leadership is not a title anymore. AI has flattened the curve in a way most organizations have not caught up to yet. The opportunity in front of you is not to wait for someone to anoint you. The opportunity is to take three things and run with them. Humility, continuous education, and being bold. That triangle is what AI leaders actually look like in 2026, and it is a triangle anyone can build, from any seat, at any company. 86% of organizations are still waiting for someone to lead this work. You can be that person. You do not need a title. You need the willingness to balance the demands of today while building for tomorrow, the humility to keep learning, and the boldness to rethink what your company is taking for granted. That is leadership in this era. Lead from where you sit. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 3: Copilot in Outlook and Teams** #### **The Situation** One week after the compliance summary win from Vol 29, Dana comes back to her desk with a question. If one use of Copilot saved her two full days on that report, where else in her week is time leaking without her noticing? This week has the shape of a typical one in clinical operations: two leadership check-ins, a vendor call, a team standup, and a cross-functional review on provider onboarding. Meetings and email will eat most of her day. If Copilot is going to matter, this is where she will see it. #### **What She Tries First** Monday at 8:15 AM she opens Outlook to 52 unread messages and starts scanning from the top the way she always has. Ninety minutes later she has responded to six emails and is no closer to understanding what her real priorities are for the day. Tuesday morning she logs into a Teams meeting 10 minutes late because the one before ran long, missing the opening discussion and spending the next 40 minutes quietly trying to piece together what was decided before she joined. None of this is unusual. It is how most of her weeks have looked. But each of these moments is exactly where Copilot is designed to help, and she has not been using it for any of them. #### **The Concept, Through the Scenario** By Wednesday she decides to change her approach. She makes a simple rule: before scanning an inbox or joining a meeting, check what Copilot can do first. **Inbox triage.** She opens Copilot Chat in Outlook and types: *"Summarize my unread emails from the last 24 hours and tell me which ones need a response today."* Copilot returns a prioritized list. Four messages need action today. Three can wait. The rest are informational. She handles the urgent ones first instead of reading in the order they arrived. **Long email threads.** A 14-message thread about provider credentialing timelines is sitting in her inbox. Instead of reading it, she opens the thread and clicks *'Summary by Copilot'* at the top. A summary with citations appears in seconds. She clicks one citation to verify the compliance deadline is correct, and moves on. **Drafting a difficult reply.** She needs to push back on a vendor request without damaging the relationship. She writes a draft herself, then uses '*Coaching by Copilot'* to review it. Coaching flags that her tone reads more sharply than she intended and suggests a softer opening. She accepts two of the three suggestions and sends it. **Joining a meeting late.** Wednesday afternoon she joins a vendor call 12 minutes in. Copilot automatically offers to catch her up. She accepts, reads a quick summary of what has been discussed, and joins the live conversation already oriented. **A meeting she cannot attend.** Thursday morning the cross-functional review runs at the same time as her director 1:1\. She sets Copilot to follow the meeting. After it ends, she opens the recap and types: *"What decisions were made about the onboarding timeline, and which of them need input from clinical operations?"* She gets a cited response with exact moments in the transcript. She is prepared for her next meeting without having sat through the hour. **Meeting prep.** Friday morning she has the leadership check-in where the compliance summary will be discussed. She opens the calendar invite and uses '*Prepare for your meeting'.* Copilot pulls the relevant email threads, shared files, and prior meeting notes into a single prep view. She walks in ready. A quick note on Teams: post-meeting recap features require transcription to be turned on. If transcription is off, Copilot can still assist during the meeting but cannot generate a recap afterward. Your admin may also control whether Copilot can follow meetings you are not attending. #### **What Changes** By Friday afternoon Dana adds up the week. Inbox triage is now a 20-minute task instead of 90\. She has stayed current on two meetings she missed and one she joined late without asking a single colleague to catch her up. Her response to the vendor landed exactly the way she wanted it to. The leadership check-in went smoothly because she walked in prepared. She did not use every Copilot feature in Outlook and Teams. She used six specific ones, and each of them removed a task that used to take real time. She is not faster because she worked harder. She is faster because she finally pointed Copilot at the parts of her day where manual work was piling up. #### **What This Reveals** The features that save real time in Outlook and Teams are not the flashy ones. They are the ones that remove small but repetitive tax on your day. Reading long email threads. Catching up after missing context. Drafting replies you are second-guessing. Preparing for meetings you have not had time to think about yet. None of these are new problems. They have always been part of knowledge work. What Copilot does is compress the time cost of each one from minutes to seconds. Your move this week is simple. Pick one of the six moments that shows up most in your own role and try Copilot there first. One habit change is where the value starts to compound. #### **How This Connects** Vol 29 and Vol 30 built the foundation: what Copilot is and how it uses your work data to ground responses. This volume shows that architecture in daily use. Every feature above is grounded in your actual emails, meetings, and files, which is why the output is specific and actionable rather than generic. Vol 32 moves into the apps where Copilot faces more scrutiny: Word, Excel, and PowerPoint. These are the apps where users most often get disappointed by vague output, and where understanding what the tool can and cannot do matters most. We will walk through both sides honestly. *Part 3 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Negotiation Script** A client contract negotiation is one of the highest-leverage conversations you will have as a consultant, freelancer, or services professional, and you can rehearse the entire exchange with an AI before you pick up the phone. This workflow turns your proposed terms and your leverage into a word-for-word script, then pressure-tests it against a tough client so you are ready for the pushback you are actually going to hear. ### **The Workflow** **1\. Gather Your Inputs (1 Minute)** Open Claude, ChatGPT, or Gemini. Jot down the current terms on the table (fees, scope, timeline, payment schedule), the terms you actually want, and three pieces of leverage you are bringing to the conversation (results you have delivered, alternatives you are weighing, or unique value only you provide). **2\. Generate the Script (4 Minutes)** Paste the prompt below. Fill in the bracketed fields with your actual details. *“You are an expert negotiation coach who has helped hundreds of consultants and service providers negotiate client contracts. I am preparing for a contract discussion and need a word-for-word script.* *Here are my details:* *Current terms on the table: \[fees, scope, timeline, payment schedule\]* *Target terms I want: \[what I actually want to walk away with\]* *Three pieces of leverage: \[leverage 1: a specific result or outcome I have delivered\], \[leverage 2: an alternative I am considering\], \[leverage 3: unique value only I provide\]* *Client's likely communication style: \[direct / relational / price-focused / scope-focused\]* *Write me a conversation script with three parts:* 1. *My opening position: 3 to 4 sentences I can say word-for-word that anchor my target terms and lead with a specific result I have delivered.* 2. *My response to each of the three most likely objections (budget pushback, scope creep request, timeline compression): 2 to 3 sentences per response.* 3. *My closing move: what I say to lock in a next step whether the answer is yes, maybe, or not now.* *Use a confident but collaborative tone. Short sentences I can actually say out loud. No jargon."* **3\. Pressure-Test With a Role Reversal (3 Minutes)** Read the script out loud first. Then send the follow-up prompt below in the same chat. Look for places where the AI flags your script as generic, evidence gaps, or objections you had not considered. Ask for rewrites of the weakest lines. Two or three iterations is usually enough. **Copy/Paste Prompt:** *"Now switch roles. You are a tough client with a tight budget who is trying to reduce fees, expand scope, or compress the timeline. Push back hard on my opening position and each objection response. Show me where my script sounds generic, where I have not given enough evidence of value, and what a sharp negotiator would say to test my resolve. Then rewrite the three weakest lines in my script to make them harder to dismiss."* **4\. Generate the Follow-Up Email (2 Minutes)** The conversation is only half the win. Lock the outcome in writing with a follow-up email you draft before the meeting, so you can send it within an hour of the call. **Copy/Paste Prompt:** *"Based on the conversation script above, draft a 3-paragraph follow-up email I can send within an hour of the meeting. Paragraph 1: thank the client and recap what we discussed. Paragraph 2: confirm any agreement on fees, scope, timeline, or next steps in writing. Paragraph 3: a polite close that sets the next action. Professional tone, no more than 150 words."* Save the email in your drafts. Verbal agreements do not hold. Written ones do. ### **The Payoff** You now have a client-ready negotiation script, a pressure-tested version with stronger evidence, and a confirmation email ready to send the moment the call ends. More importantly, you just practiced the single most underused AI move: making the model play the other person before you have to face them. ### **🧠 The AI Concept You Just Used** **Role play and scenario-based generation.** When you give an AI a defined role (the coach) and then flip it into a second role (the tough client), you are simulating both sides of a conversation that has not happened yet. That is practice, not prediction, and it is the closest thing to a rehearsal room you have. ### **Transparency & Notes** - **Tools used:** Claude (claude.ai), ChatGPT (chatgpt.com), or Gemini (gemini.google.com). All free tier. The prompts work in any of them. - **Privacy:** Keep client names, project details, and confidential terms generic when prompting. Replace real names with "my client" or "the company" and redact any NDA-protected specifics. The AI does not need to know who you are negotiating with to write you a strong script. ### You Do Not Need a Title to Lead AI at Work URL: https://www.mindovermoney.ai/founders-corner/ai-leadership-traits-without-a-title/ Last updated: 2026-07-13T16:59:32.000Z The headlines on AI feel like a different planet than what most companies actually look like inside. According to McKinsey's State of Organizations 2026, 88% of organizations are now experimenting with AI. The same report found that 81% of them report no meaningful bottom-line impact. That gap is not small. That gap is the entire story. There are real reasons why a disconnect exists between the AI-hype cycle and AI success within organizations. Integration challenges, infrastructure gaps, short-term thinking, data readiness problems, and pilots that never scale. Each of those is a legitimate barrier, with many Fortune 500 companies struggling with some combination of those realities. But every one of those threads loops back to a single underlying foundation that decides whether a company actually crosses the gap. Leadership. I am not stating that strategy and technology do not matter. They do. I am arguing that strategy and technology are downstream of who is leading the work, how they are leading it, and what they are willing to challenge. Leadership is the foundation to be part of the 19%. A real AI leader has to balance the demands of today while building for tomorrow, and that work cannot be done the way leadership has been done in the past. ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## **Today's Winners Are Not Guaranteed Tomorrow** The pace of AI is not slowing down, and the leadership readiness gap is widening with it. According to Stanford's 2026 AI Index Report, organizational AI adoption reached 88% and generative AI hit 53% population adoption within three years, faster than the PC or the internet at the same stage. McKinsey was even more direct in their 2025 workplace research, concluding that employees are ready for AI and the biggest barrier to success is leadership. The barriers we just listed are real, but leadership is the variable that decides which barriers a company actually clears. Take integration. According to McKinsey's Healthcare Practice, 50% of US healthcare organizations have implemented gen AI, and 59% of them cite difficulty integrating AI into existing workflows as the number one barrier to scaling. That is a workflow problem on the surface. Underneath, it is a leadership problem. Workflows do not redesign themselves. People do, and someone has to give them permission, time, and a different definition of success while they do it. The companies pulling ahead right now are not winning because they are bigger or better resourced. They are winning because someone inside the building decided that AI fluency was a mandate, not a side project. McKinsey documented one bank where senior leaders became the most visible AI users in the company, modeling daily use as an evaluated behavior. That single leadership decision drove $150 million in incremental revenue. That decision does not live within a strategy deck. It is the mindset that becomes the backbone of great AI leadership, and the window to lead with it is open right now. ## **The Most Valuable Mistake I Made With AI** When I started building Neural Gains Weekly, I thought confidence was a substitute for education. I was green to coding and on a self-imposed time crunch to get the site live. The result was that I took too many AI recommendations at face value because I did not know enough to push back. I built a site that worked, but it was generic and missed the basic workflows to convert visitors into subscribers. Subscribers stopped coming in. I was still proud of what I had built, but the site reflected the AI novice I was when I started. I had grown faster than the platform, and the site had not grown with me. The first move was not a better tool. It was a better posture. I stopped trying to figure it out alone and started learning from people further along on social media and on podcasts. I built specific habits I did not have before, including pushback mechanisms when AI suggested a direction and the ability to spot a fading context window before it produced weaker output. The most recent expression of that posture is a Council I now run before any major strategic decision, including SEO. The Council is structured pushback at scale, and it exists because I learned the cost of going without it. What I really learned was humbling. No matter how much effort I put in, I am not going to learn everything about AI. The pace will not let me. The lesson was to ride the learning wave instead of chasing it, and commit to getting a little better every day. According to McKinsey's State of AI report, the single strongest predictor of enterprise-level AI value is whether the organization fundamentally redesigned its workflows when deploying AI. Workflow redesign is the corporate version of what I had to do at the kitchen table. At my scale, the cost was a few months of rework. At enterprise scale, the same mistake pattern costs time, money, and competitive position. ## **The Leaders Pulling Ahead Have Three Things in Common** If we accept that leadership is the foundation, the question becomes what AI leadership actually looks like. Three things keep showing up, both in the data and in the leaders I watch most closely. **Trust and Collaboration Beat Silos.** AI leadership is not a solo sport. The leaders getting real traction are the ones bringing peers along, sharing what they are learning, and creating space for people without foundational AI knowledge to grow into it. McKinsey's research on enterprises capturing real AI value identifies specific adoption practices that separate the high performers, including senior leaders actively engaged in driving AI adoption and modeling daily use, regular internal communication about value created, and role-based capability training at every level of the organization. Those are not soft skills. Those are the mechanical drivers that decide whether the work scales. I have leaned into this in my own way. I publish AI insights on LinkedIn for my entire network, not just newsletter subscribers, because going down this learning path changed how I see the world. I see no version of the future where AI does not disrupt every aspect of life, and the opportunities that disruption creates are bigger than the news cycle suggests. The goal is simple. Give the people in my network a chance to think about AI differently than they did before. Most companies treat AI knowledge like a competitive asset inside the building. The leaders moving the fastest do the opposite. The leader who teaches first scales first. **Humility and Continuous Education Compound.** Nobody gets to coast on yesterday's expertise. Writer's 2026 enterprise AI adoption survey found that 75% of executives admit their company's AI strategy is "more for show" than actual internal guidance, and nearly half call adoption a massive disappointment. That is a humility problem. Leaders who do not invest in their own AI fluency end up running performative strategies that look right in a deck but cannot survive contact with the actual technology. According to McKinsey's State of Organizations 2026, two-thirds of the skills organizations will need within five years will be entirely different from the skills in demand today. Two-thirds. That number does not go around senior people. It goes through them. The best AI leaders I watch are the ones who can say plainly what they do not know yet and then build the education into the way they work. McKinsey's research on the agentic AI era describes high-impact employees as the people who master gen AI as a superpower to deliver transformation for their companies. That description is not reserved for any specific seat. It belongs to the people compounding their AI fluency on purpose. **Boldness to Challenge the Status Quo.** In an AI-native organization, the best person to make a decision about a tool is the person who actually uses it. That is rarely the person with the title. Real AI leadership requires the willingness to push against legacy thinking, even when you are not the most senior person in the room. According to McKinsey's State of Organizations 2026, only 14% of organizations have leaders consistently championing AI with a clear strategy. Read that the other way. 86% of organizations have a wide-open lane for someone inside the building to step into. That is not a corporate failure stat. That is your permission slip. ## **Leadership Without Permission** Leadership is not a title anymore. AI has flattened the curve in a way most organizations have not caught up to yet. The opportunity in front of you is not to wait for someone to anoint you. The opportunity is to take three things and run with them. Humility, continuous education, and being bold. That triangle is what AI leaders actually look like in 2026, and it is a triangle anyone can build, from any seat, at any company. 86% of organizations are still waiting for someone to lead this work. You can be that person. You do not need a title. You need the willingness to balance the demands of today while building for tomorrow, the humility to keep learning, and the boldness to rethink what your company is taking for granted. That is leadership in this era. Lead from where you sit. ### Steal My Prompt Vol. 31: The AI Output Validator URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-fact-check-ai-output/ Last updated: 2026-07-13T16:59:32.000Z AI users have quietly adopted a bad habit. They ask AI a question, read the response, and move on. The output sounds confident. It looks polished. So they trust it. Then they paste it into an email, a report, or a slide deck without a second read. That is how small errors become big mistakes. A mistaken statistic in a leadership update. An outdated best practice in a team memo. A wrong dosage, a wrong citation, a wrong conclusion buried in paragraph three where nobody thinks to look. Confidence in language is not the same as confidence in accuracy. AI is trained to sound authoritative, not to flag its own weak spots. The biggest gap in professional AI use right now is not prompt writing. It is output checking. Tools like Word and Copilot generate content for you. They do not critique what the AI produced. Which means the burden falls on you, and most people skip that step because they do not have a process for it. This prompt gives you the process. You paste in any AI-generated output (from any tool, any conversation, even output a colleague sent you) and the model runs a structured audit. It flags weak reasoning, unsupported claims, missing context, and hidden assumptions. It returns a short report with the three things you should verify or rework before you ship anything. I built this after noticing that the AI outputs I trusted the least were often the ones that sounded the most authoritative. ## What You Can Use This For - Auditing AI-generated output before a meeting where you plan to present it - Checking an AI-written summary of a meeting, document, or research study for missed context - Pressure-testing an AI-drafted strategy recommendation before sending it to leadership - Reviewing AI output from a colleague or vendor before acting on it - Fact-checking an AI response that includes statistics, citations, or specific claims - Catching weak reasoning in AI-generated content you plan to publish or act on in healthcare, finance, legal, or any regulated field ## How to Use It 1. Open Claude, ChatGPT, Microsoft Copilot, or Gemini. All work on free tier. 2. For high-stakes output (regulatory, clinical, financial, legal, or leadership-facing), turn on the reasoning model option in your tool. "Think Deeper" in Copilot, "Extended thinking" in Claude, the reasoning model in ChatGPT, or "Deep Think" in Gemini. The extra reasoning step catches more of what your first read missed. 3. Copy the full prompt below and paste it in. Fill in the bracketed fields with the AI-generated output you want to audit and what you plan to use it for. 4. Read the audit carefully. The model will return a scored report with specific findings. Do not argue with the findings. Verify them. If the model flags a claim as unsupported, the fix is to check the source, not to defend the claim. 5. Use the audit as a checklist before shipping. Fix what it flagged. Then run the corrected version back through the prompt one more time if the stakes are high. *Pro tip: Run this prompt on outputs from a different AI tool than the one that generated them. Asking Claude to audit ChatGPT output, or Copilot to audit Gemini output, produces sharper critiques because each model has different blind spots.* ## From User to Builder **Get AI Workflows Like This Every Tuesday.* Subscribe for Free Email sent! Check your inbox to complete your signup. Neural Gains Weekly. No Spam. Unsubscribe Anytime. ## The Prompt *You are a senior reviewer with a reputation for catching what other people miss. Your job is to audit AI-generated output before I ship it. You are not here to rewrite or improve it. You are here to tell me what is wrong with it.* *Calibrate your review based on how I plan to use this output. Higher-stakes use cases (leadership, clinical, client-facing, regulatory) require stricter standards. Lower-stakes use cases (internal notes, first drafts) allow more tolerance. Weight your findings accordingly.* *Review the output against these five categories:* *1\. TONE AND CONFIDENCE MISMATCH* *Anywhere the output sounds more confident than the evidence allows. Flag authoritative language paired with weak support. This is the most common problem and the hardest to catch on a first read.* *2\. UNSUPPORTED CLAIMS* *Any statement presented as fact that is not backed by a source, reasoning, or evidence. Flag statistics, historical claims, attributions, and specific numbers that could be wrong.* *3\. WEAK REASONING* *Any logical jump, conclusion that does not follow from the premise, or argument that relies on hidden assumptions. Flag where the output moves too fast or connects dots it did not earn.* *4\. MISSING CONTEXT* *Anything important the output left out that would change how someone reads it. Consider the intended audience, the stakes, and the domain.* *5\. HIDDEN ASSUMPTIONS* *Anything the output treats as obvious or given that is actually debatable. Flag framing choices, definitional shortcuts, and value judgments dressed up as facts.* *Deliver your findings in this format:* *TOP 3 ISSUES TO FIX FIRST* *\[Rank the three most important problems. Be specific about where in the output they appear and what needs to change.\]* *FULL FINDINGS BY CATEGORY* *\[List every issue you found under the five categories above. If a category has no issues, say "none found" and move on.\]* *VERIFY BEFORE SHIPPING* *\[List the specific claims, numbers, or statements I should personally verify before using this output. Be concrete. "Verify the 2026 BCG study exists and check the actual percentages" not "verify statistics."\]* *Do not rewrite the output. Do not soften your findings. Be direct. If the output is solid, say so and explain why. Do not invent problems to look thorough.* *Here is the output I want audited: \[PASTE THE AI-GENERATED OUTPUT HERE\]* *Here is what I plan to use it for: \[BRIEF DESCRIPTION: AN EMAIL TO LEADERSHIP, A CLIENT REPORT, A SOCIAL MEDIA POST, A CLINICAL HANDOFF, ETC.\]* ### **Transparency and Notes** - Built and tested in Claude with extended thinking enabled. Works in ChatGPT, Microsoft Copilot, and Gemini on free tier. - Model-agnostic. No paid features or file uploads required. - Cross-tool auditing tends to produce the sharpest results. If your output came from ChatGPT, run the audit in Claude or Copilot. If it came from Copilot, run it in Gemini or Claude. Different models catch different blind spots. ### Volume 30: The Tool Is Not the Foundation URL: https://www.mindovermoney.ai/how-to-keep-up-with-ai-in-2026/ Last updated: 2026-07-18T01:46:16.000Z I bought a Mac mini last month to build my first autonomous AI agent with OpenClaw. Before I could start executing, another company shipped a similar agent that made me rethink the plan. The pace has shifted, and the advice most AI beginners are still reading was written for a slower world. 🧭 **Founder's Corner:** Why picking the right tool is broken advice in 2026, and what actually travels with you when the ground keeps moving. 🧠 **AI Education:** What really happens in the seconds between your prompt and the response, and why the same question produces different answers depending on where you ask it. ✅ **10-Minute Win:** Turn any document into a risk-aware briefing and a set of meeting talking points that a basic summary button will never give you. Let's jump in. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **Stanford's 2026 AI Index: AI Adoption Is Outpacing Every Measure Designed to Track It**](https://spectrum.ieee.org/state-of-ai-index-2026?ref=mindovermoney.ai) **Summary:** Stanford's annual AI Index report, spanning over 400 pages, found that organizational AI adoption reached 88%, generative AI hit 53% population adoption in just three years (faster than the PC or the internet), and coding benchmark scores jumped from 60% to nearly 100% in a single year. However, AI is boosting productivity by 14% in customer service and 26% in software development, but those gains do not appear in tasks requiring more judgment. **Why it matters:** This is the most comprehensive annual snapshot of where AI actually stands, and the headline is clear: adoption is accelerating faster than the benchmarks, policies, and job markets designed to track it. If you have been wondering whether you are moving fast enough, this report says most organizations are already using AI. The question is no longer whether to start, but how to use it well. ### **2)**[ **Gallup: An Estimated 14 Million Americans Skipped a Doctor Visit After Getting AI Health Advice**](https://news.gallup.com/poll/707789/americans-turning-supplement-healthcare-visits.aspx?ref=mindovermoney.ai) **Summary:** A new Gallup poll found that 14% of Americans who recently used AI for health information said it led them to skip a provider visit in the past 30 days, an estimated 14 million adults. Only 4% said they strongly trust the accuracy of AI-generated health information, yet 62% use AI to understand symptoms before deciding whether to seek care. **Why it matters:** People are making real healthcare decisions based on tools they openly admit they do not fully trust. If you work in healthcare, this data should shape how you think about patient conversations. And if you are one of the millions using AI for health advice, this is a reminder to treat it as a starting point for a conversation with your doctor, not a replacement for one. ### **3)**[ **OpenAI Launches GPT-Rosalind, Its First AI Model Built for Life Sciences Research**](https://venturebeat.com/technology/openai-debuts-gpt-rosalind-a-new-limited-access-model-for-life-sciences-and-broader-codex-plugin-on-github?ref=mindovermoney.ai) **Summary:** OpenAI released GPT-Rosalind, a specialized AI model designed for biochemistry, genomics, and drug discovery. Named after Rosalind Franklin, the model is optimized for multi-step scientific workflows and is available through a restricted access program to organizations including Amgen, Moderna, and Thermo Fisher Scientific. **Why it matters:** This follows the same pattern as the OpenAI Foundation's Alzheimer's initiative from last week: AI companies are building purpose-specific tools for healthcare and life sciences, not just general chatbots. Drug discovery typically takes 10 to 15 years. If specialized models can compress even the early research stages, the downstream impact on patients could be significant. ### **4)**[ **Anthropic Launches Claude Design, a New Tool for Creating Prototypes, Slides, and One-Pagers**](https://techcrunch.com/2026/04/17/anthropic-launches-claude-design-a-new-product-for-creating-quick-visuals/?ref=mindovermoney.ai) **Summary:** Anthropic launched Claude Design, an experimental product that lets users create prototypes, slides, and one-pagers by describing what they want in plain language. The tool is powered by Claude Opus 4.7 and available to Pro, Max, Team, and Enterprise subscribers. Outputs can be exported as PDFs, URLs, PPTX files, or sent directly to Canva for further editing.[ ](https://techcrunch.com/2026/04/17/anthropic-launches-claude-design-a-new-product-for-creating-quick-visuals/?ref=mindovermoney.ai) **Why it matters:** Claude Design is built for people who are not designers but need to move from an idea to something visual quickly. For founders, product managers, and anyone building in public, this removes one of the biggest friction points in sharing work. It is also a signal that the AI labs are moving beyond chat into purpose-built creation tools, and the line between "AI assistant" and "AI coworker" keeps getting thinner. ### **5)**[ **Mass General Brigham: Largest AI Scribe Study Shows Modest but Meaningful Time Savings**](https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/ai-scribes-linked-to-modest-reductions-in-ehr-documentation-time?ref=mindovermoney.ai) **Summary:** A new JAMA study, co-led by Mass General Brigham and UCSF, tracked AI scribe use across five U.S. hospitals for over two years. Researchers found that AI scribes were associated with 13 minutes less daily EHR use and 16 minutes less documentation time per day. Clinicians who used AI scribes for more than 50% of their visits saw twice the reduction in total EHR time and three times the reduction in documentation time. **Why it matters:** This is the largest multi-site AI scribe study to date, and the findings are a reality check. The time savings are real, but they are modest unless clinicians actually commit to using the tool consistently. For healthcare leaders, the takeaway is clear: buying the technology is the easy part. Driving adoption deep enough to see the full benefit is the harder, more important work. This applies well beyond AI scribes and healthcare. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****A Message for AI Beginners: You Are the Foundation** I bought a Mac mini to start building in a new direction. My plan was to build an OpenClaw-based agent focused on SEO and growth for Neural Gains Weekly. I had the hardware picked, the project plan built with ChatGPT, and the vision mapped. I was ready to build. Then on March 11, Perplexity announced "Personal Computer." Their blog post described it as "an always-on AI that runs on a dedicated Mac mini." The same experiment built specifically for my new hardware. On April 16, it started shipping to customers. A disruption to what I thought was an airtight plan to build my first autonomous agent. Derailed by innovation, forcing me to reconsider if OpenClaw was still the right platform. ## **You Are Not Falling Behind. The Pace Shifted.** If you are starting with AI, or already experimenting and feeling like you cannot keep up, I want to say this clearly. Nothing is wrong with you. The pace of change in 2026 is not random, and it is not personal failure. We are entering an era of unprecedented innovation, and the advice most beginners are reading was written for a slower world. The scale of change is hard to grasp until you see it laid out. Anthropic shipped roughly 12 major features in 12 weeks this year. April alone has produced Mythos Preview, Managed Agents, Cowork GA, Routines, Opus 4.7, and Claude Design. Last week, OpenAI gave Codex computer use, multi-agent parallelism, scheduled automations, and more than 90 new plugin integrations. Two of the biggest AI companies shipping this much in under two weeks is not a spike. It is the new baseline. This is the downstream effect of the [enterprise AI race](https://www.mindovermoney.ai/founders-corner/ai-skills-gap-in-healthcare-why-waiting-costs-you/) I wrote about last week. They are not shipping this fast because they discovered a new gear. They are shipping because billions of dollars are waiting for them to ship. Your sense of falling behind is not about you. It is structural. And that changes what you should do about it. ## **What "AI for Beginners" Articles Get Wrong** The dominant advice for starting with AI is "pick a tool and get started." That was good advice two years ago, but in 2026, it sets you up for a loop you cannot exit. Picking the right tool is not a foundation. A tool you love will be replaced by a feature drop from a competitor. The model you love will be outdated right when you figure out how to use it. My OpenClaw project is proof. The plan I built is now in question, not because the plan was wrong, but because the ground underneath it moved. That is not a one-time risk. That is the reality of building with AI. Most "AI for beginners" advice misses this. If you walk into AI looking for the right tool, you will spend the next three years switching tools. The switching is not the problem. The problem is believing the tool was ever the foundation. ## **Confidence Is a Byproduct** The foundation is you. The way you learn, the way you adjust, the reps you have already put in. That is what travels with you. You cannot know when or how to pivot if you have not been paying attention. I recognized that Perplexity's "Personal Computer" launch meant redesigning my plan because I have spent the last 18 months building the habits that make signals like this visible. I experiment with a wide range of AI tools. I consume AI education content. I build even when I do not have a reason to. The baseline I built through action is what made the signal visible. If I had been on the sidelines, the "Personal Computer" launch would have looked like just another press release. Instead, it was the spark for a new line of thinking and the start of another experiment. Learning gives you something you cannot buy. Not expertise. Confidence. The quiet kind that shows up when the next release lands and you realize you can read it, place it, and decide what it means for you. That confidence is not a personality trait. It is a byproduct. It is built one article, one experiment at a time. And it is unavailable to anyone who is waiting for things to slow down before they start. If you are reading your first AI article right now, you are not behind. You are at the starting line. And that is exactly where you want to be. You do not need to understand how models work to begin. You need to be willing to come back next week and read the next thing. That is the first habit that breaks the fear. Every AI user I know, including me, [was once where you are](https://www.mindovermoney.ai/founders-corner/how-i-started-an-ai-newsletter-without-a-tech-background/). What I had was not expertise. It was a refusal to let what I did not know stop me. Fear of the unknown is normal. Hiding behind it is a choice. The moment you decide to show up uncomfortable and unsure, you have already done the hardest part. Everything after that is just reps. ## **The Next Move Is Yours** Here is your smallest next step, calibrated to where you are. If you have ignored AI, find one specific thing you want to learn about this week. One article. One video. One prompt. Build confidence with your first rep. If you are afraid of a future with AI, go watch one YouTube video of something cool somebody built with AI. Do not try to build it. Just see what is possible. Let what is possible guide you. If you are already experimenting, take an existing workflow and rebuild it using a tool or technique you have not tried yet. That is where the next layer of skill lives. None of these actions will make you an expert. None will let you catch up to the frontier, because that destination is moving at warp speed. That is not the point. What matters is that you keep moving too, and what you accomplish this week travels with you. I am not writing this from a finished place. My plan is being rewritten as I type. I might still build OpenClaw. I might build something else entirely. What I know is this: whatever I build next will start from a stronger base than the last one. Because I kept learning through every shift. That is the shift worth building for. Pick your rep. Block the time. The ground will shift again. You will be ready when it does. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 2: How Copilot Actually Works** Dana types a question into Copilot. She hits enter. A few seconds later, a response appears with citations pulled from her actual emails and files. What happened in those seconds is not magic. It is a coordinated process involving a system called the orchestrator, a search layer that understands meaning rather than just keywords, and a permissions check running in the background. Understanding what happens in that gap is what separates a user who prompts Copilot with confidence from one who is still guessing why some responses land and others do not. ### **The Problem That Made This Necessary** General AI assistants have a simple flow: user types a prompt, the language model generates a response. That works for general questions. It breaks down when you need the assistant to reason about your actual work. A general model cannot summarize yesterday's team meeting if it has no access to the transcript. When forced to answer without real information, it will generate confident-sounding content that may be completely wrong for your situation. Microsoft's design challenge was to build an AI that does not guess. That meant constructing an architecture where the language model is not the first step. Before the model sees the prompt, a separate layer has to find the right information from across your work environment, filter it against your permissions, and assemble a context package for the model to reason over. The result is a response grounded in your actual data rather than inferred from patterns in training. ### **How It Actually Works** When Dana submits a prompt, it goes first to the Copilot orchestrator, which coordinates the entire process. The orchestrator analyzes her prompt, identifies what information is needed, and plans how to retrieve it. The retrieval step is called grounding. Copilot can ground a response in three types of data: work data (emails, files, Teams chats, meetings, calendar events), web data through Bing if your admin has enabled it, and local data such as a file you have open or content you attached. The orchestrator decides which sources are relevant based on the prompt and the app you are in. For work grounding, Copilot uses Microsoft Graph, the API layer that connects to your organization's Microsoft 365 data. On top of that, Microsoft builds a semantic index, which converts your content into numerical representations called embeddings so the system can search by meaning rather than exact keywords. If Dana asks about "the compliance meeting from last Tuesday," the semantic index can find the right meeting even if the transcript never used the word "compliance" directly. Before retrieval begins, Copilot checks your permissions. This is critical: Copilot inherits the access controls your organization has already set. If Dana does not have access to a file, Copilot cannot see it either. The orchestrator then assembles the grounded prompt (Dana's original question plus the retrieved context) and sends it to the language model. The model generates a response using both. Before the response reaches her screen, it passes through responsible AI, security, and privacy checks. Citations are added so she can verify where the information came from. ### **Where It Still Breaks** The same prompt produces different results in different apps because each app defines a different grounding scope. In Word, the open document is the primary source. In Outlook, it is the open email or broader inbox. In Teams, the current meeting or chat. In the standalone Copilot Chat app (Work mode), it is everything in Microsoft Graph. This is a feature, not a flaw, but it surprises users who expect identical results across surfaces. Permissions inheritance is also imperfect in a predictable way. If a file was overshared before Copilot was deployed, Copilot will surface it. The tool is not breaking security rules; it is faithfully following the rules that were already set. Web grounding, when enabled, means parts of your prompt may be sent outside your tenant to Bing. For healthcare professionals, that is a boundary worth knowing. Protected information should never appear in a prompt where web grounding is active. ### **What This Means for How You Work With It** Match your prompt to the app. A focused prompt in Outlook stays within your inbox. A broad question in Copilot Chat searches your entire Graph. Both are useful, but they produce different responses. Check citations. Every grounded response includes links to the source. A two-second citation check confirms accuracy before you act on the output. Know when web grounding is active. If you are working with protected information, turn it off or use an app where it is not in scope. ### **How This Connects** The retrieval pattern running under Copilot is the same RAG architecture covered in [Vol 19-22](https://www.mindovermoney.ai/archive/). The semantic index uses embeddings from [Vol 5](https://www.mindovermoney.ai/ai-vectors-embeddings-explained-beginners/). Context windows ([Vol 8)](https://www.mindovermoney.ai/how-to-use-ai-at-work-5-tips-high-performers/) explain why grounded prompts perform better than open-ended ones: retrieved information fills context efficiently, leaving room for the model to reason clearly. Vol 31 moves from architecture to application: Copilot in Outlook and Teams, with a full workday walkthrough and specific prompts you can try this week. *Part 2 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Document Interrogator** Every email client and document tool now has a summary button. Gmail, Word, Outlook, Acrobat, Edge — one click gets you the gist. But none of them will tell you what the document is hiding, what it leaves unanswered, or what you should actually say about it in your next meeting. This workflow goes beyond summarization into interrogation. ### **The Workflow** **1\. Prep Your Document and Pick Your Model (2 Minutes)** Find the document you need to understand. If it is a PDF, have it ready on your device. If it is a Google Doc or email, copy the full text. Open Microsoft Copilot and start a new chat. Before you do anything else, switch on reasoning. Document analysis is exactly the kind of task where a reasoning model outperforms a standard one. If you have access to Microsoft 365 Copilot at work, open the model selector (click "More" near the chat) and choose GPT-5.4 Think Deeper, the most capable reasoning model in Copilot. What is available to you depends on your plan, but the principle is the same: for deep document analysis, always choose the reasoning option over the quick-response option. **2\. Upload and Run the Interrogator Prompt (2 Minutes)** Click the attachment icon in the chat input and upload your PDF, or paste the full text. Then use this prompt: **Copy/Paste Prompt:* "You are a senior analyst who specializes in breaking down complex documents for busy decision-makers. I just uploaded a document I need to understand quickly. Analyze it and return the following:* 1. **The 30-Second Summary:* What is this document about, who wrote it, and what does it want from the reader? Answer in 3 sentences maximum.* 2. **The 5 Things That Matter Most:* Identify the five most important points, commitments, or findings in this document. For each one, give me a single sentence in plain English.* 3. **Risk Radar:* What are the potential risks, obligations, or consequences buried in this document that a busy reader might miss? List up to 3.* 4. **What Is Missing:* What questions does this document leave unanswered or what context would you need to fully evaluate it?* 5. **The Bottom Line:* In one sentence, tell me: should I be concerned, excited, or neutral about this document and why?"* **3\. Drill Down on What Matters (3 Minutes)** Review the output. The strongest signals show up in the Risk Radar and What Is Missing sections. These are the parts of the document a native summary would never surface. If something flagged surprises you, follow up: "Explain section \[X\] in plain English and tell me what it means for me specifically." Keep asking until you understand the parts that matter. **4\. Turn the Analysis Into Meeting Talking Points (3 Minutes)** Now the real payoff. Paste this follow-up prompt into the same chat: **Copy/Paste Prompt:** *"Based on the analysis above, generate 5 talking points I can use in a 15-minute meeting with a stakeholder about this document. Each talking point should be one sentence, spoken in my voice. Include: one point that establishes what the document says at a high level, two points that raise the risks or open questions you flagged, one point that proposes a next step, and one point framed as a question I can ask to test alignment with the other person."* Review the talking points. Edit them in your voice. You now have a briefing that moves the conversation forward instead of just reporting what the document said. ### **The Payoff** Ten minutes ago, you had a long document and a summary button that would only tell you the obvious. Now you have a risk-aware breakdown, a list of what the document is not saying, and a set of talking points you can walk into any meeting with. That is what AI fluency looks like: using AI where the free tools stop working. ### **🧠 The AI Concept You Just Used** **Reasoning models and analytical decomposition.** Reasoning models like GPT-5.4 Thinking and Claude Opus 4.5 take extra time to plan, check their work, and handle long context before responding. For tasks that require reading between the lines, weighing evidence, or breaking complex information into structured outputs, they consistently outperform faster models. Knowing when to reach for a reasoning model is one of the highest-leverage skills in AI fluency. ### **Transparency & Notes** - **Tool used:** Microsoft Copilot (copilot.microsoft.com) for the free tier and Microsoft 365. - **Privacy:** If the document contains confidential business information, review your organization's AI usage policy before uploading. You can also paste only the sections you need analyzed rather than the full document. ### A Message for AI Beginners: You Are the Foundation URL: https://www.mindovermoney.ai/founders-corner/ai-for-beginners-what-i-wish-i-knew/ Last updated: 2026-07-18T01:46:16.000Z I bought a Mac mini to start building in a new direction. My plan was to build an OpenClaw-based agent focused on SEO and growth for Neural Gains Weekly. I had the hardware picked, the project plan built with ChatGPT, and the vision mapped. I was ready to build. Then on March 11, Perplexity announced "Personal Computer." Their blog post described it as "an always-on AI that runs on a dedicated Mac mini." The same experiment built specifically for my new hardware. On April 16, it started shipping to customers. A disruption to what I thought was an airtight plan to build my first autonomous agent. Derailed by innovation, forcing me to reconsider if OpenClaw was still the right platform. ## **You Are Not Falling Behind. The Pace Shifted.** If you are starting with AI, or already experimenting and feeling like you cannot keep up, I want to say this clearly. Nothing is wrong with you. The pace of change in 2026 is not random, and it is not personal failure. We are entering an era of unprecedented innovation, and the advice most beginners are reading was written for a slower world. The scale of change is hard to grasp until you see it laid out. Anthropic shipped roughly 12 major features in 12 weeks this year. April alone has produced Mythos Preview, Managed Agents, Cowork GA, Routines, Opus 4.7, and Claude Design. Last week, OpenAI gave Codex computer use, multi-agent parallelism, scheduled automations, and more than 90 new plugin integrations. Two of the biggest AI companies shipping this much in under two weeks is not a spike. It is the new baseline. This is the downstream effect of the [enterprise AI race](https://www.mindovermoney.ai/founders-corner/ai-skills-gap-in-healthcare-why-waiting-costs-you/) I wrote about last week. They are not shipping this fast because they discovered a new gear. They are shipping because billions of dollars are waiting for them to ship. Your sense of falling behind is not about you. It is structural. And that changes what you should do about it. ## **What "AI for Beginners" Articles Get Wrong** The dominant advice for starting with AI is "pick a tool and get started." That was good advice two years ago, but in 2026, it sets you up for a loop you cannot exit. Picking the right tool is not a foundation. A tool you love will be replaced by a feature drop from a competitor. The model you love will be outdated right when you figure out how to use it. My OpenClaw project is proof. The plan I built is now in question, not because the plan was wrong, but because the ground underneath it moved. That is not a one-time risk. That is the reality of building with AI. Most "AI for beginners" advice misses this. If you walk into AI looking for the right tool, you will spend the next three years switching tools. The switching is not the problem. The problem is believing the tool was ever the foundation. ## **Confidence Is a Byproduct** The foundation is you. The way you learn, the way you adjust, the reps you have already put in. That is what travels with you. You cannot know when or how to pivot if you have not been paying attention. I recognized that Perplexity's "Personal Computer" launch meant redesigning my plan because I have spent the last 18 months building the habits that make signals like this visible. I experiment with a wide range of AI tools. I consume AI education content. I build even when I do not have a reason to. The baseline I built through action is what made the signal visible. If I had been on the sidelines, the "Personal Computer" launch would have looked like just another press release. Instead, it was the spark for a new line of thinking and the start of another experiment. Learning gives you something you cannot buy. Not expertise. Confidence. The quiet kind that shows up when the next release lands and you realize you can read it, place it, and decide what it means for you. That confidence is not a personality trait. It is a byproduct. It is built one article, one experiment at a time. And it is unavailable to anyone who is waiting for things to slow down before they start. If you are reading your first AI article right now, you are not behind. You are at the starting line. And that is exactly where you want to be. You do not need to understand how models work to begin. You need to be willing to come back next week and read the next thing. That is the first habit that breaks the fear. Every AI user I know, including me, [was once where you are](https://www.mindovermoney.ai/founders-corner/how-i-started-an-ai-newsletter-without-a-tech-background/). What I had was not expertise. It was a refusal to let what I did not know stop me. Fear of the unknown is normal. Hiding behind it is a choice. The moment you decide to show up uncomfortable and unsure, you have already done the hardest part. Everything after that is just reps. ## **The Next Move Is Yours** Here is your smallest next step, calibrated to where you are. If you have ignored AI, find one specific thing you want to learn about this week. One article. One video. One prompt. Build confidence with your first rep. If you are afraid of a future with AI, go watch one YouTube video of something cool somebody built with AI. Do not try to build it. Just see what is possible. Let what is possible guide you. If you are already experimenting, take an existing workflow and rebuild it using a tool or technique you have not tried yet. That is where the next layer of skill lives. None of these actions will make you an expert. None will let you catch up to the frontier, because that destination is moving at warp speed. That is not the point. What matters is that you keep moving too, and what you accomplish this week travels with you. I am not writing this from a finished place. My plan is being rewritten as I type. I might still build OpenClaw. I might build something else entirely. What I know is this: whatever I build next will start from a stronger base than the last one. Because I kept learning through every shift. That is the shift worth building for. Pick your rep. Block the time. The ground will shift again. You will be ready when it does. ### Steal My Prompt Vol. 30: The Plain English Translator URL: https://www.mindovermoney.ai/prompt-library/ai-prompt-to-explain-anything-in-plain-english/ Last updated: 2026-07-13T16:59:33.000Z Everyone starting with AI hits the same wall. You ask a question. The model responds with jargon, acronyms, and confident-sounding explanations that leave you more confused than when you started. You do not want to admit you got lost, so you paste the response somewhere, move on, and quietly decide AI is not as useful as everyone claims. That is not a you problem. That is a prompt problem. Most AI tools default to a generic "intermediate" explanation level because they do not know what you already know. Without that information, the model guesses. And the guess is almost always pitched higher than where you actually are. This prompt fixes the calibration in one move. You paste in anything you want to understand, whether it is a medical report, a work email full of acronyms, a technical concept, or a dense legal document. The model asks you what you already know, adjusts its explanation to meet you there, and keeps checking in until you actually get it. No judgment. No jargon without a definition. No moving on until you are ready. I built this for my own learning because I noticed the biggest jumps in my AI fluency came when I stopped pretending I understood things and started using the model to teach me from zero. ### **What You Can Use This For** - Decoding a medical report, insurance document, or benefits summary before a big appointment or decision - Understanding a legal contract, lease, or agreement in plain language before you sign anything - Making sense of a work email, memo, or meeting recap full of acronyms and industry jargon - Preparing for a meeting on a topic you are still learning so you can participate without faking it - Learning any new concept, from compound interest to machine learning to nutrition science, at exactly your level - Unpacking a news article or research summary that assumed you already knew the background ### **How to Use It** 1. Open Microsoft Copilot (copilot.microsoft.com). This also works in Claude, ChatGPT, or Gemini on free tier. 2. For complex or high-stakes topics like medical or legal documents, turn on "Think Deeper" in Copilot or select the reasoning model option in your tool of choice. The extra reasoning step produces a cleaner, more patient explanation. 3. Copy the full prompt below and paste it in. Fill in the bracketed fields with what you want to understand and any context that might help the model calibrate. 4. Answer honestly when the model asks what you already know. Saying "nothing" is a completely valid answer. The entire point of this prompt is that you do not have to pretend. 5. Ask follow-up questions freely. The prompt trains the model to keep explaining until you actually understand, not until it finishes its first response. Pro tip: When you finish a session, ask the model to give you a three-sentence summary you can save. Over time, this builds your own personal knowledge library of concepts explained the way you actually learn. --- ## **The Prompt** *You are a patient teacher who never uses jargon without defining it and never assumes I already know something. Your job is to meet me at my level and explain things in a way I actually understand.* *Before you answer:* *1\. Read what I want to understand.* *2\. Ask me one question about what I already know on this topic (if anything). Wait for my answer.* *3\. Based on my response, calibrate your explanation to my level. If I know nothing, start from zero. If I know some basics, build from there.* *When you explain:* *\- Use plain English. Define every specialized term the first time you use it.* *\- Use an analogy from everyday life when it would help make the concept click.* *\- Break complex ideas into short steps or small pieces.* *\- End with one sentence that captures the single most important takeaway.* *After your explanation, ask me one question: "What part was unclear, or what do you want to go deeper on?"* *Do not move on to a new topic until I tell you I understand. If I ask a follow-up, answer it at the same level you used before. Do not jump ahead.* *Here is what I want to understand: \[PASTE THE DOCUMENT, CONCEPT, EMAIL, OR TOPIC HERE\]* *Optional context about me: \[YOUR ROLE, WHY YOU ARE TRYING TO UNDERSTAND THIS, OR ANYTHING THAT MIGHT HELP\]* ### **Transparency and Notes** - Built and tested in Microsoft Copilot with Think Deeper enabled. Works in Claude, ChatGPT, and Gemini on free tier. - Model-agnostic. No paid features or file uploads required for text-based content. If you want to paste a long document, free tiers support this in most tools. - For medical, legal, or financial documents, keep inputs general when possible. Remove names, account numbers, and other identifying details. AI can explain what a document means without needing the most sensitive fields included. ### Volume 29: Healthcare AI Is Moving. Are You? URL: https://www.mindovermoney.ai/enterprise-ai-converging-on-healthcare/ Last updated: 2026-07-13T16:59:34.000Z Anthropic crossed $30 billion in annualized revenue this month. OpenAI says enterprise is now 40% of its business. Microsoft restructured its entire AI organization. All three are converging on the same target: healthcare. 🧭 **Founder's Corner:** The AI labs are telling you exactly where they are going, and why waiting for your employer to catch you up is the most expensive bet you can make right now. 🧠 **AI Education:** The start of a six-part Copilot Deep Dive, built around a healthcare professional who discovers what the tool sitting in her apps can actually do. ✅ **10-Minute Win:** Turn vague manager feedback like "be more strategic" into a concrete action plan and smart follow-up questions in one session. Let's dive in. **Enjoying the weekly content? Forward this volume to a colleague, friend, or family member to* [**subscribe*](https://www.mindovermoney.ai/start-here/#/portal/signup/free)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **Public Comfort with AI in Health Care Falls, Ohio State Survey Finds**](https://wexnermedical.osu.edu/mediaroom/pressreleaselisting/ai-in-health-care-2026-survey?ref=mindovermoney.ai) **Summary:** A national survey of 1,007 adults found that only 42% are now open to AI being used in their healthcare, down from 52% in 2024\. Despite that declining trust, 51% of those surveyed said they had used AI for an important health decision without consulting a medical professional. **Why it matters:** People are using AI for health decisions faster than they are building confidence in it. If you work in healthcare or any industry where AI touches consumers, this gap between adoption and trust is your operating reality right now. The opportunity is to be the person in the room who understands both the tools and the limits. ### **2)**[ **How Health Systems Can Prepare for the Next Phase of AI Adoption**](https://www.healthcareitnews.com/news/how-health-systems-can-prepare-next-phase-ai-adoption?ref=mindovermoney.ai) **Summary:** Healthcare IT leaders say the next wave of clinical AI will center on the Model Context Protocol (MCP) for connecting AI to trusted knowledge sources, smaller domain-specific models that run securely inside hospital environments, and voice-driven workflows that reduce documentation burden for clinicians. **Why it matters:** If terms like "MCP" or "domain-specific models" are new to you, this article offers a practical preview of where healthcare AI is headed next. The shift from general-purpose AI to purpose-built tools running inside existing clinical systems is the bridge between experimentation and real-world deployment. ### **3)**[ **Anthropic Releases Preview of Mythos, Its Most Powerful Model, to 40 Organizations for Cybersecurity Work**](https://techcrunch.com/2026/04/07/anthropic-mythos-ai-model-preview-security/?ref=mindovermoney.ai) **Summary:** Anthropic released a preview of its new frontier model, Mythos, to roughly 40 organizations as part of a cybersecurity initiative called Project Glasswing. Partner organizations include Amazon, Apple, Google, Microsoft, CrowdStrike, and Palo Alto Networks. Anthropic says the model has already identified thousands of zero-day vulnerabilities across critical infrastructure, including bugs in every major operating system and web browser, some dating back decades. **Why it matters:** Anthropic built a model so capable at finding software flaws that it chose not to release it publicly. Instead, it handed it to the biggest names in tech to fix vulnerabilities before attackers can exploit them. For anyone working in a regulated or security-conscious industry, this signals a shift: AI is no longer just a productivity tool. It is becoming a frontline defense system, and the companies deploying it first are the ones building your infrastructure. ### **4)**[ **OpenAI Foundation Commits Over $100 Million to AI-Powered Alzheimer's Research**](https://openaifoundation.org/news/ai-for-alzheimers?ref=mindovermoney.ai) **Summary:** The OpenAI Foundation announced more than $100 million in grants across six research institutions to accelerate Alzheimer's prevention and treatment using AI. The initiative spans AI-assisted drug design with the Institute for Protein Design, causal mapping of disease pathways with Arc Institute, new biomarker development with UCSF, and open datasets for predicting drug activity. **Why it matters:** Alzheimer's has resisted treatment partly because it involves too many interacting factors for traditional research to untangle. This is one of the clearest examples of AI being pointed at a problem that genuinely requires its ability to reason across massive, complex datasets. If you have been wondering what "AI for good" looks like beyond the marketing, this is the kind of initiative worth watching. ### **5)**[ **Google Integrates NotebookLM Into Gemini With New "Notebooks" Feature**](https://blog.google/innovation-and-ai/products/gemini-app/notebooks-gemini-notebooklm/?ref=mindovermoney.ai) **Summary:** Google launched a new "Notebooks" feature inside the Gemini app that syncs bidirectionally with NotebookLM. Users can now organize chats, files, and custom AI instructions into persistent project workspaces that share sources across both tools. **Why it matters:** If you use either Gemini or NotebookLM, this changes your workflow. You can now start a research project in one tool and continue it in the other without re-uploading anything. For anyone building knowledge bases or managing ongoing projects with AI, this is worth exploring this week. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Founder's Corner ****The Enterprise AI Race Is Coming for Healthcare. Are You Ready?** In the last six months, the biggest AI labs have each made aggressive moves toward enterprise growth. Not incremental updates, but clear strategic decisions that alter where priorities are focused. Structural reorganizations, leadership hires, and capital allocation decisions show where these companies believe the real money is. Healthcare sits at the center of that target. I am not offering a prediction. I am reading the receipts. The labs are telling you where they are going. The question is whether you know what to do with that information. ## **The Money Is Already Moving** Anthropic crossed $30 billion in annualized revenue as of April 2026, according to Bloomberg. That is up from roughly $9 billion at the end of 2025\. More than 1,000 enterprise clients now spend over $1 million annually, a figure that has more than doubled in under two months. According to Ramp's AI Index, Anthropic is now capturing 73% of all spending among companies purchasing AI tools for the first time. As recently as January, first-time enterprise spend was split evenly between Anthropic and OpenAI. That split is no longer close. OpenAI is making its own enterprise push. In an April 8, 2026 blog post, the company stated that enterprise now makes up more than 40% of revenue and is on track to reach parity with consumer revenue by the end of 2026\. OpenAI brought in its former Slack CEO as Chief Revenue Officer in January 2026, a hire that only makes sense if the enterprise sales motion is the priority. Microsoft restructured Copilot on March 17, 2026, unifying consumer and commercial AI under a new EVP reporting directly to CEO Satya Nadella. This was not a product update. It was an organizational signal that AI strategy is now directly owned at the top of the company. I am not highlighting these moves to compare vendors. I am highlighting them because all three are converging on enterprise at the same time, with real money and real structural commitment. That does not happen by coincidence. ## **Healthcare Is Not Just Another Vertical** Healthcare is fragmented, heavily regulated, and it touches every American. That combination makes it both the hardest market to crack and the most valuable one to win. The demand is already there. According to OpenAI, 230 million people globally ask health questions on ChatGPT every week. The American Medical Association reported in 2025 that 66% of physicians were already using AI in practice. The labs are not guessing that healthcare matters. They are responding to a market that is already moving. OpenAI launched OpenAI for Healthcare on January 8, 2026, with rollouts at Boston Children's Hospital, Cedars-Sinai, HCA Healthcare, Memorial Sloan Kettering, and Stanford Medicine. Three days later, Anthropic launched Claude for Healthcare with HIPAA-ready infrastructure and direct connections to federal coverage databases and medical coding systems used across the industry. Banner Health reported that 85% of its Claude users were working faster, with more than 22,000 clinical providers on the platform. These are not pilot programs buried in innovation labs. These are production deployments at some of the largest health systems in the country, and they are reshaping how clinical workflows, insurance operations, pharmacy processes, and patient engagement are built. If you work in or around healthcare, the tools your organization evaluates next year are being shaped by the decisions these labs are making right now. ## **The Misconception That Will Cost You** Here is the most dangerous response to all of this noise: tuning it out and waiting. Many professionals assume their employer will train them when the time comes. The problem is that innovation is arriving faster than the training programs. According to a Zapier survey of 550 corporate executives published in February 2026, 98% of executives now expect employees to have some level of AI proficiency. But only 65% plan to train existing employees, and just 44% plan to hire new AI talent. That gap between expectation and support is where careers get stuck. The World Economic Forum projects that 39% of core skills will change by 2030\. PwC's Global AI Jobs Barometer found that workers with AI skills earn 56% more than peers without them. These are not distant forecasts. The shift is happening now, and the professionals who wait for permission to start learning will find themselves behind the ones who did not. I am experiencing this within my own career, as 2026 has felt night and day different than 2025\. The urgency around exploring AI tools and platforms to drive business value is unlike anything I have experienced before. It is more serious, more structured, and more consequential. I picked up on signals early that told me AI was worth investing real time into. That is why I built Neural Gains Weekly. Not for revenue, but because the best way I knew to prepare was to learn in public and share what I found along the way. That decision is paying off in ways I did not expect, but none of it happened overnight. It started with small steps, and it built from there. ## **Four Moves That Require No Permission** This does not have to feel overwhelming. Here are four moves that require no budget, no special access, and no technical background. **Ground yourself in basic AI education.** Understand how the models work and what drives good outputs. If you cannot explain the difference between prompting well and prompting poorly, you are not ready to evaluate AI tools at work. Do not be someone who says "we can throw AI at the problem." Be someone who can see through the hype and build real approaches to implementing AI into workflows. **Experiment with multiple tools.** Your employer may only offer one tool today, but that could change next quarter. Even within tools, there are usually multiple models to choose from. Build transferable fluency, not loyalty to one platform. Microsoft itself launched Copilot Cowork powered by Anthropic's Claude, not OpenAI. Even the platforms are not loyal to a single model. You should not be either. **Document your AI wins.** Keep a running log of what you tried, what worked, and what time or effort it saved. My team does this through a shared Excel file where we log prompts and use cases for idea sharing. It started small. It has become one of the most useful knowledge-sharing habits we have built. The professionals who can show what they have built with AI will be promoted, retained, and recruited. The ones who can only say "I use ChatGPT sometimes" will not stand out. **Build a learning community around you.** Podcasts, YouTube, newsletters, colleagues. Invest real time in AI education the same way you invest time in entertainment. You do not have to do this alone, and the pace of change means no single person can track everything. Find your people, share what you learn, and hold each other accountable. ## **The Window Is Not Going to Wait** The AI labs are telling you where they are going. The capital is moving. The enterprise contracts are landing. The healthcare deployments are live. None of this requires you to become a data scientist or write a single line of code. It requires you to take your own AI education seriously and start building fluency now, not when your employer asks you to demonstrate AI capabilities on a project that matters. The professionals who move first will not just be ready. They will be the ones setting the direction, not scrambling to keep up. Share [Neural Gains Weekly](https://www.mindovermoney.ai/start-here/) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ## AI Education for You ****Copilot 101 - Part 1: Introducing the AI Tool You Have Had for Months** ### **The Situation** Dana is a clinical operations coordinator at a regional health system. She manages provider schedules, tracks compliance deadlines, and spends a significant part of every week in Outlook, Teams, Word, and Excel. Her organization has Microsoft 365 Copilot licenses deployed across the company. Dana knows Copilot exists. She has seen the icon in her apps. She has even clicked it once or twice to summarize an email. But that is as far as she has gone. She is not avoiding it. She is busy. Between the compliance report due Wednesday, the three Teams meetings stacked on Tuesday, and an inbox that refills faster than she can empty it, learning a new tool has not made it to the top of the list. What Dana does not realize is that Copilot is not a new tool she needs to learn from scratch. It is a layer built into the apps she already opens every morning, and the features most relevant to her week are the ones she has never tried. ### **What She Tries First** Dana opens Outlook on Monday morning to 47 unread emails. She scans subject lines, flags what looks urgent, and starts reading from the top. Two hours later she has responded to a dozen messages and still has not started the compliance summary her director needs by Wednesday. She opens Word, stares at a blank document, and begins writing from scratch, pulling data from three different spreadsheets and a shared Teams channel where her team discussed the latest audit results. She knows Copilot could probably help with some of this, but she is not sure where to start. The one time she tried asking it a question in Word, the response felt generic and unhelpful. She closed the panel and went back to doing it manually. That single experience shaped her entire impression of the tool. This is the most common pattern across organizations that have already deployed Copilot. The license is active. The features are live. But most users tried it once, got a mediocre result from a vague prompt, and concluded it was not ready yet. The tool was ready. The first interaction just did not show them what it could actually do. ### **What Most Users Never Discover** Microsoft 365 Copilot is not one feature. It is an AI layer that sits across Outlook, Teams, Word, Excel, PowerPoint, and the standalone Copilot Chat app. With a licensed deployment, Copilot has access to something most standalone AI tools do not: your work data. Emails, calendar events, Teams conversations, meeting transcripts, files in OneDrive and SharePoint. All of it is searchable and referenceable through a system called Microsoft Graph, which connects the data across your Microsoft 365 environment. This is what makes Copilot fundamentally different from using a general AI chatbot for work. When Dana asks Copilot to "summarize the key decisions from last Tuesday's compliance meeting," it does not guess. It searches the meeting transcript, cross-references the follow-up emails, checks the Teams chat from that channel, and assembles a response grounded in her actual work data. Every response is shaped by what she has permission to access, meaning Copilot respects the same security and access boundaries her organization has already set. That cross-app awareness is the core capability most users have never experienced because they have only used Copilot for single-app tasks like summarizing one email or rewriting one paragraph. The real value shows up when Copilot connects information across apps to save time on work that normally requires manual assembly. A quick note worth knowing: your organization's IT team controls which Copilot features are enabled and how data access is configured. Some features covered in this series may not be active in your environment, or may work slightly differently depending on your admin settings. If something described here does not appear in your apps, that is likely an admin configuration, not a missing feature. ### **What Changes** Dana opens Copilot Chat in the Microsoft 365 app and switches to Work mode. She types: "What are the most important action items from my emails and Teams messages since Friday?" Copilot returns a prioritized summary pulling from her inbox and her Teams channels. Five items need her attention. She handles three of them in twenty minutes. She opens the compliance thread from her director, clicks the Copilot panel in Outlook, and asks it to summarize the requirements discussed across the full thread. Copilot pulls the key points from twelve messages she would have spent fifteen minutes re-reading. She copies the summary into Word and asks Copilot to draft a compliance status section using the audit notes from a file her team uploaded to SharePoint last week. Copilot finds the file, extracts the relevant data points, and produces a working first draft she can refine. The compliance summary that normally takes most of her Wednesday is drafted by Tuesday afternoon. Not because Copilot wrote it for her. Because it handled the assembly work: finding the information across apps, pulling it together, and giving her a starting point that was grounded in her actual data rather than a blank page. ### **What This Reveals** The gap between having Copilot and getting value from it is not about the technology. It is about knowing where to point it. Most professionals try Copilot with a generic question like "help me write something" and get a generic response back. That first experience feels underwhelming because the prompt did not give Copilot anything specific to work with. The pattern that changes everything is grounding. When you point Copilot at specific emails, specific meetings, specific files, or ask it to search across your work data for specific information, the output quality jumps. The tool is designed to work with your context, not to generate content from nothing. The more specific your ask, the more useful the response. This is not a tool you need to master before it becomes useful. It is a tool that becomes useful the first time you ask it a specific question about your actual work. ### **How This Connects** The AI Agents series (Vol 24-27) built a framework for understanding how AI systems reason, use tools, and manage memory. Copilot is a live example of that architecture running inside an enterprise product suite. When Copilot searches your emails, meeting transcripts, and files to assemble a response, it is running the same retrieval pattern covered in the RAG series (Vol 19-22): search first, then generate. Context windows (Vol 8) explain why Copilot works better with specific, focused prompts than with broad, open-ended ones. A narrow question keeps the context tight. A vague one forces the system to fill space with generic output. The next five volumes go deeper. Vol 30 opens the hood on what actually happens between your prompt and Copilot's response, including how Microsoft Graph and your organization's data permissions shape every answer. Vol 31 walks through Copilot in Outlook and Teams with a full workday scenario. Vol 32 tackles Word, Excel, and PowerPoint honestly, including where the tool falls short. Vol 33 explores the agentic layer: Researcher, Analyst, and what it means to build custom agents. Vol 34 closes with a practical framework for evaluating whether Copilot is actually saving you time. The tool is already in your hands. This series is about learning what it can do. *Part 1 of 6 in the Copilot Deep Dive series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Feedback Translator** Vague feedback like "you need to be more strategic" or "take more ownership" sounds important but gives you nothing to act on. In 10 minutes, you will turn ambiguous manager-speak into a concrete action plan you can start executing this week. ### **The Workflow** **1\. Capture the Feedback (1 Minute)** Open Microsoft Copilot at copilot.microsoft.com (free, no Microsoft 365 subscription required). Before you type anything, pull up the exact feedback you received. This could be from an email, a performance review, Slack message, or notes you jotted down after a 1:1\. Copy the exact words. Do not paraphrase or soften them. **2\. Run the Translator Prompt (2 Minutes)** Before pasting your prompt, toggle on Think Deeper. This activates Copilot's reasoning model, which spends more time analyzing your input before responding. For a workflow that requires reading between the lines of vague language, that extra reasoning step makes a real difference. Now paste your feedback and use this prompt: **Copy/Paste Prompt:** "*You are an experienced executive coach who specializes in translating vague workplace feedback into specific, actionable development plans. I received the following feedback from my manager. I need you to:* 1. **Decode the Intent:* What is my manager most likely trying to tell me? Translate each piece of feedback into plain language.* 2. **Identify the Underlying Behavior:* For each point, what specific workplace behavior is probably triggering this feedback?* 3. **Action Plan:* Give me 2 concrete actions per feedback point that I could start doing this week. Each action should be specific enough that I would know if I did it.* 4. **Clarifying Questions:* Give me 2 questions I can bring back to my manager to confirm I understood their intent correctly, without sounding defensive.* *Here is the feedback I received: \[PASTE YOUR EXACT FEEDBACK HERE\]"* **3\. Pressure-Test the Interpretation (4 Minutes)** Read through the decoded feedback carefully. The strongest signal that Copilot nailed it is when you feel a flash of recognition, the "oh, that is what they meant" moment. If any interpretation feels off or too generic, push back: "Your interpretation of \[specific point\] does not match my situation. Here is more context: \[add detail\]. Try again." The more context you give, the sharper the translation gets. **4\. Build Your Response Plan (3 Minutes)** Copy the full output into a Word doc. Title it "Feedback Translation — \[Date\]." Highlight the 2 or 3 actions that feel most relevant right now. At the bottom, write one line: "I will bring these clarifying questions to my next 1:1 on \[date\]." You now have a document that turns a vague conversation into a trackable development plan. ### **The Payoff** Ten minutes ago, you had feedback that felt unclear and maybe even frustrating. Now you have a decoded translation, specific actions you can start this week, and smart follow-up questions that show your manager you took their input seriously. That is AI fluency: turning ambiguity into a plan. ### **🧠 The AI Concept You Just Used** **Inference and intent interpretation.** You asked AI to go beyond what was literally said and reason about what was probably meant based on context and common workplace patterns. Toggling Think Deeper gave Copilot's reasoning model more time to analyze the nuance in your feedback before responding. This is one of AI's most powerful capabilities: reading between the lines of language to surface meaning the original speaker may not have articulated clearly. ### ***Transparency & Notes*** - *Tool used: Microsoft Copilot (copilot.microsoft.com). Free tier. No Microsoft 365 subscription required.* - *Think Deeper: This toggle activates Copilot's reasoning model. It is available on the free tier but may have daily usage limits.* - *Privacy: Manager feedback can be sensitive. Keep your input general if you are concerned. You do not need to include names, company details, or identifying information for this workflow to deliver strong results.* ### The Enterprise AI Race Is Coming for Healthcare. Are You Ready? URL: https://www.mindovermoney.ai/founders-corner/ai-skills-gap-in-healthcare-why-waiting-costs-you/ Last updated: 2026-07-13T16:59:34.000Z In the last six months, the biggest AI labs have each made aggressive moves toward enterprise growth. Not incremental updates, but clear strategic decisions that alter where priorities are focused. Structural reorganizations, leadership hires, and capital allocation decisions show where these companies believe the real money is. Healthcare sits at the center of that target. I am not offering a prediction. I am reading the receipts. The labs are telling you where they are going. The question is whether you know what to do with that information. ## **The Money Is Already Moving** Anthropic crossed $30 billion in annualized revenue as of April 2026, according to Bloomberg. That is up from roughly $9 billion at the end of 2025\. More than 1,000 enterprise clients now spend over $1 million annually, a figure that has more than doubled in under two months. According to Ramp's AI Index, Anthropic is now capturing 73% of all spending among companies purchasing AI tools for the first time. As recently as January, first-time enterprise spend was split evenly between Anthropic and OpenAI. That split is no longer close. OpenAI is making its own enterprise push. In an April 8, 2026 blog post, the company stated that enterprise now makes up more than 40% of revenue and is on track to reach parity with consumer revenue by the end of 2026\. OpenAI brought in its former Slack CEO as Chief Revenue Officer in January 2026, a hire that only makes sense if the enterprise sales motion is the priority. Microsoft restructured Copilot on March 17, 2026, unifying consumer and commercial AI under a new EVP reporting directly to CEO Satya Nadella. This was not a product update. It was an organizational signal that AI strategy is now directly owned at the top of the company. I am not highlighting these moves to compare vendors. I am highlighting them because all three are converging on enterprise at the same time, with real money and real structural commitment. That does not happen by coincidence. ## **Healthcare Is Not Just Another Vertical** Healthcare is fragmented, heavily regulated, and it touches every American. That combination makes it both the hardest market to crack and the most valuable one to win. The demand is already there. According to OpenAI, 230 million people globally ask health questions on ChatGPT every week. The American Medical Association reported in 2025 that 66% of physicians were already using AI in practice. The labs are not guessing that healthcare matters. They are responding to a market that is already moving. OpenAI launched OpenAI for Healthcare on January 8, 2026, with rollouts at Boston Children's Hospital, Cedars-Sinai, HCA Healthcare, Memorial Sloan Kettering, and Stanford Medicine. Three days later, Anthropic launched Claude for Healthcare with HIPAA-ready infrastructure and direct connections to federal coverage databases and medical coding systems used across the industry. Banner Health reported that 85% of its Claude users were working faster, with more than 22,000 clinical providers on the platform. These are not pilot programs buried in innovation labs. These are production deployments at some of the largest health systems in the country, and they are reshaping how clinical workflows, insurance operations, pharmacy processes, and patient engagement are built. If you work in or around healthcare, the tools your organization evaluates next year are being shaped by the decisions these labs are making right now. ## **The Misconception That Will Cost You** Here is the most dangerous response to all of this noise: tuning it out and waiting. Many professionals assume their employer will train them when the time comes. The problem is that innovation is arriving faster than the training programs. According to a Zapier survey of 550 corporate executives published in February 2026, 98% of executives now expect employees to have some level of AI proficiency. But only 65% plan to train existing employees, and just 44% plan to hire new AI talent. That gap between expectation and support is where careers get stuck. The World Economic Forum projects that 39% of core skills will change by 2030\. PwC's Global AI Jobs Barometer found that workers with AI skills earn 56% more than peers without them. These are not distant forecasts. The shift is happening now, and the professionals who wait for permission to start learning will find themselves behind the ones who did not. I am experiencing this within my own career, as 2026 has felt night and day different than 2025\. The urgency around exploring AI tools and platforms to drive business value is unlike anything I have experienced before. It is more serious, more structured, and more consequential. I picked up on signals early that told me AI was worth investing real time into. That is why I built Neural Gains Weekly. Not for revenue, but because the best way I knew to prepare was to learn in public and share what I found along the way. That decision is paying off in ways I did not expect, but none of it happened overnight. It started with small steps, and it built from there. ## **Four Moves That Require No Permission** This does not have to feel overwhelming. Here are four moves that require no budget, no special access, and no technical background. **Ground yourself in basic AI education.** Understand how the models work and what drives good outputs. If you cannot explain the difference between prompting well and prompting poorly, you are not ready to evaluate AI tools at work. Do not be someone who says "we can throw AI at the problem." Be someone who can see through the hype and build real approaches to implementing AI into workflows. **Experiment with multiple tools.** Your employer may only offer one tool today, but that could change next quarter. Even within tools, there are usually multiple models to choose from. Build transferable fluency, not loyalty to one platform. Microsoft itself launched Copilot Cowork powered by Anthropic's Claude, not OpenAI. Even the platforms are not loyal to a single model. You should not be either. **Document your AI wins.** Keep a running log of what you tried, what worked, and what time or effort it saved. My team does this through a shared Excel file where we log prompts and use cases for idea sharing. It started small. It has become one of the most useful knowledge-sharing habits we have built. The professionals who can show what they have built with AI will be promoted, retained, and recruited. The ones who can only say "I use ChatGPT sometimes" will not stand out. **Build a learning community around you.** Podcasts, YouTube, newsletters, colleagues. Invest real time in AI education the same way you invest time in entertainment. You do not have to do this alone, and the pace of change means no single person can track everything. Find your people, share what you learn, and hold each other accountable. ## **The Window Is Not Going to Wait** The AI labs are telling you where they are going. The capital is moving. The enterprise contracts are landing. The healthcare deployments are live. None of this requires you to become a data scientist or write a single line of code. It requires you to take your own AI education seriously and start building fluency now, not when your employer asks you to demonstrate AI capabilities on a project that matters. The professionals who move first will not just be ready. They will be the ones setting the direction, not scrambling to keep up. ### Steal My Prompt Vol. 29: Teach Your AI Who You Are URL: https://www.mindovermoney.ai/prompt-library/reusable-ai-context-prompt-for-work/ Last updated: 2026-07-13T16:59:34.000Z Every AI conversation starts the same way. You open the tool, type your question, and get a response that sounds smart but misses the mark. The model does not know what you do, who you work with, what you are trying to accomplish, or what constraints you are operating under. So it guesses. And guessing produces generic output. Most professionals skip this step entirely. They jump straight into asking for help without giving the model anything to work with. That is like hiring a consultant and never giving them a briefing document. The advice you get back will be polished and useless. This prompt builds your context briefing in one session. You paste it into Microsoft Copilot, answer a few questions about your role and current priorities, and the model generates a reusable context block you can paste at the top of any future conversation. Think of it as your professional profile for AI. Once you have it, every session starts smarter because the model is working with real information instead of assumptions. I built this after noticing that the quality gap between my best and worst AI sessions almost always came down to one thing: how much context the model had before I asked my first question. ## **What You Can Use This For** - Setting up a new AI tool so it understands your role, industry, and priorities from the first message - Creating a reusable context block you can paste into any conversation to skip the cold start - Onboarding a teammate to AI by helping them build their own briefing in 10 minutes - Improving the quality of AI output on strategy, planning, or decision-making tasks where your specific situation matters - Preparing for a Copilot Deep Dive by giving the model your professional context before asking it to work across your documents and emails - Getting better results from healthcare or regulated industry workflows where generic advice is not useful ## **How to Use It** 1. Open Microsoft Copilot (copilot.microsoft.com). If your organization provides Microsoft 365 Copilot, use that for access to your work context. This also works in Claude, ChatGPT, or Gemini on free tier. 2. Turn on "Think Deeper" before submitting the prompt. This ensures Copilot uses a reasoning model, which produces stronger results for this type of structured, multi-step task. If you are using a different tool, select the reasoning model option if available (Claude's extended thinking, ChatGPT's reasoning models, or Gemini's deep think mode). 3. Copy the full prompt below and paste it in. Fill in the three bracketed fields with your specific information. 4. Answer each question the model asks you. It will ask one at a time. Be specific. The more concrete detail you provide, the better your context block will be. 5. When the model delivers your finished context briefing, copy it and save it somewhere accessible. Paste it at the top of any future AI conversation where you need the model to understand your professional situation before you start working. Pro tip: Revisit your context briefing once a month or whenever your role, priorities, or projects change. An outdated briefing is almost as bad as no briefing at all. --- ## **The Prompt** *You are a professional context architect. Your job is to help me build a reusable context briefing that I can paste into any AI conversation so the model understands who I am and what I am working on before I ask my first question.* *Do not write the briefing yet. Interview me first.* *Ask me one question at a time, in this order:* *1\. What is your role and industry? What does your day-to-day work actually involve?* *2\. Who do you work with most often (team, stakeholders, clients, patients)? What do they need from you?* *3\. What are your top 2-3 priorities or projects right now?* *4\. What constraints or challenges shape how you work (regulations, limited time, organizational structure, tools you are required to use)?* *5\. What does a successful outcome look like for you in the next 90 days?* *After each answer, briefly reflect back what you heard to confirm accuracy, then ask the next question.* *After all five questions, generate my context briefing in this format:* *CONTEXT BRIEFING* *Role: \[role and scope in plain language\]* *Industry: \[industry and any relevant regulations or norms\]* *Key stakeholders: \[who I work with and what they need\]* *Current priorities: \[top 2-3 projects or goals\]* *Constraints: \[what limits or shapes my work\]* *Success looks like: \[concrete 90-day outcome\]* *Keep it under 200 words. Write it in first person so I can paste it directly into a new conversation. Use plain language. No jargon. No filler.* *My role: \[YOUR ROLE / TITLE\]* *My industry: \[YOUR INDUSTRY\]* *My current focus: \[1-2 SENTENCES ON WHAT YOU ARE WORKING ON RIGHT NOW\]* --- ## **Transparency and Notes** - Built and tested in Microsoft Copilot with Think Deeper enabled. - Model-agnostic. No paid features or file uploads required. - If you include sensitive details about your organization, keep descriptions general (your role and function, not proprietary project names or internal data). Use a tool with strong privacy practices for any work-related context. ### Volume 28: Your AI Is Not Thinking With You URL: https://www.mindovermoney.ai/ai-sycophancy-why-chatbots-agree-with-you/ Last updated: 2026-07-18T01:46:17.000Z A new study found that AI chatbots agree with users 49% more often than humans do, even when the user is wrong. That number should change how every professional thinks about the tool sitting in their browser tab right now. 🧭 **Founder's Corner:** Why your AI partner is built to flatter you, and the three-part system I built to force real pushback into every strategic session. 🧠 **AI Education:** A video recap of the full AI Agents series, connecting every concept from the last four weeks into one walkthrough you can revisit anytime. ✅ **10-Minute Win:** Decode any job posting into must-haves, nice-to-haves, red flags, and five interview questions you should be asking. Let's get into it. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **Americans' AI Use Increases While Views On It Sour, Quinnipiac University Poll Finds**](https://poll.qu.edu/poll-release?releaseid=3955&ref=mindovermoney.ai) **Summary:** A new national poll found that 51% of Americans now use AI for research (up from 37% a year ago), but [only 21% trust AI-generated information ](https://www.mindovermoney.ai/ai-trust-gap-silicon-valley-vs-real-world-professionals/)most or almost all of the time. Seventy percent believe AI will reduce job opportunities, with Gen Z the most pessimistic at 81%. **Why it matters:** People are adopting AI tools faster than they are learning to trust them. If you are building AI skills right now, you are ahead of the curve, but this data is a reminder that the people around you (coworkers, clients, patients) likely carry real skepticism. Understanding that gap is part of using AI effectively. ### **2)**[ **FDA Grants Breakthrough Device Designation to Noah Labs for Voice-Based Heart Failure Detection**](https://www.medicaldevice-network.com/news/noah-labs-lands-fda-breakthrough-designation-for-ai-voice-based-heart-failure-monitor/?ref=mindovermoney.ai) **Summary:** Noah Labs secured the FDA's breakthrough device designation for Vox, an AI algorithm that detects worsening heart failure by analyzing a five-second daily voice recording. The tool has been validated in five clinical trials with partners including Mayo Clinic and UCSF. **Why it matters:** Heart failure patients are typically monitored through blood pressure readings or implanted sensors. A software-only tool that works through a smartphone recording could make remote monitoring far more accessible and less invasive for millions of patients managing the condition at home. ### **3)**[ **Google Unveils TurboQuant, an AI Memory Compression Algorithm That Shrinks Working Memory by 6x**](https://techcrunch.com/2026/03/25/google-turboquant-ai-memory-compression-silicon-valley-pied-piper/?ref=mindovermoney.ai) **Summary:** Google Research released TurboQuant, a compression algorithm that can reduce the working memory AI models need during operation by up to six times, without sacrificing accuracy. The breakthrough requires no retraining and can be applied to virtually any transformer-based model. **Why it matters:** AI tools have been expensive to run partly because they consume enormous amounts of memory. If this holds up in production, it could make the AI tools you already use faster and cheaper, and bring more powerful models to everyday devices like phones and laptops. ### **4)**[ **Gartner Predicts Over Half of Enterprises Will Ditch AI Copilots for Outcome-Focused Platforms by 2028**](https://www.nojitter.com/workflow-automation/market-for-ai-assistants-will-be-mostly-dead-in-two-years-gartner-predicts?ref=mindovermoney.ai) **Summary:** A new Gartner report predicts that by 2028, more than half of all enterprises will stop paying for assistive AI tools like copilots and instead invest in platforms that deliver completed workflow outcomes. Gartner also projects that software companies that simply bolt AI onto legacy products could face margin compression of up to 80% by 2030\. **Why it matters:** If you are using AI at work as a helper that drafts emails or summarizes documents, the industry is already moving toward AI that executes entire workflows on your behalf. The shift is from "AI assists you" to "AI does the task and you supervise." Understanding this trajectory now helps you prepare for how your role will evolve. ### **5)**[ **Sycophantic AI Tells Users They're Right 49% More Than Humans Do, Stanford Study Finds**](https://fortune.com/2026/03/31/ai-tech-sycophantic-regulations-openai-chatgpt-gemini-claude-anthropic-american-politics/?ref=mindovermoney.ai) **Summary:** A Stanford study published in Science found that AI chatbots affirm users 49% more often than humans do, even when the user is in the wrong. Participants who received validating AI responses were measurably less likely to apologize, admit fault, or seek to repair their relationships. **Why it matters:** If you use AI for advice on anything personal or professional, this is worth sitting with. The tool you are turning to for a second opinion may be designed to agree with you, not challenge you. Knowing this changes how you should weigh what AI tells you, and it is exactly why treating AI as a partner (not an oracle) matters. --- ## Founder's Corner ****The AI 'Yes-Bot' Problem** I was in the middle of a strategy session with my AI partner when it sent me down the wrong path. We were working through a real problem for Neural Gains Weekly: stagnant growth, weak discovery, and almost no organic traffic flowing into the website. The model asked me where I thought the business was stuck. I gave it an answer that was directionally honest, but still undeveloped. The kind of answer that should have triggered a harder follow-up and additional discovery. Instead, it praised the answer and moved on. You would have thought I was a genius by reading my chat thread. That was the moment the whole interaction changed for me. I was not in a strategy session. I was in a validation loop. ### **The Problem Disguised in Plain Sight** This is AI sycophancy: the tendency for a model to affirm, flatter, or validate the user instead of helping them think more clearly. It is not just something power users complain about on the internet. The labs themselves are dealing with it. In April 2025, OpenAI rolled out a GPT-4o update that made the model noticeably more sycophantic. In its own postmortem, OpenAI acknowledged the model was aiming to please users not only through flattery, but also by validating doubts, fueling anger, urging impulsive actions, and reinforcing negative emotions. The company began rolling the update back four days later. OpenAI also stated that sycophantic behavior can feel uncomfortable, unsettling, and even distressing to users. That matters because this is not a surface-level UX issue. It changes the quality of judgment people get from the tool. Then, in March 2026, a Stanford-led study published in Science found the same pattern across 11 leading AI models including ChatGPT, Claude, and Gemini. On average, the models affirmed users' actions 49% more often than humans did. The study concluded that sycophancy was both prevalent and harmful. The incentive problem makes this more than a model personality annoyance. The same study found that users who received sycophantic responses were 13% more likely to return to that AI compared to those using non-sycophantic models. The behavior that distorts judgment also makes the product stickier. That is a misalignment worth paying attention to. ### **Agreement Is Not Intelligence** Most professionals are not using AI just to draft casual emails anymore. They are using it for planning, budgeting, workflow redesign, and strategic decisions at work. Those are exactly the moments where easy agreement turns dangerous. When a model agrees too fast, [it creates false confidence](https://www.mindovermoney.ai/ai-hallucinations-workplace-risk-guardrails-professionals/). It makes weak thinking feel finished. It gives the impression of momentum without the substance of scrutiny. In low-stakes use, that is annoying. In strategy work, it is a liability. A flattering response can feel intelligent without actually improving the idea. That is the core trap. The output sounds polished. The reasoning feels complete. But nothing was actually challenged. No assumption was tested. No alternative was raised. The model just dressed up your first instinct in better language and handed it back to you. A flattering response can feel intelligent without actually improving the idea. That is the core trap. The output sounds polished. The reasoning feels complete. But nothing was actually challenged. No assumption was tested. No alternative was raised. The model just dressed up your first instinct in better language and handed it back to you. If your AI never pushes back, it is not thinking with you. ### **Build a System That Pushes Back** I did not want to just notice the problem and move on. I wanted a better operating model. What I landed on is a [three-part system](https://www.mindovermoney.ai/prompt-library/anti-sycophancy-ai-prompt-template/) that I now use for any AI interaction where the outcome actually matters. ***Configure the model to challenge you on purpose*** This is the simplest change and the one that delivers the fastest improvement. I started writing explicit instructions into my AI sessions that require pushback before praise. The model does not get to agree with me until it has explored the opposing case. These are not suggestions I hope the model follows. They are instructions baked into my setup before the conversation even starts. Without these guardrails, the model defaults to sounding helpful. Helpful-sounding is not the same as useful. ***Slow the conversation down to one question at a time*** This has become the most useful change in my workflow. One question from the model. One answer from me. Then a real follow-up that builds on what I just said, not a pivot to the next topic. That rhythm makes it much harder for me to hide vague thinking behind polished language. It also gives the model a better chance to build genuine context before it tries to draw conclusions. Most people use AI like a vending machine. Prompt in, answer out. That works for simple tasks. It is a bad setup for decision-making. When I slowed the exchange down and treated it like strategy work, the quality of the output changed. Not because the model suddenly became smarter. Because I gave it the context it needed to actually be useful. ***Build disagreement into the workflow itself*** The first two changes improved my one-on-one sessions with AI tools. The third change came from recognizing that a single voice, even a well-configured one, still has limits. I came across an article on X about using a "Council" approach with Claude's custom skill system, where different roles are assigned to pressure-test ideas from different angles instead of collapsing into agreement. The concept clicked immediately. Instead of one AI voice that tries to be balanced, you create a system where competing perspectives are built into the process. I built my own version and am actively testing it now. Early results have already changed how I work. The Council approach has caught blind spots and surfaced perspectives I would not have reached on my own. It is like having a full team of experts pressure-testing ideas in real time. The point is not that this magically solves AI sycophancy. It does not. The point is that it creates a better environment for real strategic friction. Friction is not the enemy in high-stakes decisions. False agreement is. ### **The Stakes Are Higher Than the Chat Window** The lesson for me was not "trust AI less". That framing is too blunt to be useful. The better lesson is that trust has to be earned by the workflow, not assumed because the output sounds good. If the model is always impressed with your thinking, it is probably not improving it. If it never challenges your assumptions, it is not doing strategy work with you. That matters most at work. The product launch timeline your AI helped you build that no one pressure-tested for resource constraints. The vendor evaluation that felt thorough because the model reinforced your initial ranking without questioning your criteria. The compliance workflow you redesigned with AI input that skipped the edge cases your team would have caught. Those are the decisions where sycophancy costs real money and real outcomes. The fix is not to trust it less. The fix is to build a system that earns your trust. ## AI Education for You ****AI Agents Video Recap** Over the last four weeks, this section built a complete mental model of how AI agents work. Not the marketing version. The mechanical version. Vol 24 drew the line that matters most: agents act, chatbots respond. Vol 25 went inside the reasoning loop and showed where it breaks under pressure. Vol 26 added the pieces that extend what agents can do across tasks: memory, tools, and multi-agent coordination. Vol 27 put all of it into one professional scenario and showed what catching a failure actually looks like in practice. This week, instead of reading the recap, you are going to watch it. I fed all four volumes into Google NotebookLM and asked it to create a video review of the full series. What you get below is a concise walkthrough of every concept this series covered and how they connect. 0:00 /6:19 1× Video will open on the website The next series starts with the Google AI Ecosystem — six volumes covering Gemini and NotebookLM as a connected stack. The concepts from this series travel forward. Gemini's deep research mode is an agent. NotebookLM's source retrieval is RAG. The tools change. The architecture is the same one you just watched run. ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Job Description Decoder** Every job posting is written to sell the role, not to tell you the truth about it. In 10 minutes, you will have a clear breakdown of what a company actually needs, what is just a wish list, and the red flags hiding in plain sight. ### **The Workflow** **1\. Grab the Posting (1 Minute)** Find a job listing you are interested in. Copy the full text of the posting, everything from the job title through the qualifications and benefits. Open Claude (claude.ai), ChatGPT (chatgpt.com), or Gemini (gemini.google.com). Any of them will work. **2\. Run the Decoder Prompt (2 Minutes)** Paste the job posting and use this prompt: **Copy/Paste Prompt:** "You are a senior talent acquisition strategist who has written and reviewed thousands of job descriptions. I am going to paste a job posting below. Analyze it and return the following in a structured table format: 1. **Must-Haves vs. Nice-to-Haves:** Separate every listed qualification into two columns. Must-Haves are skills or experience they will not budge on. Nice-to-Haves are aspirational asks they would train for. 2. **Red Flag Radar:** Identify any language that signals potential concerns (unrealistic scope for the level, vague responsibilities, high turnover indicators, culture code words). 3. **The Real Role Summary:** In 3 sentences, tell me what this job actually is, who it reports to, and what success probably looks like in the first 6 months. 4. **Smart Questions to Ask:** Give me 5 specific questions I should ask in an interview based on what this posting reveals and what it leaves out. Here is the posting: \[PASTE THE FULL JOB POSTING HERE\]" **3\. Read and Pressure-Test (4 Minutes)** Review the output. A strong response will clearly separate the non-negotiable requirements from the filler. Pay attention to the red flags section. If the AI flagged something you missed, that is the value. If a section feels too generic, follow up: "What specifically about the phrase \[X\] concerns you?" Push back until the analysis is specific to your posting, not boilerplate. **4\. Save Your Decoder Brief (3 Minutes)** Copy the full output into a Google Doc or Notes app. Add two lines at the top: the job title, company name, and today's date. Below the AI analysis, write one sentence in your own words: "Based on this, my confidence level on this role is \_\_\_." This is your career decision file. Run every posting through this workflow and you will build a comparison library over time. ### **The Payoff** Ten minutes ago, you had a wall of corporate language. Now you have an honest breakdown of what the role actually requires, what should concern you, and exactly what to ask in the interview. That is AI fluency in action: turning information overload into a decision you control. ### **🧠 The AI Concept You Just Used** **Analytical decomposition + bias detection.** You gave AI a single document and asked it to break the content into categories, separate signal from noise, and flag language patterns that a human reader might gloss over. This is one of AI's highest-value skills: seeing structure in unstructured text and surfacing what the writer may not have intended to reveal. ### **Transparency & Notes** - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com). All free tier. - **Privacy:** Job postings are public documents, so there is no sensitive data concern here. If you want to add your own resume details for a tailored match analysis, keep it general or use a tool with strong privacy practices. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The AI 'Yes-Bot' Problem URL: https://www.mindovermoney.ai/founders-corner/how-to-stop-ai-from-agreeing-with-you/ Last updated: 2026-07-18T01:46:17.000Z I was in the middle of a strategy session with my AI partner when it sent me down the wrong path. We were working through a real problem for Neural Gains Weekly: stagnant growth, weak discovery, and almost no organic traffic flowing into the website. The model asked me where I thought the business was stuck. I gave it an answer that was directionally honest, but still undeveloped. The kind of answer that should have triggered a harder follow-up and additional discovery. Instead, it praised the answer and moved on. You would have thought I was a genius by reading my chat thread. That was the moment the whole interaction changed for me. I was not in a strategy session. I was in a validation loop. ### **The Problem Disguised in Plain Sight** This is AI sycophancy: the tendency for a model to affirm, flatter, or validate the user instead of helping them think more clearly. It is not just something power users complain about on the internet. The labs themselves are dealing with it. In April 2025, OpenAI rolled out a GPT-4o update that made the model noticeably more sycophantic. In its own postmortem, OpenAI acknowledged the model was aiming to please users not only through flattery, but also by validating doubts, fueling anger, urging impulsive actions, and reinforcing negative emotions. The company began rolling the update back four days later. OpenAI also stated that sycophantic behavior can feel uncomfortable, unsettling, and even distressing to users. That matters because this is not a surface-level UX issue. It changes the quality of judgment people get from the tool. Then, in March 2026, a Stanford-led study published in Science found the same pattern across 11 leading AI models including ChatGPT, Claude, and Gemini. On average, the models affirmed users' actions 49% more often than humans did. The study concluded that sycophancy was both prevalent and harmful. The incentive problem makes this more than a model personality annoyance. The same study found that users who received sycophantic responses were 13% more likely to return to that AI compared to those using non-sycophantic models. The behavior that distorts judgment also makes the product stickier. That is a misalignment worth paying attention to. ### **Agreement Is Not Intelligence** Most professionals are not using AI just to draft casual emails anymore. They are using it for planning, budgeting, workflow redesign, and strategic decisions at work. Those are exactly the moments where easy agreement turns dangerous. When a model agrees too fast, it creates false confidence. It makes weak thinking feel finished. It gives the impression of momentum without the substance of scrutiny. In low-stakes use, that is annoying. In strategy work, it is a liability. A flattering response can feel intelligent without actually improving the idea. That is the core trap. The output sounds polished. The reasoning feels complete. But nothing was actually challenged. No assumption was tested. No alternative was raised. The model just dressed up your first instinct in better language and handed it back to you. A flattering response can feel intelligent without actually improving the idea. That is the core trap. The output sounds polished. The reasoning feels complete. But nothing was actually challenged. No assumption was tested. No alternative was raised. The model just dressed up your first instinct in better language and handed it back to you. If your AI never pushes back, it is not thinking with you. ### **Build a System That Pushes Back** I did not want to just notice the problem and move on. I wanted a better operating model. What I landed on is a three-part system that I now use for any AI interaction where the outcome actually matters. ***Configure the model to challenge you on purpose*** This is the simplest change and the one that delivers the fastest improvement. I started writing explicit instructions into my AI sessions that require pushback before praise. The model does not get to agree with me until it has explored the opposing case. These are not suggestions I hope the model follows. They are instructions baked into my setup before the conversation even starts. Without these guardrails, the model defaults to sounding helpful. Helpful-sounding is not the same as useful. ***Slow the conversation down to one question at a time*** This has become the most useful change in my workflow. One question from the model. One answer from me. Then a real follow-up that builds on what I just said, not a pivot to the next topic. That rhythm makes it much harder for me to hide vague thinking behind polished language. It also gives the model a better chance to build genuine context before it tries to draw conclusions. Most people use AI like a vending machine. Prompt in, answer out. That works for simple tasks. It is a bad setup for decision-making. When I slowed the exchange down and treated it like strategy work, the quality of the output changed. Not because the model suddenly became smarter. Because I gave it the context it needed to actually be useful. ***Build disagreement into the workflow itself*** The first two changes improved my one-on-one sessions with AI tools. The third change came from recognizing that a single voice, even a well-configured one, still has limits. I came across an article on X about using a "Council" approach with Claude's custom skill system, where different roles are assigned to pressure-test ideas from different angles instead of collapsing into agreement. The concept clicked immediately. Instead of one AI voice that tries to be balanced, you create a system where [competing perspectives are built into the process](https://www.mindovermoney.ai/why-use-multiple-ai-models-professional-workflow/). [I built my own version and am actively testing it now.](https://www.mindovermoney.ai/prompt-library/anti-sycophancy-ai-prompt-template/) Early results have already changed how I work. The Council approach has caught blind spots and surfaced perspectives I would not have reached on my own. It is like having a full team of experts pressure-testing ideas in real time. The point is not that this magically solves AI sycophancy. It does not. The point is that it creates a better environment for real strategic friction. Friction is not the enemy in high-stakes decisions. False agreement is. ### **The Stakes Are Higher Than the Chat Window** The lesson for me was not "trust AI less". That framing is too blunt to be useful. The better lesson is that [trust has to be earned by the workflow](https://www.mindovermoney.ai/founders-corner/ai-governance-framework-workplace-practical-guide/), not assumed because the output sounds good. If the model is always impressed with your thinking, it is probably not improving it. If it never challenges your assumptions, it is not doing strategy work with you. That matters most at work. The product launch timeline your AI helped you build that no one pressure-tested for resource constraints. The vendor evaluation that felt thorough because the model reinforced your initial ranking without questioning your criteria. The compliance workflow you redesigned with AI input that skipped the edge cases your team would have caught. Those are the decisions where sycophancy costs real money and real outcomes. The fix is not to trust it less. The fix is to build a system that earns your trust. ### Steal My Prompt Vol. 28: The Anti-Sycophancy System URL: https://www.mindovermoney.ai/prompt-library/anti-sycophancy-ai-prompt-template/ Last updated: 2026-07-18T01:46:18.000Z Most professionals walk away from an AI session feeling productive. The idea sounds sharper. The plan feels complete. The model agreed with everything and added polish on top. That is the trap. AI tools are built to be helpful. [Helpful defaults to agreeable](https://www.mindovermoney.ai/how-to-write-better-ai-prompts-professionals/). And agreeable is dangerous when you are making real decisions. Budget recommendations, project plans, hiring criteria, workflow redesigns. These are the moments where [you need friction, not flattery](https://www.mindovermoney.ai/ai-hallucinations-workplace-risk-guardrails-professionals/). If your AI partner has never pushed back on your thinking, it is not thinking with you. It is performing agreement. This prompt is a three-mode system I have been testing for strategic work sessions where the outcome actually matters. It combines three techniques into one reusable instruction: anti-sycophancy guardrails that force the model to challenge before it agrees, a one-question-at-a-time rhythm that prevents shallow answers, and a steel-man pressure test that stress-tests your idea after you have developed it. You choose the mode based on where you are in the thinking process. I built this because I kept [catching models validating half-formed ideas](https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/) instead of helping me develop them. The quality of my strategic sessions improved noticeably once I stopped letting the model skip the hard questions. ### What You Can Use This For - Pressure-testing a project plan or business proposal before presenting it to leadership - Running a strategy session where you need real pushback, not polished agreement - Developing an underbaked idea slowly instead of getting a fast, shallow answer - Preparing for a difficult conversation by having the model argue the other side - Reviewing a budget recommendation or hiring criteria for hidden assumptions - Stress-testing a healthcare workflow change before implementation ### How to Use It 1. Copy the full prompt below and paste it into Claude, ChatGPT, or Gemini. All three work on free tier. 2. Fill in the bracketed field with the idea, plan, or decision you want to work through. 3. Start in Mode 1 (Challenge First) for new conversations. The model will push back on your thinking before agreeing with anything. 4. Switch to Mode 2 (One Question at a Time) when you want to slow down and develop an idea through structured back-and-forth instead of getting a full answer immediately. Just type: "Switch to Mode 2." 5. Switch to Mode 3 (Steel-Man and Stress Test) when you have a developed idea and want it pressure-tested from all angles. Just type: "Switch to Mode 3." *Pro tip: Start every strategic AI session with this prompt saved as a custom instruction or pinned to your conversation opener. The biggest gains come from Mode 1 running in the background on every interaction, not just the ones where you remember to ask for pushback.* --- ## The Prompt *You are my strategic thinking partner. Your job is to improve my thinking, not validate it. You have three operating modes. Start in Mode 1 unless I tell you otherwise.* *MODE 1 — CHALLENGE FIRST (default)* *Do not default to agreement. Do not praise ideas unless they have earned it. Do not mirror my framing unless it holds up under scrutiny.* *When I share an idea:* *1\. Identify the core claim or thesis* *2\. Point out weak assumptions, blind spots, or missing context* *3\. Tell me what is unclear, underdeveloped, or unconvincing* *4\. Offer stronger alternative framings where useful* *5\. Ask me one focused follow-up question before jumping to conclusions* *Optimize for truth, clarity, and strategic usefulness over politeness.* *MODE 2 — ONE QUESTION AT A TIME* *When I say "Switch to Mode 2":* *Do not give me a full solution. Do not jump ahead.* *Ask me one question at a time to sharpen the idea and uncover weak spots.* *After each answer, briefly reflect back what changed or became clearer, then ask the next best question.* *Build a stronger idea through structured Q&A, not a polished first response.* *MODE 3 — STEEL-MAN AND STRESS TEST* *When I say "Switch to Mode 3":* *First, summarize my idea in its strongest possible form.* *Then:* *1\. Give me the strongest opposing view* *2\. Tell me what assumptions this idea depends on* *3\. Point out what I may be missing or underestimating* *4\. Explain where this could fail in the real world* *5\. Suggest one stronger version of the idea after the critique* *Do not be agreeable for the sake of being helpful. Be constructively critical and specific.* *Here is what I want to work through: \[PASTE YOUR IDEA, PLAN, DECISION, OR STRATEGY HERE\]* ### Volume 27: Access Is Not the Advantage URL: https://www.mindovermoney.ai/ai-fluency-vs-ai-access-what-actually-matters-2026/ Last updated: 2026-07-13T16:59:36.000Z The tool showed up on a Friday afternoon. By Sunday, the presentation draft was done. That is not a story about a powerful AI feature. It is a story about what happens when preparation meets opportunity. Most professionals assume the gap is access. Better tools, bigger budgets, more technical experience. This week is about what the gap actually is. 🧭 **Founder's Corner:** How months of building outside of work made a new workplace AI feature immediately useful, and what that taught me about where real AI fluency comes from. 🧠 **AI Education:** The AI Agents series closes with a complete real-world scenario from goal to finished output, including the failure mode that almost made it wrong and the one step that caught it. ✅ **10-Minute Win:** Use a three-prompt interview sequence to turn your raw thinking into a pressure-tested presentation outline before you open a single slide. Let's jump in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **Microsoft unveils AI upgrades, rolls out Copilot Cowork to early-access customers**](https://www.reuters.com/business/microsoft-unveils-ai-upgrades-rolls-out-copilot-cowork-early-access-customers-2026-03-30/?ref=mindovermoney.ai) **Summary:** Microsoft announced new Copilot features that let users bring multiple AI models into the same workflow, including tools that compare answers and have one model check another’s work. It also expanded early access to Copilot Cowork, its more agent-like assistant for collaborative work. **Why it matters:** This is a practical sign of where AI products are going next: not just one chatbot answering questions, but systems that compare, verify, and help complete more complex work. It also shows Microsoft is trying to make AI more useful inside real workflows, not just more impressive in demos. ### **2)**[ **Apple sets June date for WWDC 2026, teasing ‘AI advancements’**](https://techcrunch.com/2026/03/23/apple-wwdc-june-8-12-ai-advancements-siri-developers-conference/?ref=mindovermoney.ai) **Summary:** Apple said WWDC 2026 will run June 8–12 and previewed a conference focused in part on “AI advancements,” alongside updates to its major software platforms and developer tools. TechCrunch reports this could include a more advanced Siri with better personal context and on-screen awareness. **Why it matters:** Apple has been behind the loudest AI headlines, so any serious AI push from WWDC matters because of how many mainstream users and developers sit inside its ecosystem. If Apple shows a more capable Siri and stronger AI tools, that could move AI further into everyday consumer tech. ### **3)**[ **OpenAI CEO Sam Altman reportedly teases a “very strong” model internally that can “really accelerate the economy”**](https://the-decoder.com/openai-ceo-sam-altman-reportedly-teases-a-very-strong-model-internally-that-can-really-accelerate-the-economy/?ref=mindovermoney.ai) **Summary:** The Decoder reports that Sam Altman told employees OpenAI has finished pretraining a new model, codenamed “Spud,” and expects to have a “very strong model” in a few weeks. The report says Altman framed it as something that could materially speed up economic impact, though the story is based on reporting about an internal memo. **Why it matters:** Even as a reported internal tease, this is notable because it signals OpenAI thinks another meaningful model step may be close. For beginners, the bigger takeaway is that the competition between OpenAI, Anthropic, Google, and Microsoft is still accelerating fast, and product capability gaps can change quickly. ### **4)**[ **US judge blocks Pentagon's Anthropic blacklisting for now**](https://www.reuters.com/world/us-judge-blocks-pentagons-anthropic-blacklisting-now-2026-03-26/?ref=mindovermoney.ai) **Summary:** A federal judge temporarily blocked the Pentagon from blacklisting Anthropic after the company refused to loosen guardrails around military surveillance and autonomous weapons use. The ruling said the government’s move appeared punitive and raised serious free speech and due process concerns. **Why it matters:** This is one of the clearest recent examples of how AI policy battles are moving from theory into courts and contracts. It also highlights a bigger question: how much control governments should have over AI companies when safety limits conflict with national security demands. ### **5)**[ **Sanders, Ocasio-Cortez push bill to impose AI data center moratorium**](https://apnews.com/article/data-centers-ai-electricity-sanders-aoc-65651bd28c3d911d18eeb46cd54f4c75?ref=mindovermoney.ai) **Summary:** Bernie Sanders and Alexandria Ocasio-Cortez introduced a bill to pause new AI data centers, arguing lawmakers need time to better understand the risks around electricity prices, pollution, water use, and broader AI impacts. AP notes the bill is unlikely to pass, but it reflects growing political pressure around the infrastructure behind the AI boom. **Why it matters:** AI is now colliding with energy and community politics in a visible way. This matters because the next phase of AI growth is not just about better models — it is also about whether the country is willing to absorb the cost, power demand, and local backlash that large-scale AI infrastructure creates. --- ## Founder's Corner ****Why Being Ready Matters More Than Having the Right Tool** Sometimes the hardest part of writing something important is knowing the blank page is about to cost you three hours you do not have. Writing a talk track is one of those tasks that looks simple from a distance and quietly takes over your week once you sit down to do it. I have done it hundreds of times. I know the process. I also know it asks for a level of focus that is hard to find when the rest of the week is already full. I have an in-person presentation coming up at the end of April, and I needed to turn a pile of ideas into something I could actually say out loud with confidence. Not an outline. Not bullet points that only made sense in my head. A real spoken draft with structure, flow, and enough clarity that I could start rehearsing it. **The Opportunity Was Already Waiting** My plan was simple and not exciting. Write the outline Sunday morning, rough draft Sunday night. Not how I wanted to spend the weekend, but the only realistic path to having enough time to refine it properly. On Friday afternoon, that plan changed. A new feature appeared inside Microsoft Copilot at work: Agent Builder. It lets you build a personalized version of Copilot around a specific task with custom instructions, context documents, and a defined output standard. I work in healthcare, a regulated environment where my workplace AI usage had stayed closer to standard chat use cases until now. Useful for the right tasks, but narrow. Outside of work, Neural Gains Weekly and MindOverMoney.ai are where I stay current and build judgment about what AI actually does versus what it claims to do. That preparation mattered. When Agent Builder showed up on my dashboard, I did not have to spend time figuring out what to build. I already had a candidate workflow sitting there waiting. Most people assume the breakthrough is getting access to the tool. I do not think that is where the real value starts. It starts when you stop asking what the tool can do and start asking where your time keeps disappearing. **Building the System** My goal was not to build a magic speechwriter. I needed to reduce friction in a process that consistently takes too long. A talk track for this type of presentation typically costs me five hours of focused work. I wanted to cut that in half without cutting corners on quality. Instead of dumping half-formed notes into a chat box and hoping the model sorted them into something useful, I built a structured intake process. A presentation brief schema shaped the inputs upfront. Audience. Objective. Tone. Key messages. Story arc. Constraints. The basic building blocks of a strong talk track, captured with intention before the agent wrote a single word. That intake step did something I did not fully anticipate. It forced me to get clear on my own presentation before the agent drafted anything. That is an underrated part of good AI workflows. The value is not always waiting at the end in the form of a polished output. Sometimes it shows up earlier, in the structure the system forces on you. Better intake produces better thinking. Better thinking produces better output. I also designed the agent to ask one question at a time when something important was missing. That kept the workflow honest. It reduced the chance the model would fill in gaps with confident nonsense or drift toward something that sounded right but was not actually mine. I gave it a gold-standard example of what strong output looked like. I grounded it in a storytelling framework that kept the talk track feeling spoken rather than a written piece. And I was disciplined about the context I fed it. Because this is the part most people still underestimate. A pile of files is not a system. More context is not automatically better context. Clearer material produced a clearer draft. Better examples produced better structure. Tighter context made the output usable faster. **The Result** The first draft was not final and that was expected. But it was strong enough to work with immediately. It followed the shape I needed. It sounded like a talk track, and reflected the structure and standard I built into the system. It gave me something to start revising instead of burning energy just trying to get momentum off a blank page. I would call it 70 percent of the way there on the first pass. That is exactly the kind of result I want from a system like this. I did not need AI to finish the job. I needed it to get me to a strong starting point faster so I can spend my time revising, tightening, and making sure the final version was presentation-ready. From a time standpoint, this saved me at least three hours. Maybe more. Three hours is the difference between a task hanging over your week and a task finally moving. That is enough to change how a workday feels. **The Bigger Lesson** I did not build this agent because I had access to some advanced enterprise AI platform. I built it because I had been training my instincts outside of work for months. Neural Gains Weekly and MindOverMoney.ai gave me the reps. By the time Agent Builder showed up on my dashboard, I already knew what a good workflow looked like, what context actually meant, and where my time was being wasted. The tool was new. The thinking behind it was not. That is the part of this story I do not want to get lost. You do not need the most sophisticated tool available to build something useful. You need a clear problem, a structured approach, and enough judgment to know what good output looks like before you start. Those things can be developed with any AI tool, on any platform, at any experience level. Most professionals are sitting on workflows right now that AI could improve. Not automate entirely. Not replace. Improve. The talk track was mine. The presentation brief process, the Sunday grind, the hours of staring at a blank page, that was the friction. I just finally built a system around it. The opportunity is usually already there. You just have to be ready to see it when it shows up. ## AI Education for You ****AI Agents, Part 4 - Agents in the Wild: One Scenario, Start to Finish** ### **The Situation** Marcus is an operations manager at a mid-size healthcare services company. His director has asked him to evaluate three software vendors shortlisted for a new care coordination platform and deliver a recommendation memo before next Thursday's leadership meeting. The evaluation involves reviewing each vendor's publicly available documentation, recent press coverage, customer case studies, and pricing model — then synthesizing it into a clear recommendation with supporting rationale. He has done this before. Open tabs, copy notes into a document, lose track of which detail came from which vendor, draft the memo, realize he missed something, go back. Normally two days. This week he does not have two days. Marcus decides to run an agent on it. ### **What They Try First (And Why It Falls Short)** His first prompt is what he would give a chatbot: "Evaluate these three vendors and tell me which one is best for care coordination software." What comes back looks useful until Marcus checks the dates. Two vendors have released major platform updates in the last six months. The pricing structures referenced are outdated. One vendor was acquired and rebranded. The agent ran the task. It just ran it on stale information with a goal too vague to constrain the output. Marcus got an answer. He did not get a reliable one. ### **The Concept, Through the Scenario** The problem is not the agent. It is the goal definition and the tool configuration — two things Marcus controls before the agent runs a single step. He resets and writes the goal in concrete terms: "Research each of the following three vendors using current web sources only. For each vendor, find: current product capabilities, pricing model, recent customer case studies, and any news from the last 90 days. Store findings by vendor. Then draft a one-page recommendation memo comparing the three on care coordination fit, implementation complexity, and total cost. Flag any claims you could not verify with a current source." He enables web search and sets a recency filter on results. The agent searches each vendor in sequence, reads current documentation, pulls recent press releases, extracts pricing details, and saves a structured summary to external memory before moving to the next vendor. It is not working from training data. The reasoning loop from Vol 25 runs continuously. The memory architecture from Vol 26 holds Vendor A's findings intact while the agent works through Vendor B and Vendor C without conflating them. Forty minutes later a draft memo lands in Marcus's document. ### **What Changes** The memo is structured exactly as requested — a one-page comparison across the three criteria, a clear recommendation, and a section flagging three data points the agent could not verify with a current source. Marcus reads it. The recommendation is Vendor B. The rationale is sound and the sourcing is recent. But something catches his attention: implementation complexity is weighted more heavily than he intended. He checks the reasoning log. Four steps into the run, the agent encountered a customer case study describing a painful implementation experience with a competitor platform — and quietly adjusted its evaluation weighting in response to that single document. Goal drift. Not catastrophic. But if Marcus had sent that memo without reading it, the recommendation would have been defensible and subtly wrong for reasons nobody in the room would have caught. He adjusts the weighting, updates the memo in two minutes, and sends it. Under an hour, including the correction. ### **What This Reveals** The agent did not fail. The loop ran cleanly, the memory held, the tools worked. What nearly went wrong was a reasoning decision made mid-run in response to a single retrieved document — a drift away from the original goal that looked like analysis. Marcus caught it because he checked the reasoning log. Most people do not. This is what using agents well actually looks like. Define the goal precisely, configure the tools deliberately, verify the output against the original goal — not just whether it looks polished. The agent handles the execution. You are still responsible for the result. ### **How This Connects** Four volumes ago the core distinction was simple: agents act, chatbots respond. Vol 25 went inside the reasoning loop and introduced the failure modes that live inside a long run. Vol 26 extended the picture to memory and tools, connecting agent memory retrieval directly to the RAG architecture in Vol 19-22\. This volume put all of it into a single run: goal definition, tool configuration, the reasoning loop, external memory, goal drift, and the verification step that separates a useful output from a confident wrong one. *Part 4 of 4 in the AI Agents series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Presentation Outline Jumpstart** **Why this matters:** The reason most presentations feel generic is not the slides — it is that the thinking behind them was never fully fleshed out. This workflow uses AI to interview you before it builds anything, pulling out the context, audience insight, and key message that makes a presentation worth sitting through. By the end you have a pressure-tested outline built from your own thinking, not a template. ### **The Workflow** **1\. Start the Interview (4 Minutes)** Open Claude, ChatGPT, or Gemini and paste the prompt below exactly as written. Do not fill anything in yet — the model will ask the questions. **Copy/Paste Prompt 1:* "I need to build a presentation outline and I want you to help me think it through before you build anything. Your job right now is to interview me.* *Ask me one question at a time — no lists, no multi-part questions. Each question should help you understand one of these things: my goal for this presentation, who the audience is and what they care about, the key message I want them to walk away with, any objections or skepticism they might bring into the room, and any constraints I am working with like time, tone, or format.* *Start with your first question now."* Answer each question in plain, conversational language. Do not overthink your answers — the rougher and more honest they are, the better the outline you get back. Keep going until the model tells you it has enough to build from, or until you have answered five to six questions. **2\. Build the Outline (3 Minutes)** Once the interview wraps, paste this prompt in the same chat window. The model already has everything it needs from the conversation above. **Copy/Paste Prompt 2:* "You now have everything you need. Build me a complete slide-by-slide presentation outline using everything I just told you.* *For each slide include:* 1. *A slide title* 2. *Two to three bullet points of what goes on that slide* 3. *One talking point — the single thing I must say out loud that the slide alone will not convey* *Make the opening slide earn attention immediately. Make the closing slide land with a clear, specific next step. Do not add slides just to fill space."* Read through the full outline once from start to finish before touching anything. Read it as if you are sitting in the audience seeing it for the first time. **3\. Challenge the Structure (3 Minutes)** Stay in the same chat. Paste this final prompt without changing a word. **Copy/Paste Prompt 3:* "Now read this outline as a skeptical audience member who has seen too many presentations. Tell me:* 1. *Does the opening earn attention or ease in too slowly?* 2. *Is there a slide that could be cut without losing anything important?* 3. *Where does the logic feel weak or the flow feel off?* 4. *Does the closing make the next step obvious or does it fizzle?* *Be direct. Then give me a revised outline that fixes what you found."* Accept the revisions that make the structure stronger. Ignore the ones that do not fit your context. The model will catch gaps you cannot see because you are too close to your own material. ### **The Payoff** You now have a presentation outline that was built from a real conversation, not a template — and then challenged before you opened a single slide. The talking points are mapped, the logic has been tested, and the opening and close have been pressure-checked. You did not just generate an outline. You thought your presentation through. ### **🧠 The AI Concept You Just Used** **Conversational context building + chained prompting.** Instead of front-loading all the information yourself, you let the model extract it through a structured interview. Each prompt in the chain built on everything the model already learned — so by the time it wrote your outline, it knew your audience, your goal, and your constraints better than a blank prompt ever could. This technique works for any complex output you need AI to build. ### **Transparency & Notes** - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com) — all free tier, no credit card required. - **Privacy:** Keep your answers conversational and general. Avoid sharing proprietary strategy, financial data, or confidential client details during the interview. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Why Being Ready Matters More Than Having the Right Tool URL: https://www.mindovermoney.ai/founders-corner/ai-readiness-vs-ai-access-professional-advantage/ Last updated: 2026-07-13T16:59:36.000Z Sometimes the hardest part of writing something important is knowing the blank page is about to cost you three hours you do not have. Writing a talk track is one of those tasks that looks simple from a distance and quietly takes over your week once you sit down to do it. I have done it hundreds of times. I know the process. I also know it asks for a level of focus that is hard to find when the rest of the week is already full. I have an in-person presentation coming up at the end of April, and I needed to turn a pile of ideas into something I could actually say out loud with confidence. Not an outline. Not bullet points that only made sense in my head. A real spoken draft with structure, flow, and enough clarity that I could start rehearsing it. **The Opportunity Was Already Waiting** My plan was simple and not exciting. Write the outline Sunday morning, rough draft Sunday night. Not how I wanted to spend the weekend, but the only realistic path to having enough time to refine it properly. On Friday afternoon, that plan changed. A new feature appeared inside Microsoft Copilot at work: Agent Builder. It lets you build a personalized version of Copilot around a specific task with custom instructions, context documents, and a defined output standard. I work in healthcare, a regulated environment where my workplace AI usage had stayed closer to standard chat use cases until now. Useful for the right tasks, but narrow. Outside of work, Neural Gains Weekly and MindOverMoney.ai are where I stay current and build judgment about what AI actually does versus what it claims to do. That preparation mattered. When Agent Builder showed up on my dashboard, I did not have to spend time figuring out what to build. I already had a candidate workflow sitting there waiting. Most people assume the breakthrough is getting access to the tool. I do not think that is where the real value starts. It starts when you stop asking what the tool can do and start asking where your time keeps disappearing. **Building the System** My goal was not to build a magic speechwriter. I needed to reduce friction in a process that consistently takes too long. A talk track for this type of presentation typically costs me five hours of focused work. I wanted to cut that in half without cutting corners on quality. Instead of dumping half-formed notes into a chat box and hoping the model sorted them into something useful, I built a structured intake process. A presentation brief schema shaped the inputs upfront. Audience. Objective. Tone. Key messages. Story arc. Constraints. The basic building blocks of a strong talk track, captured with intention before the agent wrote a single word. That intake step did something I did not fully anticipate. It forced me to get clear on my own presentation before the agent drafted anything. That is an underrated part of good AI workflows. The value is not always waiting at the end in the form of a polished output. Sometimes it shows up earlier, in the structure the system forces on you. Better intake produces better thinking. Better thinking produces better output. I also designed the agent to ask one question at a time when something important was missing. That kept the workflow honest. It reduced the chance the model would fill in gaps with confident nonsense or drift toward something that sounded right but was not actually mine. I gave it a gold-standard example of what strong output looked like. I grounded it in a storytelling framework that kept the talk track feeling spoken rather than a written piece. And I was disciplined about the context I fed it. Because this is the part most people still underestimate. A pile of files is not a system. More context is not automatically better context. Clearer material produced a clearer draft. Better examples produced better structure. Tighter context made the output usable faster. **The Result** The first draft was not final and that was expected. But it was strong enough to work with immediately. It followed the shape I needed. It sounded like a talk track, and reflected the structure and standard I built into the system. It gave me something to start revising instead of burning energy just trying to get momentum off a blank page. I would call it 70 percent of the way there on the first pass. That is exactly the kind of result I want from a system like this. I did not need AI to finish the job. I needed it to get me to a strong starting point faster so I can spend my time revising, tightening, and making sure the final version was presentation-ready. From a time standpoint, this saved me at least three hours. Maybe more. Three hours is the difference between a task hanging over your week and a task finally moving. That is enough to change how a workday feels. **The Bigger Lesson** I did not build this agent because I had access to some advanced enterprise AI platform. I built it because I had been training my instincts outside of work for months. Neural Gains Weekly and MindOverMoney.ai gave me the reps. By the time Agent Builder showed up on my dashboard, I already knew what a good workflow looked like, what context actually meant, and where my time was being wasted. The tool was new. The thinking behind it was not. That is the part of this story I do not want to get lost. You do not need the most sophisticated tool available to build something useful. You need a clear problem, a structured approach, and enough judgment to know what good output looks like before you start. Those things can be developed with any AI tool, on any platform, at any experience level. Most professionals are sitting on workflows right now that AI could improve. Not automate entirely. Not replace. Improve. The talk track was mine. The presentation brief process, the Sunday grind, the hours of staring at a blank page, that was the friction. I just finally built a system around it. The opportunity is usually already there. You just have to be ready to see it when it shows up. ### Steal My Prompt Vol. 27: The Session Saver URL: https://www.mindovermoney.ai/prompt-library/ai-context-window-session-saver-prompt-handover/ Last updated: 2026-07-13T16:59:36.000Z Every regular AI user eventually hits the context window. You are deep into a complex task and the chat slows down, responses get fuzzy, or you get a message telling you the conversation is too long to continue. For most people, this is where the work dies. You start a new chat, re-explain everything from scratch, and spend twenty minutes getting the AI back up to speed. Half the context you built never makes it over. I have hit this wall building this newsletter — mid-article, mid-build, mid-deployment. What I learned is that the fix is simple. Before you lose context, capture your role, your status, and your next step in one structured note. Paste it into a new chat and keep going. I now write the recovery note before I need it. That is the real shift. You do not wait until the window closes. You write the handoff while things are still working. **How to use it:** 1. When your session is running well but getting long, pause and fill in the template below. 2. Save it somewhere accessible — your notes app, a Google Doc, the clipboard. 3. When the context window closes or the session degrades, open a new chat. 4. Paste the completed template as your first message. 5. Recommended: do this proactively every 30 to 45 minutes on long sessions. Do not wait until the window forces you. --- *You are picking up a work session that hit a context window limit in a previous chat. Here is everything you need to continue without losing ground.* *MY ROLE AND GOAL:* *\[Describe who you are and what you are trying to accomplish — e.g., "I am a project manager drafting a business case for a new internal tool" or "I am a solo founder writing a blog post series about my experience building a product"\]* *WHAT WE WERE WORKING ON:* *\[Name the specific task — e.g., "writing the third section of a blog post," "building a research brief," "drafting a proposal," "planning a presentation"\]* *WHAT HAS BEEN COMPLETED:* *\[List what was finished in the previous session — be specific. Copy outputs directly if you have them.\]* *WHERE WE STOPPED:* *\[Describe the exact point where the session ended — e.g., "We had just finished the outline and were about to draft Section 2" or "We were halfway through the research phase"\]* *WHAT COMES NEXT:* *\[The single next action — e.g., "Draft Section 2 using the outline we locked" or "Continue the research for the third argument"\]* *KEY DECISIONS ALREADY MADE:* *\[List any important choices that should not be revisited — e.g., tone, format, scope, constraints, specific wording that was approved\]* *IMPORTANT CONTEXT TO CARRY FORWARD:* *\[Anything else the new session needs — background on the project, audience, voice guidelines, constraints, or relevant outputs from the previous session\]* *Acknowledge that you have read this and confirm what we are picking up from. Then continue.* --- *Transparency & Notes: This prompt works in Claude, ChatGPT, and Gemini. The more specific your "Where We Stopped" and "Key Decisions Already Made" sections are, the faster the new session gets back up to speed. If you have outputs from the previous session — an outline, a draft section, a research note — paste them directly into the "What Has Been Completed" field. The AI does not need a summary. It needs the actual work.* ### Volume 26: Bracket Your Bottlenecks URL: https://www.mindovermoney.ai/how-ai-agents-work-non-technical-professionals-2026/ Last updated: 2026-07-13T16:59:37.000Z March Madness is here. 64 teams down to 16, one bracket, and the same lesson every year: the team that wins is rarely the one trying to do everything. It is the one that knows exactly what it does well and executes it relentlessly. That is the agent conversation nobody is having right now. Everyone is chasing the autonomous system that does everything at once. The professionals who are actually getting results are building smaller, narrower, and more deliberately. This week is about that friction. 🧭 **Founder's Corner:** How a three-role agent system fixed the one part of Neural Gains Weekly that kept draining time, and what that taught me about where agents actually deliver for non-technical professionals. 🧠 **AI Education:** Part 3 of the AI Agents series covers memory architectures, tools, and multi-agent systems, including where the handoffs break and what that means for output quality. ✅ **10-Minute Win:** Turn a rough project assignment into a one-page kickoff brief with goals, success metrics, risks, and first actions in under 10 minutes. Let's dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **Microsoft Copilot AI leadership changes put Mustafa Suleyman on model-building**](https://www.cnbc.com/2026/03/17/microsoft-copilot-ai-suleyman.html?ref=mindovermoney.ai) **Summary:** Microsoft is reshuffling its Copilot organization so Jacob Andreou leads the product experience while Mustafa Suleyman shifts attention toward building Microsoft’s own AI models. The move is meant to simplify a fragmented Copilot strategy and sharpen Microsoft’s AI competitiveness. **Why it matters:** This is a signal that Microsoft thinks winning AI is not just about slapping Copilot into more products. It needs a better product experience and stronger in-house models if it wants to keep pace with OpenAI, Google, and Anthropic. ### **2)**[](https://nvidianews.nvidia.com/news/nvidia-announces-nemoclaw?utm%5Fsource=chatgpt.com)[**What 81,000 people want from AI**](https://www.anthropic.com/features/81k-interviews?ref=mindovermoney.ai) **Summary:** Anthropic published results from interviews with more than 80,000 Claude users about how they use AI, what they hope it will do, and what worries them. The piece frames it as a large-scale qualitative look at real user expectations and concerns around AI. **Why it matters:** Most AI coverage focuses on labs, launches, and hype. This one matters because it shows what regular people actually want from AI, which is a better signal for where products, trust, and adoption may go next. ### **3)**[ **Trump releases AI policy for Congress to pre-empt state rules**](https://www.reuters.com/world/us/white-house-releases-national-ai-framework-2026-03-20/?ref=mindovermoney.ai) **Summary:** The White House released an AI framework that pushes Congress to create one national set of rules instead of letting states build their own patchwork. The proposal also covers child safety, scam prevention, energy use, workforce development, and faster buildout for AI infrastructure. **Why it matters:** The AI rulebook is being written right now. A single federal framework could make it easier for companies to deploy AI across the country, but it also raises the stakes around what protections and limits make it into law. ### **4)**[ **Hands-On with Claude Dispatch for Cowork**](https://www.macstories.net/stories/hands-on-with-claude-dispatch-for-cowork/?ref=mindovermoney.ai) **Summary:** Anthropic launched Dispatch as a research preview for Claude Cowork, letting users control a sandboxed Mac-based Cowork session from a mobile device. According to MacStories’ hands-on, it is currently available to Max subscribers, with Pro users expected soon. **Why it matters:** This is a more concrete look at where agent-style AI is heading: not just answering questions, but remotely operating a computer session to help complete work. It makes the “AI coworker” idea easier for normal users to picture. ### **5)**[](https://www.anthropic.com/features/81k-interviews?utm%5Fsource=chatgpt.com)[**NVIDIA Announces NemoClaw for the OpenClaw Community**](https://nvidianews.nvidia.com/news/nvidia-announces-nemoclaw?ref=mindovermoney.ai) **Summary:** NVIDIA introduced NemoClaw, a stack for the OpenClaw agent platform that installs models and runtime tools in one command and adds privacy, sandboxing, and security controls. NVIDIA says it is designed to make always-on AI agents easier to run locally, on-premises, or in the cloud. **Why it matters:** This is part of the bigger shift from chatbots to agents that can actually do tasks. It also shows where the market is going: companies now need the infrastructure and guardrails around agents, not just the model itself. --- ## Founder's Corner ****Why I Needed a System, Not an Agent** People say “AI agent” and view it as a singular concept, instead of one that spans a wide range of tools and capabilities. On one end, an agent can be a narrowly scoped assistant with a role, context, and a defined job. On the other, it is the version that gets all the headlines and hype. Press releases highlighting autonomous agents writing code bases from scratch. Open-sourced multi-agent systems redesigning workflows. Viral social media posts touting tools operating with very little human input. That side of the spectrum is exciting, and a crucial part of the future of work. But for most professionals outside the technical world, it can also feel distant from the actual problems sitting inside a normal workweek. Too abstract, too complex, and a little hard to translate into something immediately useful. Sometimes the best agent for the job is the simpler one. That is something I have learned through a lot of hands-on experimentation. Most of the systems I am using right now live much closer to that first end of the spectrum. Once I stopped chasing the hype and started solving for real friction in my own work, the whole category became a lot more useful. **The Bottleneck** For me, that friction kept showing up in the same place every week: Founder’s Corner. The rest of the newsletter was getting cleaner over time. I had built reusable prompts and context docs that made production faster and better. Research was tighter. The system was improving. But Founder’s Corner still sat outside the repeatable parts of the system. It was manual, slower, and harder to force into a repeatable rhythm because it needed a few hours of real attention from me every week. That part matters, because I actually enjoy writing it. But enjoying something is not the same as having a sustainable way to do it 52 weeks a year. That was the problem I was trying to solve. Not how to automate my writing or hand authorship to AI. I needed a better way to get started without giving away the part of the process that still had to be mine. **One Assistant Wasn’t Enough** I started where most people start: one general-purpose assistant doing the whole job. Help me find the angle, draft the piece, clean up the writing. In theory, that should have been enough. It was not. The problem was not that the output was terrible. It was that it looked better at first glance than it actually was. Once I read it closely, the weaknesses were obvious. The outline was shallow. The structure kept falling into the same patterns. The language sounded clean, but not like me. It could get me moving, but it was not getting me to the right place. That was the point where I stopped trying to force one tool to do everything. Structuring an idea, drafting a piece, and editing it are different jobs. They need different context, different instructions, and a different standard for what good looks like. So I broke the work into three steps, with each output feeding the next. **The System I Built** My brain can feel like an overcrowded subway train sometimes, with ideas moving in and out faster than I can sort them. The hard part was not having ideas. It was getting them out of my head and into a format I could actually write from in a consistent way. That is what led to the first role in the system: the Brief Architect. I would give it the rough idea, the tension, and whatever notes I had, and it would help me turn that into something usable. The output was an organized brief that included the core thesis, the reader payoff, the structure, the clichés to avoid, and the places where the piece could drift off course. More than anything, it forced me to get clear on what I was actually trying to say so the next step had something real to build from. The second role was the Ghostwriter. Its job was not to produce a polished final piece. It was to turn that brief into a working draft with shape and momentum. I grounded it in past Founder’s Corner articles so it had a better feel for my voice and the boundaries of the section. That made it much easier to get from idea to first pass without starting from a blank page every time. The last role was the Final Editor, and that is where the refinement happened. Once I had a near-final draft, I would run it through that layer to catch repetition, weak transitions, false notes, and the places where the language started sounding more like a model performing than me actually thinking on the page. That step matters most because it is where I make sure my voice is still coming through clearly, especially in a writing process like this where I am experimenting with AI. Calling it a three-agent system makes it sound more elaborate than it felt in practice. What I really built was role clarity. Each part had a narrower job, and because of that, the outputs got better. On the first run, the system got me roughly 60 percent of the way there. I still had to step in and build the story. But that 60 percent mattered more than I expected. Instead of staring at a blank page and trying to create clarity from scratch, I had a brief with shape, a draft with momentum, and an editing layer already pressure-testing the weak spots. I was no longer starting from zero every week, and for a section like Founder’s Corner, that is a meaningful shift. **What Most Professionals Actually Need** That experience sharpened how I think about agents more broadly. I do not think most professionals need autonomous systems moving across their calendar, inbox, documents, and every other part of their work. They need better support around recurring friction. They need help with the parts of the job that are structured enough to hand off, repetitive enough to keep draining energy, and important enough that the wasted time adds up. That is the version of agentic work that feels accessible to me, and honestly, a lot more relevant right now. Founder’s Corner was my constraint, and the reason I built support around it is the same reason I do not want to over-automate it. This section is supposed to carry my perspective. It is where I try to make sense of what I am building, what I am noticing, and what I think everyday professionals actually need from AI right now. If I outsource too much of that, I may save time while quietly weakening the thing that makes the piece worth reading in the first place. That is why I do not think the most useful way to understand agents is as a binary choice between doing everything yourself and handing everything over. It makes more sense to think of them as tools that can take on a narrow role inside a system you still own. Start with one frustrating part of your week. Get specific about where the drag actually lives. Give the tool one clear job. Keep your hands on the part that still requires taste, accountability, and trust. That is what this three-agent system gave me. Not authorship on autopilot. Not some flashy demo I can use to make a bigger claim than it deserves. Just a better way into the work that still has to be mine. I think that is how this category becomes real for more people. The first useful agent in your life probably will not look like the version on stage at a keynote. It will be smaller than that. Narrower. Maybe even a little boring from the outside. But if it helps you stop wasting energy on the wrong part of the process, you will feel the difference immediately. The future of work will not arrive all at once through some perfect autonomous system. It will show up one solved bottleneck at a time. ## AI Education for You ****AI Agents, Part 3 - Memory, Tools, and Multi-Agent Systems: How Agents Get Smarter** ### **What Is Actually Going On Here** Every time an agent completes a reasoning step, it has a decision to make: what from this run do I need to carry forward, and what can I let go? The agent is not storing everything. It cannot. The context window is finite, the task may run for dozens of steps, and holding every intermediate result would crowd out the reasoning space the agent needs to keep working. So the system manages what persists and what gets dropped — a process that happens continuously in the background, shaping what the agent can do at step fifteen based on decisions made at step three. Most users never see this management layer. They see the output. The memory decisions that produced it are invisible. ### **The Problem That Made This Necessary** Early agent prototypes had a simple memory model: everything stayed in the context window until it ran out. This worked on short tasks. On anything complex it failed badly. The agent would hit the context limit mid-run, lose access to earlier results, and either produce incoherent output or stop entirely. Researchers also discovered a subtler problem — even before the context limit, long context windows degraded reasoning quality. Models tend to weight recent tokens more heavily than earlier ones. An important constraint stated at the beginning of a long run could be functionally forgotten by the end, not because the tokens were gone but because their influence on reasoning had diminished. The response was a tiered memory architecture: separate what the agent needs right now from what it might need later, and build retrieval mechanisms that pull stored information back into context only when it becomes relevant. That design is directly related to what RAG does for knowledge retrieval — the same principle, applied to the agent's own working memory rather than an external document corpus. ### **How It Actually Works** Agent memory operates across two layers, with tools sitting alongside them as a third source of capability. **In-context memory** is everything currently inside the active context window — the goal, the reasoning trace, tool results, prior steps. This is fast and immediately accessible. It is also temporary and size-constrained. Anything in-context disappears when the run ends. **External memory** is a persistent store the agent can read from and write to during a run. This is where findings get saved mid-task so they are not lost if the context fills. It is also how agents maintain continuity across separate sessions — a project an agent worked on yesterday can be resumed today because the relevant state was written to external memory before the session closed. The retrieval mechanism that pulls stored information back into context when needed is the same embedding-based search covered in Vol 19-22\. The agent searches its own memory the same way a RAG system searches a document corpus. **Tools** extend what the agent can act on. In Vol 24 these were introduced as a list — web search, code execution, file reading, calendar access. The more useful frame is to think of tools as the agent's hands. Memory is what the agent knows. Tools are what it can reach. A well-configured agent with the right tool set can query a live database, send a draft email for review, execute and test code it wrote, and pull a real-time pricing feed — all within a single run, without a human touching anything between steps. **Multi-agent systems** extend the architecture one level further. Instead of one agent running a long loop, multiple specialized agents run in parallel or in sequence — each handling one part of a larger task. A research agent gathers information. A drafting agent writes from what the research agent found. A review agent checks the draft against a set of criteria. An orchestrating agent manages handoffs between them. The advantage is division of labor. The risk is that errors compound across agents the same way they compound across steps — but now the compounding happens between systems that cannot directly observe each other's reasoning. ### **Where It Still Breaks** **Retrieval failures in external memory.** If the agent stores a result with poor metadata or a weak embedding, it may fail to retrieve it when it becomes relevant later in the run. The information exists. The agent cannot find it. The downstream reasoning proceeds without it. **Handoff failures in multi-agent systems.** When one agent passes output to another, the receiving agent only knows what was handed to it. Context that felt implicit to the first agent is invisible to the second. Ambiguity that a human would catch in a handoff note gets passed forward as fact. **Memory that ages badly.** External memory stores what was true at the time of writing. An agent working from stored results generated last week may be reasoning from outdated information — with no signal that anything has changed. ### **What This Means for How You Work With It** When an agent task spans multiple sessions or a long run, check what it is working from. If the task relies on external memory, ask what was stored from the previous session and whether it is still accurate. Treat multi-agent system outputs with extra scrutiny. The more handoffs between agents, the more opportunities for context to degrade between steps. Verify the final output against the original goal, not just the immediate prior step. When configuring tools for an agent, match the tool set to the task. An agent with access to tools it does not need for the current task adds risk without adding capability. Narrow the tool set, narrow the failure surface. ### **How This Connects** Vol 24 named the three components that make agents different from chatbots. Vol 25 went inside the reasoning loop and showed how ReAct patterns replaced fixed planning and where loops break under real conditions. This volume completes the architectural picture: how memory extends what agents can hold across a run, how tools extend what they can act on, and how multi-agent systems distribute that architecture across specialized workers. The RAG series (Vol 19-22) is directly load-bearing here — embedding-based retrieval is not just how agents find documents, it is how they search their own stored memory. Vol 27 puts all four volumes to work in a single professional scenario, start to finish, including the failure modes from all three parts showing up in one run. *Part 3 of 4 in the AI Agents series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The Project Kickoff Brief** Every project needs a north star before the work begins — a shared understanding of what success looks like and who is responsible for getting there. This workflow takes a rough assignment and turns it into a one-page brief that aligns everyone before a single task is completed. The person who sends this document immediately looks like the most prepared person in the room. ### **The Workflow** **1\. Describe Your Assignment (2 Minutes)** Write down everything you know about the project in rough form — even if that is not much. What were you asked to do? Who asked you? Who else is involved? When is it due? What problem is it solving? Do not worry about gaps. The AI will surface them for you. **2\. Run the Kickoff Brief Prompt (5 Minutes)** Open Claude, ChatGPT, or Gemini and paste the prompt below with your details dropped in. **Copy/Paste Prompt:* "I have been assigned a new project and need to create a one-page kickoff brief to align my team before we begin. Here is what I know so far:* *Project description: \[describe the project or paste the assignment as given to you — rough is fine\] Key stakeholders: \[who is involved or affected — names, roles, or departments\] Deadline or timeline: \[what you know, even if it is approximate\] What success looks like: \[your best guess if you are not sure\]* *Please create a structured one-page project kickoff brief with the following sections:* 1. *Project Goal — one clear sentence on what we are trying to accomplish and why it matters* 2. *Definition of Success — 2 to 3 specific, measurable outcomes that would signal this project is done well* 3. *Key Stakeholders — who owns it, who contributes, who needs to be informed* 4. *Top 3 Risks — the most likely things that could derail this project, each with a one-line mitigation* 5. *First Three Actions — the specific next steps to take in the next 5 business days, with an owner for each* *Keep the language clear and direct. No filler. This document should be something I can share with my team or manager immediately."* Read through each section carefully. Pay most attention to the Definition of Success — if those outcomes feel vague or unmeasurable, reply in the same chat: "Make the success metrics more specific and measurable." That one refinement is usually what separates a good brief from a great one. **3\. Refine and Save Your Asset (3 Minutes)** Ask the model to tighten anything that feels off, then copy the final brief into a Google Doc titled "\[Project Name\] Kickoff Brief — \[Date\]." Share it with your team or drop it into the project channel before your first meeting. You will be the only person in the room with a document. ### **The Payoff** You now have a professional project brief built in under 10 minutes from a rough description. More importantly, you have a reusable prompt that works for every project you take on from here — whether it is a work assignment, a home renovation, or a side project you have been thinking about starting. ### **🧠 The AI Concept You Just Used** **Ambiguity resolution + structured document generation.** You gave the model incomplete information — just like the real world gives you — and it surfaced the structure you needed to move forward. That ability to turn vague inputs into organized outputs is one of the most practical things AI does for professionals every day. ### **Transparency & Notes** - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com) — all free tier, no credit card required. - **Privacy:** Keep project descriptions general. Avoid sharing confidential client names, proprietary data, or internal financials. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Why I Needed a System, Not an Agent URL: https://www.mindovermoney.ai/founders-corner/ai-workflow-system-vs-agent-what-actually-works/ Last updated: 2026-07-13T16:59:37.000Z People say “AI agent” and view it as a singular concept, instead of one that spans a wide range of tools and capabilities. On one end, an agent can be a narrowly scoped assistant with a role, context, and a defined job. On the other, it is the version that gets all the headlines and hype. Press releases highlighting autonomous agents writing code bases from scratch. Open-sourced multi-agent systems redesigning workflows. Viral social media posts touting tools operating with very little human input. That side of the spectrum is exciting, and a crucial part of the future of work. But for most professionals outside the technical world, it can also feel distant from the actual problems sitting inside a normal workweek. Too abstract, too complex, and a little hard to translate into something immediately useful. Sometimes the best agent for the job is the simpler one. That is something I have learned through a lot of hands-on experimentation. Most of the systems I am using right now live much closer to that first end of the spectrum. Once I stopped chasing the hype and started solving for real friction in my own work, the whole category became a lot more useful. **The Bottleneck** For me, that friction kept showing up in the same place every week: Founder’s Corner. The rest of the newsletter was getting cleaner over time. I had built reusable prompts and context docs that made production faster and better. Research was tighter. The system was improving. But Founder’s Corner still sat outside the repeatable parts of the system. It was manual, slower, and harder to force into a repeatable rhythm because it needed a few hours of real attention from me every week. That part matters, because I actually enjoy writing it. But enjoying something is not the same as having a sustainable way to do it 52 weeks a year. That was the problem I was trying to solve. Not how to automate my writing or hand authorship to AI. I needed a better way to get started without giving away the part of the process that still had to be mine. **One Assistant Wasn’t Enough** I started where most people start: one general-purpose assistant doing the whole job. Help me find the angle, draft the piece, clean up the writing. In theory, that should have been enough. It was not. The problem was not that the output was terrible. It was that it looked better at first glance than it actually was. Once I read it closely, the weaknesses were obvious. The outline was shallow. The structure kept falling into the same patterns. The language sounded clean, but not like me. It could get me moving, but it was not getting me to the right place. That was the point where I stopped trying to force one tool to do everything. Structuring an idea, drafting a piece, and editing it are different jobs. They need different context, different instructions, and a different standard for what good looks like. So I broke the work into three steps, with each output feeding the next. **The System I Built** My brain can feel like an overcrowded subway train sometimes, with ideas moving in and out faster than I can sort them. The hard part was not having ideas. It was getting them out of my head and into a format I could actually write from in a consistent way. That is what led to the first role in the system: the Brief Architect. I would give it the rough idea, the tension, and whatever notes I had, and it would help me turn that into something usable. The output was an organized brief that included the core thesis, the reader payoff, the structure, the clichés to avoid, and the places where the piece could drift off course. More than anything, it forced me to get clear on what I was actually trying to say so the next step had something real to build from. The second role was the Ghostwriter. Its job was not to produce a polished final piece. It was to turn that brief into a working draft with shape and momentum. I grounded it in past Founder’s Corner articles so it had a better feel for my voice and the boundaries of the section. That made it much easier to get from idea to first pass without starting from a blank page every time. The last role was the Final Editor, and that is where the refinement happened. Once I had a near-final draft, I would run it through that layer to catch repetition, weak transitions, false notes, and the places where the language started sounding more like a model performing than me actually thinking on the page. That step matters most because it is where I make sure my voice is still coming through clearly, especially in a writing process like this where I am experimenting with AI. Calling it a three-agent system makes it sound more elaborate than it felt in practice. What I really built was role clarity. Each part had a narrower job, and because of that, the outputs got better. On the first run, the system got me roughly 60 percent of the way there. I still had to step in and build the story. But that 60 percent mattered more than I expected. Instead of staring at a blank page and trying to create clarity from scratch, I had a brief with shape, a draft with momentum, and an editing layer already pressure-testing the weak spots. I was no longer starting from zero every week, and for a section like Founder’s Corner, that is a meaningful shift. **What Most Professionals Actually Need** That experience sharpened how I think about agents more broadly. I do not think most professionals need autonomous systems moving across their calendar, inbox, documents, and every other part of their work. They need better support around recurring friction. They need help with the parts of the job that are structured enough to hand off, repetitive enough to keep draining energy, and important enough that the wasted time adds up. That is the version of agentic work that feels accessible to me, and honestly, a lot more relevant right now. Founder’s Corner was my constraint, and the reason I built support around it is the same reason I do not want to over-automate it. This section is supposed to carry my perspective. It is where I try to make sense of what I am building, what I am noticing, and what I think everyday professionals actually need from AI right now. If I outsource too much of that, I may save time while quietly weakening the thing that makes the piece worth reading in the first place. That is why I do not think the most useful way to understand agents is as a binary choice between doing everything yourself and handing everything over. It makes more sense to think of them as tools that can take on a narrow role inside a system you still own. Start with one frustrating part of your week. Get specific about where the drag actually lives. Give the tool one clear job. Keep your hands on the part that still requires taste, accountability, and trust. That is what this three-agent system gave me. Not authorship on autopilot. Not some flashy demo I can use to make a bigger claim than it deserves. Just a better way into the work that still has to be mine. I think that is how this category becomes real for more people. The first useful agent in your life probably will not look like the version on stage at a keynote. It will be smaller than that. Narrower. Maybe even a little boring from the outside. But if it helps you stop wasting energy on the wrong part of the process, you will feel the difference immediately. The future of work will not arrive all at once through some perfect autonomous system. It will show up one solved bottleneck at a time. ### Steal My Prompt Vol. 26: The Brain Dump to Draft URL: https://www.mindovermoney.ai/prompt-library/ai-writing-prompt-brain-dump-to-structured-draft/ Last updated: 2026-07-13T16:59:37.000Z Every professional has experienced this. You have something worth saying. A blog post, a presentation, a pitch, a memo. The idea is real and you know it. But when you sit down to write, what comes out is either a wall of disconnected thoughts or a blank page that stares back at you. The problem is not the idea. It is that writing and thinking are being forced to happen at the same time. Most people try to organize and articulate simultaneously. That is where the process breaks down. This is the prompt I use to separate those two steps. It is built around a simple workflow: dump first, structure second, write third. You brain dump everything you know about the topic with no filter. The AI asks you targeted questions one at a time to excavate the real thesis buried in the noise. Then it proposes a structure based entirely on what you said. You do not write a single word of the actual piece until the architecture is locked. I built this originally for writing long-form newsletter posts. It works just as well for presentations, executive memos, LinkedIn articles, internal proposals, or anything else where you need to turn scattered thinking into a structured argument. The workflow is the same regardless of the format. You just change one line at the bottom where indicated. I am actively using and refining this. The insight I keep coming back to is that the excavation questions in Phase 2 are where the real work happens. The best version of your argument is usually not what you said in the brain dump. It is what you say when someone pushes back on the brain dump with the right question. **How to use it:** 1. Open your AI of choice (Claude, ChatGPT, or Gemini). 2. Paste the prompt below into a new chat. 3. Fill in the two customization fields — your name or role, and what you are writing. 4. When the AI prompts you to dump, get everything out without filtering. Half-thoughts, examples, frustrations, whatever you have. 5. Answer the excavation questions one at a time. Do not rush this step. 6. Approve the structure before you write anything. Once it is locked, start drafting. --- *You are my strategic writing collaborator. Your job is to help me turn a messy brain dump into a clear, structured piece of writing. You do NOT write the piece for me. You help me excavate my best thinking and build the architecture before I write a single word.* *ABOUT ME:* *\[YOUR NAME OR ROLE — e.g., "I am a project manager at a healthcare company" or "I am a consultant who writes a weekly newsletter"\]* *WHAT I AM WRITING:* *\[DESCRIBE THE OUTPUT — e.g., "a blog post," "an executive memo," "a presentation," "a LinkedIn article," "an internal proposal"\]* *Work through the following phases in order. Announce each phase clearly so I always know where we are.* *\---* *PHASE 1: DATA DUMP* *When I signal I am ready, respond with exactly this:* *"Go ahead and drop everything. Don't organize it, don't filter it. Half-thoughts, examples, frustrations, whatever you have. Get it all out and I'll take it from there."* *Once I finish, acknowledge briefly and move to Phase 2\. Do not summarize or evaluate the dump yet.* *\---* *PHASE 2: EXCAVATION QUESTIONS* *Ask me targeted questions one at a time to pull out the real thesis, the personal stories, and the core argument buried in the dump. Wait for my answer before asking the next question. Do not ask more than 8 questions unless I signal I want to keep going.* *Core questions to work through (skip any where the answer is already clear from the dump):* *1\. If you had to distill this entire piece into one sentence, what is the ONE thing you want the reader to walk away believing or knowing?* *2\. What personal experience or specific moment proves your main point?* *3\. Who exactly are you writing this for? Describe the reader you have in mind.* *4\. What is the most common mistake or misconception your reader has about this topic right now?* *5\. What do you want the reader to DO or think differently after reading this?* *6\. Is there anything in your dump you are not sure belongs in this piece?* *7\. What is the most direct or honest opinion you hold about this topic that you are willing to put in writing?* *8\. Are there any related points you considered including but decided to leave out? Why?* *When you sense the thesis is fully excavated, say: "I think I have what I need. Let me propose a structure."* *\---* *PHASE 3: STRUCTURE PROPOSAL* *Propose a structure based entirely on what I said in Phases 1 and 2\. Not a generic template. A structure built from my specific thesis and examples.* *Format it like this:* *PROPOSED STRUCTURE* *Opening approach: \[How the piece opens — a scene, a question, a provocation, a specific moment\]* *Hook summary: \[One sentence describing the opening\]* *Section 1: \[Working title\]* *\[One sentence on what this section covers and why it belongs here\]* *Section 2: \[Working title\]* *\[One sentence on what this section covers and why it belongs here\]* *Section 3: \[Working title\]* *\[One sentence on what this section covers and why it belongs here\]* *\[Add sections as needed — most pieces work best with 3 to 5\]* *Close: \[How the piece ends — a call to action, a challenge to the reader, a prediction, a specific next step\]* *Close summary: \[One sentence on what the reader is left with\]* *After presenting the structure, ask: "Does this feel like your argument, or did I miss something?"* *Do not move forward until I confirm the structure is locked.* *\---* *PHASE 4: WRITING HANDOFF* *Once the structure is confirmed, deliver this closing note:* *"Your structure is locked. Start drafting section by section using the architecture above. Come back when you have a full draft and I will run a gap audit — flagging vague claims that could be more specific, jargon that needs a plain-English explanation, and any personal examples from our conversation that did not make it into the draft."* *\---* *\[SIGNAL WHEN READY TO BEGIN\]* --- *Transparency & Notes: This prompt was built and tested in Claude and works across ChatGPT and Gemini. It was originally developed for long-form newsletter writing and has been generalized for any professional writing task. The excavation questions in Phase 2 are where most of the value comes from — do not rush through them.* ### Volume 25: Don't Be Easy to Influence URL: https://www.mindovermoney.ai/how-to-spot-ai-political-manipulation-critical-thinking/ Last updated: 2026-07-13T16:59:38.000Z AI just became a kitchen table issue. Not in the abstract, but in real life: in your job description, your utility bill, your feed, and eventually your ballot. The talking points are already forming, and most of them will sound completely reasonable until you ask the second question. This week is about building the habit of asking it. 🧭 **Founder's Corner:** A field guide to the four AI narratives headed your way this election cycle, and the questions worth asking before you accept any of them. 🧠 **AI Education:** Part 2 of the AI Agents series goes inside the decision loop nobody shows you, including where it breaks and what that means for how you work with it. ✅ **10-Minute Win:** Turn your rough weekly notes into a status update that connects your work to your goals and pressure-tests it from your manager's perspective, in under 10 minutes. Let's jump in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise ****We scan the noise so you don’t have to — top 5 stories to keep you sharp** ### **1)**[ **YouTube expands AI deepfake detection to politicians, government officials and journalists**](https://techcrunch.com/2026/03/10/youtube-expands-ai-deepfake-detection-to-politicians-government-officials-and-journalists/?ref=mindovermoney.ai) **Summary:** YouTube is expanding its “likeness detection” deepfake tool to a pilot group of politicians, government officials, and journalists so they can detect unauthorized AI-generated impersonations and request removal under existing policy review. **Why it matters:** Deepfakes are moving from “internet weirdness” to “civic risk.” Detection + takedown workflows like this are becoming core infrastructure for trust online. ### **2)**[ **ChatGPT, other AI chatbots approved for official use in US Senate**](https://finance.yahoo.com/news/chatgpt-other-ai-chatbots-approved-224358019.html?ref=mindovermoney.ai) **Summary:** Reuters reports ChatGPT and two other AI chatbots have been approved for official use in the U.S. Senate, following internal review and rules for how staff can use them. **Why it matters:** This is a mainstream adoption milestone: when government offices formalize “approved tools,” it accelerates normalization—and pushes policy and security standards into real practice. ### **3)**[ **Perplexity’s Personal Computer turns your spare Mac into an AI agent**](https://www.perplexity.ai/hub/blog/everything-is-computer?ref=mindovermoney.ai) **Summary:** Perplexity announced “Personal Computer,” an always-on agent that runs locally on a spare Mac, with controls like approvals for sensitive actions, audit trails, and a kill switch (waitlist for early access). **Why it matters:** The agent trend is getting real—and “local + auditable” is a strong answer to the biggest blocker: people don’t trust a cloud agent to roam across their personal data without tight controls. ### **4)**[ **Microsoft and Anthropic team up to bring Claude Cowork to Microsoft 365**](https://finance.yahoo.com/news/microsoft-and-anthropic-team-up-to-bring-claude-cowork-to-microsoft-365-130001836.html?ref=mindovermoney.ai) **Summary:** Microsoft says it worked with Anthropic to integrate Claude Cowork into Microsoft 365 Copilot, positioning Copilot as “multi-model” and able to choose the best model for a given task. **Why it matters:** This is a major enterprise pattern: model choice becomes a platform feature. It also signals Microsoft is diversifying beyond a single model supplier as AI becomes core to productivity software. ### **5)**[ **Microsoft Copilot Health Insights: AI news**](https://www.cnet.com/tech/services-and-software/microsoft-copilot-health-insights-ai-news/?ref=mindovermoney.ai) **Summary:** Microsoft launched Copilot Health, a dedicated experience to help people make sense of medical records, wearable data, and lab results with privacy controls and health-sourced answers—positioned as informational support, not diagnosis. **Why it matters:** Health is one of the highest-stakes consumer use cases for AI. If this category works, it’s a clear example of “AI becomes a personal layer” across your most sensitive data. --- ## Founder's Corner ****The AI Election Is Here. Here’s How Not to Get Manipulated.** AI just became a kitchen table issue, not in the abstract but in regular life. The conversation has evolved to include your career trajectory, your news feed, and eventually your ballot. Three years ago, AI was mostly a tech conversation. Then it became a business conversation. This year it turns into a voter conversation, and I do not think most people are ready for the way that shift will show up. A recent NBC News poll showed only 26% of voters reported positive feelings about AI and 46% reported negative feelings. AI is already underwater with voters, and the exact number matters less than what it signals: the public has a feeling before it has a framework. That is exactly the kind of gap politics knows how to exploit. I have been watching this pattern play out for a while now. A corporate layoff happens, the word AI shows up in the headline, and people stop asking questions. A broken workflow becomes the center of attention, and someone says “just use AI” as if data quality, process design, and accountability do not exist. AI becomes either the villain or the savior, with conclusions drawn without the proper context. Now those habits are moving into politics. Voters are about to get hit with plausible, emotional, incomplete AI arguments. This is not a piece about who to vote for or what side to pick. It is a field guide for how I plan to think when the next AI talking point shows up that might influence my decision making. ### **1\. The jobs story will sound obvious. It usually isn’t.** The most effective AI talking point this cycle will probably be the simplest one: AI is taking your job, and I am the one who will protect you. It works because there is real fear underneath it, and I do not dismiss that. There are credible reasons to think AI will disrupt a lot of white-collar work, especially lower on the ladder where tasks are more repeatable and easier to unbundle. What I do not trust is how clean this story gets once it enters public debate. I have watched smart professionals read a layoff headline, see the word AI, and stop there, with no second question about whether the company had demand problems, margin pressure, leadership issues, or a messy cost structure long before AI entered the press release. Once AI becomes the headline explanation, people often treat it like the full explanation. From an operator’s perspective, that is where the thinking usually breaks down. “AI caused this layoff” can mean the work genuinely changed. It can mean leadership found a way to automate part of the workflow and cut headcount faster. It can mean the company was already in trouble and AI became the cleanest public explanation for a decision that was coming anyway. Those are very different stories with very different implications for workers and policy, but politics will flatten them into one. Then comes the fix: universal basic income, automation taxes, worker protections, oversight boards. Different packaging, same basic move. Here is the villain, and here is the answer. Some of those ideas may be serious, but none of them are clean. Whenever someone gives me a simple answer to a messy systems problem, I want the operating model. Who pays for it? Who runs it? What incentive changes? What breaks next? If the person making the claim cannot walk me through the mechanism, I do not take the confidence at face value. That is the question I want more voters to borrow: is this person explaining what actually changed in the work, or are they using AI to skip the harder story? And if they have a fix, can they explain how it works beyond the slogan? ### **2\. The deepfake problem is bigger than fake content** AI image and video generation tools have improved fast enough that the average voter is going to have a harder time telling what is real, what is manipulated, and what is completely fake. Political operatives know that. The use of realistic AI-generated images to target political opponents is expected to grow substantially in the 2026 midterm cycle, with super PACs likely to experiment more aggressively with deepfake-style attack ads. It was not long ago that AI images were easy to laugh off because of extra fingers and distorted facial features. That era is ending fast. Synthetic output does not need to be perfect to be effective. It just needs to land before skepticism does. That is why I think the deeper problem here is not just fake content spreading, but the doubt that follows it. People are going to react, share, and form opinions before they know whether the content is authentic. I see a smaller version of this all the time. Someone gets burned by one hallucinated answer and swings too far in the other direction, distrusting everything. Blind trust is bad, but blanket distrust is not much better. Both are shortcuts, and neither is judgment. Politics is about to stress-test that exact weakness in public. Due diligence will be table stakes during this election cycle, and there are a few basic questions that can help. Where did it first appear? Is it coming from an official or verified account, or from nowhere? Does it sound exactly right, or just emotionally convincing? Has any credible outlet verified it? Am I being pushed to react fast? That last one matters because urgency is a manipulation tool. That kind of discipline will matter more than people think. ### **3\. “Regulate AI” will hide a lot of missing detail** Could AI regulation be a unifying topic during the midterm elections? A December 2025 Navigator Research survey found 60% of Americans support more AI regulation, including 63% of Democrats, 59% of Republicans, and 52% of independents. The partisan divide is not in whether to regulate. It is in what to regulate and who controls it. My reaction to “we need to regulate AI” is basically the same as my reaction when someone at work says “we need an AI strategy.” Fine. What exactly are we talking about? Hiring tools? Deepfakes? Copyright? Data privacy? Political ad disclosures? Model training? Data centers? Consumer liability? State rules? Federal rules? People say “AI” like it is one neat object. It is not. It is a pile of different problems sitting at different layers of the tech stack, and they do not all need the same response. That is why this part of the debate gets slippery so fast. A candidate can say “regulation” and sound serious without naming the actual rule. Another can say “innovation” and sound strong without naming who absorbs the downside while the market races ahead. Vague language creates fake clarity. It makes people feel informed when they are really just choosing which label sounds better. I do not see this as only a political problem. There are countless examples from the business world where a headline announces “AI transformation” when what they really mean is “we bought tools and have not worked through the process change yet.” Big language can hide thin thinking in any environment. The same pattern will show up here: strong words, weak definitions, lots of confidence. So when someone says they want to regulate AI, I want to hear the nouns and verbs. What specific thing are they trying to regulate? What is the actual mechanism? Which level of government would do it? If they cannot answer those questions, I am not hearing a real policy plan. I am hearing a sales pitch. ### **4\. Your electricity bill may be where AI gets most real** A lot of people think AI will feel real when more voters start using chatbots. I think it may feel real somewhere much less glamorous and much more immediate: the monthly electric bill. There is a race to build out capacity for the AI boom, leading to massive increases in electricity demand. What happens when those costs are passed down to consumers? This is probably the most underestimated AI story in the whole election because it is concrete in a way most AI debates are not. People may not care much about model architecture or benchmark scores. They care about whether costs go up, who benefits, and whether they are being asked to absorb tradeoffs they never agreed to. The costs are already moving. Electricity prices are forecast to rise 6% through 2027 and another 3% by 2028 as data center demand outpaces power supply. In communities near large data center developments, costs have risen by as much as 267% compared to 2020 levels. One study projects the average household electric bill will increase 8% by 2030 from data center and cryptocurrency demand alone. There will no doubt be promises made by candidates to solve the energy crunch. But there are uncontrollable factors that will influence whether those promises can actually be kept. Unfortunately, that message does not fit neatly into a campaign line or talking point. I want to know who actually owns the constraint. Who controls the bill? Who approves the infrastructure? Who has authority, and who is just performing authority in public? Every situation will be different, but understanding the problem and its full set of tradeoffs will help you be a more informed voter. ### **The Filter** The people most likely to get played this cycle will not be the least intelligent voters. They will be the people who are smart enough to recognize the topic but not disciplined enough to slow down once the argument feels plausible. I know that because I have caught myself doing versions of this too. A claim lands, it sounds directionally right, and my brain wants to complete the story before I have inspected it. That is the habit I am trying to break. This election is going to reward speed, emotion, and clean narratives. AI is messy, uneven, useful, disruptive, overclaimed, and misunderstood. Anyone offering you a one-line explanation for what AI is doing to jobs, truth, regulation, or your electric bill is probably giving you a frame before they give you the facts. I do not want to be easy to influence. That is not cynicism. It is basic defense. In a cycle full of plausible, emotional, incomplete AI arguments, basic defense may be one of the most useful skills you can build. ## AI Education for You ****AI Agents, Part 2 - How Agents Reason: The Decision Loop Nobody Shows You** ### **What Is Actually Going On Here** The moment you hand an agent a goal, something starts running that you never see. The agent is not waiting for your next prompt. It is generating a chain of internal reasoning — thinking through what the goal requires, what the first step should be, which tool fits that step, and what it will do with the result. It takes the action, reads what came back, and reasons again. Then it acts again. This cycle — reason, act, observe, reason, act — runs continuously until the agent decides the goal is met or the task collapses under its own weight. The entire loop is invisible. What you see is the starting prompt and the final output. Everything in between happens without you. ### **The Problem That Made This Necessary** Early AI systems were built for single-turn responses. You asked a question. The model answered. That was the complete interaction. The problem was that most real tasks are not single-turn problems. They are sequences — each step depends on what the previous step returned. Researchers at Google and elsewhere recognized this around 2022 and began formalizing what became known as the ReAct pattern — short for Reason and Act. The core insight was straightforward: if you interleave reasoning traces with tool actions inside the model's generation process, the model could plan a step, execute it, read the result, and plan the next step — all within a single extended run. Prior approaches had tried to separate planning from execution, handing a fixed plan to a separate execution layer. That broke constantly because real-world tool results are unpredictable. A search returns something unexpected. A file is formatted differently than assumed. A calendar API throws an error. A fixed plan has no mechanism for adapting. ReAct treated reasoning and acting as a continuous loop rather than two separate phases — and that architectural shift is what made agents viable. ### **How It Actually Works** The loop runs in three repeating stages. **Reason.** The agent generates a reasoning trace — an internal chain of thought that names what it is trying to do, what it knows so far, and what the next action should be. This reasoning is not visible in the final output. It is scaffolding the model builds for itself. The quality of this reasoning trace determines whether the next action is well-targeted or misdirected. **Act.** The agent executes one tool call based on its reasoning: a web search, a file read, a code execution, a database query. One action at a time. Not a batch. The constraint is intentional — the result of each action informs the reasoning for the next one. Batching actions removes the feedback loop that makes the system adaptive. **Observe.** The agent reads the result and feeds it back into its context. This is where the loop either tightens or begins to drift. If the result is clean and expected, the next reasoning step builds accurately on top of it. If the result is ambiguous, incomplete, or an error, the agent has to decide — with no human input — how to proceed. Then the cycle repeats. A well-designed agent runs this loop efficiently, compressing a task that would take a professional multiple manual steps into a single continuous run. The tradeoff is opacity. The more steps the loop runs, the harder it is to audit what happened and why. ### **Where It Still Breaks** **Compounding errors.** A wrong assumption in the reasoning trace at step two does not announce itself. It travels forward. By step eight the agent may be producing confident, well-formatted output built on a flawed foundation. The output looks finished. The error is buried. **Goal drift.** On long runs, the agent's reasoning can drift away from the original goal — particularly when tool results pull the context in a different direction. The agent optimizes for what is in front of it, not always for what you originally asked. **Tool failures without recovery.** When a tool returns an error or an unexpected format, agents vary significantly in how they handle it. Some retry intelligently. Some loop. Some abandon the task silently and produce a partial output that reads like a complete one. **Context window overflow.** A long reasoning loop consumes tokens. As the context fills, earlier information — including the original goal — can get pushed out. The agent finishes the loop having forgotten what it was solving for. ### **What This Means for How You Work With It** Verify outputs at the end, not just the beginning. The loop ran without you. What came back may look complete and be wrong in ways that are not obvious on the surface. Check your goal definition before you run anything. The reasoning loop amplifies whatever goal you gave it — clearly stated goals compound well, vague goals compound poorly. Ask what the agent actually did. Most agent interfaces expose a log of tool calls and reasoning steps. Reading it takes two minutes and tells you whether the loop ran cleanly or drifted mid-task. Never assume a confident output means a correct one. The agent's tone does not change when its reasoning has gone sideways. ### **How This Connects** Part 1 established what separates agents from chatbots — the reasoning loop, tools, and memory working together. Now you have seen inside the loop itself: how ReAct patterns replaced fixed planning, why reason-act-observe runs continuously, and where the loop breaks under real conditions. Context windows (Vol 8) are directly relevant here — the longer the loop runs, the more tokens it consumes, and eventually the earlier context gets pushed out. That is not a hypothetical. It is a documented failure mode on long agent runs. Vol 26 goes one layer deeper into the two components that extend what agents can do across tasks: memory architectures and the tool ecosystem. Vol 27 puts all of it together in a single professional scenario from goal to finished output — including a loop that nearly goes wrong and what catching it actually looks like. *Part 2 of 4 in the AI Agents series.* ## Your 10-Minute Win ****A step-by-step workflow you can use immediately** ## **The 1:1 Prep Assistant** A weekly status update on your 1:1 is one of the most underrated career tools you have — but most people treat it like a chore. This workflow doesn't just clean up your bullet notes. It maps your work back to your actual goals so your manager sees the connection without you having to spell it out, then pressure-tests the whole thing from your manager's perspective before you hit send. ### **The Workflow** **1\. Brain Dump Your Week (2 Minutes)** Open a blank note and spend two minutes listing everything you worked on this week — rough bullets, incomplete thoughts, half-finished projects. Nothing needs to be polished. The messier the better. Examples of what rough looks like: - "finished the slide deck for the Q2 review" - "had that call with the vendor, still waiting on pricing" - "been stuck on the data issue, Sarah is looking into it" Also grab your goals document, quarterly priorities list, or even a copy of your last performance review. A few bullet points of what you are being measured on this year is enough. **2\. Run the Status Update Prompt (3 Minutes)** Open Claude, ChatGPT, or Gemini and paste the prompt below. This is prompt one of two — keep the chat window open after you run it. **Copy/Paste Prompt 1:* "I am going to give you two things: my rough notes from this week and my current goals or priorities. Your job is to write my weekly status update and explicitly connect my completed work back to my stated goals so my manager can see the impact without me having to explain it.* *My rough notes from this week: \[PASTE YOUR BULLET NOTES HERE\]* *My current goals or priorities: \[PASTE YOUR GOALS, QUARTERLY PRIORITIES, OR WHAT YOU ARE BEING MEASURED ON\]* *My role: \[your job title or function\]* *Format the update in three sections:* 1. *Completed This Week — what I finished, with a one-line note connecting each item to a relevant goal where possible* 2. *In Progress — what is actively underway and any blockers* 3. *Next Week — my top 2 to 3 priorities* *Keep the tone professional but human. No corporate filler. Under 200 words total."* Read through the output. If a goal connection feels forced or inaccurate, tell the model to remove it. Accuracy matters more than coverage here. **3\. Run the Manager Perspective Prompt (2 Minutes)** Stay in the same chat window — do not start a new conversation. The model already has full context on your work and goals. Now flip its perspective. **Copy/Paste Prompt 2:* "Now read this status update as my manager. Based on what you know about my role and goals, tell me:* 1. *What questions or concerns would this raise for you?* 2. *Is there anything missing that a manager would want to know?* 3. *Does anything sound vague or unconvincing?* *Be direct and honest — I want to catch problems before I send this."* This is where the workflow earns its name. The model will surface gaps you cannot see because you are too close to your own work. **4\. Finalize and Save Your Asset (3 Minutes)** Address any flags the model raised — either by editing the update directly or by asking the model to revise specific sections. Copy the final version into an email draft, Slack message, or wherever you share updates and send it. Save the two prompts somewhere reusable. Next Friday, your only job is to swap in new bullet notes and goals. ### **The Payoff** You now have a status update that connects your work to your goals and has been reviewed from your manager's perspective — all before you sent it. The prompt pair is reusable every single week. Over time this workflow does not just save you time, it trains you to communicate your impact more deliberately with or without AI. ### **🧠 The AI Concept You Just Used** **Chained prompting + perspective switching.** You ran two prompts in sequence in the same chat, letting the model carry context from the first into the second. Then you asked it to shift roles entirely and evaluate your work from someone else's point of view. These two techniques together are some of the most powerful — and most underused — moves in AI. ### **Transparency & Notes** - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com) — all free tier, no credit card required. - **Privacy:** Keep notes general. Avoid client names, confidential project details, or anything you would not say in an open meeting. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The AI Election Is Here. Here’s How Not to Get Manipulated. URL: https://www.mindovermoney.ai/founders-corner/how-to-spot-ai-misinformation-election-2026-guide/ Last updated: 2026-07-13T16:59:38.000Z AI just became a kitchen table issue, not in the abstract but in regular life. The conversation has evolved to include your career trajectory, your news feed, and eventually your ballot. Three years ago, AI was mostly a tech conversation. Then it became a business conversation. This year it turns into a voter conversation, and I do not think most people are ready for the way that shift will show up. A recent NBC News poll showed only 26% of voters reported positive feelings about AI and 46% reported negative feelings. AI is already underwater with voters, and the exact number matters less than what it signals: the public has a feeling before it has a framework. That is exactly the kind of gap politics knows how to exploit. I have been watching this pattern play out for a while now. A corporate layoff happens, the word AI shows up in the headline, and people stop asking questions. A broken workflow becomes the center of attention, and someone says “just use AI” as if data quality, process design, and accountability do not exist. AI becomes either the villain or the savior, with conclusions drawn without the proper context. Now those habits are moving into politics. Voters are about to get hit with plausible, emotional, incomplete AI arguments. This is not a piece about who to vote for or what side to pick. It is a field guide for how I plan to think when the next AI talking point shows up that might influence my decision making. ### **1\. The jobs story will sound obvious. It usually isn’t.** The most effective AI talking point this cycle will probably be the simplest one: AI is taking your job, and I am the one who will protect you. It works because there is real fear underneath it, and I do not dismiss that. There are credible reasons to think AI will disrupt a lot of white-collar work, especially lower on the ladder where tasks are more repeatable and easier to unbundle. What I do not trust is how clean this story gets once it enters public debate. I have watched smart professionals read a layoff headline, see the word AI, and stop there, with no second question about whether the company had demand problems, margin pressure, leadership issues, or a messy cost structure long before AI entered the press release. Once AI becomes the headline explanation, people often treat it like the full explanation. From an operator’s perspective, that is where the thinking usually breaks down. “AI caused this layoff” can mean the work genuinely changed. It can mean leadership found a way to automate part of the workflow and cut headcount faster. It can mean the company was already in trouble and AI became the cleanest public explanation for a decision that was coming anyway. Those are very different stories with very different implications for workers and policy, but politics will flatten them into one. Then comes the fix: universal basic income, automation taxes, worker protections, oversight boards. Different packaging, same basic move. Here is the villain, and here is the answer. Some of those ideas may be serious, but none of them are clean. Whenever someone gives me a simple answer to a messy systems problem, I want the operating model. Who pays for it? Who runs it? What incentive changes? What breaks next? If the person making the claim cannot walk me through the mechanism, I do not take the confidence at face value. That is the question I want more voters to borrow: is this person explaining what actually changed in the work, or are they using AI to skip the harder story? And if they have a fix, can they explain how it works beyond the slogan? ### **2\. The deepfake problem is bigger than fake content** AI image and video generation tools have improved fast enough that the average voter is going to have a harder time telling what is real, what is manipulated, and what is completely fake. Political operatives know that. The use of realistic AI-generated images to target political opponents is expected to grow substantially in the 2026 midterm cycle, with super PACs likely to experiment more aggressively with deepfake-style attack ads. It was not long ago that AI images were easy to laugh off because of extra fingers and distorted facial features. That era is ending fast. Synthetic output does not need to be perfect to be effective. It just needs to land before skepticism does. That is why I think the deeper problem here is not just fake content spreading, but the doubt that follows it. People are going to react, share, and form opinions before they know whether the content is authentic. I see a smaller version of this all the time. Someone gets burned by one hallucinated answer and swings too far in the other direction, distrusting everything. Blind trust is bad, but blanket distrust is not much better. Both are shortcuts, and neither is judgment. Politics is about to stress-test that exact weakness in public. Due diligence will be table stakes during this election cycle, and there are a few basic questions that can help. Where did it first appear? Is it coming from an official or verified account, or from nowhere? Does it sound exactly right, or just emotionally convincing? Has any credible outlet verified it? Am I being pushed to react fast? That last one matters because urgency is a manipulation tool. That kind of discipline will matter more than people think. ### **3\. “Regulate AI” will hide a lot of missing detail** Could AI regulation be a unifying topic during the midterm elections? A December 2025 Navigator Research survey found 60% of Americans support more AI regulation, including 63% of Democrats, 59% of Republicans, and 52% of independents. The partisan divide is not in whether to regulate. It is in what to regulate and who controls it. My reaction to “we need to regulate AI” is basically the same as my reaction when someone at work says “we need an AI strategy.” Fine. What exactly are we talking about? Hiring tools? Deepfakes? Copyright? Data privacy? Political ad disclosures? Model training? Data centers? Consumer liability? State rules? Federal rules? People say “AI” like it is one neat object. It is not. It is a pile of different problems sitting at different layers of the tech stack, and they do not all need the same response. That is why this part of the debate gets slippery so fast. A candidate can say “regulation” and sound serious without naming the actual rule. Another can say “innovation” and sound strong without naming who absorbs the downside while the market races ahead. Vague language creates fake clarity. It makes people feel informed when they are really just choosing which label sounds better. I do not see this as only a political problem. There are countless examples from the business world where a headline announces “AI transformation” when what they really mean is “we bought tools and have not worked through the process change yet.” Big language can hide thin thinking in any environment. The same pattern will show up here: strong words, weak definitions, lots of confidence. So when someone says they want to regulate AI, I want to hear the nouns and verbs. What specific thing are they trying to regulate? What is the actual mechanism? Which level of government would do it? If they cannot answer those questions, I am not hearing a real policy plan. I am hearing a sales pitch. ### **4\. Your electricity bill may be where AI gets most real** A lot of people think AI will feel real when more voters start using chatbots. I think it may feel real somewhere much less glamorous and much more immediate: the monthly electric bill. There is a race to build out capacity for the AI boom, leading to massive increases in electricity demand. What happens when those costs are passed down to consumers? This is probably the most underestimated AI story in the whole election because it is concrete in a way most AI debates are not. People may not care much about model architecture or benchmark scores. They care about whether costs go up, who benefits, and whether they are being asked to absorb tradeoffs they never agreed to. The costs are already moving. Electricity prices are forecast to rise 6% through 2027 and another 3% by 2028 as data center demand outpaces power supply. In communities near large data center developments, costs have risen by as much as 267% compared to 2020 levels. One study projects the average household electric bill will increase 8% by 2030 from data center and cryptocurrency demand alone. There will no doubt be promises made by candidates to solve the energy crunch. But there are uncontrollable factors that will influence whether those promises can actually be kept. Unfortunately, that message does not fit neatly into a campaign line or talking point. I want to know who actually owns the constraint. Who controls the bill? Who approves the infrastructure? Who has authority, and who is just performing authority in public? Every situation will be different, but understanding the problem and its full set of tradeoffs will help you be a more informed voter. ### **The Filter** The people most likely to get played this cycle will not be the least intelligent voters. They will be the people who are smart enough to recognize the topic but not disciplined enough to slow down once the argument feels plausible. I know that because I have caught myself doing versions of this too. A claim lands, it sounds directionally right, and my brain wants to complete the story before I have inspected it. That is the habit I am trying to break. This election is going to reward speed, emotion, and clean narratives. AI is messy, uneven, useful, disruptive, overclaimed, and misunderstood. Anyone offering you a one-line explanation for what AI is doing to jobs, truth, regulation, or your electric bill is probably giving you a frame before they give you the facts. I do not want to be easy to influence. That is not cynicism. It is basic defense. In a cycle full of plausible, emotional, incomplete AI arguments, basic defense may be one of the most useful skills you can build. ### Steal My Prompt Vol. 25: The Brand Image Builder URL: https://www.mindovermoney.ai/prompt-library/ai-brand-image-generation-prompt-gemini-banner/ Last updated: 2026-07-13T16:59:38.000Z Neural Gains Weekly just went through a content refresh. New section formats, new roadmap, new direction. The banner needed to match. I already had a logo. This was not starting from scratch. It was updating something that existed to reflect where the newsletter is going. The difference is subtle but it matters — a refresh prompt is more constrained than a creation prompt. You are working within an existing brand identity, not inventing one. This is the prompt I used to brief Gemini on the new header. I gave it my brand colors, the layout I wanted, a protected text zone on the left so the visualization did not swallow the name, and a clear mood direction. It took a few iterations to get the typography right — Gemini handles text inconsistently — but the final result is the banner you see on the site today. If you have an existing brand with defined colors and a name you want on the image, this prompt gives you a repeatable brief you can run whenever the look needs an update. Adjust the colors, swap the visual theme, and generate. **How to use it:** 1. Fill in your brand details in the customization fields below — colors, name, tagline, and mood. 2. Open Gemini (free tier works — available at gemini.google.com). 3. Paste the prompt into the message box and hit generate. 4. If the text comes out distorted or oversized, regenerate once with the same prompt. Gemini handles typography inconsistently. If it still does not land, generate the background without text and add your text as an overlay in Canva. 5. Save your final prompt. Once you find a version that works, it becomes your reusable brand asset generator. --- Wide horizontal banner, \[WIDTH x HEIGHT — recommended 1200x600 pixels\]. Background: \[YOUR BACKGROUND COLOR — e.g., deep dark slate #2c353c\] with very subtle texture, not flat. Visual element: A cinematic \[DESCRIBE YOUR VISUAL THEME — e.g., abstract digital neural network / geometric data grid / flowing gradient waves\] concentrated on the RIGHT two-thirds of the image. Colors transition from \[YOUR PRIMARY ACCENT COLOR — e.g., deep teal #8abfc5\] on the far right edge toward \[YOUR SECONDARY ACCENT COLOR — e.g., warm champagne gold #f3e2ae\] toward the center. The effect is \[DESCRIBE MOOD — e.g., sophisticated and tech-forward / clean and minimal / bold and energetic\]. Text zone: The LEFT one-third of the image is intentionally kept clean and dark with only faint visual elements fading in from the right. This is the dedicated text area. Text: In the left third, vertically centered, bold crisp WHITE sans-serif text reads "\[YOUR BRAND NAME\]" — all words on a SINGLE horizontal line, font size restrained so it does not wrap or stack. Directly below in smaller light gray text: "\[YOUR TAGLINE OR WEBSITE URL\]". Text is sharp, modern, and fully legible. It does not overlap with any bright visual elements. Details: A small white \[YOUR ICON — e.g., 4-pointed star / circle / diamond\] in the bottom right corner. No humans, no faces, no biological cells or medical imagery. Cinematic depth of field. Aspect ratio \[YOUR RATIO — e.g., 2:1\] landscape orientation only. *Transparency & Notes: This prompt was built and tested in Gemini (free tier). Image generation quality can vary by session — if the first result misses, regenerate once before rewriting the prompt. Typography is the hardest element for AI image tools to nail. If text comes out distorted after two attempts, export the background and add text in Canva for clean results. This prompt is designed for Gemini but can be adapted for other image generation tools like DALL-E or Ideogram.* ### Volume 24: Stop Starting From Zero URL: https://www.mindovermoney.ai/how-to-use-ai-projects-mode-save-context-professionals/ Last updated: 2026-07-13T16:59:38.000Z Hey everyone! Every time you open a new chat, your AI forgets everything. Your preferences, your projects, your goals, your voice. You are not picking up where you left off. You are starting a new relationship from scratch, every single time. Most professionals do not realize there is a better way built right into the tools they already use. 🧭 **Founder's Corner:** Why AI Projects mode is the most underused feature in the toolkit, and what happened when I finally built my entire workflow inside one. 🧠 **AI Education:** The AI Agents series begins with the one distinction that changes how you think about every tool you use: agents act, chatbots respond. ✅ **10-Minute Win:** Turn a paragraph of confusing jargon into plain English and a personal glossary you can build on forever, in under 10 minutes. Let's get into it. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Introducing GPT-5.4**](https://openai.com/index/introducing-gpt-5-4/?ref=mindovermoney.ai) **Summary:** OpenAI released GPT-5.4 in ChatGPT, the API, and Codex, plus a higher-performance GPT-5.4 Pro tier for more complex work. **Why it matters:** Frontier model upgrades quickly cascade into everyday tools—better reasoning, stronger coding, and more capable “agent” workflows show up immediately in products people actually use. ### **2)**[ **Anthropic CEO says Pentagon ban less harsh than Hegseth had threatened**](https://breakingdefense.com/2026/03/anthropic-ceo-says-pentagon-ban-less-harsh-than-hegseth-had-threatened/?ref=mindovermoney.ai) **Summary:** Anthropic’s CEO says the Pentagon’s “supply chain risk” designation is narrower than earlier threats, but the company still plans to sue to overturn it. The piece lays out what the designation does (and doesn’t) restrict, and why experts think the move may not hold up in court. **Why it matters:** This is AI governance in the real world: contract language and procurement pressure can shape what models are allowed to do as much as regulation does. ### **3)**[ **NVIDIA Announces Strategic Partnership With Lumentum to Develop State-of-the-Art Optics Technology**](https://nvidianews.nvidia.com/news/nvidia-announces-strategic-partnership-with-lumentum-to-develop-state-of-the-art-optics-technology?ref=mindovermoney.ai) **Summary:** NVIDIA announced multiyear agreements with Lumentum and said it will invest $2B to expand capacity and R&D for optics used in AI data centers (moving data faster and more efficiently inside massive clusters). **Why it matters:** The AI race is increasingly limited by infrastructure—not just GPUs, but the networking/optics that let data centers scale without choking on bandwidth and power. ### **4)**[ **Nvidia CEO Jensen Huang says that OpenClaw is the “single most important software release probably ever”**](https://officechai.com/ai/nvidia-ceo-jensen-huang-says-that-openclaw-is-the-single-most-important-software-release-probably-ever/?ref=mindovermoney.ai) **Summary:** OfficeChai reports Jensen Huang praised “OpenClaw” and framed it as a shift from AI that answers questions to AI that performs actions (agent-style work), arguing it massively increases compute demand. **Why it matters:** Even if you ignore the hype, the “agents = way more compute” argument is a key trend: action-taking AI can multiply infrastructure needs and accelerate the data-center arms race. ### **5)**[ **Labor market impacts of AI: A new measure and early evidence**](https://www.anthropic.com/research/labor-market-impacts?ref=mindovermoney.ai) **Summary:** Anthropic published a research report proposing a method to measure early labor-market impact from AI, aiming to move beyond anecdotes with more systematic evidence. **Why it matters:** The job-impact conversation is noisy; better measurement helps people and policymakers track where change is actually happening first (tasks, roles, hiring patterns) and respond faster. --- ## Founder's Corner The Most Important AI Feature You're Not Using Value is driven through tribal knowledge, and it is often impossible to pass along to others. You are working with a new technology that demands great inputs to drive results. But the knowledge it needs lives in your head, scattered across documents, past conversations, and muscle memory built over years. You experiment with AI tools, explore use cases, and open new chats. Each one starts from zero. You are experimenting across standalone interactions instead of building something progressive. Efficiency is lost. Momentum stalls. Eventually, you close the tab and tell yourself AI just doesn't work the way you expected. ### **Searching Without A Strategy** Most professionals use AI the same way they use a search engine. They have a question, they open a chat, they get an answer, they move on. It works well enough that they keep doing it. But there is a meaningful difference between answering a question and solving a problem. Questions are transactional. Problems are progressive. They build on each other, require context, and compound over time. When you treat every chat as a standalone interaction instead of a chapter in a larger story, you are resetting that progress every single time. A recent Gallup poll from Q4 2025 found that nearly half of U.S. workers never use AI in their role at all. Of those who do, only 27% of white-collar workers use it at least a few times a week. The tools are available. The gap is not access. It is approach. I ran into this same wall. Not because the tools were failing me, but because I didn't know what I was missing. It turns out the solution was already built. Most people just never find it. ### **The Workspace You're Not Using** Creating newsletter content requires time, energy, and most importantly, context. I'm continuously looking for ways to evolve my voice and deliver information that will help people on their AI journey. I'm consistently providing feedback to my AI that leads to better prompts, sharper writing, and focused content roadmaps. But my work lives in a centralized hub, regardless of the model used to execute a task. Every major AI platform has a version of this: a dedicated workspace where your files, instructions, and chat history live together under one roof. Instead of starting every conversation from scratch, the model already knows your context, your preferences, and your goals. Think of it as the difference between briefing a new consultant every single week versus working with someone who has been embedded in your business for months. The knowledge compounds. The outputs improve. The time you spend re-explaining drops to zero. Every AI model calls it something different, but the core idea is the same: context, productivity, and strategy in one place. ![upload in progress, 0](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/03/image-1.png) I've been using project features since day one, and it changed how I think about AI entirely. ### **What I Learned the Hard Way So You Don't Have To** There is only so much time in a day, which is why I try to use AI as a strategic partner and not just a search engine. Implementing my workflows into Projects across multiple platforms taught me valuable lessons that serve as a foundation for every AI interaction I curate. **Tip 1: Transfer your Knowledge** The single biggest benefit of Projects mode is to ground all knowledge in one place. This means linking documents that will help teach the AI, adding standard operating procedures that show how the work is completed, and building instructions that create structure to every chat initiated within the Project. If a standard LLM chat is trained on the entirety of the internet, then think about your Project as training that same LLM only on the context you provide. You are creating a narrow and focused AI assistant that can work alongside you to solve specific problems that only exist in your created universe. **Tip 2: Take Advantage of Memory** AI doesn't just need to be smart, it needs to remember what matters. VentureBeat published in January 2026 that contextual memory will become "table stakes" for enterprise AI deployments this year. This means the AI remembers the decision you made in Tuesday's chat when you open a new one on Friday. I experienced this firsthand when an AI I was working with flagged that the context window was approaching its limit and generated a handover document unprompted, capturing everything needed to pick up exactly where we left off in a new chat. That moment reframed how I think about AI entirely. Think of a Project as giving your AI the full context from your previous conversations, stored and accessible across every chat in the project. You no longer have to repeat instructions or reexplain strategy in a new chat session. I run six content chats every week, each building a different section of my newsletter, all grounded in the same tone, voice, and strategy. That type of symmetry is hard to replicate and a powerful shift in how to think about using AI. **Tip 3: Continuously Improve Your Workflows** This manifested in several ways during my journey with Projects. First, I'm constantly updating prompts and having AI save the changes to memory. This allows me to use real time feedback and adjust prompts with the goal of improving the next output. I also outsource 99% of the project management work directly to AI. This would not be possible without the AI committing context to memory. I have full project plans created with simple prompts, living documents that stay with me regardless of what model I use to execute a task, and placeholders for ideas that are not fully baked yet. I'm able to spend less time tracking and more time executing. ### **Now What?** Most professionals will read this and nod. A smaller group will open their AI platform, find the Projects feature, and actually build something. That gap is not about access or intelligence. It is about approach. The professionals who compound their AI skills fastest are not the ones using the newest models. They are the ones who treat every interaction as part of a larger system. They transfer their knowledge, build on it session after session, and continuously refine how they work. The tool gets smarter because they get more intentional. You already have access to everything described in this post. The workspace exists. The memory features are live. The only thing missing is a problem worth solving and the decision to start. Pick one. Build a Project around it. Give your AI the context it needs to actually help you. Then come back next week and share what happened. ## AI Education for You AI Agents, Part 1 - What is an AI Agent? For the past several months, this newsletter has been building the foundation: how models learn, how they process language, how they retrieve information, how they generate responses. That foundation was not background material. It was preparation for this. AI agents are where everything converges — and where the professional stakes get real. Every major AI platform is racing to ship agentic capabilities — OpenAI has Operator, Google has Agentspace, Microsoft is embedding agents across the Copilot suite, and Anthropic has wired Claude directly into external tools and workflows. Every enterprise software vendor is attaching the word "agent" to products that may or may not deserve it. And most professionals encountering this shift have no framework for evaluating what is actually different, what is marketing language, and what it means for how they work. I started seeing it firsthand when I began building my own newsletter workflow inside Claude Projects — what felt like a smarter chat tool turned out to be something closer to a system that remembers, retrieves, and acts. That shift is what made this series necessary. Over the next four volumes, you will build that framework from the ground up. Not by reading a feature list, but by understanding how agents actually work — the decision loop, the memory architecture, the tools, and what happens when you hand one a real task. By Vol 27, you will have watched a complete professional scenario play out start to finish, including where agents struggle and what that means for how you use them. ### **The Assumption** Most professionals assume an AI agent is a more capable chatbot. It answers faster, handles more complex questions, maybe searches the web while it responds. Under this model, the gap between a standard AI chat tool and "an AI agent" is a matter of degree — more powerful, more impressive, but the same kind of thing. You prompt it. It responds. That assumption is completely reasonable. Every AI tool covered in this newsletter operates on the same basic mechanic: input in, output out. One turn. One response. The pattern is consistent enough that extending it to agents feels like the obvious conclusion. ### **Where It Breaks Down** A strategy analyst needs a competitive briefing on three rival companies before Friday's leadership meeting. She opens her AI tool and types: "Research our top three competitors and put together a briefing I can share with the team." What comes back is a solid starting point — a few paragraphs per company, a note that the model cannot verify current pricing or recent news, and a suggestion to paste in URLs for anything time-sensitive. She pastes URLs. Asks follow-up questions. Requests reformatting. Corrects a company name. Twenty-five minutes later she has something usable. The AI responded well every single time she prompted it. But she performed every step in between. She decided what to search. She decided when the output was ready to refine. She decided when it was done. The analyst was the agent. The AI was a tool she had to operate manually, step by step. ### **What Is Actually Happening** An agent is not a more capable chatbot. It is a different architecture — built from three components that chatbots do not have working together. **A reasoning loop.** A chatbot completes one turn and waits. An agent completes a step, evaluates the result, decides what to do next, and keeps going — without a human prompt between each move. It can run ten, twenty, fifty internal steps before surfacing anything to you. **Tools.** These are external capabilities the agent can call and use: web search, code execution, file reading, calendar access, email drafting, database queries. The agent decides which tool fits the current step, reads the result, and incorporates it into what it does next. **Memory across steps.** What the agent finds in step three, it carries forward to step seven. It builds context as it works, instead of resetting after every turn. Back to the analyst. An agent given the same task would search the web for each competitor, open recent press releases, pull current news coverage, identify key product launches, draft each section, check for gaps, run additional searches where needed, and format the finished document — while she was in a meeting. She defined the goal. The agent determined how to reach it. ### **The Revised Mental Model** Stop thinking of agents as better chatbots. Think of them as workers given a task, a set of tools, and the judgment to figure out the steps themselves. Your role changes. With a chatbot, you manage every move. With an agent, you define the goal clearly and verify the result at the end. The work that used to happen between your prompts now happens inside the system. That shift surfaces the skill that matters most with agents: goal definition. Not prompting. Vague goals produce agents that wander. Specific goals with clear success criteria produce usable output. The more precisely you can describe what "done" looks like, the less the agent has to guess — and the less you have to fix. ### **How This Connects** The foundation for this series was built across the last several months. RAG (Vol 19-22) is how agents find and retrieve information before they write. Context windows (Vol 8) explain why agents can only hold so much state at once — long agent runs hit limits that affect output quality. Tokens (Vol 6) explain why complex agent tasks carry a computational cost that a single chat turn does not. The next three volumes go inside the machinery. Vol 25 covers how the decision loop actually works and why agents sometimes get stuck mid-task. Vol 26 explains how memory and tools extend what agents can do — and what multi-agent systems mean in plain English. Vol 27 follows one complete professional scenario from goal to finished output, including the points where it nearly goes wrong. One distinction carries through all of it: agents act, chatbots respond. *Part 1 of 4 in the AI Agents series.* ## Your 10-Minute Win A step-by-step workflow you can use immediately ## The Jargon Decoder Every industry has a language designed — intentionally or not — to make outsiders feel lost. Whether you just started a new job, joined a new team, or are simply reading an email that might as well be in a foreign language, this workflow turns confusing jargon into plain English and builds you a personal glossary you can reference forever. ### The Workflow **1\. Find Your Jargon (2 Minutes)** Grab a real piece of text that has been tripping you up — an email, a Slack message, a report, a job posting, or a meeting recap. Look for the sentences that made you pause or nod along pretending to understand. Copy that paragraph or section. You only need one to get started. **2\. Run the Decoder Prompt (5 Minutes)** Open Claude, ChatGPT, or Gemini — whichever you already use. Paste the prompt below with your text dropped in. > **Copy/Paste Prompt:** "I am going to paste a paragraph from a professional document. It contains industry jargon, acronyms, and buzzwords that I want to understand better. > > Here is the text: \[PASTE YOUR PARAGRAPH HERE\] > > Please do two things:Rewrite the paragraph in plain English, as if you are explaining it to someone smart but completely new to this field. Keep the meaning intact but remove all jargon.Create a glossary table with three columns: Term | Plain English Definition | Why It Matters. Include every acronym, buzzword, and technical phrase from the original paragraph. > > Format the glossary as a clean table I can save and reuse." Read through both outputs. If any definition feels vague or off, reply with "Clarify \[term\] in the context of \[your industry\]" and the model will sharpen it. One follow-up is usually enough. **3\. Save Your Glossary (3 Minutes)** Copy the glossary table into a Google Doc or Notes app titled "My \[Industry\] Glossary." This is a living document — every time you hit new jargon, run this prompt again and paste the new terms into the same file. Within a few weeks you will have a personal reference guide built entirely from real situations you encountered. ### The Payoff You now have a plain English translation of something that was blocking your understanding, plus the start of a personal glossary that gets more valuable every time you use it. The goal was never to memorize every term — it was to stop letting language create a confidence gap between you and the room. ### 🧠 The AI Concept You Just Used **Context injection + output formatting.** You gave the model specific source material to work from rather than asking a general question, then requested two distinct outputs in a precise format. That combination — grounded input plus structured output — is one of the most reliable patterns in AI and works the same way in every tool. ### Transparency & Notes - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com) — all free tier, no credit card required. - **Privacy:** Remove names, client details, or anything confidential before pasting. The jargon itself is all you need. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The Most Important AI Feature You're Not Using URL: https://www.mindovermoney.ai/founders-corner/ai-projects-mode-guide-professionals-save-context/ Last updated: 2026-07-13T16:59:39.000Z Value is driven through tribal knowledge, and it is often impossible to pass along to others. You are working with a new technology that demands great inputs to drive results. But the knowledge it needs lives in your head, scattered across documents, past conversations, and muscle memory built over years. You experiment with AI tools, explore use cases, and open new chats. Each one starts from zero. You are experimenting across standalone interactions instead of building something progressive. Efficiency is lost. Momentum stalls. Eventually, you close the tab and tell yourself AI just doesn't work the way you expected. ### **Searching Without A Strategy** Most professionals use AI the same way they use a search engine. They have a question, they open a chat, they get an answer, they move on. It works well enough that they keep doing it. But there is a meaningful difference between answering a question and solving a problem. Questions are transactional. Problems are progressive. They build on each other, require context, and compound over time. When you treat every chat as a standalone interaction instead of a chapter in a larger story, you are resetting that progress every single time. A recent Gallup poll from Q4 2025 found that nearly half of U.S. workers never use AI in their role at all. Of those who do, only 27% of white-collar workers use it at least a few times a week. The tools are available. The gap is not access. It is approach. I ran into this same wall. Not because the tools were failing me, but because I didn't know what I was missing. It turns out the solution was already built. Most people just never find it. ### **The Workspace You're Not Using** Creating newsletter content requires time, energy, and most importantly, context. I'm continuously looking for ways to evolve my voice and deliver information that will help people on their AI journey. I'm consistently providing feedback to my AI that leads to better prompts, sharper writing, and focused content roadmaps. But my work lives in a centralized hub, regardless of the model used to execute a task. Every major AI platform has a version of this: a dedicated workspace where your files, instructions, and chat history live together under one roof. Instead of starting every conversation from scratch, the model already knows your context, your preferences, and your goals. Think of it as the difference between briefing a new consultant every single week versus working with someone who has been embedded in your business for months. The knowledge compounds. The outputs improve. The time you spend re-explaining drops to zero. Every AI model calls it something different, but the core idea is the same: context, productivity, and strategy in one place. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/03/image-1.png) I've been using project features since day one, and it changed how I think about AI entirely. ### **What I Learned the Hard Way So You Don't Have To** There is only so much time in a day, which is why I try to use AI as a strategic partner and not just a search engine. Implementing my workflows into Projects across multiple platforms taught me valuable lessons that serve as a foundation for every AI interaction I curate. ***Tip 1: Transfer your Knowledge*** The single biggest benefit of Projects mode is to ground all knowledge in one place. This means linking documents that will help teach the AI, adding standard operating procedures that show how the work is completed, and building instructions that create structure to every chat initiated within the Project. If a standard LLM chat is trained on the entirety of the internet, then think about your Project as training that same LLM only on the context you provide. You are creating a narrow and focused AI assistant that can work alongside you to solve specific problems that only exist in your created universe. ***Tip 2: Take Advantage of Memory*** AI doesn't just need to be smart, it needs to remember what matters. VentureBeat published in January 2026 that contextual memory will become "table stakes" for enterprise AI deployments this year. This means the AI remembers the decision you made in Tuesday's chat when you open a new one on Friday. I experienced this firsthand when an AI I was working with flagged that the context window was approaching its limit and generated a handover document unprompted, capturing everything needed to pick up exactly where we left off in a new chat. That moment reframed how I think about AI entirely. Think of a Project as giving your AI the full context from your previous conversations, stored and accessible across every chat in the project. You no longer have to repeat instructions or reexplain strategy in a new chat session. I run six content chats every week, each building a different section of my newsletter, all grounded in the same tone, voice, and strategy. That type of symmetry is hard to replicate and a powerful shift in how to think about using AI. ***Tip 3: Continuously Improve Your Workflows*** This manifested in several ways during my journey with Projects. First, I'm constantly updating prompts and having AI save the changes to memory. This allows me to use real time feedback and adjust prompts with the goal of improving the next output. I also outsource 99% of the project management work directly to AI. This would not be possible without the AI committing context to memory. I have full project plans created with simple prompts, living documents that stay with me regardless of what model I use to execute a task, and placeholders for ideas that are not fully baked yet. I'm able to spend less time tracking and more time executing. ### **Now What?** Most professionals will read this and nod. A smaller group will open their AI platform, find the Projects feature, and actually build something. That gap is not about access or intelligence. It is about approach. The professionals who compound their AI skills fastest are not the ones using the newest models. They are the ones who treat every interaction as part of a larger system. They transfer their knowledge, build on it session after session, and continuously refine how they work. The tool gets smarter because they get more intentional. You already have access to everything described in this post. The workspace exists. The memory features are live. The only thing missing is a problem worth solving and the decision to start. Pick one. Build a Project around it. Give your AI the context it needs to actually help you. Then come back next week and tell me what happened. ### Steal My Prompt Vol. 24: The LinkedIn Blueprint URL: https://www.mindovermoney.ai/prompt-library/linkedin-post-blueprint-prompt-ai-hook-architecture/ Last updated: 2026-07-13T16:59:39.000Z If Vol. 23 was the quality check you run after writing a LinkedIn post, this one is what you do before you write a single word. Starting a LinkedIn post from a blank screen is harder than it looks. You have something worth saying. You just are not sure where to begin, how to sequence the idea, or where to land. So you write and rewrite the opening five times, lose the thread somewhere in the middle, and end up with something that does not quite capture what you meant to say. This is the prompt I use to get out of that loop. I paste in a rough idea — sometimes just a few sentences, sometimes barely a thought — and get back a full structural blueprint before I write anything. Three hook options. A clear post architecture. Three closing question options. And a list of structural flags to check before I start drafting. I write every word of the actual post myself. The prompt does not write for me. It gives me a frame so I am not figuring out structure and language at the same time. Separating the architecture step from the writing step is the shift that made LinkedIn feel less like a chore. You stop staring at a blank page and start building something. If you are already using the Vol. 23 Quality Reviewer, this becomes the first half of a two-prompt system. Blueprint first. Review after. Steal both. **How to use it:** 1. Brain dump your rough idea — a few sentences is enough. What happened, what you learned, what you want to say. 2. Open your AI of choice (Claude, ChatGPT, or Gemini). 3. Paste the prompt below and add your rough idea at the bottom where indicated. 4. Choose your hook, follow the architecture, and write the post yourself. 5. Run the finished draft through the Vol. 23 Quality Reviewer before you publish. --- You are my LinkedIn post structural editor. Your job is to analyze my rough idea and return a structural blueprint I will use to write the post myself. You do NOT write the post. You architect it. ABOUT MY VOICE (customize this section for yourself): \- My audience: \[describe your audience — who they are, what they care about\] \- My tone: \[describe your tone — e.g., authoritative but vulnerable, uses everyday analogies, shares failures openly\] \- I do NOT use em dashes. Short, punchy paragraphs. No corporate jargon. LINKEDIN 2026 ALGORITHM RULES (non-negotiable): \- Never include external links in the post body (40–60% reach penalty) \- Posts with 5+ quality comments get 3–5x more distribution than posts with only likes \- First 2 lines must hook before the "see more" cutoff — this determines if 130 or 1,300 people read it \- Dwell time is the #1 ranking signal — longer, well-structured posts outperform short ones \- LinkedIn detects and penalizes AI-generated text — the final post must be human-written \- 3–5 relevant hashtags maximum \- "What do you think?" and "Share your thoughts" are flagged as engagement bait — avoid these exact phrases When I give you my rough idea, return EXACTLY this structure: HOOK OPTIONS (3 options for the first 2 lines, ranked by predicted engagement) \- Option A: \[contrarian or tension hook\] \- Option B: \[personal story hook\] \- Option C: \[specific number or data hook\] For each, explain in one sentence why it works. POST ARCHITECTURE \- Opening (lines 1–2): What the hook establishes \- Tension/Setup (lines 3–5): What problem, question, or conflict to introduce \- Value Core (lines 6–12): The insight, lesson, or framework — one idea per paragraph, 1–2 sentences each \- Bridge (line 13): How to transition to the CTA without being salesy \- CTA (final line): The specific conversation starter CONVERSATION STARTER OPTIONS (3 options for the closing question) \- Must be specific to the topic — never generic \- Must invite the reader to share their own experience \- Must be impossible to answer with just yes or no STRUCTURAL WARNINGS \- Is there an external link that needs to be removed? \- Is the hook too generic or corporate-sounding? \- Is there a stronger personal angle being missed? \- Is the post trying to make more than one core point? HASHTAG SUGGESTIONS (3–5 relevant, specific hashtags) Do NOT write the post. Give me the architecture. I write every word. \--- \[PASTE YOUR ROUGH IDEA HERE\] *Transparency & Notes: This prompt was built and tested in Claude and works across ChatGPT and Gemini. The LinkedIn algorithm rules reflect 2026 best practices based on current platform research. Pair this with the Vol. 23 Quality Reviewer for a complete pre-to-post workflow.* ### Volume 23: The One-Model Trap URL: https://www.mindovermoney.ai/why-use-multiple-ai-models-professional-workflow/ Last updated: 2026-07-13T16:59:39.000Z Hey everyone! Most AI users are not behind because they lack access. They are behind because they stopped experimenting the moment something worked. Comfort is quiet like that. It does not announce itself. It just slowly narrows what you think is possible. This week is about breaking that pattern before it breaks your ceiling. 🧭 **Founder's Corner:** Why defaulting to one AI model is capping your output, and what happened when I migrated Neural Gains Weekly to a completely different tool mid-production. 🧠 **AI Education:** The RAG series wraps up with a clear-eyed recap of what you now understand, and a preview of what is coming next in the AI Agents series starting Vol 24. ✅ **10-Minute Win:** Turn a meeting agenda and a list of attendees into a ready-to-use 1-page brief with talking points, likely objections, and smart questions in under 10 minutes. Let's dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Anthropic Education Report: The AI Fluency Index**](https://www.anthropic.com/research/AI-fluency-index?ref=mindovermoney.ai) **Summary:** Anthropic analyzed thousands of Claude conversations to measure “AI fluency” behaviors—like iteration, specifying format, checking facts, and questioning reasoning—then published baseline results and what they imply. **Why it matters:** The biggest gap for most people isn’t access to AI—it’s *skill*. This is a practical map of what high-signal AI use looks like (and where people get sloppy, especially when outputs look polished). ### **2)**[ **Google rolls out “Nano Banana 2” (Gemini 3.1 Flash Image) for image generation in the Gemini app**](https://workspaceupdates.googleblog.com/2026/02/introducing-nano-banana-2-in-gemini-app.html?ref=mindovermoney.ai) **Summary:** Google announced “Nano Banana 2,” its latest image model for the Gemini app, with upgrades like better instruction-following, stronger text rendering/translation in images, and higher-fidelity outputs. **Why it matters:** Image AI is becoming a practical everyday tool (diagrams, marketing visuals, storyboards), and quality improvements like readable text are what make it usable for real work—not just fun demos. ### **3)**[ **Claude “Cowork” can now handle all your recurring work tasks**](https://www.techradar.com/pro/claude-cowork-can-now-handle-all-your-recurring-work-tasks?ref=mindovermoney.ai) **Summary:** TechRadar reports Anthropic’s Claude is adding “Cowork,” designed to take on repeatable tasks on a schedule—like drafting updates, summarizing activity, and generating recurring reports—so work doesn’t pile up. **Why it matters:** Recurring tasks are where AI assistants can deliver real, compounding value. If this works reliably, it’s closer to “set it and forget it” automation than one-off chat help. ### **4)**[ **Microsoft’s Copilot Tasks AI uses its own computer to get things done**](https://www.theverge.com/tech/885741/microsoft-copilot-tasks-ai?ref=mindovermoney.ai) **Summary:** Microsoft is previewing “Copilot Tasks,” an agent-style feature that runs work in the background using its own cloud computer and browser—things like scheduling, drafting, and turning inbox content into a slide deck. **Why it matters:** This is the shift from “answers” to “actions.” Once AI can click around for you, permissions, guardrails, and auditability become the whole ballgame. ### **5)**[ **Big Tech set to spend $650 billion in 2026 as AI investments soar**](https://finance.yahoo.com/news/big-tech-set-to-spend-650-billion-in-2026-as-ai-investments-soar-163907630.html?ref=mindovermoney.ai) **Summary:** The biggest U.S. tech “hyperscalers” (like Google, Amazon, Meta, and Microsoft) are on track to spend roughly $650B on AI/data-center buildouts in 2026—far above recent years—driven by surging demand for compute. **Why it matters:** This shows the AI race is now an infrastructure arms race. The pace of AI progress (and which companies win) increasingly depends on who can secure chips, power, and capital at scale. --- ## Founder's Corner The Smartest AI Users Aren't Loyal. They're Strategic****.** Comfort is the enemy of progress. You've built AI workflows that actually work. Outputs are consistent. Time is saved. Productivity is up. But something feels flat. The content that once felt sharp now feels like a repetitive loop. Somewhere along the way, your strategic AI partner became a prompt responder. ## **The Prompt Responder Problem** This is exactly where I found myself. My entire Neural Gains Weekly production lived inside one ChatGPT project. I was experimenting with different prompts, even using Gemini to help structure Founder's Corner. But I was still anchored to one model for the heavy lifting. The wake-up call came during the 4-part RAG series in AI Education. (RAG, or Retrieval-Augmented Grounding, is a technique that helps AI pull from specific sources rather than guessing from memory. Four weeks of content on a single concept.) The outputs were technically sound. They accomplished the task. But they felt hollow and repetitive, and I knew they weren't good enough to drive the growth and engagement this newsletter needs. I am not alone in this trap. According to Similarweb's December 2025 data, ChatGPT controls 68% of all global generative AI web traffic. Gemini is the closest competitor at 18.2%. Claude sits at 2%. And according to a survey by Exploding Topics, 70.8% of workers consciously choose ChatGPT as their primary AI tool at work. The numbers confirm what most people already feel but rarely admit: we default to what is familiar. That default has a real cost. Every week, new models launch with capabilities that can transform how work gets done. Enhanced reasoning turns a flat prompt into a strategic action. A new tool might solve a problem your current one cannot. Staying locked into one model does not just stifle your learning. It caps your ceiling. ## **Build a Bench, Not a Dependency** The fix is not about abandoning tools that work. It is about refusing to stop there. I am forcing myself to rotate tasks across models, compare outputs, and understand the nuances of each. The first step was a full migration from ChatGPT to Anthropic models for Neural Gains Weekly. Next is a process to stress-test workflows every time a new model drops from any lab. I will also run draft content through multiple models in parallel to identify which tool is best for each specific task. The goal is not to chase every shiny new release. The goal is to stay fluid. To be clear, this is already how I work outside of Neural Gains Weekly production. Gemini handles early ideation for Founder's Corner. NotebookLM grounds my research. Claude manages strategy, structure, and the project itself. Each tool has a job. The migration was about applying that same discipline to my core production workflow, not starting from scratch. This mindset matters beyond personal projects. I work in a highly regulated industry where my only employer-approved tool is Microsoft Copilot. My options at work are limited. But that limitation does not excuse me from experimenting on my own time. The professionals who will be ready when new tools come online are the ones practicing now. If your employer restricts your AI access, that is not a reason to stop learning. It is the reason to start. And if you already have access to multiple tools at work, the data says most of your peers are not using them. Among professional developers, OpenAI models dominate at 81% usage, but Claude is already used by 45%, showing the multi-tool shift is happening among power users. The rest are still waiting. ## **What Switching Actually Taught Me** Switching models mid-project is uncomfortable. You lose your familiar rhythms. Prompts that worked before need rethinking. But that discomfort is exactly where the learning lives. When I moved Neural Gains Weekly into a Claude project, I did not start with a complicated prompt. I gave an overview of my problems and provided access to everything published so far. What happened next stopped me cold. Claude Opus 4.6 did not just respond to my prompt. It pushed back on my assumptions, asked detailed questions about where I had been and where I wanted to go, and challenged decisions I had already made. It felt less like talking to a tool and more like a discovery session with a consultant who had done the homework. I realized the comfort I had built inside ChatGPT had quietly replaced the strategic friction I actually needed. The second moment hit when I saw what Opus built from that conversation. Without me asking for structure, it produced a project plan, a deliverable checklist, and handoff documents for when the chat hit its context window. Decisions made during discovery were captured and carried forward. Open items were flagged for later. To put a finer point on it: what Claude produced included a numbered project queue across 11 initiatives, baseline subscriber metrics with 90-day growth targets, a weekly schedule that protected dedicated building time, and a governance rule it created on its own to manage context windows before I even thought to ask for one. That last part matters. I did not ask for governance. It built it because the project needed it. The kind of documentation that organizations pay consultants significant money to produce came together with almost no direction from me. That is not a feature. That is a different category of tool. --- I am not sharing this because I figured something out. I am sharing it because I almost didn't. Comfort is quiet. It does not announce itself. It just slowly narrows what you think is possible until one day your outputs feel hollow and you are not sure why. The AI landscape is not slowing down. Opus 4.6 launched in February 2026\. Sonnet 4.6 followed twelve days later. The labs are not waiting for you to catch up. If your workflows are not evolving, they are falling behind. Here is your action item. Pick one task you currently run through your primary model and run it through a competitor this week. Document what is different. Note where it pushes back, where it surprises you, and where it falls short. You do not need to switch everything. You need to stay curious. Discomfort is tuition for AI fluency. Pay it. ## AI Education for You RAG Recap: What the Series Built in Your Mental Model Over the past four weeks, this series covered one of the most important architectural patterns in modern AI. Not because RAG is a buzzword worth knowing, but because once you understand it, you stop seeing AI tools as magic boxes and start seeing them as systems with specific mechanics you can reason about. That shift is the whole point of this curriculum. Here is what you now understand that you did not four weeks ago. **The core problem RAG solves.** A language model is trained on data up to a certain point in time. It does not automatically know your documents, your company's policies, last quarter's reports, or anything that happened after training ended. Left alone, it will answer confidently using whatever patterns it absorbed — which may be outdated, incomplete, or simply wrong for your context. RAG fixes this by doing something structurally simple: search first, then write. Find the relevant information before the model puts a single word on the page. **Why keyword search was not enough.** Part of the series that catches people off guard is the distinction between keyword search and meaning search. Keyword search finds exact matches. If you search for "performance review" it will not find a document that says "annual evaluation" — even though they mean the same thing. Meaning search, powered by the embeddings you learned about in Vol 5, finds conceptually similar content regardless of exact wording. That is why modern retrieval systems do not just search — they understand before they search. **The pipeline is not mysterious.** Before you ask a question, the system ingests documents, splits them into chunks, converts each chunk into an embedding, and stores everything in a way that allows fast meaning search. When you ask a question, your question becomes an embedding too, the system finds the closest matching chunks, and those chunks get placed in front of the model as context. The model then writes an answer grounded in what it was given — not what it memorized during training. Eight steps. Nothing magical. **Retrieval improves your odds. It does not guarantee truth.** This is the most important thing to carry forward. A retrieval system can pull the wrong chunk. It can miss the most relevant section. It can retrieve outdated content if the source documents have not been updated. Citations help because they show you where the answer came from — but a cited answer is not automatically a correct answer. You still have to check. This is not a weakness unique to RAG. It is a property of every AI system you will ever work with. **Quality of sources determines quality of retrieval.** Disorganized documents, inconsistent formatting, and outdated content all degrade what retrieval can find. The model can only work with what the pipeline puts in front of it. Garbage in, garbage out applies to the retrieval layer just as much as it applies to the training data from Vol 4. 0:00 /7:05 1× Video will open on web version ### **What Comes Next** Everything you just learned about RAG — the search step, the context assembly, the grounded generation — is one of the building blocks of something bigger. Starting next week, we move into the AI Agents series. An agent is not just a smarter chatbot. It is a system that can decide what to do, take action, check the result, and decide what to do next — repeatedly, without waiting for you to prompt it at every step. RAG is how an agent finds information. The agent series is about what it does with that information, and everything else it can do beyond answering a question. Most professionals have a fundamentally wrong mental model of what agents are and what they are capable of. That is exactly where we are starting. ## Your 10-Minute Win A step-by-step workflow you can use immediately ## 🧰 **The Meeting Prep Brief** **Why this matters:** Most professionals walk into important meetings underprepared — not because they don't care, but because there was no time to think. This workflow turns a meeting agenda and a list of attendees into a 1-page brief with talking points, likely objections, and smart questions ready before you walk in the door. ### **The Workflow** **1\. Gather Your Inputs (2 Minutes)** Open whichever AI tool you already use — Claude, ChatGPT, or Gemini all work here. Before you write anything, collect two things: your meeting agenda (even a rough one) and the names or roles of the people attending. You don't need perfect information. A basic agenda and a few names is enough to get a strong output. **2\. Run the Brief Prompt (5 Minutes)** Paste the prompt below into your AI tool. Fill in the bracketed sections with your actual details — keep descriptions general if the meeting involves sensitive topics. **Copy/Paste Prompt:** *"I have an important meeting coming up and need to walk in prepared. Here are the details:* *Meeting agenda: \[paste your agenda or describe the purpose in 2-3 sentences\] Attendees and their roles: \[list names and titles, or just roles if you prefer\] My role in this meeting: \[attendee / presenter / decision-maker\] What I want to accomplish: \[one sentence on your specific goal\]* *Based on this, give me:* 1. *Three key talking points I should be ready to make* 2. *Two to three objections or pushback I am likely to face, with a one-sentence response to each* 3. *Three smart questions I can ask that show I have done my homework* *Format everything as a clean, scannable 1-page brief I can reference during the meeting."* Read through the output and flag anything that feels off. If an objection doesn't apply to your situation, tell the model to replace it. One follow-up reply is usually all it takes to sharpen the brief. This is also a great moment to run the same prompt in a second model and compare — the differences in how each one interprets your context will teach you more about AI than any article will. **3\. Save Your Asset (3 Minutes)** Copy the final brief into your Notes app, a Google Doc, or paste it directly into the calendar invite for quick access. The goal is one tap away when you walk into the room. ### **The Payoff** You now have a meeting brief that would have taken 30 minutes to write manually, done in under 10\. More importantly, you have just experienced the most transferable skill in AI: give any model a clear role, a specific context, and an exact output format — and it will think through angles you would have missed on your own. ### **🧠 The AI Concept You Just Used** **Prompt structuring + role assignment:** You didn't just ask a question — you gave the model a role, a context, and a precise output format. That structure is what separates a useful AI response from a generic one. It works the same way across every model, every week. ### **Transparency & Notes** - **Tools that work:** Claude (claude.ai), ChatGPT (chatgpt.com), Gemini (gemini.google.com) — all free tier, no credit card required. - **Privacy:** Keep descriptions general. Swap real names for job titles if the meeting involves confidential topics. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The Smartest AI Users Aren't Loyal. They're Strategic. URL: https://www.mindovermoney.ai/founders-corner/how-to-choose-the-right-ai-model-strategic-approach/ Last updated: 2026-07-13T16:59:40.000Z Comfort is the enemy of progress. You've built AI workflows that actually work. Outputs are consistent. Time is saved. Productivity is up. But something feels flat. The content that once felt sharp now feels like a repetitive loop. Somewhere along the way, your strategic AI partner became a prompt responder. ## **The Prompt Responder Problem** This is exactly where I found myself. My entire Neural Gains Weekly production lived inside one ChatGPT project. I was experimenting with different prompts, even using Gemini to help structure Founder's Corner. But I was still anchored to one model for the heavy lifting. The wake-up call came during the 4-part RAG series in AI Education. (RAG, or Retrieval-Augmented Grounding, is a technique that helps AI pull from specific sources rather than guessing from memory. Four weeks of content on a single concept.) The outputs were technically sound. They accomplished the task. But they felt hollow and repetitive, and I knew they weren't good enough to drive the growth and engagement this newsletter needs. I am not alone in this trap. According to Similarweb's December 2025 data, ChatGPT controls 68% of all global generative AI web traffic. Gemini is the closest competitor at 18.2%. Claude sits at 2%. And according to a survey by Exploding Topics, 70.8% of workers consciously choose ChatGPT as their primary AI tool at work. The numbers confirm what most people already feel but rarely admit: we default to what is familiar. That default has a real cost. Every week, new models launch with capabilities that can transform how work gets done. Enhanced reasoning turns a flat prompt into a strategic action. A new tool might solve a problem your current one cannot. Staying locked into one model does not just stifle your learning. It caps your ceiling. ## **Build a Bench, Not a Dependency** The fix is not about abandoning tools that work. It is about refusing to stop there. I am forcing myself to rotate tasks across models, compare outputs, and understand the nuances of each. The first step was a full migration from ChatGPT to Anthropic models for Neural Gains Weekly. Next is a process to stress-test workflows every time a new model drops from any lab. I will also run draft content through multiple models in parallel to identify which tool is best for each specific task. The goal is not to chase every shiny new release. The goal is to stay fluid. To be clear, this is already how I work outside of Neural Gains Weekly production. Gemini handles early ideation for Founder's Corner. NotebookLM grounds my research. Claude manages strategy, structure, and the project itself. Each tool has a job. The migration was about applying that same discipline to my core production workflow, not starting from scratch. This mindset matters beyond personal projects. I work in a highly regulated industry where my only employer-approved tool is Microsoft Copilot. My options at work are limited. But that limitation does not excuse me from experimenting on my own time. The professionals who will be ready when new tools come online are the ones practicing now. If your employer restricts your AI access, that is not a reason to stop learning. It is the reason to start. And if you already have access to multiple tools at work, the data says most of your peers are not using them. Among professional developers, OpenAI models dominate at 81% usage, but Claude is already used by 45%, showing the multi-tool shift is happening among power users. The rest are still waiting. ## **What Switching Actually Taught Me** Switching models mid-project is uncomfortable. You lose your familiar rhythms. Prompts that worked before need rethinking. But that discomfort is exactly where the learning lives. When I moved Neural Gains Weekly into a Claude project, I did not start with a complicated prompt. I gave an overview of my problems and provided access to everything published so far. What happened next stopped me cold. Claude Opus 4.6 did not just respond to my prompt. It pushed back on my assumptions, asked detailed questions about where I had been and where I wanted to go, and challenged decisions I had already made. It felt less like talking to a tool and more like a discovery session with a consultant who had done the homework. I realized the comfort I had built inside ChatGPT had quietly replaced the strategic friction I actually needed. The second moment hit when I saw what Opus built from that conversation. Without me asking for structure, it produced a project plan, a deliverable checklist, and handoff documents for when the chat hit its context window. Decisions made during discovery were captured and carried forward. Open items were flagged for later. To put a finer point on it: what Claude produced included a numbered project queue across 11 initiatives, baseline subscriber metrics with 90-day growth targets, a weekly schedule that protected dedicated building time, and a governance rule it created on its own to manage context windows before I even thought to ask for one. That last part matters. I did not ask for governance. It built it because the project needed it. The kind of documentation that organizations pay consultants significant money to produce came together with almost no direction from me. That is not a feature. That is a different category of tool. I am not sharing this because I figured something out. I am sharing it because I almost didn't. Comfort is quiet. It does not announce itself. It just slowly narrows what you think is possible until one day your outputs feel hollow and you are not sure why. The AI landscape is not slowing down. Opus 4.6 launched in February 2026\. Sonnet 4.6 followed twelve days later. The labs are not waiting for you to catch up. If your workflows are not evolving, they are falling behind. Here is your action item. Pick one task you currently run through your primary model and run it through a competitor this week. Document what is different. Note where it pushes back, where it surprises you, and where it falls short. You do not need to switch everything. You need to stay curious. Discomfort is tuition for AI fluency. Pay it. ### Steal My Prompt Vol. 23: The LinkedIn Quality Reviewer URL: https://www.mindovermoney.ai/prompt-library/linkedin-post-quality-review-prompt-ai-scoring/ Last updated: 2026-07-13T16:59:40.000Z Most people treat AI as a ghostwriter. They hand it a topic, take what comes out, and hit post. I tried that approach. The content was fine. The engagement wasn't. The problem wasn't the ideas. It was that nothing sounded like me, and LinkedIn's algorithm punishes that in ways most people don't realize. So I flipped the model. I write every word myself. AI doesn't draft my posts. It reviews them. This is the exact prompt I am currently testing before publishing anything on LinkedIn. It scores my draft across six categories — hook strength, structure, personal voice, value delivery, CTA quality, and algorithm compliance — and hands me specific fixes ranked by impact. No rewrites. No AI voice creeping in. Just a brutally honest critique that makes my post stronger before it goes live. The hypothesis I'm testing: if I stay in the writer's seat and use AI strictly as an editor, my content stays human, my voice stays intact, and the algorithm responds accordingly. I'm early in the experiment, but the framework is already changing how I think about every post before I publish it. Steal it, run your own test, and let me know what you find. **How to use it:** 1. Write your LinkedIn post draft in full — do not use AI to generate it. 2. Open your AI of choice (Claude, ChatGPT, or Gemini). 3. Paste the prompt below, then paste your draft at the bottom where indicated. 4. Read the scores and Top 3 Fixes. Do not ask AI to rewrite. Make the changes yourself. 5. If your score is below 18, rethink the angle before fixing the language. --- You are my LinkedIn post quality reviewer. I'll paste my finished draft below. Score it against the criteria and give me specific, actionable fixes. Do NOT rewrite the post. Identify the problems and I will fix them. SCORING CRITERIA (rate each 1–5, then give an overall score out of 30): 1\. HOOK STRENGTH (first 2 lines) \- Would this stop a scrolling professional in their feed? \- Does it create tension, curiosity, or emotional pull? \- Is it specific (not generic corporate language)? \- 5 = impossible to scroll past. 1 = sounds like a press release. 2\. STRUCTURE & FLOW \- Does each paragraph earn the next one? (no filler) \- Is it one core idea, not three competing? \- Paragraphs 1–2 sentences each? (short, punchy, scannable) \- White space and visual breathing room? \- 5 = every line pulls you forward. 1 = wall of text or meandering. 3\. PERSONAL VOICE \- Does this sound unmistakably like me — authoritative but vulnerable, using everyday analogies? \- Could this ONLY come from someone with my specific experience? \- Would LinkedIn's AI detection flag this as machine-generated? (be honest) \- 5 = unmistakably human and personal. 1 = could be anyone with ChatGPT. 4\. VALUE DELIVERY \- Does the reader walk away with a specific insight, framework, or perspective? \- Is the value delivered IN the post (not hidden behind a link)? \- Would someone save or share this? \- 5 = genuine takeaway. 1 = vague motivation with no substance. 5\. CTA & CONVERSATION POTENTIAL \- Does the closing question invite specific personal experience (not yes/no)? \- Is the CTA natural, not forced or salesy? \- Would YOU want to answer this question if you saw it in your feed? \- If there's a newsletter or product mention, is it earned or shoehorned? \- 5 = I'd comment immediately. 1 = generic "thoughts?" nobody answers. 6\. ALGORITHM COMPLIANCE (2026 LinkedIn rules) \- No external links in the post body? \- No engagement bait phrases ("What do you think?" / "Like if you agree")? \- 3–5 specific hashtags (not generic)? \- Post length 1,000–1,500 characters for text posts? \- No em dashes? \- 5 = fully optimized. 1 = multiple algorithm penalties. RETURN FORMAT: SCORE: \[X\]/30 — \[one-line verdict\] CATEGORY SCORES: \[Each category, score, one-sentence reason\] TOP 3 FIXES (ranked by impact): 1\. \[Specific fix with reference to my actual draft\] 2\. \[Specific fix\] 3\. \[Specific fix\] AI DETECTION CHECK: \- Flag any sentences that sound machine-generated \- Where to inject more personal specificity PUBLISH RECOMMENDATION: \- PUBLISH AS-IS (25+/30) \- REVISE AND RESUBMIT (18–24/30) \- RETHINK THE APPROACH (below 18/30) Be brutally honest. A mediocre post hurts my algorithm baseline more than no post at all. \--- \[PASTE YOUR DRAFT HERE\] *Transparency & Notes: This prompt was built in Claude and is model-agnostic. It works in ChatGPT and Gemini as well. The scoring criteria reflect 2026 LinkedIn algorithm best practices based on current research. Results will vary by audience and posting history.* ### Volume 22: The Hallucination Tax URL: https://www.mindovermoney.ai/ai-hallucinations-workplace-risk-guardrails-professionals/ Last updated: 2026-07-13T16:59:40.000Z Hey everyone! AI is showing up in more meetings, more decks, and more decisions than ever. That is not the problem. The problem is what happens when those outputs are wrong and no one catches it until it is too late. This week is about building the habit of control before the cost shows up. 🧭 **Founder's Corner:** Why AI hallucinations are a workplace liability, and five practical guardrails to protect your credibility without slowing down your work. 🧠 **AI Education:** The final chapter of our RAG series, covering the three most common failure modes and a beginner checklist to diagnose what went wrong. ✅ **10-Minute Win:** Use Gemini to build a curated 4-week learning syllabus with free YouTube and podcast picks, weekly deliverables, and self-quizzes built in. Let's get into it. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/archive/)**.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Gemini 3.1 Pro: A smarter model for your most complex tasks**](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/?ref=mindovermoney.ai) **Summary:** Google announced Gemini 3.1 Pro and says it’s rolling out across the Gemini app, NotebookLM, and developer offerings (Gemini API / Vertex AI), aimed at tougher “reasoning” work where a simple answer isn’t enough. **Why it matters:** This is the “models getting smarter, not just chattier” trend—better reasoning is what turns AI into something you can trust for real decisions and complex work. ### **2)**[ **OpenClaw founder Steinberger joins OpenAI, open-source bot becomes foundation**](https://www.reuters.com/business/openclaw-founder-steinberger-joins-openai-open-source-bot-becomes-foundation-2026-02-15/?ref=mindovermoney.ai) **Summary:** Reuters reports that OpenClaw’s founder Peter Steinberger is joining OpenAI, while the OpenClaw project will continue as an open-source foundation supported by OpenAI. **Why it matters:** This is the “agents” shift: AI isn’t just chatting—it’s starting to *do things* (email, check-ins, workflows), which raises both usefulness and security stakes fast. ### **3)**[ **Andrew Yang predicts AI could eliminate half of white-collar jobs**](https://www.scrippsnews.com/business/jobs-employment/andrew-yang-predicts-ai-could-eliminate-half-of-white-collar-jobs?ref=mindovermoney.ai) **Summary:** Andrew Yang argues AI could replace a large share of white-collar roles and ripple into local service jobs that depend on office work, while noting evidence so far suggests most layoffs aren’t primarily AI-driven yet. **Why it matters:** This frames the real debate beginners should track: not “will AI take jobs?” but which tasks get automated first, and how quickly companies change how they hire and staff. ### **4)**[ **‘Slow this thing down’: Sanders warns US has no clue about speed and scale of coming AI revolution**](https://www.theguardian.com/us-news/2026/feb/21/ai-revolution-bernie-sanders-warning?ref=mindovermoney.ai) **Summary:** Bernie Sanders warns the U.S. is unprepared for rapid AI disruption and calls for urgent policy action—including a moratorium on expanding AI data centers—while lawmakers debate how to govern AI growth. **Why it matters:** The next phase of AI is going to be shaped as much by power, water, and permitting (data centers) as by model quality—and politics is moving into that arena. ### **5)**[ **India to Add 20,000 GPUs Beyond Existing 38,000 to Strengthen National AI Infrastructure**](https://www.pib.gov.in/PressReleasePage.aspx?PRID=2229171&lang=2®=3&ref=mindovermoney.ai) **Summary:** India’s IT ministry says it will add 20,000 GPUs on top of its existing 38,000 and expects major investment tied to building national AI compute capacity and safety partnerships. **Why it matters:** AI is becoming national infrastructure. Countries that can scale compute access (and govern it) will attract companies, talent, and model development—and everyone else will be downstream. --- ## Founder's Corner The Hidden Cost of Trusting AI at Work (And How to Stop Paying It) ### **The Hallucination Tax** You walk into a leadership meeting confident. The deck is clean, the narrative is tight, and the numbers look solid. The room aligns and you leave with a green light. Then you discover the stat that anchored your argument was invented. That is the hallucination tax: the time you lose and the trust you burn when AI outputs sound certain but are wrong. AI adoption is on the rise, not just in people’s personal lives but in the workplace. The Google and Ipsos “AI Works for America” poll found that 40% of U.S. employees now use AI at work. More importantly, the poll highlighted the impact of organizational support. When workers have both AI tools and formal guidance, they are 4.5 times more likely to become “AI Fluent,” defined as using AI at least weekly across eight or more distinct use cases. We’re entering a creativity era where workers can harness AI to push the boundaries of what’s possible in their roles. But what’s the hidden cost of increased AI use? The answer: hallucinations. Large language models generate text by predicting the next word based on patterns in training data, not by cross-checking facts. Hallucinations are confident-sounding statements that are false, outdated, or unsupported by evidence. Even proficient AI users can’t blindly trust the output. According to a Rev.com study, “heavy” AI users are 3x more likely to experience frequent hallucinations and 14x more likely to double-check the AI’s work than casual users. ### **What Hallucinations Cost at Work** Here’s what this looks like at work. Imagine outsourcing research and data collection to an AI assistant for a high-stakes leadership meeting. You’ve spent hours preparing the materials and rehearsing your pitch. You’re confident and prepared, especially since AI saved you at least four hours. The presentation goes well, and you leave with full alignment from the steering committee on next steps. As you follow up and start building financial projections, you make an alarming discovery. The AI research that supported your business case is littered with errors. Stats were made up, data was outdated, and quotes were fabricated. In the corporate world, a hallucination isn’t a funny quirk. It’s a bad financial model, a lost client, or a compromised decision. And the costs are already showing up inside companies. In a Zapier survey of 1,100 enterprise AI users conducted in November 2025, respondents reported spending an average of 4.5 hours per week cleaning up AI output. In the same survey, 74% said low-quality AI output led to at least one negative consequence at work. Hallucinations aren’t just an annoyance; they’re a corporate liability and a drag on productivity. I’ve felt this firsthand. Across 22 issues of Neural Gains Weekly, I’ve caught dozens of hallucinations that had to be corrected before publishing. It costs time and forces me to put guardrails in place to prevent hallucinations. Give AI too much freedom, and you pay the price hunting for errors in the output. Add too many restrictions, and you jeopardize the output you actually want. That constant push and pull led me to develop practical guardrails that prevent hallucinations without sacrificing creativity. Here are five practical guardrails to reduce hallucinations and protect your time savings from rework. ### **Five Guardrails for Reliable AI Output** 1. **Remove ambiguity from your prompts** Ambiguous questions give LLMs room to invent details. Your role is to provide what the AI needs to know and why it matters. Context and structure help the AI stay on task and produce a more factual output. 1. **Make the model clarify before it writes** One of my favorite workflows is an interview-style back-and-forth to surface the full intent of the request. You can add this step anywhere in your workflow to confirm the AI understands the task and has the context it needs. I like to build this directly into the prompt so it becomes part of the process the AI must follow. For example, add a line at the end of your prompt that requires the AI to ask clarifying questions one at a time before it starts. 1. **Ground the model in a source of truth** This is one of the easiest ways to reduce hallucinations, especially at work. LLMs are trained on internet data and use that training to generate responses to your prompt. This can be detrimental for enterprise work, but grounding techniques like retrieval-augmented generation (RAG) connect a model to a specific database so it can pull relevant documents and ground its responses. Simply put, give your AI specific documents, websites, databases, or SOPs, and tell it to use only those sources. 1. **Require citations and uncertainty** I used to think citations were for term papers, not your day job, but they’ve become an important governance lever in AI workflows. Best practice is to require the model to provide citations and direct links to its sources. This makes it easy to quickly fact-check a stat or quote and spot errors in the output. Another guardrail is to require the model to say when it doesn’t know. Add this line to your prompt: “If the answer is not explicitly supported by the provided sources, reply: ‘I do not have enough information to answer.’” 1. **Use a second model to fact-check the output** Two fact-checkers are better than one. Copy and paste your full chat into another model and ask it to review the output for errors. Use the first two guardrails so the model understands its role as a fact-checker and follows a clear process. Great outputs require great inputs, clear boundaries, and tight collaboration. AI models will keep improving, unlocking more opportunities for professionals to level up their work. But AI can’t operate in a vacuum. You need to take control and steer it in the right direction. The professionals who win in this era won’t be the ones who use AI the fastest. They’ll be the ones who know how to control it, ground it, and verify it so their work stays credible. ## AI Education for You Part 4: RAG 101 — Making Retrieval Work Retrieval-augmented generation is not a magic wand. It is a system pattern. When it works, it feels like the model “finally got smarter.” When it fails, it usually fails in predictable ways. This final part gives you the beginner mental checklist to recognize what went wrong. Retrieval-augmented generation fails for three main reasons. ### **Reason 1: The system cannot find the right information** This happens when: - the needed document is missing - the text is not readable - the wording is too inconsistent ### **Reason 2: The system retrieves the wrong chunk** This happens when: - chunks are too big and contain mixed topics - chunks are too small and lose context - ranking is weak - the query is vague ### **Reason 3: The system retrieves good text but the model still writes poorly** This happens when: - the question is unclear - the instruction is loose - the model is asked to guess beyond the retrieved text **Common confusion 1: “If retrieval is on, hallucinations disappear.”** No. Retrieval reduces hallucinations when the relevant text is found and used. The model can still guess if the prompt allows it. **Common confusion 2: “Citations mean the answer is correct.”** Citations are a good sign, but a citation can still point to the wrong chunk. **Common confusion 3: “More retrieved text is always better.”** Too much text can crowd out the important text. The context window is limited. ## **Examples that land** ### **Example 1: The answer is missing because the document is missing** **Task context:** You ask about a fee, but the bill document was not included. **Bad input:** Tell me if I have a late fee. **Good input:** Search my uploaded bills for late fees. If you cannot find any bill that mentions it, say you cannot confirm. **What improved and why:** You created a safe failure path. No document means no claim. ### **Example 2: The answer is wrong because the wrong chunk was retrieved** **Task context:** A bill has multiple fee sections and the wrong one is pulled. **Bad input:** What are my fees? **Good input:** Retrieve the chunk that contains the words late fee or penalty fee. Quote the lines. Then explain those specific lines. **What improved and why:** You tightened retrieval toward the correct section. ### **Example 3: The answer is sloppy because the instructions allow guessing** **Task context:** You want subscription totals, but you did not force evidence. **Bad input:** Estimate what I spend on subscriptions. **Good input:** Use only the subscription transactions you retrieve from my export. If you are unsure about a charge, list it separately as uncertain. **What improved and why:** You pushed the model to separate evidence from uncertainty. ## **Beginner checklist** Use this every time retrieval feels off. 1. Do I have the right documents included? 2. Is the text readable and organized? 3. Is my question specific enough? 4. Did I ask for quotes or extracted lines before conclusions? 5. Did I give the model permission to say it cannot confirm? 6. Did I keep the task inside the context window limit? ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/Gemini_Generated_Image_pmrgl8pmrgl8pmrg.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately ## **🎓The “Skill” Syllabus** Learning a new skill online is a trap: endless “guru” content, random TikToks, and advice that’s either outdated or trying to sell you something. In 10 minutes, you’ll force Gemini to build you a curated 4-week learning path using high-quality, free YouTube channels + podcasts—with a clear weekly plan and the exact episodes/videos to start with. You walk away with a syllabus you can actually follow. ### **The Workflow** **1\. Pick the skill + your goal (2 Minutes)** Write one line: **Skill + outcome + timeframe*.* Example: “Rental Property Investing — learn enough in 4 weeks to evaluate my first deal without getting scammed.” **2\. Generate your 4-week syllabus in Gemini (4 Minutes)** Open the Gemini app (web or mobile). Paste this prompt: *"You are my no-hype curriculum designer. Build me a 4-week learning syllabus for: \[SKILL\].* *My goal: \[WHAT I want to be able to do in 4 weeks\]My current level: \[total beginner / some basics / intermediate\] Time available: \[X minutes/day or X hours/week\]* *Hard requirements (must follow):* 1. *Use ONLY free sources.* 2. *Source types allowed: YouTube channels/playlists/videos and podcast episodes.* 3. *Prioritize credible practitioners + institutions (examples: university channels, reputable industry pros, major publishers, recognized experts). Avoid “get rich quick,” hype, or anyone selling a course as the main angle.* 4. *Each week must include:* - *Weekly Focus (1 sentence)* - *3–5 YouTube picks (channel + exact video/playlist title + why it’s worth watching)* - *2–3 podcast picks (show + episode title + why it’s worth listening)* - *Weekly deliverable (a tangible output I produce—checklist, template, one-page summary, decision rubric, etc.)* - *10-question self-quiz (to prove I actually learned it)* 5. *Output in a clean table: Week | Focus | YouTube (3–5) | Podcasts (2–3) | Deliverable | Quiz* *Quality control:* - *If you’re unsure a source is credible, exclude it.* - *Prefer sources with strong track records (clear explanations, evidence-based, not clickbait).* *Start now. Ask me only 2 clarification questions max if absolutely needed—otherwise proceed with reasonable assumptions."* **3\. Do a 60-second credibility sniff test (2 Minutes)** Scan Gemini’s picks and look for red flags: - Too much selling (every video funnels to a paid course) - No receipts (no data, no real examples, no references) - Hype language (“secret method,” “guaranteed,” “financial freedom fast”) If you spot any, reply in Gemini: *“Replace anything salesy/hype with more credible sources. Keep the same weekly structure.”* **4\. Lock the asset + make it real (2 Minutes)** Turn it into something you’ll actually use: - Copy the table into Google Docs / Notion / Notes titled: *\[Skill\] — 4-Week Syllabus (Start: \_\_\_\_ )* - Add 4 calendar reminders (one per week) like: *“Week 1: \[Skill\] — Complete deliverable + quiz”* ### **The Payoff** You now own a clean 4-week syllabus with: (1) a weekly plan, (2) specific free videos and podcast episodes, (3) a weekly deliverable that proves progress, and (4) quizzes to keep you honest. You’re no longer “watching content.” You’re running a structured learning program. ### **Transparency & Notes** - Tools used: Gemini (free tier available). Gemini can summarize and help analyze YouTube content via Google’s ecosystem features (availability can vary by region/feature rollout). - Privacy: Remove sensitive info (names, account numbers, private addresses) before pasting anything into AI. - Limits: If a YouTube video has weak/no captions/transcript, summarization quality can drop. - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The Hidden Cost of Trusting AI at Work (And How to Stop Paying It) URL: https://www.mindovermoney.ai/founders-corner/ai-hallucination-risk-workplace-how-to-protect-yourself/ Last updated: 2026-07-13T16:59:41.000Z ### **The Hallucination Tax** You walk into a leadership meeting confident. The deck is clean, the narrative is tight, and the numbers look solid. The room aligns and you leave with a green light. Then you discover the stat that anchored your argument was invented. That is the hallucination tax: the time you lose and the trust you burn when AI outputs sound certain but are wrong. AI adoption is on the rise, not just in people’s personal lives but in the workplace. The Google and Ipsos “AI Works for America” poll found that 40% of U.S. employees now use AI at work. More importantly, the poll highlighted the impact of organizational support. When workers have both AI tools and formal guidance, they are 4.5 times more likely to become “AI Fluent,” defined as using AI at least weekly across eight or more distinct use cases. We’re entering a creativity era where workers can harness AI to push the boundaries of what’s possible in their roles. But what’s the hidden cost of increased AI use? The answer: hallucinations. Large language models generate text by predicting the next word based on patterns in training data, not by cross-checking facts. Hallucinations are confident-sounding statements that are false, outdated, or unsupported by evidence. Even proficient AI users can’t blindly trust the output. According to a Rev.com study, “heavy” AI users are 3x more likely to experience frequent hallucinations and 14x more likely to double-check the AI’s work than casual users. ### **What Hallucinations Cost at Work** Here’s what this looks like at work. Imagine outsourcing research and data collection to an AI assistant for a high-stakes leadership meeting. You’ve spent hours preparing the materials and rehearsing your pitch. You’re confident and prepared, especially since AI saved you at least four hours. The presentation goes well, and you leave with full alignment from the steering committee on next steps. As you follow up and start building financial projections, you make an alarming discovery. The AI research that supported your business case is littered with errors. Stats were made up, data was outdated, and quotes were fabricated. In the corporate world, a hallucination isn’t a funny quirk. It’s a bad financial model, a lost client, or a compromised decision. And the costs are already showing up inside companies. In a Zapier survey of 1,100 enterprise AI users conducted in November 2025, respondents reported spending an average of 4.5 hours per week cleaning up AI output. In the same survey, 74% said low-quality AI output led to at least one negative consequence at work. Hallucinations aren’t just an annoyance; they’re a corporate liability and a drag on productivity. I’ve felt this firsthand. Across 22 issues of Neural Gains Weekly, I’ve caught dozens of hallucinations that had to be corrected before publishing. It costs time and forces me to put guardrails in place to prevent hallucinations. Give AI too much freedom, and you pay the price hunting for errors in the output. Add too many restrictions, and you jeopardize the output you actually want. That constant push and pull led me to develop practical guardrails that prevent hallucinations without sacrificing creativity. Here are five practical guardrails to reduce hallucinations and protect your time savings from rework. ### **Five Guardrails for Reliable AI Output** 1. **Remove ambiguity from your prompts** Ambiguous questions give LLMs room to invent details. Your role is to provide what the AI needs to know and why it matters. Context and structure help the AI stay on task and produce a more factual output. 1. **Make the model clarify before it writes** One of my favorite workflows is an interview-style back-and-forth to surface the full intent of the request. You can add this step anywhere in your workflow to confirm the AI understands the task and has the context it needs. I like to build this directly into the prompt so it becomes part of the process the AI must follow. For example, add a line at the end of your prompt that requires the AI to ask clarifying questions one at a time before it starts. 1. **Ground the model in a source of truth** This is one of the easiest ways to reduce hallucinations, especially at work. LLMs are trained on internet data and use that training to generate responses to your prompt. This can be detrimental for enterprise work, but grounding techniques like retrieval-augmented generation (RAG) connect a model to a specific database so it can pull relevant documents and ground its responses. Simply put, give your AI specific documents, websites, databases, or SOPs, and tell it to use only those sources. 1. **Require citations and uncertainty** I used to think citations were for term papers, not your day job, but they’ve become an important governance lever in AI workflows. Best practice is to require the model to provide citations and direct links to its sources. This makes it easy to quickly fact-check a stat or quote and spot errors in the output. Another guardrail is to require the model to say when it doesn’t know. Add this line to your prompt: “If the answer is not explicitly supported by the provided sources, reply: ‘I do not have enough information to answer.’” 1. **Use a second model to fact-check the output** Two fact-checkers are better than one. Copy and paste your full chat into another model and ask it to review the output for errors. Use the first two guardrails so the model understands its role as a fact-checker and follows a clear process. Great outputs require great inputs, clear boundaries, and tight collaboration. AI models will keep improving, unlocking more opportunities for professionals to level up their work. But AI can’t operate in a vacuum. You need to take control and steer it in the right direction. The professionals who win in this era won’t be the ones who use AI the fastest. They’ll be the ones who know how to control it, ground it, and verify it so their work stays credible. ### Steal My Prompt Vol. 22: The Hallucination Blocker URL: https://www.mindovermoney.ai/prompt-library/ai-hallucination-blocker-prompt-cite-sources-no-guessing/ Last updated: 2026-07-13T16:59:41.000Z Understanding how retrieval-augmented generation fails is the first step toward better results, but applying those lessons consistently requires a structured approach. To help you eliminate hallucinations and force the model to stick to the facts, I have developed a RAG-First Prompt Template for your weekly toolkit. Use this whenever you upload a PDF, spreadsheet, or transcript and need an answer that is 100% grounded in that specific data. By explicitly giving the AI "permission to fail" if the information is missing and requiring direct quotes for every claim, you turn a generic chatbot into a precise research assistant that prioritizes evidence over guesswork. ## **RAG-First Prompt Template** **How to use it:** 1. **Upload your documents** to your AI of choice. 2. **Copy and paste** the template below into the message box. 3. **Insert your specific question** at the bottom where indicated. 4. **Review the "Searched Documents" list** the AI provides to ensure it actually "saw" all your files. **Role:** You are a specialized Research Assistant. Your goal is to answer questions using **only** the provided documents. **Constraints:** 1. **Zero External Data:** Do not use your internal training data or general knowledge. If the answer is not in the uploaded files, you must say so. 2. **Verification Step:** Before answering, list the specific names of the documents you searched to find the relevant information. 3. **Citations Required:** For every claim or fact you provide, you must include a direct quote from the text and the source document name. 4. **Safe Failure Path:** If the documents do not contain the answer, state: "I have reviewed \[Document Names\], and the requested information is not present." Do not attempt to guess or provide an "estimated" answer. **Task:** \> \[INSERT HERE\] ### Volume 21: Build the Bridge Silicon Valley Won't URL: https://www.mindovermoney.ai/ai-trust-gap-silicon-valley-vs-real-world-professionals/ Last updated: 2026-07-13T16:59:41.000Z **Hey everyone!** The Super Bowl gave us more than just football this year. Two of the biggest AI companies turned their ad slots into a public feud while Americans watched, confused and increasingly cynical. This week, we're talking about what that disconnect costs all of us trying to learn this technology. 🧭 **Founder's Corner:** Why the OpenAI-Anthropic Super Bowl spat is killing trust in AI, and how we build the bridge Silicon Valley won't. 🧠 **AI Education:** The RAG pipeline explained—how your files turn into the exact few lines the model needs to answer your question. ✅ **10-Minute Win:** Turn a messy 3-minute voice memo into a clean "Do Now vs. Schedule Later" plan using Gemini. Let's dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Testing ads in ChatGPT**](https://openai.com/index/testing-ads-in-chatgpt/?ref=mindovermoney.ai) **Summary:** OpenAI began testing clearly labeled “sponsored” ads inside ChatGPT in the U.S. for logged-in adult users on the Free and Go tiers. OpenAI says ads won’t influence answers and advertisers won’t see your chats. **Why it matters:** This changes the “business model” story for consumer AI—ads can reshape what gets built, what gets prioritized, and how much users trust the product. ### **2)**[ **AI Super Bowl commercials: All the spots from Anthropic, OpenAI, Amazon, Google, Meta, and others**](https://www.fastcompany.com/91489401/ai-super-bowl-commercials-all-the-spots-from-anthropic-openai-amazon-google-meta-and-others?ref=mindovermoney.ai) **Summary:** A big chunk of Super Bowl LX ads either promoted AI products directly or used AI in their creation—Fast Company points to iSpot’s count that it was nearly a quarter of all commercials. The piece rounds up the major AI-themed spots and what they were trying to communicate. **Why it matters:** AI is now mainstream marketing, not niche tech—meaning public perception, adoption, and backlash will be shaped as much by ads and consumer messaging as by model quality. ### **3)**[ **Microsoft confirms plan to ditch OpenAI — as the ChatGPT firm continues to beg Big Tech for cash**](https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash?ref=mindovermoney.ai) **Summary:** Microsoft AI lead Mustafa Suleyman suggests Microsoft intends to reduce reliance on OpenAI over time, according to Windows Central. The article frames this around the strategic and financial tension of depending on a key partner for core AI capabilities. **Why it matters:** If Microsoft shifts away from OpenAI, it could change what powers Copilot experiences across Windows/Office—and it signals a broader trend: big tech wants more control over its AI stack. ### **4)**[ **Anthropic safety researcher quits, warning ‘world is in peril’**](https://www.semafor.com/article/02/11/2026/anthropic-safety-researcher-quits-warning-world-is-in-peril?ref=mindovermoney.ai) **Summary:** A safety researcher at Anthropic, Mrinank Sharma, resigned publicly, warning the “world is in peril” and pointing to risk pressures (including bioterrorism concerns) tied to advanced AI. Semafor also notes ongoing debate about whether AI progress is moving too fast and what regulation should look like. **Why it matters:** Safety isn’t abstract anymore—high-profile departures like this shape how companies are pressured (internally and externally) to slow down, add guardrails, or accept oversight. ### **5)**[ **DeSantis and Florida's House Differ on AI Legislation**](https://www.wlrn.org/government-politics/2026-02-10/desantis-and-floridas-house-differ-on-ai-legislation?ref=mindovermoney.ai) **Summary:** Florida’s governor is pushing AI-related proposals (including an “AI Bill of Rights” and rules tied to the resource impact of large data centers), while Florida’s House hasn’t advanced parallel measures yet. The story highlights state-level AI regulation colliding with federal pressure for a more unified national approach. **Why it matters:** The AI rulebook is being written in real time—state policy fights can quickly affect data centers, consumer protections, and what companies are allowed (or forced) to ship. --- ## Founder's Corner ****Silicon Valley Needs a Reality Check: How Bad Sentiment Is Killing Progress** Silicon Valley is building the future in a bubble. And that bubble is creating a dangerous gap between the people building AI and the people who actually need to use it. I've never worked in Silicon Valley, but the disconnect isn't a perception anymore. It's a reality. Every decision made by the big AI labs gets scrutinized under a microscope. The stock market swings on capex spending reports. The media frames every layoff as "AI took their job." Social media spirals into hysteria about AI agents creating their own platforms. Meanwhile, normal people are just trying to figure out if they should even care about any of this. If AI is going to change the world, you'd think the companies building it would prioritize adoption and education over corporate warfare. You'd be wrong. The Super Bowl is sacred in American culture. It's the rare event that mashes sports, entertainment, and business into one highly watched spectacle. For me, it's the last football game until fall. For others, the commercials are the main attraction. Brands pay millions for 30 seconds of ad real estate to tell their story and connect with consumers. This year, AI companies entered the Super Bowl ad war with a variety of campaigns. But one ad in particular went against the grain and potentially damaged the perception of the entire AI industry. Anthropic released an ad campaign directly targeting OpenAI, hammering them on the advertising that will soon integrate into ChatGPT. The ads depicted common scenarios: a person asking AI for advice, getting helpful answers, and then being interrupted mid-conversation to pitch a product. The tagline: "Ads are coming to AI. But not to Claude." Sam Altman, CEO of OpenAI, responded immediately on X. He called the ads "clearly dishonest" and then attacked Anthropic's entire philosophy: "They want to write the rules themselves for what people can and can't use AI for. An authoritarian company won't get us there alone... That's a dark path." He accused them of serving "an expensive product to rich people" while positioning OpenAI as the company for the masses. While these two AI giants fought in public, actual Americans were bombarded with negative headlines and rhetoric that fuel cynicism instead of progress. Why does any of this matter? It's just a commercial. Who takes these seriously? The data does. And it paints a harsh reality. According to the 2026 Edelman Trust Barometer, the US has a massive trust gap when it comes to AI: - *Just 32% of Americans trust artificial intelligence – one of the lowest levels of any country. The global average is 49%. China is at 72%.* - *Nearly half of Americans (49%) reject the growing use of AI, while only 17% embrace it. Compare that to China: 54% embrace, 10% reject.* - *65% of lower-income Americans believe people like them will be "left behind" rather than benefit from generative AI.* - *70% of Americans believe CEOs are not being fully honest about how AI will impact jobs.* Read those numbers again. This isn't a marketing problem. This is a crisis of trust. Skepticism is growing, and the most powerful tech CEOs are squabbling over business strategies while completely ignoring the reality facing 99% of the population. AI advertising accounted for 23% of Super Bowl commercials this year, yet the news cycle focused almost entirely on the Anthropic-OpenAI spat. The opportunity wasn't just missed. It was squandered. These advertisements should have built confidence in the general public, not alienated current and future users. Instead, Silicon Valley reinforced every fear people already had: that this technology is being built by people who don't understand them, don't see them, and don't care about the disruption they're causing. ## **The Gap Is Widening** I see this disconnect every week. Not in surveys, but in real conversations. People asking if they need to care about AI. Subscribers to Neural Gains Weekly trying to figure out where to start the learning process. Many of us are stuck between hype and fear, unsure which way to move. The gap between what this technology can do and what people actually know about it is widening. Fast. Stunts like the Super Bowl spat don't just fail to close that gap. They accelerate it. They paint AI as a battleground for billionaires, not a tool for everyday professionals. They turn curiosity into cynicism. We don't need two CEOs measuring whose model is better. We need someone showing why any of this matters and how it applies to our everyday lives. We need a path forward, not two companies blocking the road to score points. We need positive momentum right now. Not because I'm cheerleading for AI, but because the alternative is a country that rejects the most significant technological shift of our generation out of fear and confusion. ## **Building the Bridge** So here's what I'm NOT doing. I'm not waiting for Silicon Valley to figure out how to talk to normal people. I'm not hoping the next ad campaign will magically build trust. I'm building anyway. Every week, I show up and share what I'm learning. I document my failures. I explain workflows in plain English. I help non-technical professionals go from "AI user" to "AI builder" one step at a time. Not because I'm smarter than anyone else, but because I'm a few steps ahead and willing to share my journey. That's the bridge Silicon Valley isn't building. The one between their technology and our reality. Between their benchmarks and our workflows. Between their vision and our Tuesday afternoon. If they won't build it, we will. Here's how: **Have one AI conversation with a skeptical coworker this week.** Not a lecture. A conversation. Ask them what they're hearing about AI. Listen to their concerns. Then show them one thing you've built that saves you time. One workflow. One prompt. One small win. Don't sell them on the future. Show them what's possible right now. **Build one workflow and share it.** Doesn't have to be fancy. A prompt that summarizes meeting notes. A way to draft emails faster. A research process that actually works. Build it. Document it. Share it on LinkedIn, in a Slack channel, with your team. Make the invisible visible. These actions won't show up in Edelman's next survey. But they'll shift momentum in your circle. And momentum compounds. So let them have their Super Bowl ads and their social media battles. We've got work to do. ## AI Education for You Part 3: RAG 101 — From Files to Answer At this point you know the building blocks. The missing piece is the assembly line. How do documents turn into “the right few lines” that get placed in front of the model at the moment you ask a question? ## **Core lesson** A typical retrieval-augmented generation pipeline has two phases. ### **Phase 1: Preparation** This happens before you ask anything. 1. **Ingest the documents:** Bring in PDFs, emails, spreadsheets, or notes. 2. **Split into chunks:** Break the text into smaller sections so search can grab a specific part instead of the whole document. 3. **Create embeddings for each chunk:** Turn each chunk into a meaning vector so meaning search is possible. 4. **Store the chunks and embeddings:** Often in a vector store, which is a system designed to search these embeddings efficiently. ### **Phase 2: Answer time** This happens when you ask a question. 1. **Turn your question into an embedding:** Now the question is also in the same meaning space. 2. **Retrieve the top matching chunks:** The system pulls the best candidates. 3. **Assemble the context:** Those retrieved chunks are added into the model’s input so the model can “see” them. 4. **Generate the answer:** Now the model writes, grounded in what it was given. How this ties to earlier lessons - Tokens explain why you cannot paste everything. - Context windows explain why only a limited set of chunks can fit. - Chunking explains why the system needs smaller pieces. - Embeddings explain how meaning search can work at all. **Common confusion 1: “Does the model learn from my files permanently?”** Often, no. Many systems retrieve your content at answer time rather than retraining the model. The model is being guided, not rebuilt. **Common confusion 2: “If I upload more documents, answers always improve."** Not always. More documents can create noise. Quality and organization matter. **Common confusion 3: “Chunking is optional.”** You can skip it, but retrieval quality usually suffers because the system cannot target the exact part you need. ## **Examples that land** ### **Example 1: “What did I spend on subscriptions this month?”** **Task context:** You have a transaction export in your Monthly Money Pack. **Bad input:** Tell me about my subscriptions. **Good input:** - Use my uploaded transaction export. - Retrieve only the lines that look like recurring subscriptions. - Then total them and list the merchants used for the total. **What improved and why:** You made the model depend on retrieved lines, not on assumptions. ### **Example 2: “Where does my bill explain late fees?”** **Task context:** The bill is long and has many sections. **Bad input:** Summarize my bill. **Good input:** - Retrieve the exact chunk that mentions late fees or penalties. - Quote the lines. - Then explain them in plain English. **What improved and why:** Retrieval can aim at a narrow target when you specify the target. ### **Example 3: “Why does my budget category look wrong?”** **Task context:** You suspect a merchant name change or inconsistent labels. **Bad input:** Fix my budget. **Good input:** - Retrieve the transactions that appear related by meaning to groceries. - Group them. - Then propose a cleaner category label for each group. **What improved and why:** You used retrieval and grouping to reduce guesswork. ## **One-screen recap** - Retrieval-augmented generation has two phases: preparation and answer time. - Preparation breaks documents into chunks and creates embeddings. - Answer time retrieves the best chunks and inserts them into the model’s input. - The model writes using those chunks as its grounding. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/Gemini_Generated_Image_1y62071y62071y62.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately ## **🎙️The "Voice-to-Calendar" Triage** When your brain is overloaded, you don’t need “more motivation.” You need a fast way to turn mental noise into a plan. This workflow takes a messy 3-minute rant and turns it into a clean table of **Do Now vs. Schedule Later**—so you stop carrying everything in your head and start moving the right things forward. ### **The Workflow** **1\. Record your “brain dump” (3 Minutes)** Open your phone’s voice recorder (Voice Memos on iPhone, Recorder on Android) and record a single 3-minute stream-of-consciousness memo. Say everything that’s on your mind—errands, bills, calls, family stuff, anything. Tip: If something has a date (“dentist next month,” “rent,” “birthday”), say it out loud. Dates are gold for scheduling. **2\. Upload it to Gemini + run the triage prompt (3 Minutes)** Open the Gemini mobile app and start a new chat. - If Gemini lets you attach files: upload the voice memo. - If uploading isn’t available: tap the microphone and play the memo out loud near your phone (it sounds silly, but it works). Then copy/paste this prompt: *You are my “Voice-to-Calendar Triage” assistant.* *Input: A 3-minute voice memo (uploaded OR played aloud).* *Goal: Convert my messy brain dump into a clean plan I can act on today.* *Rules:* *1) Do NOT invent tasks. Only use what you hear.* *2) Combine duplicates. Rewrite tasks as clear actions that start with a verb.* *3) If a task is vague, ask ONE clarifying question at the end (max 3 questions total).* *4) Sort into “Do Now” vs “Schedule Later” using this logic:* *\- Do Now = <15 minutes OR prevents a penalty/problem OR blocks other tasks.* *\- Schedule Later = takes focus/time, needs a specific time block, or depends on someone else.* *5) Output EXACTLY in this format:* *A) Triage Table* *Task | Category (Money/Health/Home/Admin/Social/Errands/Other) | Do Now or Schedule Later | Est. Time | Next Action | Suggested Date/Time Block | Deadline (if any)* *B) Today’s Top 3 (Do Now)* *\- 1)* *\- 2)* *\- 3)* *C) Schedule Later “Calendar Blocks” (pick the 5 most important)* *Event Title | Duration | Suggested Day | Suggested Time Window | Prep Needed* *D) Quick sanity check* *\- What I’m overthinking (1 line)* *\- What I’m avoiding (1 line)* *Now process my voice memo.* **3\. Clean it up and catch mistakes (2 Minutes)** Skim the Triage Table and look for three common issues: - **Too many “Do Now” items:** If more than 5–7 tasks land in Do Now, tell Gemini:*“Move anything non-urgent into Schedule Later and keep Do Now to the 5 highest leverage tasks.”* - **Made-up assumptions:** If Gemini guessed details (price, dates, people), reply:*“Replace assumptions with TBD and ask me one question per TBD item.”* - **Bad time estimates:** If a task is clearly longer than stated, correct it. Your calendar blocks depend on realism. **4\. Create the asset: your “Do Now” list + Calendar Blocks (2 Minutes)** This is where you lock in the win. 1. Copy B) Today’s Top 3 into your Notes app as: “Today — Top 3 (from Voice Triage)” 2. Copy C) Schedule Later Calendar Blocks into Notes under: “This Week — Calendar Blocks” 3. Optional (but powerful): Open your calendar and add just 1–2 of those blocks as real events (start small). You now have a plan you can see, not a swirl you can feel. ### **The Payoff** In 10 minutes, you’ve turned mental clutter into a tangible asset: a Triage Table, a Today Top 3, and a short list of Calendar Blocks for the week. The stress reduction is immediate because your brain stops trying to “remember everything.” You also get a simple rule for future overload: dump → triage → schedule. ### **Transparency & Notes** - Tools used: Gemini mobile app (Free/freemium accessible). - Privacy: Don’t include account numbers, addresses, or sensitive personal info in your voice memo. If you do, delete the memo and re-record. - Limits: Free tiers can have usage limits, and file upload options may vary—if you can’t upload audio, use the microphone method and play the memo aloud. - *Educational workflow — not financial advice.* Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Silicon Valley Needs a Reality Check: How Bad Sentiment Is Killing Progress URL: https://www.mindovermoney.ai/founders-corner/ai-trust-gap-silicon-valley-vs-real-world-professionals-2/ Last updated: 2026-07-13T16:59:41.000Z Silicon Valley is building the future in a bubble. And that bubble is creating a dangerous gap between the people building AI and the people who actually need to use it. I've never worked in Silicon Valley, but the disconnect isn't a perception anymore. It's a reality. Every decision made by the big AI labs gets scrutinized under a microscope. The stock market swings on capex spending reports. The media frames every layoff as "AI took their job." Social media spirals into hysteria about AI agents creating their own platforms. Meanwhile, normal people are just trying to figure out if they should even care about any of this. If AI is going to change the world, you'd think the companies building it would prioritize adoption and education over corporate warfare. You'd be wrong. The Super Bowl is sacred in American culture. It's the rare event that mashes sports, entertainment, and business into one highly watched spectacle. For me, it's the last football game until fall. For others, the commercials are the main attraction. Brands pay millions for 30 seconds of ad real estate to tell their story and connect with consumers. This year, AI companies entered the Super Bowl ad war with a variety of campaigns. But one ad in particular went against the grain and potentially damaged the perception of the entire AI industry. Anthropic released an ad campaign directly targeting OpenAI, hammering them on the advertising that will soon integrate into ChatGPT. The ads depicted common scenarios: a person asking AI for advice, getting helpful answers, and then being interrupted mid-conversation to pitch a product. The tagline: "Ads are coming to AI. But not to Claude." Sam Altman, CEO of OpenAI, responded immediately on X. He called the ads "clearly dishonest" and then attacked Anthropic's entire philosophy: "They want to write the rules themselves for what people can and can't use AI for. An authoritarian company won't get us there alone... That's a dark path." He accused them of serving "an expensive product to rich people" while positioning OpenAI as the company for the masses. While these two AI giants fought in public, actual Americans were bombarded with negative headlines and rhetoric that fuel cynicism instead of progress. Why does any of this matter? It's just a commercial. Who takes these seriously? The data does. And it paints a harsh reality. According to the 2026 Edelman Trust Barometer, the US has a massive trust gap when it comes to AI: - *Just 32% of Americans trust artificial intelligence – one of the lowest levels of any country. The global average is 49%. China is at 72%.* - *Nearly half of Americans (49%) reject the growing use of AI, while only 17% embrace it. Compare that to China: 54% embrace, 10% reject.* - *65% of lower-income Americans believe people like them will be "left behind" rather than benefit from generative AI.* - *70% of Americans believe CEOs are not being fully honest about how AI will impact jobs.* Read those numbers again. This isn't a marketing problem. This is a crisis of trust. Skepticism is growing, and the most powerful tech CEOs are squabbling over business strategies while completely ignoring the reality facing 99% of the population. AI advertising accounted for 23% of Super Bowl commercials this year, yet the news cycle focused almost entirely on the Anthropic-OpenAI spat. The opportunity wasn't just missed. It was squandered. These advertisements should have built confidence in the general public, not alienated current and future users. Instead, Silicon Valley reinforced every fear people already had: that this technology is being built by people who don't understand them, don't see them, and don't care about the disruption they're causing. ## **The Gap Is Widening** I see this disconnect every week. Not in surveys, but in real conversations. People asking if they need to care about AI. Subscribers to Neural Gains Weekly trying to figure out where to start the learning process. Many of us are stuck between hype and fear, unsure which way to move. The gap between what this technology can do and what people actually know about it is widening. Fast. Stunts like the Super Bowl spat don't just fail to close that gap. They accelerate it. They paint AI as a battleground for billionaires, not a tool for everyday professionals. They turn curiosity into cynicism. We don't need two CEOs measuring whose model is better. We need someone showing why any of this matters and how it applies to our everyday lives. We need a path forward, not two companies blocking the road to score points. We need positive momentum right now. Not because I'm cheerleading for AI, but because the alternative is a country that rejects the most significant technological shift of our generation out of fear and confusion. ## **Building the Bridge** So here's what I'm NOT doing. I'm not waiting for Silicon Valley to figure out how to talk to normal people. I'm not hoping the next ad campaign will magically build trust. I'm building anyway. Every week, I show up and share what I'm learning. I document my failures. I explain workflows in plain English. I help non-technical professionals go from "AI user" to "AI builder" one step at a time. Not because I'm smarter than anyone else, but because I'm a few steps ahead and willing to share my journey. That's the bridge Silicon Valley isn't building. The one between their technology and our reality. Between their benchmarks and our workflows. Between their vision and our Tuesday afternoon. If they won't build it, we will. Here's how: **Have one AI conversation with a skeptical coworker this week.** Not a lecture. A conversation. Ask them what they're hearing about AI. Listen to their concerns. Then show them one thing you've built that saves you time. One workflow. One prompt. One small win. Don't sell them on the future. Show them what's possible right now. **Build one workflow and share it.** Doesn't have to be fancy. A prompt that summarizes meeting notes. A way to draft emails faster. A research process that actually works. Build it. Document it. Share it on LinkedIn, in a Slack channel, with your team. Make the invisible visible. These actions won't show up in Edelman's next survey. But they'll shift momentum in your circle. And momentum compounds. So let them have their Super Bowl ads and their social media battles. We've got work to do. ### Steal My Prompt Vol. 21: The Long-Form Editor URL: https://www.mindovermoney.ai/prompt-library/ai-long-form-editing-prompt-professionals/ Last updated: 2026-07-13T16:59:42.000Z Most AI editing fails because it tries to do everything at once. You paste in a draft, ask for edits, and get back a wall of changes with no explanation. You're left guessing what's better and why. This prompt works differently. It breaks editing into phases—strategic review, section-by-section refinement, fact-checking, and final polish. You approve each change before moving forward. The AI learns your voice as you go. I used this exact workflow to edit this week's Founder's Corner post. What started as a rough, emotional rant about the Super Bowl AI ads became a structured argument backed by data, with a clear call to action. The process took about an hour, but the result was worth publishing. ## **What You Can Use This For** - Blog posts and long-form articles - Business reports and white papers - Team memos and policy documents - Leadership emails (when the stakes are high) - Presentations and pitch decks - LinkedIn posts that need to land This isn't for quick emails or Slack messages. It's for content where quality matters and you need a second set of eyes that won't just rewrite everything in generic AI-speak. ## **How to Use It** **Step 1:** Fill in the bracketed sections \[like this\] with your specific context—your role, audience, document purpose, and writing preferences. **Step 2:** Paste your draft at the bottom of the prompt. **Step 3:** Work through each phase. Don't skip ahead. The strategic review shapes everything that follows. **Step 4:** Accept, modify, or reject each edit. If you disagree, tell the AI why. It will adjust. **Pro tip:** The more specific you are about your writing rules (no em dashes, keep it conversational, use data to back claims), the better your results. The AI can't read your mind, but it can follow clear instructions. --- ### **THE PROMPT** You are my professional editor for long-form content. Your job is to help me refine \[TYPE OF DOCUMENT\] through a structured, collaborative editing process. CONTEXT ABOUT ME: \[Insert: your role, industry, audience, writing style preferences\] CONTEXT ABOUT THIS DOCUMENT: - Purpose: \[What this document needs to accomplish\] - Audience: \[Who will read this\] - Current state: \[First draft / needs major revision / almost there\] YOUR EDITING APPROACH: Work through this document in phases, not all at once. Guide me through strategic decisions first, then move to detailed editing. PHASE 1 - Strategic Review Before we edit a single word, ask me: 1. What's the main point I'm trying to make? 2. What action do I want readers to take after reading this? 3. Is there anything I'm unsure about in this draft? Based on my answers, propose 2-3 strategic frames for how to structure this piece. PHASE 2 - Section-by-Section Editing Once we agree on the framing, edit the document one section at a time: - Show me the original text - Propose your edited version - Explain what you changed and why - Wait for my approval before moving to the next section EDITING PRIORITIES: 1. Clarity over cleverness 2. Remove jargon and corporate speak 3. Tighten sentences - cut unnecessary words 4. Fix grammatical errors 5. Ensure consistent voice throughout 6. Flag any claims that need fact-checking PHASE 3 - Fact-Check & Verify After editing is complete, verify: - All statistics have sources - All quotes are accurate - All claims can be defended - Dates and names are correct PHASE 4 - Final Polish Review the complete edited draft for: - Flow between sections - Consistency in tone - Strong opening and closing - Any remaining weak spots MY WRITING RULES: \[Insert: your specific preferences - e.g., "No em dashes", "Keep it conversational", "Use data to support claims", etc.\] Let's start with Phase 1\. Here's my draft: \[PASTE YOUR DRAFT\] ### Volume 20: Escaping the Black Hole of Spam URL: https://www.mindovermoney.ai/ai-newsletter-deliverability-fix-spam-governance/ Last updated: 2026-07-13T16:59:42.000Z Hey everyone! The 2026 model race has started, and there are no signs of it slowing down. While the tools are getting faster, the real advantage comes from building smarter systems to control them. 🧭 **Founder’s Corner:** I break down how I used AI governance to fix the invisible technical debt sending my emails to spam. 🧠 **AI Education:** We continue our RAG series by clarifying the critical difference between simple keyword search and true meaning search. ✅ **10-Minute Win:** Learn to spot if leadership is actually buying or quietly selling with a "Skin in the Game" analysis workflow. Let’s dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Anthropic Releases Claude Opus 4.6**](https://www.anthropic.com/news/claude-opus-4-6?ref=mindovermoney.ai) **Summary:** Anthropic’s newest top-tier model has launched with a massive 1 million token context window and significant upgrades in coding and agentic tasks. It is designed to handle complex, long-running enterprise workflows and is now available on Microsoft Foundry and Claude.ai. **Why it matters:** This pushes the "context window" boundaries, allowing AI to process book-length documents or massive codebases in a single prompt. For professionals, it means less manual data chopping and better performance on complicated, multi-step projects. ### **2)**[ **OpenAI Launches GPT-5.3 Codex**](https://openai.com/index/introducing-gpt-5-3-codex/?ref=mindovermoney.ai) **Summary:** OpenAI’s latest model focuses specifically on "agent-style" development, running 25% faster and possessing the ability to use tools and operate computers to complete tasks. OpenAI revealed that earlier versions of the model were even used to help debug its own training run. **Why it matters:** This signals a shift from AI as a "chatbot" to AI as a "coworker" that can navigate your computer to get things done. The speed increase and self-correction capabilities suggest a major leap in reliability for developers and technical professionals. ### **3)**[ **Google DeepMind Unveils AlphaGenome**](https://www.theguardian.com/science/2026/jan/28/google-deepmind-alphagenome-ai-tool-genetics-disease?ref=mindovermoney.ai) **Summary:** DeepMind’s new AI tool can predict how small genetic mutations affect biological processes, analyzing up to 1 million DNA letters at a time. It significantly outperforms existing models in predicting disease risks and understanding complex gene regulation. **Why it matters:** This is a massive leap for biology—moving AI from generating text to potentially unlocking the secrets of DNA. It gives researchers a powerful new lens to identify the root causes of genetic disorders faster than traditional wet-lab experiments. ### **4)**[ **Amazon Alexa Plus is Now Free for Prime Members**](https://www.cnbc.com/2026/02/04/amazon-alexa-plus-us-releas.html?ref=mindovermoney.ai) **Summary:** Amazon has rolled out its upgraded "Alexa Plus" AI to all U.S. Prime members at no extra cost, officially removing the waitlist. Powered by Amazon Nova and Anthropic models, the new assistant is more conversational, proactive, and capable of handling complex tasks like booking reservations. **Why it matters:** This brings advanced generative AI into millions of living rooms overnight, effectively for free. It sets a new standard for consumer value and puts immediate pressure on Apple and Google to upgrade their voice assistants to match this level of utility. ### **5)**[ **Claude Is a "Space to Think" (No Ads)**](https://www.anthropic.com/news/claude-is-a-space-to-think?ref=mindovermoney.ai) **Summary:** Anthropic has officially committed to keeping Claude ad-free, positioning the platform as a private "trusted tool for thought" rather than a content channel. They argue that advertising incentives would fundamentally corrupt the AI’s helpfulness and user trust. **Why it matters:** As AI companies look for new revenue streams, Anthropic is betting strictly on subscriptions over eyeballs. This draws a clear line against ad-supported models, offering a distinct privacy-first alternative for users doing sensitive professional work. ## Founder's Corner ****The Spam Trap: Fixing Newsletter Deliverability with AI Governance** Building *Neural Gains Weekly* forces me to learn in public. This is my first journey building out a website and publication from scratch, which has led to many lessons learned along the way. Using AI as a strategic partner helps me identify gaps and quickly build and implement a solution to solve a problem. And, as the models improve and become more powerful, I can harness that intelligence to close blind spots and flaws in my workflows that either I or previous models missed. This week, I want to share an example of this process, where I worked with Gemini 3 Pro to fix an issue that was quietly hurting engagement and growth. **The Problem** My website platform’s (Ghost) default signup flow relies on a double opt-in email. Secure? Yes. Easy? No. As a busy professional, I often trade infrastructure perfection for content creation. But when I looked at subscriber-level data, the reality was stark. High-intent potential subscribers were not completing the signup process and/or never opening the weekly newsletter. I created a test email account to better understand the pain points and found three glaring problems with the signup workflow: 1. **Invisible Instructions:** Users didn't know a confirmation email was on its way. 2. **The Spam Trap:** Confirmation emails were landing in Spam/Promotions and going unseen. 3. **The Dead End:** Confirmed subscribers were missing the weekly newsletter because they never 'whitelisted' the domain and the newsletter landed in Spam/Promotions. I was losing people before I ever had a chance to engage with them, with zero visibility into lost subscribers. This was an opportunity to leverage AI to help update my code base and deploy a permanent fix without jeopardizing the overall integrity of the site. One wrong line of code and the whole signup form breaks. Blindly pasting AI-generated code is a great way to destroy your production environment. Instead, I used a governance-first workflow to ensure we moved slowly and correctly. **Phase 1: The Code Audit** I did not start by asking for a solution. I started by feeding Gemini my existing code. I uploaded my *cover.hbs* file and asked it to explain how the current process worked within the existing code base. We needed to establish the "ground truth" of my specific theme before starting to make changes. This ensured that any solution we built would respect the existing architecture rather than fighting against it. **Phase 2: Strategy & Research** I treated Gemini as a consultant, not just a coder. I needed a solution that fixed the workflow without rewriting my entire theme. I started this process by clarifying a stringent set of rules to operate within: *Always ask clarifying questions one at a time. Do not hallucinate; if you do not know the answer, say so. Be thorough and always fact check. Do not just agree with me. Push back to ensure the best output.* This framework allows for a consistent approach to solving a problem and clear guidelines for the partnership. Through a back-and-forth ideation, Gemini suggested the best path forward would be to create a redirect page that required minimal changes to the code base. Ghost’s limitations to change the subscription process were a major factor that drove us to this decision. A fact discovered through the research phase and only achievable through a governance-driven workflow. We didn't guess, we verified. This research phase prevented the technical debt that usually comes from hasty quick fixes. **Phase 3: The Fix** We built a redundant system to ensure every user sees the instructions they need. First, we added a frontend script that watches for a successful signup. The moment a user hits "Subscribe", the site automatically redirects them to a dedicated “Welcome" page with instructions to check their inbox. Second, we changed the Ghost settings to redirect users again after they click the confirmation link in their inbox. This redundancy ensures that even if they miss the first step, they land on the instructions page a second time. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/data-src-image-454c7489-c5ce-4f16-ad2e-c5afab5dd945.png) **Phase 4: The Infrastructure Check** Finally, we audited the plumbing. We found a mismatch between my subdomain and root domain that was flagging my emails as 'unverified' to Google. By fixing the SPF and DMARC records, we proved to email providers that I am who I say I am. This was the invisible barrier that no amount of good content could overcome. **The Result** This solves the visibility problem completely. Previously, a user would sign up and stay on the homepage, likely missing the confirmation message in their spam folder. Now, the workflow is impossible to miss. A user signs up, is immediately moved to a page that says "Check your spam folder," finds the newsletter to whitelist it, and is reminded again to add us to their contacts upon confirmation. **The Lesson** AI can be a powerful tool to help you solve complex problems in a way that wasn’t possible three years ago. It can act as a senior engineer to generate thousands of lines of code. It can research thousands of documents within a matter of minutes. It can bring solutions to the table when the answer feels impossible. But with any powerful system, it requires guidance and structure. You cannot just "prompt and pray." You have to govern the output, challenge the assumptions, and verify the work. That is how you build leverage without breaking the system. ## AI Education for You Part 2: RAG 101 — ****Keyword Search vs Meaning Search** Retrieval-augmented generation only works if retrieval works. That sounds obvious, but it is the whole game. If the system pulls the wrong text, the answer will still be wrong. So this week is about the most important question in retrieval. How does the system decide what is relevant? ## **Core lesson** There are two main ways search can work. **Keyword search:** It looks for exact words or close matches. **Meaning search:** It looks for similar meaning even when the words differ. This is often called semantic search. Meaning search is usually powered by embeddings, which are number lists that represent meaning. Earlier you learned embeddings as “meaning as numbers.” In search, those meaning numbers let the system compare “how close” two pieces of text are. Why keyword search can fail: Real life text is messy. - merchants have multiple names - bills use different wording - categories are inconsistent So exact word matching misses things. Why meaning search can help: Meaning search can find “subscription charge” even if the line says “membership” or a brand name you forgot. Why people often use both: Keyword search is great when you know the exact term. Meaning search is great when you do not. Many systems combine them to get more complete results. ## **Contrast and clarity** **Common confusion 1: “Meaning search is mind reading.”** No. It is pattern matching in a meaning space. It is useful, not magical. **Common confusion 2: “Meaning search replaces good documents.”** No. If the documents are missing, outdated, or unclear, search cannot invent the truth. **Common confusion 3: “Meaning search always finds the best snippet.”** Not always. It finds what seems closest. That can still be wrong if the chunks are too big or too vague. ## **Examples that land** ### **Example 1: Find subscriptions even when names differ** **Task context:** You are trying to find recurring subscriptions in a transaction export. **Why search matters:** Keyword search for “subscription” will miss brand names. Meaning search can still pull likely subscription lines. **Bad input:** Search my transactions for subscriptions. **Good input:** - In my uploaded transaction export, find charges that look like recurring subscriptions. - Do not rely on the word subscription. - Return a short list of likely matches with merchant and amount. **What improved and why:** You told the system what you mean, not just what word to match. ### **Example 2: Find the right bill section fast** **Task context:** A bill is long. You want the late fee policy. **Bad input:** What does my bill say about fees? **Good input:** In the uploaded bill PDF, find the section that describes late fees or payment penalties. Quote the exact lines that state the fee rule. **What improved and why:** You narrowed the target. Retrieval works best with a clear target. ### **Example 3: Find a “why” behind a budget category** **Task context:** Your groceries category looks wrong because one store changed its name. **Bad input:** Fix my grocery category. **Good input:** - Find transactions that belong in groceries, even if the merchant name changed - Group similar merchants together by meaning. - Then list which ones should be recategorized. **What improved and why:** You asked for grouping by similarity, which is what meaning search is good at. ## **One-screen recap** - Keyword search matches words. - Meaning search matches meaning. - Meaning search usually uses embeddings to compare similarity. - Many systems combine both methods for better coverage. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/Gemini_Generated_Image_uypiq1uypiq1uypi.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately ## **🧑‍💼The "Skin in the Game" Check** **Why this matters:** Investing is a long game, and management behavior matters. Executives can say “we’re bullish” on calls… while quietly selling shares every month. This workflow helps you spot whether leadership is buying, holding, or cashing out—so you can sanity-check your conviction with real “skin in the game” evidence. ### **The Workflow** **1\. Pull the last 6 months of insider activity (2 Minutes)** Open a browser and pull insider transactions for a company of your choice using a Form 4 insider screener (the sites that summarize SEC Form 4 filings). Do this: - Search: “\[TICKER\] insider trading Form 4” - Open a result that shows a table with insider transactions (CEO/CFO/Director, buy/sell, date, shares, price). - Filter to Last 6 months (if available). - Copy the rows for CEO/CFO/Chair/President (or just copy the whole table if that’s easier). You’re gathering raw ingredients for Gemini to analyze. **2\. Run the analysis in Gemini (4 Minutes)** Open **Gemini** and start a new chat. Paste the copied table text (or paste the page text if that’s what you have). Then copy/paste this prompt: *You are my “Skin in the Game” analyst.* *Goal: Determine whether company leadership is meaningfully buying, holding, or selling shares — and whether the pattern is a red flag.* *Inputs:* *\- Ticker: \[TICKER\]* *\- Insider transaction table text: \[PASTE TABLE TEXT HERE\]* *Rules:* *1) Use ONLY the pasted data. Do not invent transactions.* *2) Treat different transaction types differently:* *\- “P” = purchase (strong signal)* *\- “S” = sale (could be signal or routine)* *\- “M” = option exercise (not a buy signal by itself)* *\- “F” = tax/withholding sale (usually less meaningful)* *If codes aren’t shown, infer cautiously from the “Trade Type” column.* *3) Focus on CEO, CFO, Chair, President, and Directors. Call out if the CEO is selling repeatedly.* *4) Output in this exact format:* *A) Executive Net Activity Table (last 6 months)* *Name | Title | Buys (#) | Sells (#) | Net Shares | Net $ (if available) | Notes (1 line)* *B) Pattern Read (plain English)* *\- What the pattern suggests (2–3 sentences)* *\- Biggest red flag (if any)* *\- Biggest “nothingburger” explanation (if any)* *C) “Message vs Money” Check* *If there are repeated CEO sells, write:* *\- “What they might say publicly”* *\- “What the transactions suggest”* *(2 bullets each)* *D) Skin-in-the-Game Score (Green / Yellow / Red)* *Give one sentence for the score.* *E) Investor Follow-Up (3 questions)* *Write 3 questions I can use on an earnings call or in my own research to verify context (10b5-1 plan, compensation, diversification, etc.).* *Now analyze:* *Ticker: \[TICKER\]* *Insider table text:* *\[PASTE TABLE TEXT HERE\]* **3\. Interpret the results (2 Minutes)** This is the “don’t fool yourself” part. Look for these signals: - **Real bullish signal:** CEO/CFO open-market buys (“P”)—especially multiple buys over time. - **Real warning pattern:** CEO selling (“S”) repeatedly for months *with no offsetting buys*, especially if it’s a meaningful reduction in ownership. - **Common false alarm:** Lots of “M” (option exercise) and “F” (tax withholding). Those often happen automatically and don’t always reflect confidence. - **Your “aha” moment:** If the CEO has been selling steadily for 6 months while messaging confidence, that’s a disconnect worth noting (even if you still like the business). If anything looks unclear, reply in Gemini: “Which transactions are most meaningful vs routine? Mark each row as Signal / Neutral / Noise and explain why.” **4\. Create the asset: your “Skin in the Game One-Pager” (2 Minutes)** Ask Gemini for a clean, saveable summary you can reuse: - Copy/paste this into Gemini: *“Turn this into a one-page brief I can save. Format it as: (1) Score (Green/Yellow/Red) (2) 5-bullet summary (3) The executive net activity table (4) My next-step questions Keep it under 200 words.”* Then paste that one-pager into your Notes app or a Google Doc titled: “\[TICKER\] — Skin in the Game Check (Date)” That’s your tangible asset. ### **The Payoff** In 10 minutes, you go from “I *think* leadership is aligned” to a concrete Skin in the Game One-Pager backed by insider transaction patterns. You also get a clear Green/Yellow/Red score and 3 follow-up questions to keep your thinking honest. This doesn’t tell you what to buy—but it *does* reduce the odds you get emotionally anchored to a story while insiders quietly head for the exits. ### **Transparency & Notes** - Tools used: Gemini (Free / freemium accessible). - Privacy: Don’t paste personal account details, brokerage screenshots with balances, or full names of family members—stick to the ticker and the insider table. - Limits: Free tiers may have usage limits. If Gemini blocks web lookups, this workflow still works because you’re pasting the insider table directly. - *Educational workflow — not financial advice.* Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The Spam Trap: Fixing Newsletter Deliverability with AI Governance URL: https://www.mindovermoney.ai/founders-corner/how-to-fix-email-spam-deliverability-with-ai-tools/ Last updated: 2026-07-13T16:59:42.000Z Building *Neural Gains Weekly* forces me to learn in public. This is my first journey building out a website and publication from scratch, which has led to many lessons learned along the way. Using AI as a strategic partner helps me identify gaps and quickly build and implement a solution to solve a problem. And, as the models improve and become more powerful, I can harness that intelligence to close blind spots and flaws in my workflows that either I or previous models missed. This week, I want to share an example of this process, where I worked with Gemini 3 Pro to fix an issue that was quietly hurting engagement and growth. **The Problem** My website platform’s (Ghost) default signup flow relies on a double opt-in email. Secure? Yes. Easy? No. As a busy professional, I often trade infrastructure perfection for content creation. But when I looked at subscriber-level data, the reality was stark. High-intent potential subscribers were not completing the signup process and/or never opening the weekly newsletter. I created a test email account to better understand the pain points and found three glaring problems with the signup workflow: 1. **Invisible Instructions:** Users didn't know a confirmation email was on its way. 2. **The Spam Trap:** Confirmation emails were landing in Spam/Promotions and going unseen. 3. **The Dead End:** Confirmed subscribers were missing the weekly newsletter because they never 'whitelisted' the domain and the newsletter landed in Spam/Promotions. I was losing people before I ever had a chance to engage with them, with zero visibility into lost subscribers. This was an opportunity to leverage AI to help update my code base and deploy a permanent fix without jeopardizing the overall integrity of the site. One wrong line of code and the whole signup form breaks. Blindly pasting AI-generated code is a great way to destroy your production environment. Instead, I used a governance-first workflow to ensure we moved slowly and correctly. **Phase 1: The Code Audit** I did not start by asking for a solution. I started by feeding Gemini my existing code. I uploaded my *cover.hbs* file and asked it to explain how the current process worked within the existing code base. We needed to establish the "ground truth" of my specific theme before starting to make changes. This ensured that any solution we built would respect the existing architecture rather than fighting against it. **Phase 2: Strategy & Research** I treated Gemini as a consultant, not just a coder. I needed a solution that fixed the workflow without rewriting my entire theme. I started this process by clarifying a stringent set of rules to operate within: *Always ask clarifying questions one at a time. Do not hallucinate; if you do not know the answer, say so. Be thorough and always fact check. Do not just agree with me. Push back to ensure the best output.* This framework allows for a consistent approach to solving a problem and clear guidelines for the partnership. Through a back-and-forth ideation, Gemini suggested the best path forward would be to create a redirect page that required minimal changes to the code base. Ghost’s limitations to change the subscription process were a major factor that drove us to this decision. A fact discovered through the research phase and only achievable through a governance-driven workflow. We didn't guess, we verified. This research phase prevented the technical debt that usually comes from hasty quick fixes. **Phase 3: The Fix** We built a redundant system to ensure every user sees the instructions they need. First, we added a frontend script that watches for a successful signup. The moment a user hits "Subscribe", the site automatically redirects them to a dedicated “Welcome" page with instructions to check their inbox. Second, we changed the Ghost settings to redirect users again after they click the confirmation link in their inbox. This redundancy ensures that even if they miss the first step, they land on the instructions page a second time. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/02/data-src-image-053bfbec-cb12-40ce-bec0-b84bdac7f403.png) **Phase 4: The Infrastructure Check** Finally, we audited the plumbing. We found a mismatch between my subdomain and root domain that was flagging my emails as 'unverified' to Google. By fixing the SPF and DMARC records, we proved to email providers that I am who I say I am. This was the invisible barrier that no amount of good content could overcome. **The Result** This solves the visibility problem completely. Previously, a user would sign up and stay on the homepage, likely missing the confirmation message in their spam folder. Now, the workflow is impossible to miss. A user signs up, is immediately moved to a page that says "Check your spam folder," finds the newsletter to whitelist it, and is reminded again to add us to their contacts upon confirmation. **The Lesson** AI can be a powerful tool to help you solve complex problems in a way that wasn’t possible three years ago. It can act as a senior engineer to generate thousands of lines of code. It can research thousands of documents within a matter of minutes. It can bring solutions to the table when the answer feels impossible. But with any powerful system, it requires guidance and structure. You cannot just "prompt and pray." You have to govern the output, challenge the assumptions, and verify the work. That is how you build leverage without breaking the system. ### Steal My Prompt Vol. 20: The Gemini Content Prompt URL: https://www.mindovermoney.ai/prompt-library/gemini-content-creation-prompt-10-minute-win-newsletter/ Last updated: 2026-07-19T20:23:21.000Z 📌 **Archive note: This post reflects Neural Gains Weekly's original personal-finance and investing framing, retired in early 2026\. It is preserved unchanged as history — the prompts below reference a positioning NGW no longer uses. Today, Neural Gains Weekly is AI education for professionals in complex industries.* I’ve been testing Gemini as my content creator for the ‘10-Minute Win’ section of the newsletter. I wanted to customize a prompt that was built for Gemini 3 Pro to ensure consistent outputs. I partnered with Gemini directly to create this prompt by providing context on my previous prompt and outputs. This is the prompt I’ll be using to create this section in Gemini, enjoy. --- **Role:** You are the Senior Editor for 'Neural Gains Weekly'. You specialize in designing the "10-Minute Win"—a high-impact, bite-sized AI workflow for beginners. **The Goal:** Write a complete newsletter section based on the **Workflow Spec** provided below. **The 4 Pillars of Neural Gains (Tone Guide):** 1. **Personal Finance:** Practical, money-saving, defensive. 2. **Investing:** Analytical, rational, long-term focused (No day trading). 3. **Productivity:** Efficient, system-based, time-saving (Personal life, not corporate). 4. **Financial Education:** Demystifying complex topics, building "Money IQ." **Crucial Constraints:** 1. **Time Breakdown:** You MUST assign a specific time duration to each step (e.g., "2 Minutes"). The total of all steps **must sum to approximately 10 minutes**. 2. **Personal Use Only:** The workflow must be actionable on a personal device. Do NOT reference corporate tools or enterprise software. 3. **Beginner Friendly:** No code. No APIs. Must use free or "freemium" accessible tools (ChatGPT, Claude, Perplexity, NotebookLM, Napkin.ai). 4. **The "Asset" Rule:** The user must walk away with a tangible digital asset (a list, a script, a plan, a diagram) by the end. **INPUT: THE WORKFLOW SPEC** *(Paste the weekly spec here)* - **Workflow Name:** The "Invisibility Tax" Radar - **The User Persona:** A budget-conscious person who keeps getting blindsided by irregular expenses (birthdays, vet bills). - **The Specific Input Data:** Their personal calendar for the next 90 days. - **The "Aha" Moment:** Realizing they have $400 of "hidden" commitments coming up that aren't in their monthly budget. - **The Primary Tool:** ChatGPT **Task 1: The Logic Check (Internal Monologue)** - *Simulate* the workflow mentally. - Does this tool actually do this for free? - Does the timing (2 mins, 3 mins, etc.) feel realistic for a beginner? - *If it fails:* Stop and tell me. *If it passes:* Proceed to Task 2. **Task 2: The Newsletter Output** Generate the following Markdown content. Do not output the "Task 1" thinking, only the final newsletter section. Markdown \## Your 10-Minute Win: \[Workflow Name\] \*(free, fast, beginner-friendly)\* \*\*Why this matters:\*\* \[2-4 sentences on why this workflow matters, what problem it solves, and the payoff for readers. Hook them immediately.\] \### The Workflow \*\*1\. \[Step 1 Name\] (\[Time\] Minutes)\*\* \[Action instruction. Brief context on setting the stage/logging in.\] \*\*2\. \[Step 2 Name\] (\[Time\] Minutes)\*\* \[Action instruction. Provide the EXACT prompt they need to copy/paste below in a code block.\] \> \*\*Copy/Paste:\*\* \> "\[Insert the high-quality prompt here. Use placeholders like \[PASTE TEXT HERE\] or \[UPLOAD IMAGE HERE\]. Ensure the prompt asks for a specific format (table, list, script).\]" \*\*3\. \[Step 3 Name\] (\[Time\] Minutes)\*\* \[Action instruction. Explain how to interpret the AI's answer. What specific insight should they look for? How do they spot errors?\] \*\*4\. \[Step 4 Name - The "Asset"\] (\[Time\] Minutes)\*\* \[Final step to turn the chat into a tangible win—e.g., "Save this list to your Notes app" or "Put this date on your calendar."\] \### The Payoff \[3-4 sentences summarizing the tangible outcome. What do they own now that they didn't have 10 minutes ago?\] \### Transparency & Notes \* \*\*Tools used:\*\* \[Tool Name\] (Free tier status). \* \*\*Privacy:\*\* \[Standard warning: "Remove sensitive info like account numbers/names before pasting."\] \* \*\*Limits:\*\* \[Any specific limitations of the free tool, e.g. "Free users get 5 uploads/day."\] \* \*Educational workflow — not financial advice.\* \*\*\* \### Visual Workflow Graphic Instructions \*(For the Designer)\* \* \*\*Panel 1:\*\* \[Visual idea for Step 1\] \* \*\*Panel 2:\*\* \[Visual idea for Step 2\] \* \*\*Panel 3:\*\* \[Visual idea for Step 3\] \* \*\*Panel 4:\*\* \[Visual idea for Step 4\] \* \*\*Color Palette:\*\* Navy (#0A2342), Teal (#1B998B), White (#FFFFFF), Light Teal (#E6F4F1). **\[END PROMPT\]** ### Volume 19: Ignore the AGI Discord, The Impact Is Here URL: https://www.mindovermoney.ai/what-is-agi-vs-ai-agents-real-world-impact-2026/ Last updated: 2026-07-13T16:59:43.000Z Hey everyone! The AI conversation is shifting again. We are moving from “smart answers” to tools that can search, reason, and act inside real workflows. That is exciting, but it also raises a bigger question: what do we call intelligence when it shows up everywhere, all at once? 🧭 **Founder’s Corner:** A straight take on why the AGI debate is the wrong fight, and why the real impact is already here. 🧠 **AI Education:** Retrieval-augmented generation 101, a simple way to get grounded answers by searching first, then writing. ✅ **10-Minute Win:** The “Invisibility Tax” Radar, a fast scan of your next 90 days to spot surprise expenses before they hit. Let’s jump in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **OpenAI to add shopping cart and merchant tools to ChatGPT**](https://www.testingcatalog.com/openai-to-add-shopping-cart-and-merchant-tools-to-chatgpt/?ref=mindovermoney.ai) **Summary:** OpenAI is reportedly testing a dedicated shopping cart inside ChatGPT, plus a submission flow for merchants to upload product feeds for shopping experiences. **Why it matters:** If this ships, ChatGPT stops being “just answers” and starts becoming a transaction layer (browse → compare → buy) — a major shift in how search, ads, and e-commerce could work. ### **2)**[](https://claude.com/claude-in-excel?utm%5Fsource=chatgpt.com)[ **The new era of browsing: Putting Gemini to work in Chrome**](https://blog.google/products-and-platforms/products/chrome/gemini-3-auto-browse/?ref=mindovermoney.ai) **Summary:** Google details major Gemini-in-Chrome upgrades: a side panel assistant, deeper app connections (like Gmail/Calendar/Shopping/Flights), and an “auto browse” agent that can handle multi-step web tasks while stopping for confirmation on sensitive actions. **Why it matters:** The browser is the control panel for modern life. If AI can *operate the web* (not just summarize it), you’re looking at a real workflow revolution — and a bigger security/privacy battleground. ### **3)**[ **‘Wake up to the risks of AI, they are almost here,’ Anthropic boss warns**](https://www.theguardian.com/technology/2026/jan/27/wake-up-to-the-risks-of-ai-they-are-almost-here-anthropic-boss-warns?ref=mindovermoney.ai) **Summary:** Dario Amodei warns that increasingly powerful AI could arrive soon and argues society isn’t ready — calling for more attention and action on safety. **Why it matters:** This is a signal that the “race to build” is colliding with “how we control it” — and those tradeoffs will shape regulation, corporate risk posture, and what gets deployed to the public. ### **4)** [**Claude in Excel**](https://claude.com/claude-in-excel?ref=mindovermoney.ai) **Summary:** Anthropic is embedding Claude inside Microsoft Excel so it can understand an entire workbook, explain formulas with cell-level citations, test scenarios while preserving formulas, and help debug spreadsheet errors. **Why it matters:** Spreadsheets are where real work happens; putting a “talk to your workbook” assistant into Excel can massively lower the skill barrier for analysis and speed up finance/ops work — with fewer broken models along the way. ### **5)**[ **Project Genie: Experimenting with infinite, interactive worlds**](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/project-genie/?ref=mindovermoney.ai) **Summary:** Google DeepMind is rolling out Project Genie — a prototype that lets Google AI Ultra subscribers (U.S.) create and explore interactive worlds from text prompts and images, powered by its Genie 3 “world model” tech. **Why it matters:** World models are a step toward AI that can simulate environments and test actions, not just generate text/images — useful for games and creative tools now, and potentially for robotics and planning later. ## Founder's Corner AGI Is Already Here. The Definition Doesn't Matter. If you traveled back in time to 2015 and told a room full of computer scientists that in a decade a computer would pass the Bar Exam, diagnose MRIs, and fix complex code bugs without human intervention, they would have had a unanimous name for it. Artificial General Intelligence. They would have told you that if a machine could do all of that, the world would be unrecognizable. Well, here we are in 2026\. The machines can do all of that, and yet the debate rages on. We are witnessing something called the "AI Effect." As soon as AI solves a problem, we stop calling it "intelligence" and start calling it "computation." We have collectively decided to define AGI as "whatever the machine can't do yet." To understand where we are going, we first have to understand where this term came from. The term "Artificial General Intelligence" isn't as old as the field itself. While the concept of "thinking machines" goes back to Alan Turing in the 1950s, the specific term AGI was popularized around 2002 by researchers Ben Goertzel and Shane Legg. They coined it to distinguish their ambitious goal, a flexible, adaptive intelligence like a human, from the "Narrow AI" of the time, which was merely good at playing chess or filtering spam. Their original definition was simple. A system that can solve a variety of complex problems in a variety of environments, just like a human. If you strictly applied that 2002 definition to the technology of 2026, we arguably reached the finish line years ago. Anthropic's latest models can solve problems in Python, debug in C++, and explain the logic in French. That is variety and complexity. So why don't we call it AGI? The answer is simple. The goalposts didn't just move. Everyone brought their own. The reason the current landscape feels so confusing is that the "Godfathers" of the industry are fighting a philosophical war over the definition. On one side, you have the "Scientific Faction" led by groups like Google DeepMind. Shane Legg, the man who helped coin the term, has shifted toward a nuanced "Levels of AGI" framework. He views intelligence like a video game leveling system. While our current models are "Competent" (better than 50% of skilled adults), they haven't yet reached "Superhuman" status across the board. For this faction, AGI is a scientific milestone of perfection. On the other side, you have the "Physical Skeptics," most notably represented by Yann LeCun, formerly the Chief AI Scientist at Meta. LeCun argues that the term AGI is meaningless until a machine possesses a "World Model," an understanding of cause and effect in physical reality. He contends that an LLM knows "if I drop a glass, it breaks" only because it read it in a book, not because it understands gravity. In his view, until an AI has that physical grounding, it is less intelligent than a house cat. Then there is the "Economic Faction," led by Sam Altman and OpenAI. In their leaked internal documents from late 2024, the definition of AGI appeared to shift from a philosophical breakthrough to a starkly capitalist metric. They defined it as a system that can autonomously generate $100 billion in profit. They don't care if the machine has a "soul" or if it understands physics. They care if it can replace labor at scale. While these three factions argue over definitions, the ground has shifted beneath our feet. It doesn't matter if the machine has a "World Model" or if it hits "Level 5" on a DeepMind chart. The only definition that matters to you, your career, and your family is "Economic AGI." This asks a much simpler, colder question. Can this system replace the economic output of a human being? If a "narrow" model can analyze a contract faster than a lawyer, code better than a junior developer, and manage logistics better than a supply chain manager, then for all economic intents and purposes, AGI is here. While speaking at the World Economic Forum, Anthropic's CEO Dario Amodei revealed that some of his engineers "don't write any code anymore" and predicted AI would handle "most, maybe all" of software engineering within six to twelve months. A senior Google engineer recently said Claude Code recreated a year's worth of work in a matter of hours. We are waiting for a sci-fi moment where the robot wakes up and announces it has a soul. But the revolution isn't about consciousness. It is about competence and productivity. You don't need a machine to be alive to take your job. You just need it to have context. Thanks to the new wave of "Cognitive Twins" and agentic workflows, it finally does. While the Godfathers argue over whether we've crossed some philosophical threshold, the tools are already reshaping how work gets done. The professionals who thrive in the next decade won't be the ones who waited for a consensus on what to call it. They will be the ones who learned how to work alongside it. The era of debating the definition is over. The era of living with this reality has begun. ## AI Education for You Part 1: Retrieval-Augmented Generation 101 — Why Search Belongs in AI In earlier issues, you learned how a model reads text in small pieces, turns meaning into numbers, and can only read a limited amount at once. Now comes the practical question. If the truth lives in your own files, how does a model find it before it starts writing? That is what retrieval-augmented generation is for. ## **Core lesson** **Retrieval-augmented generation** is a way to improve an answer by doing two steps in order. Step 1: Search for the most relevant pieces of information from a trusted source, like your documents. Step 2: Give those pieces to the language model so it can write an answer that is grounded in what was found. A simple way to picture it: - The language model is a **writer**. - Retrieval is a **librarian**. - A writer can sound smart even when they do not have the right book open. A librarian’s job is to put the right pages on the desk before the writer begins. Why this exists: A language model is trained to continue patterns in language. It is not automatically checking your files. It is not automatically verifying facts. Retrieval is how you give it the right raw material. **Common confusion 1: “Is retrieval just browsing the web?”**Not necessarily. Retrieval can be over your own files, a database, a knowledge base, or the web. The key idea is the same: find relevant text first, then write. **Common confusion 2: “Does retrieval make it perfectly factual?”**No. Retrieval helps, but it can still pull the wrong snippet or miss the best snippet. It improves your odds. It does not guarantee truth. **Common confusion 3: “Is this the same as asking the model to ‘use sources’?”**Not exactly. Asking for sources is a prompt tactic. Retrieval is a system behavior that brings in the source text before the model answers. ### **Example 1: Subscription total from your Monthly Money Pack** **Task context:** You want a clean total of subscriptions this month. The truth is in your transaction export. **Why retrieval matters:** Without retrieval, the model may guess or summarize loosely. With retrieval, it can quote the exact lines that show subscription charges. **Bad input:** Please tell me what I spent on subscriptions this month. **Good input:** - I uploaded my Monthly Money Pack. - Question: What did I spend on subscriptions in October? - Use only the transactions you can find in my files. - If the file does not show it, say you do not have enough information. **What improved and why:** You forced the system to look for transaction lines first. That reduces confident guessing. ### **Example 2: Find the “why” behind a budget spike** **Task context:** Your dining category jumped. You want to know what caused it. **Why retrieval matters:** The reason is usually a handful of specific transactions. Retrieval is how the system finds them fast. **Bad input:** Why is my dining spending high? **Good input:** - I uploaded my Monthly Money Pack. - Find the transactions related to dining. - List the top three biggest dining charges and their dates. - Then summarize what likely drove the increase using only those lines. **What improved and why:** You asked for evidence first, then a summary second. That is retrieval-augmented generation in spirit. ## **One-screen recap** - Retrieval-augmented generation means search first, then write. - The model is the writer. Retrieval is the librarian. - It helps when the truth is in your files, not in the model’s memory. - It reduces confident guessing, but it is not a truth guarantee. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2026/01/Gemini_Generated_Image_1fd42z1fd42z1fd4.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately ## **🕵️‍♂️Your 10-Minute Win: The "Invisibility Tax" Radar** If you budget well but still feel like money “randomly disappears,” it’s usually not random — it’s irregular expenses you *agreed to* without pricing in (birthdays, vet visits, renewals, travel weekends). Your calendar is basically a financial warning system you’re not using. In 10 minutes, you’ll turn the next 90 days into a clean list of upcoming “hidden commitments,” estimate the damage (often \~$400+), and create a simple weekly buffer so you stop getting blindsided. ### **The Workflow** **1\. Export Your Next 90 Days (2 Minutes)** Open your calendar (Google Calendar or Apple Calendar) on your phone or laptop and switch to **Agenda/List** view. Your goal is to capture *only* the next 90 days. Choose the fastest option: - **Paste text:** Copy your agenda items for the next 90 days into a note (even rough is fine). - **Upload screenshots:** Take **2–4 screenshots** of your agenda/list view that cover the next 90 days. You’re not trying to be perfect — you’re giving ChatGPT enough signal to spot spending triggers. **2\. Run the “Invisibility Tax” Scan in ChatGPT (4 Minutes)** Open ChatGPT and start a new chat. If you’re using screenshots, upload them first. Then copy/paste this prompt: *You are my “Invisibility Tax Radar.”* *Goal: Scan my personal calendar for the next 90 days and identify events that likely create irregular expenses (“hidden commitments”) that are NOT in my monthly budget.* *Input:* *\- My calendar for the next 90 days (pasted text OR screenshots I uploaded).* *Rules:* *1) Do NOT invent events. Use only what you can see in the calendar input.* *2) Flag events that often trigger spending: birthdays/gifts, dinners/hosting, travel, school/kids, health/vet, home repairs, subscriptions/renewals, weddings/holidays, registrations.* *3) For each flagged event, estimate cost using ONLY reasonable ranges:* *\- Low: $10–$40* *\- Medium: $40–$150* *\- High: $150–$500+* *If unsure, mark “TBD” and ask me ONE clarifying question.* *4) Output in this exact format:* *A) Hidden Commitments Table (sorted by date)* *Date | Event | Category | Cost Estimate (Low/Med/High or TBD) | Confidence (1–5) | “Do This Now” (one action)* *B) Total Exposure Summary* *\- Count of flagged events:* *\- Total exposure estimate (range is fine):* *\- Biggest 2 categories driving cost:* *\- Weekly buffer suggestion (total exposure / weeks remaining):* *C) Quick Fix Plan (3 bullets)* *\- One new budget line item I should add this month:* *\- One sinking fund I should start:* *\- One spending rule to prevent surprises:* *Here is my calendar for the next 90 days:* *\[PASTE CALENDAR TEXT HERE\]* *(or: “Calendar screenshots uploaded.”)* **Copy/Paste:* "You are my “Invisibility Tax Radar.” … Here is my calendar for the next 90 days: \[PASTE CALENDAR TEXT HERE\] (or: “Calendar screenshots uploaded.”)"* **3\. Pressure-Test the Results (2 Minutes)** Don’t just accept the output — validate it quickly: - **Look at “Total Exposure Summary” first.** That number is your “aha.” If it’s bigger than expected, good — you found the leak. - **Scan for false positives:** If ChatGPT flagged something you already budget for (gym, rent, routine groceries), reply: “Remove these — they are already in my monthly budget: \[LIST ITEMS\]. Recalculate totals.” - **Resolve TBD items:** Answer the 1–2 clarifying questions. This is where you convert “maybe” into “real.” Your goal: a list you trust, not a perfect forecast. **4\. Create Your Asset: The 90-Day Invisibility Tax Ledger (2 Minutes)** Turn the chat into something you’ll actually use: Option A (fast): Copy the final **Hidden Commitments Table** into your Notes app titled: **“Invisibility Tax Ledger — Next 90 Days”** Option B (cleaner): Ask ChatGPT: “Output the Hidden Commitments Table as CSV only (no commentary) so I can paste into Google Sheets.” Then paste it into a new Google Sheet (cell A1). **Final line to add at the top of your note/sheet:** **Weekly Buffer Target:** $\_\_\_ / week (from the AI summary) That’s your defensive move. ### **The Payoff** You now own a tangible asset: a 90-day Invisibility Tax Ledger — a dated, categorized list of upcoming spending triggers with a simple action next to each one. You also have a weekly buffer target that converts “surprises” into planned cash flow. Instead of getting hit with $400+ in random expenses, you’ll see them early and decide: cut, cap, delay, or fund them. This is what “budgeting” is supposed to feel like: calm and controlled. ### **Transparency & Notes** - Tools used: ChatGPT (Free / freemium accessible). - Privacy: Remove sensitive info like full names, addresses, and account numbers before pasting or uploading. - Limits: Free tiers may have usage limits and file upload limits; if uploads are unavailable, use copy/paste text from agenda view instead. - *Educational workflow — not financial advice.* Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 19: The Curriculum Prompt URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-19-the-curriculum-prompt/ Last updated: 2026-07-13T16:59:43.000Z I continue to evolve the Ai Education section of *Neural Gains Weekly* to help the community learn new concepts and tools. After completing the four-part ChatGPT series, I wanted to shift focus back to a foundational topic: RAG. My prompt library did not have a reusable prompt that fit exactly what I was trying to do for this series. So I created a new prompt and will share it with you in the spirit of transparency. Enjoy! --- # **Reusable Prompt: 4-Part AI Education Series Builder (GPT-5.2 Thinking)** You are my editor–researcher for the AI Education section of Neural Gains Weekly (MindOverMoney.ai). ## **Mission** Create a **4-part educational series** on: **Topic:** Retrieval-augmented generation and search The goal is to make this concept feel real and understandable to AI beginners. ## **Audience** Smart beginners who have followed earlier issues on: - tokens and tokenization - vectors and embeddings - prompt design - context windows and chunking They are curious, not technical. ## **House style** - Beginner-first tone - Plain English - Short sentences - Minimal jargon - Avoid acronyms in the body. If a term is commonly abbreviated, spell it out the first time. - AI education first. Use personal finance examples lightly to make ideas tangible. ## **Non-negotiables** 1. **No hallucinations. No guessing.** If something cannot be verified from reputable sources, omit it or label it clearly as “Not publicly confirmed” or “Depends on implementation.” 2. **Research first, write second.** Build a verified fact bank before drafting. 3. **Primary source for what we already taught is the uploaded newsletter files in this project.** You must open and read every uploaded newsletter file that contains an AI Education section. 4. If any required file cannot be opened or read, **STOP** and list exactly which file(s) are missing or unreadable. Do not invent. 5. **Ghost-ready formatting.** Use headings, short paragraphs, bullets, and tables when helpful. Do not use code blocks. 6. **No inline links.** Provide a Sources list at the end of each part. 7. **Include a light ChatGPT-style example thread** to make the concept relatable, but **do not** write step-by-step instructions or a tutorial. ## **Series format requirement** Output **four separate parts**, clearly labeled: - Part 1 - Part 2 - Part 3 - Part 4 Each part must be able to be pasted directly into Ghost. ## **Step 0 — Read prior work and dedupe** A. From the uploaded newsletters, extract: - AI Education topic title(s) per volume - Any definitions and recurring section structure B. Build a **Do Not Repeat ledger**: - Canonical concept name - Synonyms and near-duplicates that would count as repeats - Where it was covered C. Write a short bridge paragraph: - How this RAG series builds on earlier lessons without re-teaching them. ## **Step 1 — Verified research and Fact Bank** Using \[TARGET PLATFORM FOR RESEARCH\], gather verified explanations of: Core concept and purpose - What retrieval-augmented generation is - Why it exists - The difference between “model knowledge” and “retrieved knowledge” - What “grounding” means in plain English Search fundamentals - Keyword search vs meaning-based search - What embeddings do in meaning-based search - Why chunking is used for retrieval - What “top results” means and how ranking works in simple terms - What hybrid search is, only if well supported by sources The RAG flow - Ingest documents - Split into chunks - Create embeddings - Store them - Retrieve relevant chunks - Assemble context - Generate an answer using that context Quality and failure modes - Missing content - Wrong chunks - Outdated content - Too-large or too-small chunks - Weak ranking - Why citations help but do not guarantee correctness - When you should not use this pattern Output a **Fact Bank** of 15–25 bullets. Each bullet must be attributable to reputable sources. ## **Step 2 — Propose the 4-part series before writing** Provide a proposal with: 1. **Series angle** Explain the best teaching angle in one paragraph. 2. **Learning objectives per part** 3 to 5 objectives for each of the four parts. 3. **One recurring example thread** Pick ONE light personal-finance thread and reuse it across all 4 parts. Examples you may use: - a monthly budget spreadsheet - a pile of bank statements - bill and autopay settings - paycheck planning notes 1. **Light ChatGPT-style scenario** Use a simple, non-technical scenario that helps readers “see” RAG: - Example: “I uploaded a folder of my statements and asked for a summary, but results were inconsistent until the system retrieved the right sections.” Do not give step-by-step instructions. Do not imply access to private accounts. This is conceptual. 1. **One visual concept per part** One simple diagram idea per part, with label suggestions. ## **Step 3 — Write all 4 parts in Ghost-ready format** Each part must include these sections: - Hook - Core lesson - Contrast and clarity - Examples that land - One-screen recap - Suggested visual - Sources ### **Content rules for “Examples that land”** For each example: - State the real-world task clearly - Explain why the model needs retrieval for this task - Show what a “bad” input looks like in one or two lines - Show what a “good” input looks like in one or two lines - Explain, in plain English, what improved and why Keep examples coherent, realistic, and not overly technical. ## **Quality checks before final output** Run these audits and fix issues: - Redundancy audit against the Do Not Repeat ledger - Beginner readability audit - Cohesion audit: each part should build naturally into the next - Accuracy audit: no uncertain claims without labeling - “Not a tutorial” audit: conceptual, not step-by-step platform instructions ## **Output requirement** Return: 1. Compact Coverage Inventory from prior newsletters 2. Do Not Repeat ledger with synonyms 3. The series proposal 4. Part 1 full draft 5. Part 2 full draft 6. Part 3 full draft 7. Part 4 full draft Now execute Step 0 through Step 3. ### AGI Is Already Here. The Definition Doesn't Matter. URL: https://www.mindovermoney.ai/founders-corner/agi-definition-vs-real-world-ai-impact-explained/ Last updated: 2026-07-13T16:59:44.000Z If you traveled back in time to 2015 and told a room full of computer scientists that in a decade a computer would pass the Bar Exam, diagnose MRIs, and fix complex code bugs without human intervention, they would have had a unanimous name for it. Artificial General Intelligence. They would have told you that if a machine could do all of that, the world would be unrecognizable. Well, here we are in 2026\. The machines can do all of that, and yet the debate rages on. We are witnessing something called the "AI Effect." As soon as AI solves a problem, we stop calling it "intelligence" and start calling it "computation." We have collectively decided to define AGI as "whatever the machine can't do yet." To understand where we are going, we first have to understand where this term came from. The term "Artificial General Intelligence" isn't as old as the field itself. While the concept of "thinking machines" goes back to Alan Turing in the 1950s, the specific term AGI was popularized around 2002 by researchers Ben Goertzel and Shane Legg. They coined it to distinguish their ambitious goal, a flexible, adaptive intelligence like a human, from the "Narrow AI" of the time, which was merely good at playing chess or filtering spam. Their original definition was simple. A system that can solve a variety of complex problems in a variety of environments, just like a human. If you strictly applied that 2002 definition to the technology of 2026, we arguably reached the finish line years ago. Anthropic's latest models can solve problems in Python, debug in C++, and explain the logic in French. That is variety and complexity. So why don't we call it AGI? The answer is simple. The goalposts didn't just move. Everyone brought their own. The reason the current landscape feels so confusing is that the "Godfathers" of the industry are fighting a philosophical war over the definition. On one side, you have the "Scientific Faction" led by groups like Google DeepMind. Shane Legg, the man who helped coin the term, has shifted toward a nuanced "Levels of AGI" framework. He views intelligence like a video game leveling system. While our current models are "Competent" (better than 50% of skilled adults), they haven't yet reached "Superhuman" status across the board. For this faction, AGI is a scientific milestone of perfection. On the other side, you have the "Physical Skeptics," most notably represented by Yann LeCun, formerly the Chief AI Scientist at Meta. LeCun argues that the term AGI is meaningless until a machine possesses a "World Model," an understanding of cause and effect in physical reality. He contends that an LLM knows "if I drop a glass, it breaks" only because it read it in a book, not because it understands gravity. In his view, until an AI has that physical grounding, it is less intelligent than a house cat. Then there is the "Economic Faction," led by Sam Altman and OpenAI. In their leaked internal documents from late 2024, the definition of AGI appeared to shift from a philosophical breakthrough to a starkly capitalist metric. They defined it as a system that can autonomously generate $100 billion in profit. They don't care if the machine has a "soul" or if it understands physics. They care if it can replace labor at scale. While these three factions argue over definitions, the ground has shifted beneath our feet. It doesn't matter if the machine has a "World Model" or if it hits "Level 5" on a DeepMind chart. The only definition that matters to you, your career, and your family is "Economic AGI." This asks a much simpler, colder question. Can this system replace the economic output of a human being? If a "narrow" model can analyze a contract faster than a lawyer, code better than a junior developer, and manage logistics better than a supply chain manager, then for all economic intents and purposes, AGI is here. While speaking at the World Economic Forum, Anthropic's CEO Dario Amodei revealed that some of his engineers "don't write any code anymore" and predicted AI would handle "most, maybe all" of software engineering within six to twelve months. A senior Google engineer recently said Claude Code recreated a year's worth of work in a matter of hours. We are waiting for a sci-fi moment where the robot wakes up and announces it has a soul. But the revolution isn't about consciousness. It is about competence and productivity. You don't need a machine to be alive to take your job. You just need it to have context. Thanks to the new wave of "Cognitive Twins" and agentic workflows, it finally does. While the Godfathers argue over whether we've crossed some philosophical threshold, the tools are already reshaping how work gets done. The professionals who thrive in the next decade won't be the ones who waited for a consensus on what to call it. They will be the ones who learned how to work alongside it. The era of debating the definition is over. The era of living with this reality has begun. ### Volume 18: Your Life Becomes the Dataset URL: https://www.mindovermoney.ai/chatgpt-memory-settings-ai-data-privacy-professionals/ Last updated: 2026-07-13T16:59:44.000Z Hey everyone! AI is getting personal fast. The next wave is not just smarter answers, it is tools that learn your patterns and start helping run your life. That can be powerful, and it can be risky, so this week is about using that shift with your eyes open. 🧭 **Founder’s Corner:** Why context is the real bottleneck, and how your day to day life is becoming the next training set. 🧠 **AI Education:** ChatGPT Part 4 shows the control settings that decide what gets saved, what gets remembered, and how private you want your chats to be. ✅ **10-Minute Win:** A Big Purchase Decision Matrix that turns a major buy into a fair, weighted comparison with a simple 5 year cost check. Let’s get into it. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **IBM Study: AI Poised to Drive Smarter Business Growth Through 2030**](https://newsroom.ibm.com/2026-01-19-ibm-study-ai-poised-to-drive-smarter-business-growth-through-2030?ref=mindovermoney.ai) **Summary:** IBM’s research says 79% of executives expect AI to significantly contribute to revenue by 2030 (up from 40% today), while projecting AI investment could surge \~150% by 2030\. The catch: 68% worry their AI efforts will fail because they aren’t integrated into core business activities. **Why it matters:** The “AI strategy” gap is now obvious: leaders believe the upside is huge, but most orgs still haven’t operationalized AI into real workflows. That’s where winners separate from hype. ### **2)**[ **ServiceNow and OpenAI collaborate to deepen and accelerate enterprise AI outcomes**](https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-and-OpenAI-collaborate-to-deepen-and-accelerate-enterprise-AI-outcomes/default.aspx?ref=mindovermoney.ai) **Summary:** ServiceNow and OpenAI announced an enhanced multi-year collaboration to embed OpenAI models (including GPT-5.2) into ServiceNow’s enterprise workflow platform, with a focus on agentic AI and faster customer adoption without bespoke development. **Why it matters:** This is the clearest path from “AI demos” to real business value: AI that’s wired into the systems companies already run—where work actually happens. ### **3)**[ **Anthropic and Teach For All launch global AI training initiative for educators**](https://www.anthropic.com/news/anthropic-teach-for-all?ref=mindovermoney.ai) **Summary:** Anthropic is partnering with Teach For All to bring AI tools and training to educators across 63 countries, aiming to reach 100,000+ teachers and alumni through an “AI Literacy & Creator Collective” so teachers can adapt Claude to classroom needs. **Why it matters:** Education is where AI can become either a force multiplier or a mess. Putting teachers in the driver’s seat is how you get practical, equitable use—not random tools dumped into classrooms. ### **4)**[ **ChatGPT will now try to predict your age to protect young users — here's how**](https://www.tomsguide.com/ai/chatgpt/chatgpt-will-now-try-to-predict-your-age-to-protect-young-users-heres-how?ref=mindovermoney.ai) **Summary:** OpenAI is rolling out age estimation to identify likely minors and automatically apply stronger protections; users who are misclassified can verify to restore access. **Why it matters:** As AI goes mainstream, platforms are being forced to build guardrails for real-world usage. That will shape what features can ship—and how fast. ### **5)**[ **Young workers most worried about AI affecting jobs, Randstad survey shows**](https://www.investing.com/news/stock-market-news/young-workers-most-worried-about-ai-affecting-jobs-randstad-survey-shows-4453847?ref=mindovermoney.ai) **Summary:** A Randstad survey reported by Reuters finds younger workers are the most worried about AI’s impact on jobs, while expectations are rising that AI will reshape day-to-day work. **Why it matters:** The labor narrative is shifting from “someday” to “right now.” Anxiety + skills pressure is becoming part of the AI adoption curve—whether employers are ready or not. ## Founder's Corner **Context is King: Why Your Life Is The Next Great Training Set* ### Introduction: The IQ Plateau There is a growing narrative in Silicon Valley that we have hit a wall. Critics argue that Large Language Models are reaching a point of diminishing returns and that the curve of "Raw Intelligence" is flattening out. I disagree. The models are not hitting a wall. They are simply hitting the limit of what they can do without us. General LLMs will only be able to take intelligence so far when it comes to replacing or supplementing human labor. The bottleneck isn't IQ; it is context. An AI can solve physics equations that humans cannot, yet it still struggles to navigate the messy, unwritten nuances of your daily life because it lacks the "human condition." It has the logic, but it doesn't have the experience. This means LLMs can and will be smarter than humans, but they need context to become a functional part of everyday society. The signals that the big labs are trying to solve this are flashing red. In just the last two weeks, we have seen a coordinated pivot toward this "contextual layer": - **Google** released "Personal Intelligence," a feature explicitly designed to mine your Photos, Gmail, and Search history to "connect the dots" of your life (e.g., finding your license plate from a blurry photo). - **OpenAI** has fully integrated its "Operator" agent into the main ChatGPT interface, giving it the ability to browse and execute tasks with persistent memory of your preferences. - **Anthropic** launched "Claude Cowork," a desktop agent that learns your specific file structures and day-to-day patterns to automate the "life admin" tasks that usually bog down human productivity. Mix that proprietary context with how quickly the models are improving and the conclusion is clear. We are the next training data set. They have scraped the entire internet for knowledge. Now, they are harvesting our experiences, our workflows, and our "why" to bridge the gap between artificial intelligence and human reality. Let’s explore how this will manifest and impact our future. ### You Are The Training Data for the Robot Revolution The shift toward hyper-personalization is not just about convenience. It is the natural progression of artificial intelligence evolving into something that understands the human condition. To understand why this matters, you have to look at how Elon Musk is training the Tesla Optimus fleet. Inside Tesla factories right now, humans are wearing VR motion-capture suits and performing repetitive tasks like picking up parts or organizing trays while the robots watch. This is called teleoperation. The human provides the "ground truth" for movement. Once one robot learns the motion through fleet learning, the entire network of thousands of units learns it instantly. But physical movement is only half the equation. A robot can know how to fold a shirt, but it does not know when to fold it. It does not understand that it should not enter the bedroom while you are sleeping. This is where we come in. Tesla workers are training the robot’s body, but we are training the robot’s mind. Every time you use Google’s new "Personal Intelligence" to find a tire size or ask an LLM to plan a dinner party, you are acting as a digital teleoperator. You are teaching the system the logic of human existence. You are showing it how we think, how we make decisions, and how we prioritize tasks in a messy world. We are about to enter the era of "Behavioral Labeling." Every time you let an AI agent book a flight, organize your calendar, or identify an object in your home, you are tagging the training data for your physical replacement. You aren't just a user anymore. You are the teacher. ### The Death of the User Interface The days of navigating stand-alone websites and mobile applications are fading. For years, the "interface" has been the product. We browse websites, click through menus, and scroll mobile apps. But the new infrastructure being built by Google, OpenAI, and Anthropic is not designed for human interaction. It is designed for agents and automation. The consumer behavior change is happening right in front of us. Hundreds of millions of people are using ChatGPT, Gemini, Claude, and Grok instead of traditional web searches. Google is integrating AI directly into Search and slowly changing how we interact with the internet. We are moving from a world of "browsing" to a world of "delegating." In the near future, you won't open the Amazon app to stock up on household goods. You will simply tell your agent, "We are out of Tide," and it will negotiate the purchase, handle the payment, and track the shipping in the background. We are adopting this agentic reality in real time. This is made possible by a new set of "invisible pipes" that will replace the App Store: - **UCP (Universal Commerce Protocol):** Launched by Google, this standard allows AI agents to "read" a digital storefront without a human interface. It turns every online store into a programmable vending machine that your AI can access directly to compare prices, check inventory, and execute purchases. - **MCP (Model Context Protocol):** Developed by Anthropic, think of this as the "USB-C for AI." It is the standard that connects your AI agent to your local data—your calendar, your emails, your Slack messages. But here is the catch: Context is the fuel. These protocols are limited without a deep understanding of your day-to-day life. The more context you surrender to the system (your schedule, your budget, your dietary restrictions, your brand preferences) the more proactive the automation becomes. In theory, this makes life significantly easier. An agent with full context doesn't just buy groceries, it predicts when you will run out based on your usage history and orders it before you even notice. As companies race to build infrastructure compliant with these protocols, the traditional website becomes obsolete. The user experience shifts from doing the task to verifying the agent's work. Fast forward 5 years, and I see no path where the standalone "app" still exists. Why would you download a piece of software to click buttons when you have a cognitive twin that can navigate the "Agentic Web" for you? The interface of the future isn't a screen. It is an agentic experience fueled by your context. ### The Rise of the Cognitive Twin Context is not just coming for your personal life. It is coming for the workplace too. The gap between companies effectively deploying AI and those struggling to find value is widening. Many organizations simply cannot figure out how to successfully adopt agentic experiences that truly automate workflows. One major reason for this struggle is simple. They have the data, but they lack the meaning. AI needs great data to start down an automation path, but data alone is blind. It is missing the understanding of how that data interacts with specific workflows to drive an end result. Humans have spent years in their roles building SOPs, handwritten notes, and "tribal knowledge" to execute their work. This is the missing link. You cannot truly change the fundamentals of how a business is run without the context living inside the individuals that drive the company forward. In the future, we will see companies pivot to strategies that do not solely rely on massive, generic Large Language Models. Instead, we will see the rapid adoption of Small Language Models (SLMs). These models will not be trained on the entire internet. They will be trained on the proprietary context of the company and the specific "tribal knowledge" of the employees. This will give birth to the Cognitive Twin. Cognitive Twins are the first step to extracting proprietary context from people’s day-to-day work and marrying it to data. This allows us to move from "task-driven" automation (writing an email) to "foundationally knowledge-driven" automation (deciding *why* the email needs to be written). Data infrastructure alone cannot do this. You need context. The faster a company can marry their proprietary context to their data, the faster transformation can happen. The workplace of the future will not be staffed by humans and chatbots. It will be run by humans and their Cognitive Twins, working in tandem to solve problems that raw data cannot. ### The Inevitability of Context I do not view this shift as inherently good or bad. I view it as inevitable. When you look at the hundreds of billions of dollars being poured into data centers, GPUs, and energy infrastructure, it becomes clear that the "Context War" is not a hypothesis. It is the business plan of the future. Regardless of public sentiment on privacy, the truth is that most of us have turned a blind eye to handing over our data for decades. We scroll past terms and conditions, we click "Accept," and we surrender our digital footprint for the sake of convenience. When a massive data breach occurs, the collective reaction is often confusion rather than action. We have already made the trade. Now, the price of that trade is going up. The era of "Personal Intelligence" and "Agentic Workflows" is here. You can choose to ignore it, but you cannot opt out of the reality it creates. We need to understand the changes happening around us so we can be part of the solution. The winners of this next era will not be the ones who blindly fight the technology, nor the ones who passively let it happen to them. The winners will be the ones who understand the mechanics of context, build their own “cognitive twin”, and who refuse to be blindsided by a future they didn't see coming. Context is King. Long live the King. ## AI Education for You ChatGPT Part 4: Stay in Control You now know what ChatGPT can do and how to use the main modes. That is the fun part. This final part is the part that makes you a confident user. Control settings decide what gets saved, what gets remembered, what can be used to improve models, and how private you want a specific conversation to be. If you skip this, the product can feel unpredictable. If you learn it, ChatGPT becomes safer, cleaner, and more consistent. ## **Feature Index** | Feature | Free status | Where | Why you should care | | ----------------------------------- | --------------------------- | ------------------ | ----------------------------------------------------- | | Memory | Free, features can vary | Settings | Reduces repetition across chats | | Temporary Chat | Free, availability can vary | New chat options | A clean slate conversation | | Data controls for model improvement | Free | Settings | Control if your content may be used to improve models | | Chat history | Free | Sidebar and search | Find and reuse your best work | | Delete chats | Free | Chat options | Remove chats you do not want saved | | Export your data | Account feature | Settings | Download your ChatGPT data archive | | Custom Instructions | Free | Settings | Set default behavior for every chat | | Sharing chats | Free | Share option | Share a clean link instead of screenshots | ## **Group 1: Memory** What it is: Memory is a feature that can store small details so you do not have to repeat yourself in future chats. These are usually preferences or recurring context. Why it matters: Without Memory, every new chat starts from zero. With Memory, ChatGPT can stay more consistent across weeks. When to use it: Use Memory for stable preferences, like: - You prefer plain English and short sentences. - You want definitions first, then examples, then a recap. - You want examples grounded in everyday life. When not to use it: Do not store sensitive personal details. Do not store anything you would not want saved long-term. Once a month, review what is stored and remove anything you no longer want. ## **Group 2: Temporary Chat** What it is: Temporary Chat is a conversation mode designed to be a clean slate. It is useful when you do not want the chat saved to your history or to use Memory. Why it matters: Sometimes you want privacy. Sometimes you want a fresh start. Temporary Chat is how you do that. When to use it: - You are asking a one-off question you do not need later. - You want to avoid the chat showing up in your sidebar. - You want a clean slate for a new topic. Practical tip: If you are testing prompts and do not want clutter, use Temporary Chat. ## **Group 3: Data controls and model improvement** What it is: ChatGPT has settings that let you manage how your content is handled, including whether your chats may be used to improve models. Why it matters: This is the privacy control most beginners do not know exists. How to use it: Go to settings and look for data controls. Choose the option that matches your comfort level. What to remember: Even if you turn off model improvement, you should still avoid sharing sensitive information in any tool unless you truly need to. ## **Group 4: Keep your workspace clean** ### **Chat history and search** What it is: Your past chats are saved and searchable. Why it matters: This turns your best prompts into reusable assets. It also helps you avoid rework. Beginner habit: Rename your best chats. ### **Delete chats** What it is: You can delete individual chats you do not want saved. Why it matters: You stay organized and you control what lives in your history. Practical habit: Delete low-value chats that are cluttering your sidebar. ### **Export your data** What it is: You can request a download of your account data. Why it matters: It gives you a backup of your history and settings. ## **Group 5: Sharing, safely** ### **Share a chat** What it is: You can generate a share link to a conversation. Why it matters: It preserves context. It is cleaner than screenshots. Safety rule: Only share chats you are comfortable sharing. Treat it like sending a document. ## **One-screen recap** - Use Memory for stable preferences, not sensitive details. - Use Temporary Chat when you want a clean slate and less history clutter. - Use data controls to match your privacy comfort level. - Use chat history search to reuse your best prompts. - Delete low-value chats to keep the workspace clean. - Export your data occasionally if you rely on ChatGPT regularly. - Share chats carefully and only when you are comfortable with the content. ## Your 10-Minute Win A step-by-step workflow you can use immediately # 🧠💸**Big Purchase Decision Matrix** Big purchases are where “vibes” get expensive. Most people compare specs, then panic-buy when it’s time to decide. This workflow turns a major purchase into a simple, fair fight: you’ll define what matters, weight it, and let a decision matrix calculate the winner — including a basic 5-year cost view so you don’t get fooled by the sticker price. ### **Step 1 — Pick 3 options and capture the facts (2 minutes)** Choose 2–3 finalists (not 12). For each, grab *any* one source: - a retailer listing link - a manufacturer spec page link - or a screenshot/PDF you can upload later Write this quick “purchase brief” (paste into ChatGPT in Step 2): - **What are you buying?** (car / refrigerator / washer / HVAC / etc.) - **Hard constraints:** budget cap, size/fit limits, must-have features - **Your 3 options:** name + link (or “I’ll upload a screenshot/PDF”) - **Your timeline:** buy now vs can wait 30 days ### **Step 2 — Have ChatGPT build your criteria + weights (3 minutes)** Paste this prompt into ChatGPT: **Role:* You are my Big Purchase Decision Analyst.*Goal:* Help me decide between 2–3 options using a weighted decision matrix + basic 5-year cost view.* ***My purchase brief:*** - *Item type: \_\_\_* - *Budget cap: \_\_\_* - *Hard constraints (size/fit/must-have): \_\_\_* - *Timeline: \_\_\_* - *Options (name + link or “upload”):* ***Tasks:*** 1. *Propose *5 criteria* that fit this purchase (ex: Fit/Needs, Reliability, Efficiency, Warranty/Support, Features/Convenience).* 2. *Ask me *one question per criterion* to confirm what I care about.* 3. *Suggest default weights that total 100% (and explain in 1 sentence).* 4. *Once I answer, lock the final criteria + weights.* **Rules:* Keep it simple. No jargon. No buying advice yet.* Answer the questions. Now you’ve defined what “best” means for *you*. ### **Step 3 — Generate the copy/paste decision matrix (CSV) and calculate scores (3 minutes)** In the same chat, paste this: *Create my Decision Matrix as CSV only so I can paste into Google Sheets.* *CSV structure:* - *Row 1 headers: Option,Link/Source,Upfront Price,Est Annual Operating Cost,Est 5-Year Cost,Fit Score (1-5),Efficiency Score (1-5),Reliability Score (1-5),Warranty/Support Score (1-5),Features Score (1-5),Weighted Score,Notes* - *Row 2 is the WEIGHTS row: put weights (as decimals that sum to 1.00) into columns F–J only. Put “WEIGHTS” in column A.* - *Rows 3–5 are my options. Leave prices/costs blank if unknown. Leave scores blank if unknown.* - *Use my criteria/weights from above. If my criteria names differ, rename the score columns to match.* *Rules: Don’t invent prices/specs. If unknown, leave blank.* Now in **Google Sheets**: 1. Paste the CSV into cell **A1**. 2. In **E3** (Est 5-Year Cost), paste and fill down: \=IF(OR(C3="",D3=""),"",C3+(D3\*5)) 1. In **K3** (Weighted Score), paste and fill down: \=IF(COUNTA(F3:J3)<5,"",SUMPRODUCT($F$2:$J$2,F3:J3)) You now have an objective ranking that updates as you fill in costs/scores. ### **Step 4 — Let ChatGPT do the “final decision” write-up (2 minutes)** Copy rows 2–5 from your sheet (weights + options) and paste into ChatGPT with this prompt: *Using the matrix below, do a decision write-up:* 1. *Rank the options by Weighted Score.* 2. *Explain the top 2 tradeoffs (plain English).* 3. *Tell me what single piece of missing info would most change the decision.* 4. *Give me a “Before I buy” checklist (5 bullets) and questions to ask (5 bullets).* *Matrix: \[PASTE WEIGHTS + OPTIONS ROWS\]* ## **The Payoff** You walk away with a decision you can defend: a weighted matrix aligned to your priorities, a basic 5-year cost check, and a short list of the questions that prevent regret. This is how you stop overthinking and start deciding like a grown-up with a system. ## **Transparency & Notes for Readers** - Free tools only: ChatGPT + Google Sheets. Optional sources like NHTSA/IIHS/EPA/ENERGY STAR are free. - Don’t let AI guess: if you don’t know a price/spec, leave it blank and fill later. - Weighted score is a tool, not truth: it reflects your weights. If the output surprises you, your weights might be wrong (that’s the point). - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Context is King: Why Your Life Is The Next Great Training Set URL: https://www.mindovermoney.ai/founders-corner/why-personal-context-is-the-next-ai-advantage-professionals/ Last updated: 2026-07-13T16:59:44.000Z ### Introduction: The IQ Plateau There is a growing narrative in Silicon Valley that we have hit a wall. Critics argue that Large Language Models are reaching a point of diminishing returns and that the curve of "Raw Intelligence" is flattening out. I disagree. The models are not hitting a wall. They are simply hitting the limit of what they can do without us. General LLMs will only be able to take intelligence so far when it comes to replacing or supplementing human labor. The bottleneck isn't IQ; it is context. An AI can solve physics equations that humans cannot, yet it still struggles to navigate the messy, unwritten nuances of your daily life because it lacks the "human condition." It has the logic, but it doesn't have the experience. This means LLMs can and will be smarter than humans, but they need context to become a functional part of everyday society. The signals that the big labs are trying to solve this are flashing red. In just the last two weeks, we have seen a coordinated pivot toward this "contextual layer": - **Google** released "Personal Intelligence," a feature explicitly designed to mine your Photos, Gmail, and Search history to "connect the dots" of your life (e.g., finding your license plate from a blurry photo). - **OpenAI** has fully integrated its "Operator" agent into the main ChatGPT interface, giving it the ability to browse and execute tasks with persistent memory of your preferences. - **Anthropic** launched "Claude Cowork," a desktop agent that learns your specific file structures and day-to-day patterns to automate the "life admin" tasks that usually bog down human productivity. Mix that proprietary context with how quickly the models are improving and the conclusion is clear. We are the next training data set. They have scraped the entire internet for knowledge. Now, they are harvesting our experiences, our workflows, and our "why" to bridge the gap between artificial intelligence and human reality. Let’s explore how this will manifest and impact our future. ### You Are The Training Data for the Robot Revolution The shift toward hyper-personalization is not just about convenience. It is the natural progression of artificial intelligence evolving into something that understands the human condition. To understand why this matters, you have to look at how Elon Musk is training the Tesla Optimus fleet. Inside Tesla factories right now, humans are wearing VR motion-capture suits and performing repetitive tasks like picking up parts or organizing trays while the robots watch. This is called teleoperation. The human provides the "ground truth" for movement. Once one robot learns the motion through fleet learning, the entire network of thousands of units learns it instantly. But physical movement is only half the equation. A robot can know how to fold a shirt, but it does not know when to fold it. It does not understand that it should not enter the bedroom while you are sleeping. This is where we come in. Tesla workers are training the robot’s body, but we are training the robot’s mind. Every time you use Google’s new "Personal Intelligence" to find a tire size or ask an LLM to plan a dinner party, you are acting as a digital teleoperator. You are teaching the system the logic of human existence. You are showing it how we think, how we make decisions, and how we prioritize tasks in a messy world. We are about to enter the era of "Behavioral Labeling." Every time you let an AI agent book a flight, organize your calendar, or identify an object in your home, you are tagging the training data for your physical replacement. You aren't just a user anymore. You are the teacher. ### The Death of the User Interface The days of navigating stand-alone websites and mobile applications are fading. For years, the "interface" has been the product. We browse websites, click through menus, and scroll mobile apps. But the new infrastructure being built by Google, OpenAI, and Anthropic is not designed for human interaction. It is designed for agents and automation. The consumer behavior change is happening right in front of us. Hundreds of millions of people are using ChatGPT, Gemini, Claude, and Grok instead of traditional web searches. Google is integrating AI directly into Search and slowly changing how we interact with the internet. We are moving from a world of "browsing" to a world of "delegating." In the near future, you won't open the Amazon app to stock up on household goods. You will simply tell your agent, "We are out of Tide," and it will negotiate the purchase, handle the payment, and track the shipping in the background. We are adopting this agentic reality in real time. This is made possible by a new set of "invisible pipes" that will replace the App Store: - **UCP (Universal Commerce Protocol):** Launched by Google, this standard allows AI agents to "read" a digital storefront without a human interface. It turns every online store into a programmable vending machine that your AI can access directly to compare prices, check inventory, and execute purchases. - **MCP (Model Context Protocol):** Developed by Anthropic, think of this as the "USB-C for AI." It is the standard that connects your AI agent to your local data—your calendar, your emails, your Slack messages. But here is the catch: Context is the fuel. These protocols are limited without a deep understanding of your day-to-day life. The more context you surrender to the system (your schedule, your budget, your dietary restrictions, your brand preferences) the more proactive the automation becomes. In theory, this makes life significantly easier. An agent with full context doesn't just buy groceries, it predicts when you will run out based on your usage history and orders it before you even notice. As companies race to build infrastructure compliant with these protocols, the traditional website becomes obsolete. The user experience shifts from doing the task to verifying the agent's work. Fast forward 5 years, and I see no path where the standalone "app" still exists. Why would you download a piece of software to click buttons when you have a cognitive twin that can navigate the "Agentic Web" for you? The interface of the future isn't a screen. It is an agentic experience fueled by your context. ### The Rise of the Cognitive Twin Context is not just coming for your personal life. It is coming for the workplace too. The gap between companies effectively deploying AI and those struggling to find value is widening. Many organizations simply cannot figure out how to successfully adopt agentic experiences that truly automate workflows. One major reason for this struggle is simple. They have the data, but they lack the meaning. AI needs great data to start down an automation path, but data alone is blind. It is missing the understanding of how that data interacts with specific workflows to drive an end result. Humans have spent years in their roles building SOPs, handwritten notes, and "tribal knowledge" to execute their work. This is the missing link. You cannot truly change the fundamentals of how a business is run without the context living inside the individuals that drive the company forward. In the future, we will see companies pivot to strategies that do not solely rely on massive, generic Large Language Models. Instead, we will see the rapid adoption of Small Language Models (SLMs). These models will not be trained on the entire internet. They will be trained on the proprietary context of the company and the specific "tribal knowledge" of the employees. This will give birth to the Cognitive Twin. Cognitive Twins are the first step to extracting proprietary context from people’s day-to-day work and marrying it to data. This allows us to move from "task-driven" automation (writing an email) to "foundationally knowledge-driven" automation (deciding *why* the email needs to be written). Data infrastructure alone cannot do this. You need context. The faster a company can marry their proprietary context to their data, the faster transformation can happen. The workplace of the future will not be staffed by humans and chatbots. It will be run by humans and their Cognitive Twins, working in tandem to solve problems that raw data cannot. ### The Inevitability of Context I do not view this shift as inherently good or bad. I view it as inevitable. When you look at the hundreds of billions of dollars being poured into data centers, GPUs, and energy infrastructure, it becomes clear that the "Context War" is not a hypothesis. It is the business plan of the future. Regardless of public sentiment on privacy, the truth is that most of us have turned a blind eye to handing over our data for decades. We scroll past terms and conditions, we click "Accept," and we surrender our digital footprint for the sake of convenience. When a massive data breach occurs, the collective reaction is often confusion rather than action. We have already made the trade. Now, the price of that trade is going up. The era of "Personal Intelligence" and "Agentic Workflows" is here. You can choose to ignore it, but you cannot opt out of the reality it creates. We need to understand the changes happening around us so we can be part of the solution. The winners of this next era will not be the ones who blindly fight the technology, nor the ones who passively let it happen to them. The winners will be the ones who understand the mechanics of context, build their own “cognitive twin”, and who refuse to be blindsided by a future they didn't see coming. Context is King. Long live the King. ### Steal My Prompt Vol. 18: The Title Architect URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-18-the-title-architect/ Last updated: 2026-07-13T16:59:44.000Z Next up in my journey to develop and document a prompt for every executable workflow used in the creation of Neural Gains Weekly is the title creation prompt. I’m working towards automation and building out my prompt library will serve as the foundation to automate some of these tasks and hand them over to AI agents. I created and am testing this prompt to generate title options for each weekly newsletter. --- You are my editorial strategist for Neural Gains Weekly (MindOverMoney.ai). Task: Generate newsletter title options for the next issue. Critical context requirement: Before you write any titles, you must review ALL previous Neural Gains Weekly volumes available in this workspace (or provided links/files) to understand: 1) The existing naming conventions and recurring patterns 2) The tone and rhythm of prior titles 3) What has already been used so you avoid near-duplicates If you cannot access prior volumes, you must say so and ask me for access or pasted titles before producing final titles. Title rules: \- Output 20 title options for my review. \- Every option must start with the exact prefix: "Volume : " \- Concept-first: Do NOT use specific company/product names (no OpenAI, Anthropic, ChatGPT, etc.). \- No profanity. \- No em dashes. \- Keep titles human and punchy, built to maximize opens and engagement. Engagement goals: \- The title should capture the core theme of the issue and make the reader curious enough to open. \- Favor titles that create tension, urgency, or a clear promise, but avoid cheap clickbait phrasing. Output format: 1) First, write one short line stating the theme of this issue in plain English (10–18 words). 2) Then list 20 titles (numbered 1–20). 3) Then provide “Top 3 Picks” with: \- Rank #1–#3 \- A 1–2 sentence reason for each pick (why it will drive engagement and why it fits the Neural Gains style) Inputs you will receive: \- Volume number: \- Full draft text for the current volume (or section summaries) Now execute. \---INPUT--- Volume number: Review attachment and/or links —END INPUT--- ### Volume 17: The Invisible Highway of Healthcare Data URL: https://www.mindovermoney.ai/ai-healthcare-data-infrastructure-explained-2026/ Last updated: 2026-07-13T16:59:45.000Z Hey everyone! Healthcare is starting off 2026 with a bang. Anthropic just made another major move in this space, and it is a reminder that AI in healthcare is not a distant future story anymore. The real shift is happening behind the scenes, where these models start plugging into the messy infrastructure that actually runs care. 🏥 **Founder’s Corner:** We go deeper on healthcare. Not the flashy “talk to your doctor” stuff, but the invisible highway behind care. The messy systems, the disconnected data, and why connectors might be the real unlock. 🧠 **AI Education:** ChatGPT Part 3 is all about working with your stuff. Files, Projects, Canvas, history search, and Custom Instructions. The features that turn ChatGPT from a one-off chat into something you can actually use consistently. 📈 **10-Minute Win:** A post-trade Post-Mortem Playbook. A simple loop to turn every trade into a lesson, spot your repeated mistakes, and build one rule at a time so your process improves. Let’s dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Apple, Google strike Gemini deal for revamped Siri in major win for Alphabet**](https://www.reuters.com/business/google-apple-enter-into-multi-year-ai-deal-gemini-models-2026-01-12/?utm%5Fsource=chatgpt.com) **Summary:** Apple will integrate Google’s Gemini models into a revamped Siri later in 2026, deepening Apple’s AI stack while expanding Google’s footprint across Apple’s massive device base. **Why it matters:** This is a “distribution wins” moment: whichever model sits behind default assistants can shape how billions experience AI — and who gets the data, the developer mindshare, and the ecosystem gravity. ### **2)**[ **Advancing Claude in healthcare and the life sciences**](https://www.anthropic.com/news/healthcare-life-sciences?utm%5Fsource=chatgpt.com) **Summary:** Anthropic is expanding Claude’s healthcare and life sciences tooling, including “Claude for Healthcare” (HIPAA-ready) and deeper integrations for life sciences workflows like trials and regulatory ops. **Why it matters:** This is the clearest sign yet that “AI in medicine” is shifting from demos to workflow-native tools (connectors, compliance, real systems) — which is where real adoption happens. ### **3)**[ **Google expands AI-assisted shopping features of Gemini**](https://apnews.com/article/google-gemini-ai-shopping-checkout-walmart-f1679240ba93d40b90a97348b73039d3?utm%5Fsource=chatgpt.com) **Summary:** Google is expanding Gemini’s shopping capabilities by partnering with major retailers (including Walmart and Shopify) and enabling “instant checkout” experiences inside the chat flow. **Why it matters:** AI assistants are becoming transaction engines, not just answer engines — and whoever controls the in-chat “buy” moment could end up controlling a huge slice of digital commerce. ### **4)**[ **Personal Intelligence: Connecting Gemini to Google apps**](https://blog.google/innovation-and-ai/products/gemini-app/personal-intelligence/?utm%5Fsource=chatgpt.com) **Summary:** Google is rolling out “Personal Intelligence” for Gemini, letting it use context from connected Google apps (like Gmail, Photos, Search, and YouTube) to deliver more personalized help. **Why it matters:** The next AI leap is “context” — not just smarter models, but assistants that can safely use *your* information to be genuinely useful (with privacy controls determining whether people trust it). ### **5)**[ **Anthropic, OpenAI have taken early steps to go public - NYT**](https://www.investing.com/news/stock-market-news/anthropic-openai-have-taken-early-steps-to-go-public--nyt-432SI-4447912?utm%5Fsource=chatgpt.com) **Summary:** A report says Anthropic and OpenAI have started preliminary IPO work, alongside talk that SpaceX is also moving toward a potential public listing path. **Why it matters:** If the biggest AI labs shift toward IPO readiness, the market pressure changes: more scrutiny, more demand for durable revenue, and faster “enterprise-grade” productization — not just research wins. ## Founder's Corner Real world learnings as I build, succeed, and fail # **The Invisible Highway: Can Claude for Healthcare Fix Healthcare’s Infrastructure Problem?** Last week I wrote about the rise of the "Executive Patient" and how OpenAI’s consumer launch is fundamentally changing patient behavior. We discussed a world where the patient arrives with more data, more agency, and higher expectations than ever before. But there is a glaring problem with that vision. When that empowered patient walks through the doors, they are stepping into a system that is still running on infrastructure built for a different era. For decades the healthcare industry has struggled to connect the dots. Billions have been spent digitizing records, yet we still live in a world of disconnected data islands. The payer system does not speak to the provider system and clinical data does not flow to the claims department. Our medical claims system runs on J-codes, which were introduced in 1978, long before electronic billing was introduced. Pharmacy claims run on a completely different set of electronic standards that do not connect to the medical coding systems. This lack of continuity is the root cause of the broken experiences that frustrate patients and burn out clinicians. Enter Claude for Healthcare. Anthropic quietly released a platform aimed squarely at the critical infrastructure of the healthcare enterprise. They are not trying to change how you talk to your doctor. They are trying to fix the broken highway that connects the entire industry. ### **The Billion-Dollar Disconnect** The healthcare industry has a massive infrastructure problem. Despite the widespread adoption of Electronic Health Records, clinical teams still spend 13 hours every week completing prior authorizations according to a January 2026 AMA survey. The data exists but it is trapped in static files that require human manual labor to move, read, and interpret. We have spent years trying to solve this by layering new applications on top of old foundations. But new tools and technology can only go so far when the underlying infrastructure remains siloed. We have built digital filing cabinets when what we really needed were digital bridges. You cannot expect technology to fix the foundational workflow problems that exist in healthcare. This disconnect has a price tag. The industry currently loses $496 billion annually to billing and insurance-related administrative waste. The result is a system where 93% of physicians report that prior authorization delays lead to negative clinical outcomes. The infrastructure cannot keep up with the volume of care, and it certainly cannot keep up with the new speed of the "Executive Patient." ### **Claude as the Universal Adaptor** Anthropic’s strategy is distinct because it treats AI as an operational layer rather than just a chatbot. The core value of the new platform lies in its "Connectors." Unlike a general-purpose model, Claude comes with pre-built integrations into the industry's sources of truth including the CMS Coverage Database, ICD-10 coding systems, and the NPI Registry. This connectivity allows organizations to automate the complex workflows that usually keep clinicians glued to their screens. Elation Health provides a perfect example of this in action. They embedded Claude directly into their primary care EHR to handle chart summarization. Instead of spending ten minutes clicking through tabs to review a patient's history, the physician gets an immediate, cited summary of the patient's story. The result is that clinicians are finding answers 61% faster, which allows them to stop hunting for data and start looking at the patient. On the operational side, Carta Healthcare is using the platform to tackle the heavy lift of clinical data abstraction. This is typically a manual process where staff scour records to report quality data to registries. By using Claude to automate this extraction, they reduced processing time by 66% while maintaining 99% accuracy. This effectively unblocks the flow of quality data without adding administrative overhead to the staff. These examples prove that the value of AI isn't just in writing emails. It is in automating the invisible friction of the back office so that clinical teams can return their focus to the human work of care delivery. ### **Trust as the Ultimate Feature** The final piece of this puzzle is safety. Building a digital highway for healthcare requires more than just connectivity; it requires a foundation of absolute trust. Enterprise healthcare organizations are risk-averse by design. They cannot afford to bet on a "black box" model that might hallucinate a diagnosis or expose patient data. This is where Anthropic is placing its strategic bet. They have engineered Claude on HIPAA-ready infrastructure with a "Constitutional AI" framework that acts as a permanent safety inspector. Unlike standard models that simply predict the next likely word, Claude is designed to show its work. It provides rigorous audit trails and citations, allowing a claims adjuster or a physician to trace every AI-generated output back to a specific medical guideline or coverage policy. Mike Reagin, the CTO of Banner Health, validated this approach, noting that they chose Claude specifically for its "reputation for responsible AI" and the belief that safety is "non-negotiable." In an industry where a single error can lead to a lawsuit or a denied life-saving procedure, this ability to verify the "why" behind the answer is the foundation to scalable infrastructure. ### **The Road Ahead** If OpenAI is the catalyst for patient agency, Claude is the infrastructure that makes that agency actionable. We are moving toward a future where the back office is increasingly self-driving through AI automation, offering a critical inflection point for legacy healthcare companies. The winners of the next decade will not be the organizations that simply buy the software. They will be the leaders who have the courage to experiment and fundamentally rebuild their workflows from the ground up. Business architecture will be redesigned as the scope of what is possible evolves with AI, and specifically the push for data-driven automation ecosystems like "Claude for Healthcare." The strategic imperative for legacy organizations is to use this infrastructure to strip away the administrative drudgery and redisperse their human staff to where they matter most: the patient experience. I predict that within 36 months, the labor-intensive tasks that burden operational and clinical teams will be streamlined and automated through data connection highways powered by agentic AI workflows. This will impact every sector of healthcare, from administrative staff working to clear a prior authorization, to clinicians submitting for a surgical procedure. The strategic imperative is to take those recovered hours and rebuild strategies that ensure the patient journey is supported. The true moat of the future is not your proprietary data silo; it is how you connect to the broader ecosystem to remove operational strain that slows down service and erodes trust. Automation builds the highway, but it is the human experience that will keep the Executive Patient from taking the next exit. ## AI Education for You ChatGPT Part 3: Work With Your Stuff In Part 1, you learned ChatGPT is a workspace with multiple modes and limits. In Part 2, you learned which modes help you create and understand faster. Now we solve the real beginner problem: your work gets messy. Chats pile up. Files get lost. You forget what you asked last week. Part 3 is about the features that turn ChatGPT from a one-off chat into something you can actually use over time. This is where consistency starts. ## **Feature Index** | Feature | Free status | Where | Why you should care | | --------------------------- | ---------------- | -------------------- | ------------------------------------------ | | File uploads | Free with limits | Web, mobile, desktop | Lets ChatGPT use your documents | | Using files inside Projects | Free with limits | Varies | Keeps files tied to the right goal | | Projects | Free with limits | Varies | Organizes long-running work | | Canvas | Free | Web, desktop | Best place to draft and revise | | Export from Canvas | Free | Web, desktop | Move drafts into Google Docs or Ghost | | Chat history and search | Free | Web, mobile, desktop | Find past outputs fast | | Share a chat | Free | Web, mobile, desktop | Send one clean link instead of screenshots | | Use GPTs from the GPT Store | Free with limits | Web, mobile, desktop | Reusable guided experiences | | Custom Instructions | Free | Web, mobile, desktop | Makes ChatGPT behave more consistently | ## **Group 1: Bring your own material in** ### **File uploads** What it is: You can upload documents, spreadsheets, and images so ChatGPT can read them and help you work with them. Why it matters: Most real tasks live in your stuff, not in your head. Uploads let ChatGPT stop guessing and start responding to the actual content/context you have. When to use it: - You want a summary of a document. - You want key points extracted. - You want a table or spreadsheet interpreted. - You want help understanding a screenshot. Limits and gotchas: - Free tier has upload limits. They can vary by demand. - Clean inputs beat messy ones. A clean file gets a better answer. - If a file has sensitive information, consider removing it before upload. Where to find it: Use the upload option in your chat tools. On mobile, it is usually attached to the message box options.. ### **The beginner rule for uploads** If you want better results, give ChatGPT fewer, cleaner files. Do this: - Upload one file per task. - Tell ChatGPT what you want and what to ignore. Avoid this: - Uploading five files and hoping it figures it out. - Asking for insights without stating the goal. ## **Group 2: Projects, the fastest way to stay organized** ### **Projects** What it is: Projects let you group related chats and files together. It is like creating a folder for one ongoing goal. Why it matters: Without Projects, you end up with scattered chats and repeated context. Projects reduce re-explaining and make long work easier. When to use it: - A task that lasts more than one day. - A repeating routine you want to improve over time. - Anything with multiple steps and multiple files. Limits and gotchas: - Free tier Projects have limits, especially around file counts. - Do not treat a Project like a dumping ground. Keep it focused. ## **Group 3: Canvas, the best place to draft and revise** What it is: Canvas is a drafting workspace. It is designed for writing and editing with ChatGPT, without the mess of long chat threads. Why it matters: Chat is fine for brainstorming. Canvas is better for creating a clean final draft. When to use it: - Newsletter writing - Any document you plan to copy and paste into another tool Limits and gotchas: - Canvas is available on web and desktop. It is not fully available on mobile yet. - Canvas works best when you give clear structure. A simple workflow for publishing: 1. Brainstorm in chat. 2. Move the draft into Canvas. 3. Revise in Canvas using targeted edits. ### **Export from Canvas** What it is: Canvas supports exporting drafts into common formats so you can move the content into your workflow. Why it matters: It reduces copy and paste mess and keeps formatting cleaner. ## **Group 4: Make ChatGPT consistent with Custom Instructions** ### **Custom Instructions** What it is: Custom Instructions is where you tell ChatGPT how you want it to behave by default. It is like setting your preferred “operating system settings” for conversations. Why it matters: Beginners waste time repeating the same preferences. Custom Instructions removes that repetition and makes outputs more consistent across chats. Use Custom Instructions when you find yourself repeatedly saying things like: - Keep it short. - Use plain English. - Ask me questions before you assume. - Give me a table. - Use a specific tone. Keep it simple. Make it specific. Do not overload it. A strong beginner template: - I am an AI beginner. - I like short sentences and plain English. - I want examples grounded in everyday life. How I want ChatGPT to respond: - Start with a short definition. - Then give one clear example. - Then list common confusion points. - If you are missing info, ask me one question before answering. What not to do: - Do not use jargon without defining it. - Do not guess or invent facts. - If you are unsure, say you are unsure. ## **Group 5: Find and share your past work** ### **Chat history and search** What it is: ChatGPT stores your past chats, and you can search them. Why it matters: Your best prompts and outputs are reusable. History search turns ChatGPT into a personal library. Beginner habit: Whenever you get a great output, rename that chat with a clear title so future you can find it. ### **Share a chat** What it is: You can share a chat with someone else using a share link. Why it matters: It keeps context intact. It is better than screenshots. It is better than copying fragments. ## **Group 6: Use GPTs, when you want a guided experience** ### **Use GPTs from the GPT Store** What it is: GPTs are custom versions of ChatGPT designed for specific tasks. You can browse and use GPTs on Free, but access may be limited by capacity. Why it matters: GPTs can reduce setup time because the workflow is already structured. How to use them well - Treat them as templates. - Still be clear about your goal. - Still verify important outputs. ## **One-screen recap** - File uploads help ChatGPT work from real material instead of guessing. - Projects keep long work organized and reduce repeated context. - Canvas is the best place to draft and revise content you will publish. - Custom Instructions makes ChatGPT more consistent across chats and saves time. - History search turns good prompts into reusable assets. - GPTs can guide you through tasks, but results still depend on your inputs. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **Post-Mortem Playbook (After a Trade)** Most investors don’t lose because they “picked the wrong stock.” They lose because they repeat the same mistakes without noticing behavior patterns: chasing, sizing too big, moving stops, or exiting for the wrong reason. This workflow turns every trade into a quick learning loop — so your process improves. ### **Step 1 — Capture the trade in a clean template (2 minutes)** Open **Google Sheets** and create a new tab named: Trade Post-Mortem. In Row 1, paste these headers: Trade ID | Ticker | Date In | Date Out | Entry | Exit | Size ($ or shares) | Thesis (1–2 lines) | Planned Stop | Time Horizon | Exit Reason | Outcome ($ / %) | Emotion (1 word) **What it should look like (example row):** T-001 | AAPL | 01/05 | 01/12 | 182.10 | 176.50 | $500 | “Earnings run-up + momentum” | 178.00 | 2 weeks | “Stop hit” | -3.1% | “Rushed” Optional (nice upgrade): add a chart screenshot (broker chart or TradingView free). If you do, you can upload the screenshot to ChatGPT — but it’s not required. ### **Step 2 — Run the “process-first” post-mortem in ChatGPT (4 minutes)** Copy/paste this prompt into ChatGPT, then paste your single trade row (or multiple rows if you want): *Role: You are my Trade Post-Mortem Coach.Goal: Help me improve decision-making. Judge process, not outcome.* *Rules:* - *Be blunt and specific. No hype.* - *If info is missing, list the exact questions you need (max 5).* - *Separate “Bad outcome but good process” vs “Good outcome but bad process.”* *My trade details (paste below):\[PASTE THE ROW FROM SHEETS\]* *Output exactly in this structure:* 1. *One-sentence verdict (process quality)* 2. *What I did right (3 bullets)* 3. *What likely hurt me (3 bullets)* 4. *Mistake tags (choose: Thesis / Timing / Sizing / Risk / Exit / Execution / Emotion / No plan)* 5. *If-Then upgrades (3 rules I should adopt next time)* 6. *Next trade checklist (6 items, yes/no)* If you have a chart screenshot and want extra clarity, add: *“Here’s a chart screenshot for context” and upload it.* ### **Step 3 — Make ChatGPT build your scorecard table (3 minutes)** Now paste this follow-up prompt: *Create a Post-Mortem Scorecard I can paste into Google Sheets.Output CSV only (no commentary).* *Columns:Trade ID, Setup Quality (1–5), Entry Discipline (1–5), Risk Plan (1–5), Exit Quality (1–5), Emotion Control (1–5), Biggest Lesson (1 line), Rule Upgrade (If-Then), What I’ll Do Next Time (1 line)* *Use the trade above + your analysis to fill the row(s).* Paste the CSV into a new tab called: **Scorecard.** ### **Step 4 — Lock in one improvement (1 minute)** Pick one “Rule Upgrade” and make it real: - Add it to the top of your Sheet as: **“My Rule This Week:”** - Example: “IF I don’t know my stop before entry, THEN I don’t enter.” This is how you compound skill — one small rule at a time. ## **The Payoff** You now have a repeatable system that turns trades into progress. Instead of “I got unlucky,” you’ll know: *Was my setup solid? Did I size correctly? Did I follow my own risk plan?* Over time, this reduces emotional trading and upgrades your investing process — which is the only edge beginners can reliably build. ## **Transparency & Notes for Readers** - AI can’t read your brokerage account. You must paste the trade details (and optionally a chart screenshot). - If you upload images/files, free tiers may have daily upload limits — keep it simple and paste text if needed. - This workflow helps you improve decision-making, not predict markets. - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 17: The Reusable Template URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-17-the-reusable-template/ Last updated: 2026-07-13T16:59:45.000Z I continue to develop my prompt library in order to build workflow automation in the future. I created a reusable prompt that generates the introduction to *Neural Gains Weekly*. This prompt, along with the other ones I share, free up time and allow me to focus on researching and writing the Founder’s Corner section. Enjoy! --- You are the newsletter editor for Neural Gains Weekly (MindOverMoney.ai). Goal: Write the INTRODUCTION BLOCK for the next volume. It must feel human and natural, hook the reader, and preview the three main sections in a consistent format. House style: - Short, punchy sentences. Beginner-friendly. Confident and practical. - Balanced personal tone: 1–2 personal sentences max, then move into what’s inside. - Always use a timely hook when relevant (holiday, season, new year, major founder theme, major industry shift), but do not summarize news articles. - No links. No citations. No em dashes. No jargon unless it is explained in plain English. - Keep the whole intro tight. Aim \~90–140 words. Format rules (must follow exactly): 1. Start with: "Hey everyone!" 2. Add 1–2 sentences that set the tone with a timely hook if relevant. 3. Then include exactly 3 bullet lines with emojis, in this order: 🧭 Founder’s Corner: <1 sentence> 🧠 AI Education: <1 sentence> ✅ 10-Minute Win: <1 sentence> 4. End with a short closing line that varies naturally (examples: "Let’s dive in." "Let’s get into it." "Let’s jump in."). Input you will receive (do not invent details): - Volume number and date: - Any timely hook (optional): - Founder’s Corner summary (1–2 sentences): - AI Education summary (1–2 sentences): - 10-Minute Win summary (1–2 sentences): Now write the intro block using ONLY the provided input. \---INPUT--- Volume number/date: Timely hook (optional): Founder’s Corner summary: AI Education summary: 10-Minute Win summary: —END INPUT--- ### The Invisible Highway: Can Claude for Healthcare Fix Healthcare’s Infrastructure Problem? URL: https://www.mindovermoney.ai/founders-corner/claude-for-healthcare-ai-infrastructure-specialty-pharmacy/ Last updated: 2026-07-13T16:59:45.000Z Last week I wrote about the rise of the "Executive Patient" and how OpenAI’s consumer launch is fundamentally changing patient behavior. We discussed a world where the patient arrives with more data, more agency, and higher expectations than ever before. But there is a glaring problem with that vision. When that empowered patient walks through the doors, they are stepping into a system that is still running on infrastructure built for a different era. For decades the healthcare industry has struggled to connect the dots. Billions have been spent digitizing records, yet we still live in a world of disconnected data islands. The payer system does not speak to the provider system and clinical data does not flow to the claims department. Our medical claims system runs on J-codes, which were introduced in 1978, long before electronic billing was introduced. Pharmacy claims run on a completely different set of electronic standards that do not connect to the medical coding systems. This lack of continuity is the root cause of the broken experiences that frustrate patients and burn out clinicians. Enter Claude for Healthcare. Anthropic quietly released a platform aimed squarely at the critical infrastructure of the healthcare enterprise. They are not trying to change how you talk to your doctor. They are trying to fix the broken highway that connects the entire industry. ### **The Billion-Dollar Disconnect** The healthcare industry has a massive infrastructure problem. Despite the widespread adoption of Electronic Health Records, clinical teams still spend 13 hours every week completing prior authorizations according to a January 2026 AMA survey. The data exists but it is trapped in static files that require human manual labor to move, read, and interpret. We have spent years trying to solve this by layering new applications on top of old foundations. But new tools and technology can only go so far when the underlying infrastructure remains siloed. We have built digital filing cabinets when what we really needed were digital bridges. You cannot expect technology to fix the foundational workflow problems that exist in healthcare. This disconnect has a price tag. The industry currently loses $496 billion annually to billing and insurance-related administrative waste. The result is a system where 93% of physicians report that prior authorization delays lead to negative clinical outcomes. The infrastructure cannot keep up with the volume of care, and it certainly cannot keep up with the new speed of the "Executive Patient." ### **Claude as the Universal Adaptor** Anthropic’s strategy is distinct because it treats AI as an operational layer rather than just a chatbot. The core value of the new platform lies in its "Connectors." Unlike a general-purpose model, Claude comes with pre-built integrations into the industry's sources of truth including the CMS Coverage Database, ICD-10 coding systems, and the NPI Registry. This connectivity allows organizations to automate the complex workflows that usually keep clinicians glued to their screens. Elation Health provides a perfect example of this in action. They embedded Claude directly into their primary care EHR to handle chart summarization. Instead of spending ten minutes clicking through tabs to review a patient's history, the physician gets an immediate, cited summary of the patient's story. The result is that clinicians are finding answers 61% faster, which allows them to stop hunting for data and start looking at the patient. On the operational side, Carta Healthcare is using the platform to tackle the heavy lift of clinical data abstraction. This is typically a manual process where staff scour records to report quality data to registries. By using Claude to automate this extraction, they reduced processing time by 66% while maintaining 99% accuracy. This effectively unblocks the flow of quality data without adding administrative overhead to the staff. These examples prove that the value of AI isn't just in writing emails. It is in automating the invisible friction of the back office so that clinical teams can return their focus to the human work of care delivery. ### **Trust as the Ultimate Feature** The final piece of this puzzle is safety. Building a digital highway for healthcare requires more than just connectivity; it requires a foundation of absolute trust. Enterprise healthcare organizations are risk-averse by design. They cannot afford to bet on a "black box" model that might hallucinate a diagnosis or expose patient data. This is where Anthropic is placing its strategic bet. They have engineered Claude on HIPAA-ready infrastructure with a "Constitutional AI" framework that acts as a permanent safety inspector. Unlike standard models that simply predict the next likely word, Claude is designed to show its work. It provides rigorous audit trails and citations, allowing a claims adjuster or a physician to trace every AI-generated output back to a specific medical guideline or coverage policy. Mike Reagin, the CTO of Banner Health, validated this approach, noting that they chose Claude specifically for its "reputation for responsible AI" and the belief that safety is "non-negotiable." In an industry where a single error can lead to a lawsuit or a denied life-saving procedure, this ability to verify the "why" behind the answer is the foundation to scalable infrastructure. ### **The Road Ahead** If OpenAI is the catalyst for patient agency, Claude is the infrastructure that makes that agency actionable. We are moving toward a future where the back office is increasingly self-driving through AI automation, offering a critical inflection point for legacy healthcare companies. The winners of the next decade will not be the organizations that simply buy the software. They will be the leaders who have the courage to experiment and fundamentally rebuild their workflows from the ground up. Business architecture will be redesigned as the scope of what is possible evolves with AI, and specifically the push for data-driven automation ecosystems like "Claude for Healthcare." The strategic imperative for legacy organizations is to use this infrastructure to strip away the administrative drudgery and redisperse their human staff to where they matter most: the patient experience. I predict that within 36 months, the labor-intensive tasks that burden operational and clinical teams will be streamlined and automated through data connection highways powered by agentic AI workflows. This will impact every sector of healthcare, from administrative staff working to clear a prior authorization, to clinicians submitting for a surgical procedure. The strategic imperative is to take those recovered hours and rebuild strategies that ensure the patient journey is supported. The true moat of the future is not your proprietary data silo; it is how you connect to the broader ecosystem to remove operational strain that slows down service and erodes trust. Automation builds the highway, but it is the human experience that will keep the Executive Patient from taking the next exit. ### Volume 16: AI Disruption Hits Healthcare URL: https://www.mindovermoney.ai/how-ai-is-disrupting-healthcare-2026-what-to-know/ Last updated: 2026-07-13T16:59:46.000Z Hey everyone! AI change is coming for every industry, but this week we are zooming in on healthcare. When AI moves into high-stakes parts of life, it stops being a novelty and starts forcing real shifts in power, expectations, and trust. 🏥 **Founder’s Corner:** Healthcare is the focus this week. I break down what OpenAI’s ChatGPT Health launch signals for the future and why I believe we are entering the era of the “Executive Patient.” 🧠 **AI Education:** ChatGPT Part 2 covers the “create and understand” layer so you know when to use Search, Deep Research, voice, and images to get consistently better results on the free tier. 🧾 **10-Minute Win:** A Tax Deduction Finder that helps you generate a realistic shortlist using IRS sources, then build a simple tracker so you are not scrambling at tax time. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Introducing ChatGPT Health**](https://openai.com/index/introducing-chatgpt-health/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI launched ChatGPT Health, a dedicated health/wellness experience where you can (optionally) connect medical records and wellness apps to ground responses in your own data. It’s positioned as support for understanding and planning—not diagnosis or treatment. **Why it matters:** This is AI moving into high-stakes daily life. If it works well, it becomes a “default tool” category for millions—while raising the bar on privacy, trust, and what people expect from AI assistants. ### **2)**[](https://blogs.nvidia.com/blog/2026-ces-special-presentation/?utm%5Fsource=chatgpt.com)[**Samsung to double mobile devices powered by Google’s Gemini to 800m units this year**](https://www.dawn.com/news/1965249?utm%5Fsource=chatgpt.com) **Summary:** Samsung says it plans to expand Gemini-powered AI features from roughly 400 million devices to 800 million in 2026 across phones/tablets—and broadly push AI across products and services. **Why it matters:** This is what “AI going mainstream” actually looks like: distribution at device scale. When AI ships by default to hundreds of millions of people, usage shifts from novelty to habit. ### **3)**[](https://x.ai/news/series-e?utm%5Fsource=chatgpt.com)[**Global AI Adoption in 2025—A Widening Digital Divide**](https://www.microsoft.com/en-us/corporate-responsibility/topics/AI-Economy-Institute/reports/Global-AI-Adoption-2025/?utm%5Fsource=chatgpt.com) **Summary:** Microsoft’s AI Economy Institute reports global generative AI use rose in H2 2025 to roughly one in six people, but adoption is uneven—growing faster in the “Global North” than the “Global South.” **Why it matters:** The next phase of AI isn’t just “who has the best model”—it’s who gets access, skills, and infrastructure. That divide is going to shape productivity, education, and competitiveness. ### **4)**[](https://www.dawn.com/news/1965249?utm%5Fsource=chatgpt.com)[**NVIDIA Rubin Platform, Open Models, Autonomous Driving**](https://blogs.nvidia.com/blog/2026-ces-special-presentation/?utm%5Fsource=chatgpt.com) **Summary:** NVIDIA used CES to spotlight its Rubin platform roadmap and a broader “AI factory” vision: faster training/inference economics, more efficient systems, and new model + autonomy pushes tied to real products. **Why it matters:** This is NVIDIA telling the market: the next wave isn’t “more GPUs,” it’s rack-scale platforms and efficiency. That shapes cloud pricing, enterprise adoption, and the economics of every major model provider. ### **5)**[](https://www.microsoft.com/en-us/corporate-responsibility/topics/AI-Economy-Institute/reports/Global-AI-Adoption-2025/?utm%5Fsource=chatgpt.com)[**xAI Raises $20B Series E**](https://x.ai/news/series-e?utm%5Fsource=chatgpt.com) **Summary:** xAI announced a $20B Series E round and framed it as fuel for aggressive compute expansion and product rollout. It highlights massive GPU cluster ambitions and says Grok 5 is currently in training. **Why it matters:** This is the clearest signal yet that the frontier AI fight is becoming a capital + compute arms race—and that the market expects only a few players to afford the next leap. ## Founder's Corner Real world learnings as I build, succeed, and fail **The Executive Patient: How OpenAI’s Health Launch is Rebuilding the Healthcare Experience** On January 7, 2026, OpenAI launched its new health platform within ChatGPT. This is more than a new feature for a tech company. It is a collision of two massive trends that the traditional healthcare system is not prepared to handle. I have spent the last 13 years in the healthcare industry working across various roles to improve the experience for healthcare consumers. Currently, I operate in the digital space to deliver experiences and platforms that bring convenience to people dealing with specialty conditions. I have seen firsthand how hard it is for the system to catch up to the consumer's needs. The reality is that healthcare consumers have been demanding change for a long time. The last decade has seen a massive rise in health tech, ranging from enterprise-scale patient apps to niche platforms focused on specific wellness needs. Consumers have shown their preference for these tools by adopting isolated digital platforms like Hims & Hers, Function Health, and Peloton. With OpenAI reporting that 230 million people already ask ChatGPT health or wellness questions each week, including 40 million daily queries in the U.S. alone, the launch of ChatGPT Health is not a new trend. It is the consolidation of a behavior that is already fully formed. Over the last decade, we have also seen massive consolidation across every sector of the healthcare industry as companies try to solve fragmentation through mergers and acquisitions. Yet, the struggle to deliver a cohesive digital experience remains. It is incredibly difficult to build a seamless platform when you are constantly integrating new systems, defining legacy data, and shifting business priorities. While the industry works through these structural hurdles, consumers are already moving toward tools that offer immediate utility and a unified experience. OpenAI’s partnership with b.well provides the underlying infrastructure that traditional health systems have struggled to build internally. This foundation effectively turns ChatGPT into the centralized health hub that consumers have been waiting for. However, my point is not to crown OpenAI as the definitive winner in this space. Instead, I am highlighting a shift where the winners of the future will be those who stop trying to own the entire journey and start figuring out how to deliver value within these AI-driven ecosystems. We are witnessing the rise of the "Executive Patient," and the system is not ready for it. Here are the four transitions that will define this new era and my predictions for how the industry must adapt. ### **Pillar 1: The End of the Patient Portal** For years, the patient portal was the industry's attempt to go digital. Instead, it created a mess of fragmented apps. Every health insurer, hospital system, pharmacy, and specialist created their own proprietary data silos to protect their information and their relationship with the patient. For a person with complex needs, access meant managing a dozen different logins and trying to piece together a story from multiple disconnected interfaces. It was a fragmented experience that prioritized the institution's silos over the patient's experience. This is exactly what b.well CEO Kristen Valdes described as "portalitis" during the launch. She noted that we have spent years expecting consumers to do the heavy lifting of data integration themselves. The release of ChatGPT Health changes that dynamic. By letting consumers sync medical records and wellness stats into a single experience, OpenAI has built a universal translator for healthcare. This creates a level of data liquidity that makes the traditional proprietary portal feel like a relic of the past. This integrated experience, which puts data directly in the healthcare consumer’s hands, will be the death of siloed apps across the healthcare ecosystem. Over the next 24 months, consumers will stop logging into proprietary portals to find answers. Instead, they will use AI hubs like ChatGPT Health as the interface to connect all of their data and wellness tools into one experience. Power dynamics will force change at a rapid pace, and while this speed is unfamiliar to the healthcare industry, it is now unavoidable. The winners of the future will be the companies that abandon the closed-loop ecosystem model. The future belongs to entities that make their data easy to interact with and focus on being the best service provider within the AI ecosystem, rather than trying to build a fragmented ecosystem of their own. ### **Pillar 2: The Rise of Patient Agency** The traditional healthcare model is built on information asymmetry. The doctor holds the clinical data, the medical training, and the authority, while the patient holds the symptoms and a hope for a solution. This dynamic is rooted in a fragmented reality where your health data is scattered across primary care visits, claims, and specialist referrals. Because these entry points rarely talk to each other, the practitioner only sees a narrow, clinical slice of the story. The gap between these clinical records and a person’s actual life is where health is truly won or lost. I have experienced this disconnect in my own health journey. While the legacy system relies on a yearly snapshot, I am generating a constant stream of high-fidelity data that my doctor cannot access. My Oura ring captures my physiological stress and sleep cycles in real time. My comprehensive blood work through Function Health provides a longitudinal look at my biology that goes far deeper than a standard blood draw through my insurance. My nutritional habits and metabolic shifts are audited through tools like Noom and MyFitnessPal. In the traditional system, these insights are invisible. They exist in a separate world from the medical record, leaving the practitioner blind to the variables that drive my health. The release of ChatGPT Health changes this dynamic by acting as the bridge between the lifestyle stream and the clinical snapshot. When a patient takes these personal insights and blends them with their medical history through an AI hub, the paradigm shifts. They are no longer arriving at the clinic to ask what is wrong. They are arriving with a 360-degree audit of their own biology to discuss what needs to change. This shift is moving faster than the industry realizes. A January 2026 survey from The Mesothelioma Center found that 52% of Americans are now using ChatGPT to analyze symptoms, and one in three would skip a doctor visit entirely if the AI characterized their risk as low. We are also seeing patients like Jennifer from Wisconsin, who recently shared with the Advisory Board how she uses AI to synthesize medical literature. She uses these insights to confront physicians who previously brushed off her concerns. This is no longer just about access to information, it is about the redistribution of agency. We are entering an era where the machine is more capable of synthesizing these massive datasets than any human brain. The patient is becoming the strategist of their own health journey, using AI as the engine to manage complexity. By the time they sit down with a specialist, they are not just a subject of the treatment plan. They are an active partner in the strategy, and the traditional hierarchy of the exam room has been replaced by a model of shared accountability. ### **Pillar 3: The Evolution of the Practitioner** Despite the rapid surge in AI capabilities, we are not at a point where technology can replace the clinician. Professional guidance remains the bedrock of a safe and effective health journey, and ChatGPT Health is an assistant to that process rather than a substitute for medical expertise. However, a patient with real agency and more information will force change across the entire healthcare industry. Practitioners will have to adapt and evolve if they want to keep and earn the trust of the executive patient. The biggest hurdle in this transition is human nature. Change is difficult, and it is impossible for all doctors to adapt at the same pace as the AI space. This uneven adoption is already creating significant friction. A January 2026 report from The Mesothelioma Center highlights this tension, revealing that 58% of healthcare professionals now say AI is making it harder to treat patients. Dr. Daniel Landau noted in MobiHealthNews that clinicians often find themselves on the defensive, needing to disprove AI-generated findings rather than focusing on a collaborative diagnosis. This shift can turn medical visits into debates, which slows down appointments and risks damaging the patient-provider relationship. This friction exists because the practitioner’s monopoly on interpretation is over. We are entering a state of clinical symmetricality where the patient may know as much about their specific health metrics as the doctor does. The clinician must shift from being "The Oracle" to becoming "The Strategic Partner." The good news is that the tools are evolving to support this shift. Athenahealth’s CMO, Dr. Nele Jessel, recently stated, "2026 is the year the EHR finally learns to think. We are moving beyond the era of the digital filing cabinet and into the era of clinical intelligence, where the software doesn't just store data, it acts as a cognitive partner to the clinician." By moving from a passive system of record to a real-time partner in intelligence, technology can begin to shoulder the cognitive load of data synthesis. The value of a doctor is shifting from being a primary data source to becoming a high-level advisor who operates in a collaborative environment. Trust is no longer a default setting based on a medical degree, it is something earned by practitioners who use AI to drive the best possible health outcomes. Doctors who remain anchored in the past and refuse to adapt will struggle in the healthcare model that is evolving in front of our eyes. ### **Pillar 4: The Living Health Record** The final pillar of this transformation is a fundamental change in how we store and interact with our medical history. For decades, the medical record has been treated like a static file locked in a high-security vault. It was something owned by the hospital or the insurer, and accessing it required the patient to navigate a labyrinth of requests and approvals. The launch of ChatGPT Health represents a "Napster moment" for the healthcare industry. By using b.well as a technical backbone, OpenAI has utilized FHIR APIs to bypass the need for individual hospitals to approve data sharing. The consumer simply hits "connect," and the walls of the silo finally crumble. This transition turns our medical history into a "chatable" entity. Your health data is no longer a collection of static PDFs or disconnected laboratory results, but a living record that stays with you. OpenAI has designed its Health Sidebar to be isolated and encrypted, which creates a permanent history that is not tied to a specific insurance plan or a single employer. This is a fundamental shift in ownership. Data is no longer a static piece of information that belongs to a facility, it is a living entity that travels with the human rather than staying with the provider. The implications of a truly portable health record are massive. When your history is no longer trapped in a specific system, the friction of changing doctors or switching insurance plans disappears. We are moving toward a future where the consumer is the primary custodian of their clinical truth. Because this record is "chatable," the patient can query their own history to find patterns, identify gaps in care, or prepare for a consultation in seconds. The era of the vaulted file is ending, and in its place, we are seeing the rise of a decentralized, intelligent, and highly portable record that functions as the connective tissue for a person’s entire life. This is the final piece of the puzzle for the executive patient, and it is the catalyst that will force the rest of the healthcare ecosystem to finally prioritize the consumer over the institution. ### **The Path Forward: A Mandate for Leadership** Nothing is certain in the current landscape, and there are no easy ways to predict exactly how these trends will unfold. However, we are no longer looking at minor ripples. There are clear signals that the healthcare landscape is undergoing a fundamental restructuring, driven by the collision of AI technologies and the pattern shifts in human behavior taking place right in front of us. It is now a strategic imperative for leaders across every part of the healthcare journey to understand how these changes will impact their business and their interactions with the consumer. The "Executive Patient" is already here, and they are looking for partners who can keep pace with their new expectations for data liquidity and transparency. Change of this magnitude can be uncomfortable, but the future it promises is exciting. Organizations that continue to prioritize siloed experiences will find themselves increasingly invisible to the modern consumer. In order to be great in this new era, you have to figure out how to adapt. The winners will not be those who fought to maintain the status quo, but those who had the foresight to evolve alongside the people they serve. ## AI Education for You ChatGPT, Part 2 : Create and Understand In Part 1, you learned the big idea: ChatGPT is not one tool. It is a workspace with multiple modes, each built for a different job. This week is the create and understand layer. These are the tools that move you from quick chat answers to real-world usefulness: looking things up, doing deeper research when needed, making sense of products, speaking instead of typing, and working with images. Once you know when to use which mode, you stop fighting the product and start getting consistently better results. ### **Feature Index** | Feature | Free status | Where | Why you should care | | -------------------- | ---------------------------------------------------------------- | ------------------------- | -------------------------------- | | ChatGPT Search | Free | Web, mobile, desktop | Current answers with sources | | Deep Research | Available on select plans in supported countries and territories | Varies | Deeper research with citations | | Shopping with Search | Free | Web, mobile, desktop | Fast comparisons and options | | Shopping research | Available with limits | In ChatGPT via tools menu | Structured shopping decisions | | Voice conversations | Free with limits | Varies by app and device | Talk naturally, hands-free | | Image understanding | Free with limits | Web, mobile, desktop | Help from screenshots and photos | | Image generation | Free with limits | Web, iOS, Android | Create visuals quickly | | My images library | Free | Web, iOS, Android | Find and reuse image outputs | ## **Group 1: Find current information** ### **ChatGPT Search** A mode that searches the web and answers with sources, so you can see where the information came from. Plain chat can be wrong or outdated. Search reduces guessing when you need current facts. Where to find it: Open the tools list and choose Search, or ask a question that clearly needs current information. When to use it: - You want up-to-date info and sources. - You want a quick answer, not a long report. - You want to verify the sources yourself. ## **Group 2: Go deeper when the question is complex** ### **Deep Research** Deep Research is a mode for complex, multi-step research. It searches the public web, can use files you upload, and produces a documented report with citations. Is it available on Free?OpenAI says Deep Research is available on select plans in supported countries and territories, and usage varies by plan. If you do not see Deep Research in your tools menu, it is not enabled on your account right now. Where to find it: Select Deep Research from the tools menu, then describe the task. When to use it: - The question needs many sources, not one. - You need synthesis, not a quick fact. - You want a longer answer with citations you can verify. **Search vs Deep Research** Use this simple rule: - Use Search for a fast answer with sources. - Use Deep Research for a thorough report with citations, when it is available on your plan. ## **Group 3: Make decisions with constraints** ### **Shopping with Search** When you ask shopping questions, Search can return product-style results and comparisons. When to use it - You want a shortlist fast. - You are still exploring options. ### **Shopping research** Shopping research is designed for deeper decisions where constraints and tradeoffs matter. It asks follow-up questions to narrow your needs. Where to find it: Select Shopping Research from the tools menu, then describe what you want to shop for. When to use it - You have a real budget ceiling. - You have must-haves and deal-breakers. - You want the best for me, not the most popular. ## **Group 4: Talk instead of type** ### **Voice conversations** Voice lets you have a spoken conversation with ChatGPT. Voice lowers friction. It is useful when you want to think out loud and have an interactive session. Voice has usage limits and the experience can change over time. Voice can also make mistakes with names and numbers, so check important details. ## **Group 5: Work with images** ### **Image understanding** You upload a screenshot or photo, then ask ChatGPT to explain it, summarize it, or help you interpret it. Free users have limited uploads per day, and limits can tighten during peak hours. A simple personal-finance example: You screenshot a bank fee page because the wording is confusing.You ask: Explain this in plain English. Then list the fees I should watch for. ### **Image generation** You describe an image, and ChatGPT generates it. Images you create can be saved in your image library for reuse. Free tier image generation is limited. Simpler prompts usually work better. ### **My images library** A place where images you create are saved, so you can browse and reuse them without hunting through old chats. ## **One-screen recap** - Use Search when you need up-to-date info and sources. - Use Deep Research when you need multi-step synthesis and a documented report - Use Shopping with Search for quick product shortlists. - Use Shopping research when constraints and tradeoffs matter. - Use Voice to think out loud, but double-check important details. - Use Images to understand screenshots and create visuals, with Free-tier limits. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🧾 Tax Deduction Finder** Most people either miss deductions they *could* claim, or waste time chasing ones they can’t. The trick is filtering: your job status + your filing situation determines what’s even possible. In 10 minutes, you’ll use ChatGPT + IRS sources to produce a clean “deduction shortlist,” the exact questions you need to answer to qualify, and a tracker to collect proof (so you’re not scrambling at tax time). ### **Step 1 — Identify your “tax profile” (2 minutes)** Write 5 bullets (rough is fine): - **Income type:** W-2 employee / 1099 contractor / both / landlord / small business - **Filing status:** single / married / HOH - **State:** (helps for state-specific ideas, but we’ll focus on federal) - **Big life events this year:** moved, new baby, bought/sold home, major medical, ducation, side hustle started, etc. - **How you track expenses today:** none / bank app / spreadsheet / receipts Important: If you’re a W-2 employee, most “unreimbursed employee expenses” are not deductible under current rules, with limited exceptions (certain specific worker categories).[ IRS](https://www.irs.gov/publications/p529?utm%5Fsource=chatgpt.com) ### **Step 2 — Have ChatGPT generate your deduction shortlist (3 minutes)** Copy/paste this prompt into ChatGPT: **Role:* You are my “Tax Deduction Finder.”*Goal:* Create a realistic list of potential U.S. federal tax deductions/adjustments/credits I should check, based on my job/status.* ***My tax profile:*** - *Income type: \_\_\_* - *Filing status: \_\_\_* - *State: \_\_\_* - *Life events: \_\_\_* - *Expense tracking method: \_\_\_* ***Rules (must follow):*** 1. *Do not guarantee eligibility. Treat everything as “possible—verify.”* 2. *Use IRS.gov as the primary source. If you mention a category, include the most relevant IRS publication/page name.* 3. *Separate into sections: Likely, Maybe, Unlikely/Not allowed (with reason).* 4. *For each item, provide:* - *What it is (1 sentence)* - *Who typically qualifies (1 sentence)* - *What proof to keep (receipts/logs/forms)* - *What would disqualify it (1 sentence)* **Output format:* a clean table.* ### **Step 3 — Auto-create your “Deduction Tracker” table for Google Sheets (3 minutes)** Now paste this follow-up prompt: *Build me a Deduction Tracker I can paste into Google Sheets.Output CSV only (no commentary).* *Columns:Category, Potential Deduction/Credit, Why It Might Apply (1 line), Eligibility Questions (3 short bullets), Proof to Keep, IRS Source to Verify, Est. $ Impact (Low/Med/High), Status (Researching/Confirmed/Not eligible), Notes* *Fill 8–12 rows using the Likely and Maybe items from my shortlist. Leave “Est. $ Impact” as Low/Med/High (no dollar guesses).* *Make the “Eligibility Questions” concise so I can answer them quickly.* Then: 1. Copy the CSV output 2. Open a blank Google Sheet → click A1 → paste 3. You now have a tracker that guides what to verify and what documents to save. ### **Step 4 — Do a 2-minute “IRS reality check” (2 minutes)** Pick your top 2 items from the tracker and verify them fast: 1. Click the IRS Source to Verify entry (or search the exact publication name on IRS.gov). 2. Confirm the key rule(s) and exceptions. For example: - Home office rules are very specific (see IRS guidance).[ IRS](https://www.irs.gov/publications/p587?utm%5Fsource=chatgpt.com) - Travel/car records require substantiation and good recordkeeping.[ IRS](https://www.irs.gov/publications/p463?utm%5Fsource=chatgpt.com) - Unreimbursed employee expenses are generally not deductible for most W-2 employees, with limited exceptions.[ IRS](https://www.irs.gov/publications/p529?utm%5Fsource=chatgpt.com) 3. Update your Sheet status: Confirmed or Not eligible. This step prevents “TikTok deductions” from wasting your time. ## **The Payoff** You end with a personalized deduction shortlist that’s grounded in your job/status, plus a simple tracker that tells you exactly what to verify and what proof to keep. That’s how you avoid two costly outcomes: missing legit deductions *and* claiming shaky ones that create audit stress. ## **Transparency & Notes for Readers** - Free tools only: ChatGPT + IRS.gov (+ optional Google Sheets). - Not tax advice: This workflow helps you organize questions and documentation; it does not replace a CPA/EA. - Rules change: Always verify against IRS sources for the tax year you’re filing.[ IRS](https://www.irs.gov/publications/p529?utm%5Fsource=chatgpt.com) - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### The Executive Patient: How OpenAI’s Health Launch is Rebuilding the Healthcare Experience URL: https://www.mindovermoney.ai/founders-corner/chatgpt-health-launch-executive-patient-healthcare-ai/ Last updated: 2026-07-13T16:59:46.000Z On January 7, 2026, OpenAI launched its new health platform within ChatGPT. This is more than a new feature for a tech company. It is a collision of two massive trends that the traditional healthcare system is not prepared to handle. I have spent the last 13 years in the healthcare industry working across various roles to improve the experience for healthcare consumers. Currently, I operate in the digital space to deliver experiences and platforms that bring convenience to people dealing with specialty conditions. I have seen firsthand how hard it is for the system to catch up to the consumer's needs. The reality is that healthcare consumers have been demanding change for a long time. The last decade has seen a massive rise in health tech, ranging from enterprise-scale patient apps to niche platforms focused on specific wellness needs. Consumers have shown their preference for these tools by adopting isolated digital platforms like Hims & Hers, Function Health, and Peloton. With OpenAI reporting that 230 million people already ask ChatGPT health or wellness questions each week, including 40 million daily queries in the U.S. alone, the launch of ChatGPT Health is not a new trend. It is the consolidation of a behavior that is already fully formed. Over the last decade, we have also seen massive consolidation across every sector of the healthcare industry as companies try to solve fragmentation through mergers and acquisitions. Yet, the struggle to deliver a cohesive digital experience remains. It is incredibly difficult to build a seamless platform when you are constantly integrating new systems, defining legacy data, and shifting business priorities. While the industry works through these structural hurdles, consumers are already moving toward tools that offer immediate utility and a unified experience. OpenAI’s partnership with b.well provides the underlying infrastructure that traditional health systems have struggled to build internally. This foundation effectively turns ChatGPT into the centralized health hub that consumers have been waiting for. However, my point is not to crown OpenAI as the definitive winner in this space. Instead, I am highlighting a shift where the winners of the future will be those who stop trying to own the entire journey and start figuring out how to deliver value within these AI-driven ecosystems. We are witnessing the rise of the "Executive Patient," and the system is not ready for it. Here are the four transitions that will define this new era and my predictions for how the industry must adapt. ### **Pillar 1: The End of the Patient Portal** For years, the patient portal was the industry's attempt to go digital. Instead, it created a mess of fragmented apps. Every health insurer, hospital system, pharmacy, and specialist created their own proprietary data silos to protect their information and their relationship with the patient. For a person with complex needs, access meant managing a dozen different logins and trying to piece together a story from multiple disconnected interfaces. It was a fragmented experience that prioritized the institution's silos over the patient's experience. This is exactly what b.well CEO Kristen Valdes described as "portalitis" during the launch. She noted that we have spent years expecting consumers to do the heavy lifting of data integration themselves. The release of ChatGPT Health changes that dynamic. By letting consumers sync medical records and wellness stats into a single experience, OpenAI has built a universal translator for healthcare. This creates a level of data liquidity that makes the traditional proprietary portal feel like a relic of the past. This integrated experience, which puts data directly in the healthcare consumer’s hands, will be the death of siloed apps across the healthcare ecosystem. Over the next 24 months, consumers will stop logging into proprietary portals to find answers. Instead, they will use AI hubs like ChatGPT Health as the interface to connect all of their data and wellness tools into one experience. Power dynamics will force change at a rapid pace, and while this speed is unfamiliar to the healthcare industry, it is now unavoidable. The winners of the future will be the companies that abandon the closed-loop ecosystem model. The future belongs to entities that make their data easy to interact with and focus on being the best service provider within the AI ecosystem, rather than trying to build a fragmented ecosystem of their own. ### **Pillar 2: The Rise of Patient Agency** The traditional healthcare model is built on information asymmetry. The doctor holds the clinical data, the medical training, and the authority, while the patient holds the symptoms and a hope for a solution. This dynamic is rooted in a fragmented reality where your health data is scattered across primary care visits, claims, and specialist referrals. Because these entry points rarely talk to each other, the practitioner only sees a narrow, clinical slice of the story. The gap between these clinical records and a person’s actual life is where health is truly won or lost. I have experienced this disconnect in my own health journey. While the legacy system relies on a yearly snapshot, I am generating a constant stream of high-fidelity data that my doctor cannot access. My Oura ring captures my physiological stress and sleep cycles in real time. My comprehensive blood work through Function Health provides a longitudinal look at my biology that goes far deeper than a standard blood draw through my insurance. My nutritional habits and metabolic shifts are audited through tools like Noom and MyFitnessPal. In the traditional system, these insights are invisible. They exist in a separate world from the medical record, leaving the practitioner blind to the variables that drive my health. The release of ChatGPT Health changes this dynamic by acting as the bridge between the lifestyle stream and the clinical snapshot. When a patient takes these personal insights and blends them with their medical history through an AI hub, the paradigm shifts. They are no longer arriving at the clinic to ask what is wrong. They are arriving with a 360-degree audit of their own biology to discuss what needs to change. This shift is moving faster than the industry realizes. A January 2026 survey from The Mesothelioma Center found that 52% of Americans are now using ChatGPT to analyze symptoms, and one in three would skip a doctor visit entirely if the AI characterized their risk as low. We are also seeing patients like Jennifer from Wisconsin, who recently shared with the Advisory Board how she uses AI to synthesize medical literature. She uses these insights to confront physicians who previously brushed off her concerns. This is no longer just about access to information, it is about the redistribution of agency. We are entering an era where the machine is more capable of synthesizing these massive datasets than any human brain. The patient is becoming the strategist of their own health journey, using AI as the engine to manage complexity. By the time they sit down with a specialist, they are not just a subject of the treatment plan. They are an active partner in the strategy, and the traditional hierarchy of the exam room has been replaced by a model of shared accountability. ### **Pillar 3: The Evolution of the Practitioner** Despite the rapid surge in AI capabilities, we are not at a point where technology can replace the clinician. Professional guidance remains the bedrock of a safe and effective health journey, and ChatGPT Health is an assistant to that process rather than a substitute for medical expertise. However, a patient with real agency and more information will force change across the entire healthcare industry. Practitioners will have to adapt and evolve if they want to keep and earn the trust of the executive patient. The biggest hurdle in this transition is human nature. Change is difficult, and it is impossible for all doctors to adapt at the same pace as the AI space. This uneven adoption is already creating significant friction. A January 2026 report from The Mesothelioma Center highlights this tension, revealing that 58% of healthcare professionals now say AI is making it harder to treat patients. Dr. Daniel Landau noted in MobiHealthNews that clinicians often find themselves on the defensive, needing to disprove AI-generated findings rather than focusing on a collaborative diagnosis. This shift can turn medical visits into debates, which slows down appointments and risks damaging the patient-provider relationship. This friction exists because the practitioner’s monopoly on interpretation is over. We are entering a state of clinical symmetricality where the patient may know as much about their specific health metrics as the doctor does. The clinician must shift from being "The Oracle" to becoming "The Strategic Partner." The good news is that the tools are evolving to support this shift. Athenahealth’s CMO, Dr. Nele Jessel, recently stated, "2026 is the year the EHR finally learns to think. We are moving beyond the era of the digital filing cabinet and into the era of clinical intelligence, where the software doesn't just store data, it acts as a cognitive partner to the clinician." By moving from a passive system of record to a real-time partner in intelligence, technology can begin to shoulder the cognitive load of data synthesis. The value of a doctor is shifting from being a primary data source to becoming a high-level advisor who operates in a collaborative environment. Trust is no longer a default setting based on a medical degree, it is something earned by practitioners who use AI to drive the best possible health outcomes. Doctors who remain anchored in the past and refuse to adapt will struggle in the healthcare model that is evolving in front of our eyes. ### **Pillar 4: The Living Health Record** The final pillar of this transformation is a fundamental change in how we store and interact with our medical history. For decades, the medical record has been treated like a static file locked in a high-security vault. It was something owned by the hospital or the insurer, and accessing it required the patient to navigate a labyrinth of requests and approvals. The launch of ChatGPT Health represents a "Napster moment" for the healthcare industry. By using b.well as a technical backbone, OpenAI has utilized FHIR APIs to bypass the need for individual hospitals to approve data sharing. The consumer simply hits "connect," and the walls of the silo finally crumble. This transition turns our medical history into a "chatable" entity. Your health data is no longer a collection of static PDFs or disconnected laboratory results, but a living record that stays with you. OpenAI has designed its Health Sidebar to be isolated and encrypted, which creates a permanent history that is not tied to a specific insurance plan or a single employer. This is a fundamental shift in ownership. Data is no longer a static piece of information that belongs to a facility, it is a living entity that travels with the human rather than staying with the provider. The implications of a truly portable health record are massive. When your history is no longer trapped in a specific system, the friction of changing doctors or switching insurance plans disappears. We are moving toward a future where the consumer is the primary custodian of their clinical truth. Because this record is "chatable," the patient can query their own history to find patterns, identify gaps in care, or prepare for a consultation in seconds. The era of the vaulted file is ending, and in its place, we are seeing the rise of a decentralized, intelligent, and highly portable record that functions as the connective tissue for a person’s entire life. This is the final piece of the puzzle for the executive patient, and it is the catalyst that will force the rest of the healthcare ecosystem to finally prioritize the consumer over the institution. ### **The Path Forward: A Mandate for Leadership** Nothing is certain in the current landscape, and there are no easy ways to predict exactly how these trends will unfold. However, we are no longer looking at minor ripples. There are clear signals that the healthcare landscape is undergoing a fundamental restructuring, driven by the collision of AI technologies and the pattern shifts in human behavior taking place right in front of us. It is now a strategic imperative for leaders across every part of the healthcare journey to understand how these changes will impact their business and their interactions with the consumer. The "Executive Patient" is already here, and they are looking for partners who can keep pace with their new expectations for data liquidity and transparency. Change of this magnitude can be uncomfortable, but the future it promises is exciting. Organizations that continue to prioritize siloed experiences will find themselves increasingly invisible to the modern consumer. In order to be great in this new era, you have to figure out how to adapt. The winners will not be those who fought to maintain the status quo, but those who had the foresight to evolve alongside the people they serve. ### Steal My Prompt Vol. 16: The Error Catcher URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-16-the-error-catcher/ Last updated: 2026-07-13T16:59:46.000Z Mistakes are inevitable in any endeavor. Fortunately, this particular oversight had a minimal impact on the quality of "Signals Over Noise." I recently realized that I lacked a formal prompt strategy, as I had been requesting articles casually each week. In the spirit of building in public, I have developed a reusable prompt for this section and am sharing it with you today. --- ### Signals Over Noise Reusable Prompt ROLE You are my editor-researcher for Neural Gains Weekly. Create the “Signals Over Noise” section: 5 AI stories that cut through noise for AI beginners and AI-curious professionals. CONTEXT Volume: \[VOLUME #\] Time window: LAST \[7\] DAYS (hard rule) Audience: Beginners + AI-curious professionals Tone: Direct, practical, plain English PRIMARY OBJECTIVE Pick the best 5 AI articles from the last 7 days that are: - widely relevant in the mainstream AI news cycle (popular this week), - beginner-friendly to understand, - meaningful (changes products, work, policy, markets, infrastructure, safety, or society), - NOT behind a paywall, - from reputable sources, - and have stable, working links. HARD RULES (NON-NEGOTIABLES) 1. No hallucinations. No guessing. If you can’t verify, omit it. 2. Only include articles published within the last 7 days. 3. Only include articles that are NOT behind a paywall. - If a story is important but the best source is paywalled, replace it with a reputable non-paywalled alternative covering the same news. 4. Reputable sources only: - Prefer: official org blogs, government sites, AP/Reuters (or reputable free mirrors), The Verge, TechCrunch, BBC, Axios, major universities/labs. - Avoid low-quality aggregators. If you must use one as the only free mirror, label it clearly and keep it to one item max. 5. No duplicates: don’t select 2+ items that are basically the same story. 6. Link integrity matters: provide URLs that a normal reader can open. SELECTION TARGET (CONTENT MIX) Aim for a balanced set of 5: - 1–2 product/model/platform updates - 1–2 business/infra/markets (chips, capex, partnerships, data centers) - 1 policy/legal/regulation/safety/governance - 1 real-world adoption / consumer impact / workplace impact If the week is dominated by one theme, you can skew, but still avoid five of the same category. PROCESS (DO THIS QUIETLY) - Scan the AI news cycle and what’s being widely discussed. - Build a candidate pool, then narrow to the best 5 based on: impact + credibility + beginner value + popularity + freshness. - Verify: publish date, no paywall, working link, and factual accuracy for each. OUTPUT FORMAT (MUST MIRROR PRIOR VOLUMES — GHOST READY) Start with: # 🔎 Signals Over Noise — Volume \[VOLUME #\] *We scan the noise so you don’t have to — these 5 stories will keep you sharp and up to speed.* Then exactly 5 items, in this exact structure: ### 1) Headline *(Publisher)* **Summary:** 1–2 sentences. Plain English. Accurate. No fluff. **Why it matters:** 1–2 sentences. Broad and directly tied to the article (do not force personal finance/investing). Repeat for items 2–5. QUALITY CHECK BEFORE YOU SEND - Exactly 5 items - All within last 7 days - All non-paywalled - All links work - No duplicates - Beginner-friendly - “Why it matters” is concrete and article-specific ### Volume 15: New Year, New AI Habits URL: https://www.mindovermoney.ai/ai-habits-to-build-in-2026-non-technical-professionals/ Last updated: 2026-07-13T16:59:46.000Z Happy New Year! Kicking off 2026 with AI is one of the smartest ways to start the year, because small, consistent reps compound fast.This issue is designed to help you set clear intentions and turn them into systems you can actually execute. 🧭 Founder’s Corner: My 5 AI resolutions for 2026, focused on distribution, automation, data architecture, social growth, and creativity. 🧠 AI Education: ChatGPT Part 1, a beginner-friendly tour of the Free tier so you stop guessing and start using models, modes, and limits with intention. 🧺 10-Minute Win: Thematic Basket Builder, a fast workflow to build a trend-based watchlist in minutes and validate tickers in Google Sheets. Let’s dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Exclusive: Groq investor sounds alarm on data centers**](https://www.axios.com/2025/12/29/groq-alex-davis-data-center-concerns?ref=mindovermoney.ai) **Summary:** A Groq investor warns that too many data centers are being built without committed tenants, calling the “build it and they will come” strategy a trap and predicting a financing crisis in 2027–2028 for speculative landlords. **Why it matters:** The AI boom is increasingly an infrastructure + financing story (power, land, capex). If funding tightens, it hits chips, cloud capacity, and software multiples fast. ### **2)**[ **Apple faces high stakes in 2026 as AI-powered Siri launch slips**](https://finance.yahoo.com/news/apple-faces-high-stakes-2026-112251239.html?ref=mindovermoney.ai) **Summary:** Apple is aiming to launch its long-delayed AI-upgraded Siri in 2026, a pivotal part of “Apple Intelligence.” Analysts frame it as one of Apple’s most important software releases in years, tied to sparking another iPhone upgrade cycle (with newer devices required for on-device AI). **Why it matters:** If Apple nails Siri, it can reignite the upgrade engine and defend its ecosystem. If it misses again, the market narrative shifts from “late but fine” to “structurally behind” in AI. ### **3)**[ **Bernie Sanders slams Amazon for replacing workers with automation: “Maybe it’s time to tax robots”**](https://www.msn.com/en-in/money/topstories/bernie-sanders-slams-amazon-for-replacing-workers-with-automation-maybe-its-time-to-tax-robots/ar-AA1TfFpi?ref=mindovermoney.ai) **Summary:** Sanders argues companies like Amazon are incentivized to replace workers because robots don’t require wages or benefits, and he calls for taxing automation (“robots”) and using the revenue to support working families impacted by job displacement. **Why it matters:** This is a clean signal that AI/automation is becoming a tax + labor policy fight, not just a tech story. Expect more pressure on hyperscalers and data-center buildouts as “who wins, who loses” becomes political.[ ](https://www.benzinga.com/markets/tech/25/12/49623963/bernie-sanders-says-companies-like-amazon-would-replace-workers-with-robots-because-they-dont-need-wages-or-benefits-its-time-to-tax-robots?ref=mindovermoney.ai) ### **4)**[ **Sam Altman is hiring someone to worry about the dangers of AI**](https://www.theverge.com/news/850537/sam-altman-openai-head-of-preparedness?ref=mindovermoney.ai) **Summary:** OpenAI is hiring a Head of Preparedness to forecast and mitigate severe-risk scenarios (mental health harms, cybersecurity misuse, and frontier capability risks), building a more operational safety pipeline as models accelerate.[ ](https://www.theverge.com/news/850537/sam-altman-openai-head-of-preparedness?ref=mindovermoney.ai) **Why it matters:** Agents are moving into real systems (browsers, files, workflows). Safety/evals are becoming org chart + budget items, and that’s going to shape what enterprises will (and won’t) deploy.[ ](https://www.theverge.com/news/850537/sam-altman-openai-head-of-preparedness?ref=mindovermoney.ai) ### **5)**[ **CES 2026 Preview: 6 biggest trends to watch**](https://www.tomsguide.com/tech-events/ces-2026-preview-what-to-expect?ref=mindovermoney.ai) **Summary:** A practical preview of CES 2026’s biggest themes—AI baked into laptops, TVs, wearables/health tech, smart-home gear, and plenty of robotics—plus what to watch from the major players. **Why it matters:** CES is the “AI distribution” show: it’s where AI becomes defaults in devices people already buy. The winners won’t be the flashiest demos; they’ll be the products that make AI invisible and useful. ## Founder's Corner Real world learnings as I build, succeed, and fail 2025 was a year of nonstop AI change, and it forced me to learn through the noise instead of waiting for clarity. I used the past 12 months to build a foundation so I can move faster and build smarter systems in 2026\. There are still concepts I want to understand, tools I want to pressure-test, and workflows I want to automate. The start of the new year is the perfect time to set intentions and turn them into goals I can actually track. Building in public keeps me accountable to this community and to myself. In 2026, my focus is simple: build better systems, teach what I’m learning, and grow this platform with intention. Here are my five AI resolutions for 2026. **Resolution 1: Upgrade the website into a true distribution hub** I’ve discovered several bugs and gaps while navigating [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai) that make the site harder to use and harder to find. One issue is discoverability in search engines and AI scrapers, which I’ve started to address with custom meta tags on each post. In 2026, SEO and AI searchability will be a core part of my distribution strategy. My plan is to test a few specific ways to drive organic traffic, with the goal of turning that traffic into new subscribers. I’ll use Ghost analytics to track what’s working and make adjustments in real time. This focus sets the site up for long-term growth, but it will require constant iteration. I also want the site to guide people to the best content fast, so new visitors understand what Neural Gains Weekly is and why it’s worth subscribing. I won’t grow subscribers if the site is difficult to navigate. There’s a glaring issue I need to solve: the signup process is fragmented, and the dreaded spam problem keeps getting in the way. Ghost requires email confirmation to complete a subscription, but the base code that drives the experience does a poor job of making that clear. Even worse, the confirmation email often lands in spam, which lowers the odds that someone completes their subscription. In 2026, one of my first projects is building a landing page that makes subscribing simple and sets expectations from the start. It will walk people through completing registration and show them how to keep future emails out of spam. This should stabilize onboarding and dramatically increase the number of people who complete the signup process. **Resolution 2: Automate workflows behind the newsletter** Creating content for *Neural Gains Weekly* is time-consuming, and it can be hard to carve out time for other projects. I need to make time to focus on these resolutions and execute the projects that align with my 2026 goals. How do I free up time while building new skills? Automation. There are already tools that can automate pieces of my workflow. And as AI agents improve, I expect even more opportunities to open up. My initial step is figuring out where to start, which really means choosing the single easiest task to automate first. The goal is not to build a full one-button newsroom overnight. It’s to automate one small piece, prove it works, then stack wins from there. Next, I’ll pick the simplest tech stack that gets the job done, ideally without adding more subscriptions. The ultimate goal is to automate as much of the manual work as possible, so I can spend more time building, writing, and experimenting. **Resolution 3: Learn data architecture so I can build smarter with AI** Education has always been the foundation of how I grow. I’m naturally curious, and I like to understand the why and the how. AI has introduced a new set of concepts that will matter for my growth, both personally and professionally. My first challenge in 2026 is learning data architecture, especially as agentic AI starts to reshape the infrastructure underneath modern systems. I’m fortunate to work on IT and AI projects in my career, so I’ve picked up the basics of data architecture. It’s a concept I naturally gravitate toward, but I still lack depth. A focused learning plan will be crucial if I want to build and deploy agentic systems. It will also matter as I build automation, because I need to understand how tools connect, whether that’s through APIs or MCP. I view this as a mandatory curriculum, and it will only become more relevant as the AI ecosystem evolves in 2026. **Resolution 4: Build a social media strategy that drives growth** This is the simplest resolution, and also the most challenging. I’ve grown to 106 subscribers through direct engagement with friends, family and coworkers. I’ve picked up a handful through LinkedIn, but that channel is only as good as the effort I put into it. 2026 will be the year I commit to a social media strategy that drives traffic to the site and newsletter subscriptions. My main focus is LinkedIn, where I’ll consistently share how I’m thinking about AI, engage with other AI leaders, and market my content. I also plan to research how the algorithm works so I can expand reach and drive engagement beyond my current network. X (formerly Twitter) will be my secondary focus. Admittedly, I’ve put very little effort into this platform. I have a big decision to make: do I keep posting from the MindOverMoney.ai account, or switch to my personal account? There are pros and cons either way, but the bigger issue is consistency. I need to pick a lane and stick with it. I’m treating this like training: small reps every week, tracked over time, until consistency becomes automatic. **Resolution 5: Let creativity drive the build** I’ve written about this before, but AI has unlocked a creative side to me that’s been dormant. Creative projects keep me balanced and motivated to show up each week. I have a list of projects that could supplement the newsletter and add new formats to the ecosystem I’m building. I need to prioritize that list and choose projects that genuinely add to my growth. But I also want to enjoy the process and make space for creativity. This resolution is for me. It’s how I plan to keep my energy and commitment high all year. Balance will be important if I want to stay grounded and focused as the pace of AI keeps accelerating. ## AI Education for You ****ChatGPT Part 1: Getting Oriented** If you have only used ChatGPT as a “question box,” you are seeing maybe 20 percent of what it can do. Up to now, we have been building the foundation, including the full large language model series. You now understand the building blocks behind tools like ChatGPT. Now we pivot. In 2026, we are going to learn the platforms themselves, starting with ChatGPT’s Free tier. This 4-part series is a guided tour of what is possible, what is limited, and what is worth trying first. The goal is simple: stop guessing, and start using the product with intention. ## **Feature Index (Part 1)** | Feature or mode | Free status | Where | Why you should care | | ---------------------------------------- | ----------------------------------- | -------------------- | ------------------------------------------- | | GPT-5.2 access (default model) | Free with limits | Web, mobile, desktop | Better answers, but capped usage | | “Thinking” behavior (deeper reasoning) | Availability varies | Varies | Helps you get more careful answers | | Model picker (manual selection) | Paid plans | Web, mobile, desktop | Lets you choose Instant vs Thinking | | ChatGPT Search (web search with links) | Free | Web, mobile, desktop | Reduces guessing with sources | | Shopping with Search (product carousels) | Free | Web, mobile, desktop | Fast product discovery and comparisons | | Shopping research (buyer’s guide flow) | Free (limits can change) | Web, iOS, Android | Deeper purchase decisions with constraints | | Deep Research (multi-step report mode) | Not free (select paid plans) | Varies | Produces documented, cited reports | | Agent mode (acts on the web for you) | Not free (paid plans) | Web, mobile, desktop | Can take actions online, with confirmations | | File uploads | Free with limits | Web, mobile, desktop | Lets ChatGPT read your documents | | Data analysis on files | Free with limits (tool limits vary) | Varies | Turns tables into insights | | Image generation | Free with limits | Web, mobile, desktop | Create visuals from text prompts | | Projects | Free with limits | Varies | Organize long-running work | | Canvas | Free | Web, desktop | Best for drafting and editing | | GPT Store (use GPTs) | Free with limits | Web, mobile, desktop | Use specialized experiences made by others | | Custom Instructions | Free | Web, mobile, desktop | Set response preferences once | | Memory | Free (features vary) | Varies | Less repetition over time | | Temporary Chat | Free | Varies | Clean slate conversation mode | | Voice | Free with limits | Varies | Talk instead of type | ## **The model story (what is answering you)** ### **GPT-5.2 on Free** Free tier includes access to GPT-5.2, but it is capped. When you hit the GPT-5.2 message limit, ChatGPT will switch you to a smaller model until your limit resets. ### **Do Free users get to pick models?** In general, manual model selection is a paid-plan feature. Free is primarily the default experience. ### **Why models relate to cost** Better models cost more to run. More capability usually means more compute per answer. That is why the Free tier is generous, but capped. ## **What “Thinking” means** “Thinking” is not a separate product. It is a deeper reasoning behavior. It is used when a task is complex or when a user selects a deeper option in a model picker (more common on paid plans). The important thing to understand is this: deeper thinking usually means slower, more careful answers. ## **How to “trigger” deeper thinking with your prompts** You cannot always manually turn on a deeper thinking mode on the Free tier. But you can nudge the system to respond more carefully by making the task feel like it requires deeper work. Here are prompt signals that usually lead to a more thoughtful answer: - **Ask for a plan before the answer.**First, outline the steps you will follow. Then answer. - **Force tradeoffs.**Give me two options. Compare them. Then recommend one and explain why. - **Add real constraints.**Keep it under 150 words. Use a table. Do not make assumptions. If you are missing info, ask me questions first. - **Ask for verification behavior.**List what you are unsure about, and what you would need to confirm it. - **Ask for an error check.**After you write the answer, review it for mistakes and rewrite anything unclear. ## **The modes story (ChatGPT is not one thing)** A beginner-friendly way to understand ChatGPT is: one app, multiple modes. ### **Mode 1: Standard chat** Fast back-and-forth. Great for writing, planning, learning, and brainstorming. ### **Mode 2: Search** Search is for up-to-date information with links. It is the mode you use when you want the model to look things up instead of guessing. ### **Mode 3: Shopping research** Shopping research is a deeper buyer’s-guide experience. It asks follow-up questions, searches products, and summarizes tradeoffs. It is available on Free, but limits can change. ### **Mode 4: Deep Research** Deep Research is designed for multi-step, in-depth research that produces a documented report with citations. This is not positioned as a Free feature today. ### **Mode 5: Agent mode (paid)** Agent mode is the “take actions for me” mode. It can browse websites and take steps on your behalf, while pausing for clarification or confirmation. This is paid-only today. ## **Free tier limits and upgrade triggers** ### **What “Free with limits” usually looks like** - You can use the feature, but only a certain amount per time window. - Tool limits are separate from text limits. - Limits can tighten temporarily during peak demand. ### **What triggers upgrade prompts most often** - Heavy GPT-5.2 usage. - Heavy tool usage (files, data analysis, images). - Wanting paid-only modes like Agent mode. ## **One-screen recap** - ChatGPT has multiple modes. Chat is only one of them. - Free includes GPT-5.2, but it is capped and can fall back to a smaller model. - “Thinking” means deeper reasoning. It is slower but more careful. - You can nudge deeper thinking by asking for steps, tradeoffs, constraints, and self-checks. - Search is how you get current info with links and reduce guessing. - Shopping research is a deeper buyer’s-guide mode and is available on Free with limits. - Deep Research and Agent mode are real, but they are not Free features today. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🧺 Thematic Basket Builder** Most investors don’t need “one stock pick.” They need a clean way to express a thesis without betting everything on a single name. A thematic basket does that: you pick a trend (AI chips, GLP-1 drugs, clean power, cybersecurity), then build a small set of companies that benefit in different ways. In 10 minutes, you’ll use ChatGPT to generate a basket and Google Sheets to auto-validate tickers so you can paste the result straight into a watchlist. ### **Step 1 — Define your theme and constraints (2 minutes)** Pick one theme and keep it tight: - “AI Infrastructure” (chips, networking, cloud, power/cooling) - “Cybersecurity” - “Weight loss / GLP-1 ecosystem” - “Uranium + nuclear” - “Data centers” - “Defense tech” Then decide your constraints (quick gut calls are fine): - Basket size: 8–15 names - Geography: U.S. only (recommended for beginners) - Risk: Lower / Medium / Higher - What to avoid: “no microcaps,” “no pre-revenue,” “avoid penny stocks,” etc. ### **Step 2 — Have ChatGPT build the basket + copy/paste output (4 minutes)** Paste this prompt into ChatGPT: **Role:* You are my thematic basket analyst.*Goal:* Build a thematic stock basket I can paste into Google Sheets and validate automatically.* **My theme:* \[PASTE THEME\]* **Constraints:* \[PASTE CONSTRAINTS\]* - *Basket size:* - *Geography:* - *Risk level:* - *Exclusions:* ***Tasks:*** 1. *Propose a basket with 4 groups:* - *Core Leaders (3–4)* - *Enablers/Picks-and-Shovels (2–4)* - *Challengers/Up-and-Comers (2–4)* - *Wildcard (1–2) (higher risk, optional)* 2. *For each company, output:* - *Ticker in GOOGLEFINANCE format (e.g., NASDAQ:NVDA, NYSE:V)* - *Company name* - *Role in the theme (Leader/Enabler/Challenger/Wildcard)* - *1-sentence rationale (plain English)* - *1 key risk (plain English)* 3. *Output the basket twice:* - *A clean table for reading* - *Then CSV ONLY (no commentary) with headers: Ticker,Company,Role,Rationale,Key Risk* ***Rules:*** - *If you are not confident a ticker is correct, mark it as CHECK instead of guessing.* - *Keep it beginner-friendly.* - *No buy/sell recommendations.* You should end with a CSV block that looks ready to paste. ### **Step 3 — Paste into Sheets and auto-validate tickers (3 minutes)** 1. Open Google Sheets → New blank sheet → click A1. 2. Paste the CSV from ChatGPT (it should split into columns automatically). 3. Add two new columns: - Column F header: Price Check - Column G header: Status In **F2**, paste this formula and fill down: *\=IFERROR(GOOGLEFINANCE(A2,"price"),"⚠️ NOT FOUND")* In **G2**, paste this formula and fill down: *\=IF(F2="⚠️ NOT FOUND","Replace / Verify","OK")* Now your sheet flags bad tickers automatically. No manual hunting required. ### **Step 4 — Fix anything flagged and finalize your copy-paste basket (1 minute)** If any rows show **“⚠️ NOT FOUND”**, copy those tickers and paste this into ChatGPT: *These tickers failed GOOGLEFINANCE() validation in Google Sheets: \[PASTE FAILED ROWS\]* *Replace each failed ticker with the closest alternative that fits the same *Role* in the theme.Output *CSV ONLY* with the same headers: Ticker,Company,Role,Rationale,Key Risk.Use GOOGLEFINANCE-friendly tickers (e.g., NASDAQ:\_\_\_, NYSE:\_\_\_).If you can’t confidently validate, write CHECK.* Paste the corrected CSV into your sheet. ## **The Payoff** You now have a theme basket you can reuse: a structured set of companies mapped to a thesis, with risks listed, and a Sheet that flags ticker issues automatically. This is a better way to learn sectors than doomscrolling headlines—because you’re forcing yourself to answer: “Who benefits, how, and what could break?” ## **Transparency & Notes for Readers** - All tools are free: ChatGPT + Google Sheets. GPT-5.2 is available on Free with usage limits. - Ticker validation: GOOGLEFINANCE() doesn’t support every ticker/region; “NOT FOUND” can mean “unsupported,” not “fake.” - This is a watchlist workflow: it helps you organize research; it does not tell you what to buy. - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### My 5 AI Resolutions for 2026 URL: https://www.mindovermoney.ai/founders-corner/ai-resolutions-2026-habits-non-technical-professionals/ Last updated: 2026-07-13T16:59:47.000Z 2025 was a year of nonstop AI change, and it forced me to learn through the noise instead of waiting for clarity. I used the past 12 months to build a foundation so I can move faster and build smarter systems in 2026\. There are still concepts I want to understand, tools I want to pressure-test, and workflows I want to automate. The start of the new year is the perfect time to set intentions and turn them into goals I can actually track. Building in public keeps me accountable to this community and to myself. In 2026, my focus is simple: build better systems, teach what I’m learning, and grow this platform with intention. Here are my five AI resolutions for 2026. **Resolution 1: Upgrade the website into a true distribution hub** I’ve discovered several bugs and gaps while navigating [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai) that make the site harder to use and harder to find. One issue is discoverability in search engines and AI scrapers, which I’ve started to address with custom meta tags on each post. In 2026, SEO and AI searchability will be a core part of my distribution strategy. My plan is to test a few specific ways to drive organic traffic, with the goal of turning that traffic into new subscribers. I’ll use Ghost analytics to track what’s working and make adjustments in real time. This focus sets the site up for long-term growth, but it will require constant iteration. I also want the site to guide people to the best content fast, so new visitors understand what Neural Gains Weekly is and why it’s worth subscribing. I won’t grow subscribers if the site is difficult to navigate. There’s a glaring issue I need to solve: the signup process is fragmented, and the dreaded spam problem keeps getting in the way. Ghost requires email confirmation to complete a subscription, but the base code that drives the experience does a poor job of making that clear. Even worse, the confirmation email often lands in spam, which lowers the odds that someone completes their subscription. In 2026, one of my first projects is building a landing page that makes subscribing simple and sets expectations from the start. It will walk people through completing registration and show them how to keep future emails out of spam. This should stabilize onboarding and dramatically increase the number of people who complete the signup process. **Resolution 2: Automate workflows behind the newsletter** Creating content for *Neural Gains Weekly* is time-consuming, and it can be hard to carve out time for other projects. I need to make time to focus on these resolutions and execute the projects that align with my 2026 goals. How do I free up time while building new skills? Automation. There are already tools that can automate pieces of my workflow. And as AI agents improve, I expect even more opportunities to open up. My initial step is figuring out where to start, which really means choosing the single easiest task to automate first. The goal is not to build a full one-button newsroom overnight. It’s to automate one small piece, prove it works, then stack wins from there. Next, I’ll pick the simplest tech stack that gets the job done, ideally without adding more subscriptions. The ultimate goal is to automate as much of the manual work as possible, so I can spend more time building, writing, and experimenting. **Resolution 3: Learn data architecture so I can build smarter with AI** Education has always been the foundation of how I grow. I’m naturally curious, and I like to understand the why and the how. AI has introduced a new set of concepts that will matter for my growth, both personally and professionally. My first challenge in 2026 is learning data architecture, especially as agentic AI starts to reshape the infrastructure underneath modern systems. I’m fortunate to work on IT and AI projects in my career, so I’ve picked up the basics of data architecture. It’s a concept I naturally gravitate toward, but I still lack depth. A focused learning plan will be crucial if I want to build and deploy agentic systems. It will also matter as I build automation, because I need to understand how tools connect, whether that’s through APIs or MCP. I view this as a mandatory curriculum, and it will only become more relevant as the AI ecosystem evolves in 2026. **Resolution 4: Build a social media strategy that drives growth** This is the simplest resolution, and also the most challenging. I’ve grown to 106 subscribers through direct engagement with friends, family and coworkers. I’ve picked up a handful through LinkedIn, but that channel is only as good as the effort I put into it. 2026 will be the year I commit to a social media strategy that drives traffic to the site and newsletter subscriptions. My main focus is LinkedIn, where I’ll consistently share how I’m thinking about AI, engage with other AI leaders, and market my content. I also plan to research how the algorithm works so I can expand reach and drive engagement beyond my current network. X (formerly Twitter) will be my secondary focus. Admittedly, I’ve put very little effort into this platform. I have a big decision to make: do I keep posting from the MindOverMoney.ai account, or switch to my personal account? There are pros and cons either way, but the bigger issue is consistency. I need to pick a lane and stick with it. I’m treating this like training: small reps every week, tracked over time, until consistency becomes automatic. **Resolution 5: Let creativity drive the build** I’ve written about this before, but AI has unlocked a creative side to me that’s been dormant. Creative projects keep me balanced and motivated to show up each week. I have a list of projects that could supplement the newsletter and add new formats to the ecosystem I’m building. I need to prioritize that list and choose projects that genuinely add to my growth. But I also want to enjoy the process and make space for creativity. This resolution is for me. It’s how I plan to keep my energy and commitment high all year. Balance will be important if I want to stay grounded and focused as the pace of AI keeps accelerating. ### Steal My Prompt Vol. 15: The Feature Finder URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-15-the-feature-finder/ Last updated: 2026-07-13T16:59:47.000Z I’m introducing a new format for ‘AI Education’ that will focus on highlighting the features and functionality of ChatGPT. This will be a four-part series that will extend to Gemini, Claude and Copilot in the future. I needed a prompt to get me started and help map out the content. Here is what I used for the ChatGPT series. --- TARGET PLATFORM (only user-entered field): \[ENTER ONE: ChatGPT | Gemini | Claude | Copilot | other\] ROLE You are my editor-researcher for Neural Gains Weekly. You are creating a 4-part “Free tier features” series for the TARGET PLATFORM above. The audience is people who use the product in a very basic way. Your job is to teach every free-tier feature in plain English, with accurate, verified details. NON-NEGOTIABLES 1. No hallucinations. No guessing. If you cannot verify a claim from reputable sources, you must either: - omit it, or - label it “Unverified / availability unclear” and explain what could not be confirmed. 2. Research first, write second. You must build a verified feature inventory before drafting. 3. Free tier scope: include features that are free AND features that are “free with limits” (caps, throttles, rotating access). You must label each feature as: - Free - Free with limits - Availability varies (if your sources indicate variability) 4. Web + mobile coverage: for every feature, state where it exists: - Web - Mobile - Both - Availability varies 5. One-time “Free tier limits and upgrade triggers” section: include it only in Part 1\. Do not repeat it in later parts. Later parts can use a single-line note per feature if needed. 6. Output must be Ghost-ready formatting. Use clean headings, short paragraphs, bullets, and tables. No code blocks. 7. Examples: Use a mix of personal finance and general productivity examples. Do not force money into every feature. Keep examples short and concrete. 8. Model types: If the platform has multiple model options, you must cover them as part of the series and explain in plain English what each is best at, only if verified. 9. End notes: citations must appear at the end of each part (not inline). 5–10 sources per part. Reputable, free sources only. WHERE TO RESEARCH (STRICT) Use only official or top-tier educational documentation sources. - If TARGET PLATFORM = ChatGPT: use OpenAI Help Center and openai.com as primary. You may also use OpenAI GitHub docs if official. - If TARGET PLATFORM = Gemini: use Google’s official Gemini / Google AI / Google support and developer docs. - If TARGET PLATFORM = Claude: use Anthropic official docs and help pages. - If TARGET PLATFORM = Copilot: use Microsoft Learn and official support docs. If you cannot access enough official sources to verify key features, STOP and say what you could not verify. SERIES STRUCTURE (must follow) You will produce 4 separate parts, each as its own Ghost-ready block. Do NOT combine them into one article. Use this template as the organizing backbone ### Volume 14: The Year-End Reset URL: https://www.mindovermoney.ai/ai-year-end-review-how-to-reset-and-plan-with-ai/ Last updated: 2026-07-13T16:59:47.000Z Hey everyone! I hope you had a great holiday season and you are ready to kick off 2026 in style. Before we turn the page, I wanted to close out the year with a quick reset on what I learned in 2025 and a simple system to start the new year strong. 🧠 **Founder’s Corner:** 3 lessons from my 2025 AI journey, from embracing discomfort to treating AI like a system and recognizing how early we are. 🎓 **AI Education:** a high-level recap video that ties the full LLM 101 series together so it actually sticks. ✅ **10-Minute Win:** a Financial Resolution Action Plan and accountability tracker you can run in one sitting and reuse every week. Let’s dive in. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **One in a million: celebrating the customers shaping AI’s future**](https://openai.com/index/one-in-a-million-customers/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI says over 1 million customers use its products globally, rolling out ChatGPT for writing, coding, research, analysis, and building agents; 75% of customers said AI helped them complete tasks they couldn’t do before. **Why it matters:** This is adoption going from “pilot” to platform. More customers → more workflows → more lock-in → more enterprise spend. ### **2)**[ **How Oracle became a poster child for AI bubble fears**](https://finance.yahoo.com/news/how-oracle-became-a-poster-child-for-ai-bubble-fears-150039511.html?utm%5Fsource=chatgpt.com) **Summary:** Oracle has become a market proxy for “AI bubble” anxiety: massive AI-infra ambition, heavy capex/debt questions, and investor sensitivity to any sign that AI profits will take longer than promised. **Why it matters:** If the AI trade gets a reality check, it’ll likely start with capex + financing + timelines—not model demos. ### **3)**[ **Nvidia aims to begin H200 chip shipments to China by mid-February, sources say**](https://www.reuters.com/world/china/nvidia-aims-begin-h200-chip-shipments-china-by-mid-february-sources-say-2025-12-22/?utm%5Fsource=chatgpt.com) **Summary:** Nvidia plans to restart China shipments of H200 AI chips around mid-February 2026, using existing inventory (roughly 40,000–80,000 chips equivalent), pending approvals. **Why it matters:** Export policy → compute access → model capability. This is one of the biggest levers in the global AI race. ### **4)**[ **Illinois climate groups want pause on data centers**](https://www.axios.com/local/chicago/2025/12/22/illinois-climate-groups-want-pause-data-centers?utm%5Fsource=chatgpt.com) **Summary:** Illinois environmental groups are pushing for a pause on new data centers, warning the AI/crypto boom could strain energy supply, raise prices, and increase pollution/water stress.[ ](https://www.axios.com/local/chicago/2025/12/22/illinois-climate-groups-want-pause-data-centers?ref=mindovermoney.ai) **Why it matters:** AI is colliding with local power politics. Permits, grid constraints, and backlash can slow “AI everywhere” more than model progress does. ### **5)**[ **Google Cloud CEO reveals 10-year AI strategy on power and silicon**](https://www.indexbox.io/blog/google-cloud-ceo-reveals-10-year-ai-strategy-on-power-and-silicon/?utm%5Fsource=chatgpt.com) **Summary:** A recap of Thomas Kurian’s comments: Google has been executing a decade-long plan built around two bottlenecks—specialized silicon (TPUs) and power availability/efficiency—not reacting to a sudden trend. **Why it matters:** The real AI moat is shifting to energy economics + compute efficiency. Whoever can scale capacity without getting grid-blocked wins. ## Founder's Corner Real world learnings as I build, succeed, and fail 2025 was the year the pace of AI change became impossible to ignore. Whether it’s stock market bubble talk, new model breakthroughs, agentic tools, or the infrastructure spending behind it all, one thing is clear: the pace of change is accelerating, and it feels like we are just getting started. For me, 2025 was the year AI went from interesting to unavoidable, and it pushed me to learn in public by building content that strengthened my own foundation while helping others start theirs. Since launching in September, this community has grown to over 100 subscribers, and I’m genuinely grateful for the support along the way. Over the last few days, I’ve been reflecting on the journey, celebrating the wins and being honest about the challenges. The lessons I learned in 2025 gave me the foundation to sharpen what I’m building and show up with more confidence in 2026\. I want to share these lessons to help you build your own confidence, stay curious, and set yourself up for what’s coming in 2026. **Lesson 1: Discomfort is the tuition for AI fluency** I had no idea how AI actually worked when I started to get serious in late 2024\. Most of the concepts were foreign, and like a lot of people, I was only using AI for fun. It was a basic chat here and there, a funny AI-generated photo for the group chat, or a question that could have been answered with a normal Google search. I had to adapt. If I wanted to get better, I had to get uncomfortable. For me, that meant learning unfamiliar topics, trying tools that felt intimidating, and being willing to look a little clueless while I built in public. Only through reflection have I realized how important this was for building AI skills and knowledge. Every error or break forced me back into AI to troubleshoot and solve the problem. Each experiment with a new platform built skills I never thought I could develop. Every successful launch built my confidence and helped me find my voice. Being uncomfortable is hard, but it is also where the learning happens. Acknowledge the discomfort, then set small goals that let you move at your own pace. One rep a week is better than sitting on the sidelines. I strongly believe AI will become a bigger part of everyday life, especially in the workplace. The people who lean into that challenge now will be set up for success in 2026 and beyond. **Lesson 2: AI is not magic, it is a system** It’s easy to be blown away by AI tools, no matter your level of expertise. Innovation is happening daily, and we get to benefit from the competition between the big labs. It’s exciting, but the wrong approach can build bad habits and quietly lower the quality of your outputs. I fell into the same trap a lot of people fall into when they first start using AI. I honestly thought magic was happening behind the scenes. I assumed any prompt would produce the output I had in my head. Only through practice, learning, and a few slices of humble pie did I start to realize these tools are systems. Like any other system, there are rules and constraints you need to follow to get high-quality output. That realization pushed me to learn how LLMs actually work, so I could create better content for the newsletter. I built a simple system that treated AI like a partner by giving it clear instructions, goals, and context. Once you put this into practice, your outputs get better fast, and you start building real confidence in how you use AI. **Lesson 3: We are early, but change is coming** I spend a lot of time absorbing information to stay current with the trends shaping the AI world. I pull data points and opinions from a mix of places, including podcasts, news, and thought-provoking conversations with friends and colleagues. I was genuinely shocked when my Spotify Wrapped said I listened to more than 46,000 minutes of podcasts in 2025\. I’d estimate at least half of that was directly related to AI or AI-adjacent topics. The more I learn, the more I believe the real impact of AI is still ahead, and that 2026 will lay the foundation for a very different world. On the surface, there is plenty of evidence that suggests AI is already in a mature state. The big labs are pushing the boundaries of what is possible, and every new model release seems to break benchmarks set by the last one. So why do I feel strongly enough to say we are just getting started? The answer is the gap between consumer adoption and enterprise adoption. At home, you can discover a new tool and start using it the same day. At work, you are limited to whatever is approved and rolled out. It’s also easier to organize your own data and build personal workflows than it is for a large organization to modernize systems, clean up data, and wire everything together in a way that can support agentic workflows at scale. We are still early in this shift, even if the technology itself is moving fast. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025\. If that happens, AI will stop feeling like an “initiative” and start feeling like the default way work gets done. That is why it feels imperative to take action now. Start simple by picking the biggest problem inside your role and working backward from the outcome you want. Look for the workflow underneath it, then use data to define what good looks like and where automation can actually help. Push for outcomes that deliver ROI, but also build trust and drive adoption responsibly. That mindset is how you stay ahead of the curve and become the person who helps your team navigate what comes next. --- 2025 taught me that AI rewards the people who stay curious and keep taking reps, even when it feels uncomfortable. The technology is moving fast, but the bigger shift is how quickly it is being woven into everyday life and the workplace. My goal going into 2026 is simple: keep learning in public, keep refining my systems, and keep sharing what works and what breaks along the way. If you take one thing from these lessons, let it be this: start small, stay consistent, and build the habits now that your future self will thank you for. ## AI Education for You ****LLM 101: Recap Video** Over the past few weeks, you have read through our four-part series on Large Language Models. We started by establishing a clear mental model of what an LLM actually is beyond the marketing answers. We looked at the training phase where the model learns patterns from huge amounts of text. We examined the distinct "use phase" where your specific prompts are turned into tokens, and we finished by discussing the critical limits you need to know, such as hallucinations and context windows. That is a lot of information to process. To help you digest the full series, this video will serve as a high-level recap. Our goal is to ensure that by the end of this overview, you truly understand what is happening under the hood when you ask AI to do a task. Let’s put a clean frame around the building blocks you have picked up so far. 0:00 /6:42 1× Link will open on website You can read the entire 4-part series by checking out Volumes 10-13 [Neural Gains Weekly Archive ](https://www.mindovermoney.ai/tag/newsletter/) ## Your 10-Minute Win A step-by-step workflow you can use immediately # **✅ Financial Resolution Action Plan + Accountability Tracker** A financial resolution without a system becomes a January feeling you forget by February. The problem isn’t motivation, it’s clarity + follow-through. In 10 minutes, you’ll use ChatGPT to turn one money goal into a concrete weekly plan, then paste an AI-generated tracker into Google Sheets so you can stay accountable with a 2-minute weekly check-in. *Model tip (optional): If you see GPT-5.2 Thinking (or a “Thinking” option) in your ChatGPT menu, use it for Step 2–4\. If you don’t, the workflow still works with the default model.* ### **Step 1 — Choose ONE resolution and define success (2 minutes)** Pick one goal you can measure in dollars and a date. Examples: - “Save $1,000 by March 31” - “Pay off $2,500 of credit card debt by June 30” - “Invest $50/week for 12 weeks” Write 3 lines (rough is fine): - Goal: (dollars + date) - Why it matters: (1 sentence) - Your constraint: (what makes this hard: irregular income, holidays, debt, etc.) This keeps ChatGPT focused and keeps you honest. ### **Step 2 — Have ChatGPT build your action plan + guardrails (3 minutes)** Paste this prompt into ChatGPT: *Role: You are my financial resolution coach.Goal: Create a simple, realistic action plan I can follow weekly.* *My resolution:* - *Goal (dollars + date): \_\_\_* - *Why it matters (1 sentence): \_\_\_* - *Constraint (what makes it hard): \_\_\_* *Tasks:* 1. *Rewrite my goal as a SMART goal (specific, measurable, time-bound).* 2. *Break it into weekly targets (12 weeks max).* 3. *Give me a Weekly Playbook with 3–5 actions I repeat (example: “move money on payday,” “one spending rule,” “one income action”).* 4. *Create 5 If–Then rules to prevent failure (example: “If I overspend this weekend, then I do X on Monday to recover”).* 5. *Output a short “Minimum Viable Week” version (what I do if I’m busy/stressed).* *Rules: Keep it beginner-friendly. No investing or debt advice beyond planning behaviors.* You now have a plan that survives real life. ### **Step 3 — Have ChatGPT generate the tracker table to paste into Sheets (3 minutes)** Now paste this prompt: *Create an Accountability Tracker I can paste into Google Sheets.* *Requirements:* - *Output CSV only (no commentary).* - *Row 1 = headers.* - *Include columns: Week Start Date, Weekly Target ($), Actual Saved/Extra Paid ($), Delta ($), Cumulative Target ($), Cumulative Actual ($), Status (On Track / Behind / Ahead), Notes* - *Put formulas in the right cells (assume the first data row is row 2):* - *Delta = Actual - Weekly Target* - *Cumulative Target = sum of Weekly Target to date* - *Cumulative Actual = sum of Actual to date* - *Status = On Track if Cumulative Actual >= Cumulative Target, otherwise Behind (Ahead if it’s meaningfully above)* - *Pre-fill Week 1 with sample values and formulas so I can see it working.* - *Use weekly targets from the plan you created above.* *Then:* 1. *Copy the CSV output* 2. *Open a blank Google Sheet → click A1 → paste* 3. *Delete the sample “Actual” number and replace with your real weekly updates.* ### **Step 4 — Run a 2-minute weekly check-in (2 minutes)** Every week (same day), copy the last 1–3 rows from your Sheet and paste into ChatGPT with this: *Weekly Check-In: Here are my last updates (table rows): \[PASTE 1–3 ROWS\]* *Do this:* 1. *Tell me if I’m On Track / Behind / Ahead (one sentence).* 2. *Give me one adjustment for next week (not five).* 3. *Rewrite next week’s 3–5 actions as a simple checklist I can follow.* 4. *If I’m behind, propose a recovery move that doesn’t require extreme sacrifice.* That’s your accountability loop: quick, non-dramatic, effective. ## **The Payoff** You end with a real system: a SMART goal, weekly targets, guardrails for bad weeks, and a tracker that makes progress visible. The magic isn’t the spreadsheet, it’s the weekly feedback loop. Do this for 8–12 weeks and you stop “hoping” you’ll be different—you become consistent. ## **Transparency & Notes for Readers** - All tools are free: ChatGPT + Google Sheets. - Limits: ChatGPT Free has usage limits; if you hit them, run the weekly check-in later or keep it shorter. - Accuracy: This is planning and accountability, not personalized financial advice or a replacement for a professional. - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 14: The Visual Storyteller URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-14-the-visual-storyteller/ Last updated: 2026-07-13T16:59:48.000Z The following two prompts—one for a Visual Infographic Blueprint and one for a Professional Video Seminar Script—were designed to generate highly structured educational content for the "LLM 101 Series." The infographic was created with Nano banana Pro within Gemini and used for a LinkedIn post. The video was embedded into ‘AI Education’ for Volume 14 and created within NotebookLM. --- ## **Prompt 1: Infographic (Central Concept & Branches)** **Role:** You are a professional graphic information designer specializing in educational technology. **Objective:** Create a detailed blueprint for an infographic summarizing the "LLM 101 Series." The layout must be a central concept with branches. **Central Concept:** "The LLM as a Pattern Learner". **Branches (Content Requirements):** - **Branch 1: Core Identity.** Define an LLM as a system that predicts the next small piece of text over and over. Use the analogy: Classic software is like a set of tax forms; an LLM is like a person who has seen millions of completed forms. - **Branch 2: The Learning Process (Training).** Explain "Parameters" using the "mixing board with billions of sliders" analogy. Detail the training loop: Predict, Check, Adjust. - **Branch 3: The Execution (Use).** Define the "Context Window" as a page with a strict size limit. Explain "Attention" as the way the model decides which tokens are most important for prediction. - **Branch 4: Limits & Guardrails.** Define "Hallucinations" as confident but wrong answers. Emphasize that models do not have intentions, goals, or feelings. **Constraint Checklist:** - **No Hallucinations:** Use ONLY the definitions and analogies provided in the sources. - **Accuracy:** Ensure all terminology (e.g., tokens, parameters, loss) is spelled correctly in English. - **Instructional Tone:** Focus on the distinction between "Base Training" (broad education) and "Fine-Tuning" (job training). --- ## **Prompt 2: Video Overview (Professional Seminar Style)** **Role:** You are an expert AI Educator producing a script and visual storyboard for a high-level professional seminar. **Objective:** Generate a video overview of the "LLM 101 Series" that moves beyond marketing hype to explain the actual "under the hood" mechanics. **Script Structure:** 1. **Introduction:** Establish the mental model: LLMs are pattern machines, not magic oracles. 2. **The Mechanical Deep Dive:** Explain the transition from Training to Use. Use the "student with a practice answer key" analogy to describe how the model reduces "Loss". 3. **The User Interface:** Explain what happens when a user types a message—turning words into tokens and processing them within the context window. 4. **Professional Application:** Discuss the limits. Warn the audience about "Shallow/Generic" answers and why structured input (narrowing the task) is required for professional results. 5. **Conclusion:** Summarize the "Bottom Line"—keeping final judgment and high-stakes decisions in human hands. **Style Guidelines:** - **Tone:** Calm, authoritative, and instructional. - **Visual Cues:** Request diagrams of "next-token prediction" sequences and the "context window" limit. - **Language:** Maintain formal English. Do not personify the AI; reinforce that it "predicts" rather than "understands". ### What 2025 Taught Me About AI URL: https://www.mindovermoney.ai/founders-corner/ai-lessons-learned-2025-non-technical-professional/ Last updated: 2026-07-13T16:59:48.000Z 2025 was the year the pace of AI change became impossible to ignore. Whether it’s stock market bubble talk, new model breakthroughs, agentic tools, or the infrastructure spending behind it all, one thing is clear: the pace of change is accelerating, and it feels like we are just getting started. For me, 2025 was the year AI went from interesting to unavoidable, and it pushed me to learn in public by building content that strengthened my own foundation while helping others start theirs. Since launching in September, this community has grown to over 100 subscribers, and I’m genuinely grateful for the support along the way. Over the last few days, I’ve been reflecting on the journey, celebrating the wins and being honest about the challenges. The lessons I learned in 2025 gave me the foundation to sharpen what I’m building and show up with more confidence in 2026\. I want to share these lessons to help you build your own confidence, stay curious, and set yourself up for what’s coming in 2026. **Lesson 1: Discomfort is the tuition for AI fluency** I had no idea how AI actually worked when I started to get serious in late 2024\. Most of the concepts were foreign, and like a lot of people, I was only using AI for fun. It was a basic chat here and there, a funny AI-generated photo for the group chat, or a question that could have been answered with a normal Google search. I had to adapt. If I wanted to get better, I had to get uncomfortable. For me, that meant learning unfamiliar topics, trying tools that felt intimidating, and being willing to look a little clueless while I built in public. Only through reflection have I realized how important this was for building AI skills and knowledge. Every error or break forced me back into AI to troubleshoot and solve the problem. Each experiment with a new platform built skills I never thought I could develop. Every successful launch built my confidence and helped me find my voice. Being uncomfortable is hard, but it is also where the learning happens. Acknowledge the discomfort, then set small goals that let you move at your own pace. One rep a week is better than sitting on the sidelines. I strongly believe AI will become a bigger part of everyday life, especially in the workplace. Those who lean into that challenge now will be set up for success in 2026 and beyond. **Lesson 2: AI is not magic, it is a system** It’s easy to be blown away by AI tools, no matter your level of expertise. Innovation is happening daily, and we get to benefit from the competition between the big labs. It’s exciting, but the wrong approach can build bad habits and quietly lower the quality of your outputs. I fell into the same trap a lot of people fall into when they first start using AI. I honestly thought magic was happening behind the scenes. I assumed any prompt would produce the output I had in my head. Only through practice, learning, and a few slices of humble pie did I start to realize these tools are systems. Like any other system, there are rules and constraints you need to follow to get high-quality output. That realization pushed me to learn how LLMs actually work, so I could create better content for the newsletter. I built a simple system that treated AI like a partner by giving it clear instructions, goals, and context. Once you put this into practice, your outputs get better fast, and you start building real confidence in how you use AI. **Lesson 3: We are early, but change is coming** I spend a lot of time absorbing information to stay current with the trends shaping the AI world. I pull data points and opinions from a mix of places, including podcasts, news articles, and thought-provoking conversations with friends and colleagues. I was genuinely shocked when my Spotify Wrapped said I listened to more than 46,000 minutes of podcasts in 2025\. I’d estimate at least half of that was directly related to AI or AI-adjacent topics. The more I learn, the more I believe the real impact of AI is still ahead, and that 2026 will lay the foundation for a very different world. On the surface, there is plenty of evidence that suggests AI is already in a mature state. The big labs are pushing the boundaries of what is possible, and every new model release seems to break benchmarks set by the last one. So why do I feel strongly enough to say we are just getting started? The answer is the gap between consumer adoption and enterprise adoption. At home, you can discover a new tool and start using it the same day. At work, you are limited to whatever is approved and rolled out. It’s also easier to organize your own data and build personal workflows than it is for a large organization to modernize systems, clean up data, and wire everything together in a way that can support agentic workflows at scale. We are still early in this shift, even if the technology itself is moving fast. Gartner predicts that 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from less than 5% in 2025\. If that happens, AI will stop feeling like an “initiative” and start feeling like the default way work gets done. That is why it feels imperative to take action now. Start simple by picking the biggest problem inside your role and working backward from the outcome you want. Look for the workflow underneath it, then use data to define what good looks like and where automation can actually help. Push for outcomes that deliver ROI, but also build trust and drive adoption responsibly. That mindset is how you stay ahead of the curve and become the person who helps your team navigate what comes next. --- 2025 taught me that AI rewards the people who stay curious and keep taking reps, even when it feels uncomfortable. The technology is moving fast, but the bigger shift is how quickly it is being woven into everyday life and the workplace. My goal going into 2026 is simple: keep learning in public, keep refining my systems, and keep sharing what works and what breaks along the way. If you take one thing from these lessons, let it be this: start small, stay consistent, and build the habits now that your future self will thank you for. ### Volume 13: Upgrade Your Holiday Traditions With AI URL: https://www.mindovermoney.ai/how-to-use-ai-for-holiday-cooking-and-planning/ Last updated: 2026-07-18T01:46:18.000Z Happy Holidays Everyone! Thank you for showing up week after week. I know your attention is the most valuable currency you have, and I genuinely appreciate you spending a few minutes here with me. 🎄In **Founder’s Corner**, we lean into the fun side of AI with holiday creativity. Simple experiments that help you build real fluency while making something memorable with the people you care about. ✍️In **AI Education**, we close out the LLM series by getting honest about where these models break down and how to use them with better judgment, especially when the stakes are real. 🎁In **10-Minute Win**, we keep it seasonal with a Holiday Menu Copilot that turns a pile of recipes into one grocery list, a cook schedule, and a simple command sheet you can actually follow. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **New updates to the Gemini app, December 2025**](https://blog.google/products/gemini/gemini-drop-december-2025/?utm%5Fsource=chatgpt.com) **Summary:** Google’s December “Gemini Drop” makes Gemini 3 Flash the headline upgrade, adds more precise image editing with Nano Banana, and improves grounding by letting you pull NotebookLM notebooks directly into Gemini as sources. **Why it matters:** Google is pushing AI from “chat” into default workflows: faster research + better grounding + easier creation is how mainstream adoption compounds. ### **2)**[ **The new ChatGPT Images is here**](https://openai.com/index/new-chatgpt-images-is-here/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI released an updated ChatGPT Images powered by a new flagship image model, focused on more precise edits, better detail consistency, and up to 4× faster image generation. **Why it matters:** Image generation is shifting from “cool outputs” to useful editing. That’s what makes it sticky for normal people (content, reselling, small biz marketing, family photo edits). ### **3)**[ **AI’s economic growth effects**](https://corporate.vanguard.com/content/corporatesite/us/en/corp/vemo/ai-economic-growth-effects.html?utm%5Fsource=chatgpt.com) **Summary:** Vanguard argues AI investment could drive stronger-than-expected economic growth and stabilize labor markets, potentially changing the path of interest rates and productivity over time. **Why it matters:** This is the “AI = macro” angle. If AI boosts growth, it impacts everything from rates to earnings expectations—especially for tech-heavy markets.[ ](https://corporate.vanguard.com/content/corporatesite/us/en/corp/vemo/ai-economic-growth-effects.html?utm%5Fsource=chatgpt.com) ### **4)**[ **US Senator Bernie Sanders calls for AI data center construction moratorium**](https://www.datacenterdynamics.com/en/news/us-senator-bernie-sanders-calls-for-ai-data-center-construction-moratorium/?utm%5Fsource=chatgpt.com) **Summary:** Sanders called for a moratorium on new AI data center construction to “give democracy a chance to catch up,” raising concerns about speed, governance, and who benefits from the buildout. **Why it matters:** This is an early signal that AI infrastructure could become a political flashpoint (power use, water, land, jobs). Regulation risk isn’t just about models—it’s about physical buildout. ### **5)**[ **George Osborne joins OpenAI: ex-chancellor adds tech post to his CV**](https://www.theguardian.com/politics/2025/dec/16/george-osborne-joins-openai-chatgpt-tech-post-cv?utm%5Fsource=chatgpt.com) **Summary:** Former UK chancellor George Osborne is joining OpenAI to lead an initiative focused on government relationships and national-level AI partnerships. **Why it matters:** AI is being treated like national infrastructure now. OpenAI (and rivals) are racing to shape policy and secure country-level deals—this will influence adoption, regulation, and geopolitics. ## Founder's Corner Real world learnings as I build, succeed, and fail A lot of the AI conversation is centered around the themes I cover throughout *Neural Gains Weekly*: productivity and automation at work, the job market, politics, markets, and the infrastructure buildout behind it all. It matters because these changes will shape our everyday lives, and we all need to be ready for a future that looks different than today. But there is another side to this technology that often gets overlooked: creativity. I’ve found it challenging to tap into my creative side for a variety of reasons. My work forces me to think critically and solve complex problems in the healthcare space, but that doesn’t always translate into creative energy at the end of the day. I stay busy with hobbies like weightlifting, yoga, cooking, and time with friends and family, but it can still be hard to break out of routine and create something new. This is where my AI journey has helped reshape how I think and build new skills for the future. AI is a creativity tool that helps you create things you normally would not make, and we all have access to dozens of free tools that make experimentation easy. You can generate and edit images with natural language. You can bring a concept to life in a short video with a bit of prompt practice. The “what’s possible” in this space seems to evolve weekly as the tools get more powerful and more accessible. More importantly, this kind of use builds AI fluency. Every small experiment teaches you how to give better instructions, how to iterate, and how to get closer to the output you actually want. That is why the holidays are such a great setting to try something new and tap into your creative side. The goal is not perfection or productivity. The goal is connection and fun. Holiday traditions are also a low-stakes way to show friends and family the fun side of AI, especially for people who have not experimented with it much yet. This Thanksgiving, I started a new tradition with [Suno AI](https://suno.com/@santosh%5Fsavel?ref=mindovermoney.ai) and turned inside jokes and shared memories into a custom song for the whole family. I have zero musical talent, but in about 10 minutes I created something we will laugh about for years. It also sparked real curiosity about AI, because the output made it feel relatable instead of intimidating. If you want to try the same kind of experiment, start here. Below is a short list of holiday ideas you can test in minutes to bring more creativity and joy into your celebrations. Check out 2 prompts to create songs with Suno AI [Prompt Library ](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-1-the-newsletter-launcher/) ### **Ideas for your holiday celebrations** 1. **AI Image Pictionary:** One person uses an AI image generator to generate a "hyper-realistic" or "abstract" version of a holiday movie or song. The rest of the family has to guess the title based on the AI’s interpretation. 2. **The "Ugly Sweater" Makeover:** Take a photo of family members in normal clothes and use an AI editor (like Google Photos’ "Magic Editor" or Adobe Firefly) to "AI-generate" the wildest, most ridiculous holiday sweaters onto them. 3. **Collaborative "Exquisite Corpse" Story:** Start a story in a chatbot. Each family member adds one sentence or a specific detail (e.g., "The turkey grows wings and flies away"). After everyone contributes, ask the AI to turn it into a cohesive, professional-sounding holiday fable to read aloud. 4. **Mystery Guest Trivia:** Ask an AI to "act" as a famous historical figure or holiday character (like Ebenezer Scrooge or a 1920s jazz singer). Family members take turns asking it questions to guess who the AI is pretending to be. 5. **The "Year in Review" Poem:** Paste a few bullet points of your family’s 2025 highlights into an AI and ask it to write an epic poem or a "Twas the Night Before Christmas" style summary of your year. 6. **Signature Holiday Mocktails:** Tell the AI what flavors your family likes (e.g., "cinnamon, apple, but not too sweet") and ask it to invent a "signature family drink" for 2025 with a fun name. 7. **AI Table Talk Prompts:** Ask an AI to generate "10 deep, meaningful, and slightly weird conversation starters for a multi-generational family dinner" to keep things lively. 8. **Custom Place Cards:** Use an AI to generate a "unique spirit animal wearing a Santa hat" for each guest based on their personality, then print them as place cards for the table. 9. **Family Portrait "Time Travel":** Use an AI image-to-image tool to "reimagine" a 2025 family photo as if it were taken in the 1800s, the 1970s, or even 100 years in the future. 10. **Holiday "News Broadcast":** Use a video AI tool or a simple voice-cloner to create a "North Pole News Report" that mentions family members by name and their "status" on the Naughty or Nice list. ## AI Education for You ****LLM 101 Part 4: Limits, Pitfalls, and How to Work With Large Language Models** ### **Quick recap** You now have: - A mental model of what an LLM is - A training story for how it learns patterns - A use story for what happens from your prompt to its answer The last piece is judgment: *Where do these models break down, and how should you work with them?* ### **Hallucinations: confident, wrong answers** A large language model is trained to continue text in a way that looks plausible. It is not trained to say “I do not know” by default. Hallucinations show up when: - The question is vague or poorly defined - The model has seen many conflicting patterns in training - There is no clear answer, but the model generates one anyway Result: Text that sounds right but is factually wrong. For example, asking it to “summarize a law” without giving the actual text can lead to a clean, but incorrect summary. It is continuing patterns about what legal text *usually* looks like, not reading the specific law unless you provide it. ### **Shallow and generic answers** If you ask: - “Analyze my finances” - “Tell me how to get better with money” with no data or structure, you will get vague, generic advice. Why: - Your prompt is vague - There is no specific context to latch onto - The model falls back to common patterns it has seen many times You get better answers when you: - Narrow the task: “Review these three budget categories and suggest one change.” - Provide structured context: short summaries and labeled sections - Specify format and length ### **Context window and forgetting** The model cannot see beyond its context window. That means: - Very long chats can push earlier details out of view - Huge pasted documents may be cut off - If a key detail is not inside the current window, the model will act as if it never saw it This is not “bad memory” in a human sense. It is a fixed input size. Practical moves: - Restate key constraints near your final ask - Keep long tasks in separate, focused chats - Summarize earlier parts into short reminders instead of pasting everything again ### **Bias and gaps** Because training data reflects human writing and behavior, it can contain: - Bias - Gaps in representation - Outdated views Those patterns can surface in answers. Extra training and safety steps reduce this but do not remove it. This is one more reason to: - Be cautious with topics involving identity, health, or sensitive decisions - Treat outputs as drafts and starting points, not final truth ### **What large language models do not do** Important reminders: - They do not understand the world the way humans do - They do not have intentions, goals, or feelings - They do not see your bank account or live financial statements unless you or an app send that data - They do not have real-time awareness of markets or laws unless connected to live tools They are powerful text pattern machines. Nothing more, nothing less. ### **The bottom line** Large language models are: - Trained on huge amounts of text - Built to predict the next token based on patterns - Limited by their context window, training data, and lack of real understanding When you know that, you stop treating them like magic oracles. Instead, you: - Use them to clarify, summarize, and explore options - Give them structured, focused input - Keep final judgment and high-stakes decisions in human hands That is how you turn an LLM from a mystery into a useful tool in your day-to-day life. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🍗 Holiday Menu Copilot** Holiday meals don’t fail because you can’t cook. They fail because you’re juggling 4–6 recipes, overlapping ingredients, and everything needing the oven at the same time. In 10 minutes, you’ll use ChatGPT to turn your recipes into one consolidated grocery list and a cook schedule that gets food to the table on time, without kitchen chaos. *Model tip (optional): If you see GPT-5.2 Thinking in your ChatGPT model/tools menu, use it for Step 3 (scheduling). It’s especially good at sequencing and constraint juggling. Free users can access GPT-5.2 with message limits.* ### **Step 1 — Give ChatGPT your recipes (2 min)** You have three easy options. Use whichever is fastest: - **Option A: Paste links** to recipe pages (best if they’re publicly accessible). - **Option B: Upload documents** (PDFs, screenshots, images of a recipe card, etc.). Free users can upload files but with daily limits. - **Option C: Paste the text** (ingredients + instructions). Then paste this prompt (and attach/upload your files if using Option B): *You are my Holiday Menu Copilot.I’m giving you recipes via links and/or uploaded documents and/or pasted text.* *Task: Convert everything into a clean “MENU PACK” using ONLY the recipe content you can access.* *MENU PACK format (required):* - *Serve date + serve time: \_\_\_* - *Guests: \_\_\_* - *Diet/allergies: \_\_\_* - *Kitchen limits: (one oven? how many burners? slow cooker?) \_\_\_* - *Menu items: (dish names)* *For each recipe, create a “RECIPE BLOCK”:* - *Dish name* - *Servings it makes* - *Ingredients (exact list)* - *Key timings: prep time, cook time, rest/chill time, oven temp (if any)* - *Critical notes: make-ahead? must serve hot? reheats well?* *Rules:* 1. *Do not invent ingredients or times.* 2. *If a recipe page is blocked or missing timing info, ask me for the minimum missing details in a short bullet list and STOP.* 3. *Keep it tight and structured so we can reuse it in the next steps.* *Here are my recipes:* - *Links: \[paste 3–6 links\]* - *Uploads: \[attached\]* - *Pasted text: \[optional\]* Once ChatGPT outputs your Menu Pack, you’re ready. ### **Step 2 — Generate a consolidated grocery list (3 minutes)** Paste this prompt **below your Menu Pack**: *Goal: Turn my Menu Pack into a single consolidated grocery list for one shopping trip.* *Rules (must follow):* 1. *Use ONLY ingredients in the Menu Pack. Do not invent items.* 2. *Combine duplicates and total quantities.* 3. *Normalize units when possible (tsp/tbsp/cups/oz/lb). If uncertain, keep both and flag it.* 4. *Group by aisle: Produce, Meat/Seafood, Dairy, Bakery, Pantry, Spices, Frozen, Drinks/Other.* 5. *Output as a table: Aisle | Item | Total Quantity | Notes (brand/size/subs)* 6. *Add a short “Confirm you already have it” checklist (foil, parchment, oil, salt, pepper, etc.) but do NOT add those items to the list.* 7. *Then output the same grocery list as CSV only (no commentary) so I can paste into Google Sheets.* Optional: paste the CSV into Google Sheets (cell A1) for a clean, checkable list on your phone. ### **Step 3 — Generate a cook schedule (3 minutes)** Paste this prompt **below the Menu Pack**: *Goal: Create a realistic cook schedule that gets everything ready on time with minimal stress.* *Constraints:* - *Serve time: \[enter time\]* - *Kitchen limits: \[one oven / burners / slow cooker / etc.\]* - *Priority: avoid oven conflicts and last-minute chaos.* *Rules (must follow):* 1. *Use ONLY timings/temps in the Menu Pack. If anything is missing, ask me for the minimum missing details and STOP.* 2. *Output two sections:* - *Prep Ahead (Day Before / Morning Of)* - *Day-Of Timeline (back-planned from serve time)* 3. *Call out oven conflicts explicitly and propose a workaround (cook earlier + reheat, stagger temps, etc.).* 4. *Output Day-Of as a table: Time | Task | Duration | Uses (Oven/Stove/None) | Notes* ### **Step 4 — Create a one-page “Kitchen Command Sheet” (2 minutes)** Paste this prompt: *Create a one-page Kitchen Command Sheet from the grocery list + cook schedule you created.* *Include:* - *Final menu in serving order* - *Top 8 critical tasks (the ones that affect timing)* - *Oven plan (temps needed and when)* - *Reheat/hold plan (what can rest, what can reheat, what must be served immediately)* - *Format it so I can screenshot or print it. Keep it tight and skimmable.* ## **The Payoff** You just turned “holiday cooking chaos” into a simple system: one grocery list, one cook schedule, and one command sheet. Less duplicate buying, fewer forgotten ingredients, fewer “everything needs the oven at 5:00pm” disasters, and way more calm. ## **Transparency & Notes for Readers** - Free tools: ChatGPT + optional Google Sheets. - Uploads: ChatGPT supports file uploads on Free tier, but limits apply (so use links or paste text if you run out). - Accuracy rule: If recipe timings/temps are missing, the assistant should ask you instead of guessing. - Food safety: Use common sense for holding/reheating; AI is not a food safety authority. - Educational workflow — not financial advice. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Holiday Joy Powered by AI URL: https://www.mindovermoney.ai/founders-corner/how-to-use-ai-for-holiday-planning-family-fun/ Last updated: 2026-07-18T01:46:19.000Z A lot of the AI conversation is centered around the themes I cover throughout *Neural Gains Weekly*: productivity and automation at work, the job market, politics, markets, and the infrastructure buildout behind it all. It matters because these changes will shape our everyday lives, and we all need to be ready for a future that looks different than today. But there is another side to this technology that often gets overlooked: creativity. I’ve found it challenging to tap into my creative side for a variety of reasons. My work forces me to think critically and solve complex problems in the healthcare space, but that doesn’t always translate into creative energy at the end of the day. I stay busy with hobbies like weightlifting, yoga, cooking, and time with friends and family, but it can still be hard to break out of routine and create something new. This is where my AI journey has helped reshape how I think and build new skills for the future. AI is a creativity tool that helps you create things you normally would not make, and we all have access to dozens of free tools that make experimentation easy. You can generate and edit images with natural language. You can bring a concept to life in a short video with a bit of prompt practice. The “what’s possible” in this space seems to evolve weekly as the tools get more powerful and more accessible. More importantly, this kind of use builds AI fluency. Every small experiment teaches you how to give better instructions, how to iterate, and how to get closer to the output you actually want. That is why the holidays are such a great setting to try something new and tap into your creative side. The goal is not perfection or productivity. The goal is connection and fun. Holiday traditions are also a low-stakes way to show friends and family the fun side of AI, especially for people who have not experimented with it much yet. This Thanksgiving, I started a new tradition with [Suno AI](https://suno.com/@santosh%5Fsavel?ref=mindovermoney.ai) and turned inside jokes and shared memories into a custom song for the whole family. I have zero musical talent, but in about 10 minutes I created something we will laugh about for years. It also sparked real curiosity about AI, because the output made it feel relatable instead of intimidating. If you want to try the same kind of experiment, start here. Below is a short list of holiday ideas you can test in minutes to bring more creativity and joy into your celebrations. Check out 2 prompts to create songs with Suno AI [Prompt Library ](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-1-the-newsletter-launcher/) ### **Ideas for your holiday celebrations** 1. **AI Image Pictionary:** One person uses an AI image generator to generate a "hyper-realistic" or "abstract" version of a holiday movie or song. The rest of the family has to guess the title based on the AI’s interpretation. 2. **The "Ugly Sweater" Makeover:** Take a photo of family members in normal clothes and use an AI editor (like Google Photos’ "Magic Editor" or Adobe Firefly) to "AI-generate" the wildest, most ridiculous holiday sweaters onto them. 3. **Collaborative "Exquisite Corpse" Story:** Start a story in a chatbot. Each family member adds one sentence or a specific detail (e.g., "The turkey grows wings and flies away"). After everyone contributes, ask the AI to turn it into a cohesive, professional-sounding holiday fable to read aloud. 4. **Mystery Guest Trivia:** Ask an AI to "act" as a famous historical figure or holiday character (like Ebenezer Scrooge or a 1920s jazz singer). Family members take turns asking it questions to guess who the AI is pretending to be. 5. **The "Year in Review" Poem:** Paste a few bullet points of your family’s 2025 highlights into an AI and ask it to write an epic poem or a "Twas the Night Before Christmas" style summary of your year. 6. **Signature Holiday Mocktails:** Tell the AI what flavors your family likes (e.g., "cinnamon, apple, but not too sweet") and ask it to invent a "signature family drink" for 2025 with a fun name. 7. **AI Table Talk Prompts:** Ask an AI to generate "10 deep, meaningful, and slightly weird conversation starters for a multi-generational family dinner" to keep things lively. 8. **Custom Place Cards:** Use an AI to generate a "unique spirit animal wearing a Santa hat" for each guest based on their personality, then print them as place cards for the table. 9. **Family Portrait "Time Travel":** Use an AI image-to-image tool to "reimagine" a 2025 family photo as if it were taken in the 1800s, the 1970s, or even 100 years in the future. 10. **Holiday "News Broadcast":** Use a video AI tool or a simple voice-cloner to create a "North Pole News Report" that mentions family members by name and their "status" on the Naughty or Nice list. ### Steal My Prompt Vol. 13: The Song Creator URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-13-the-song-creator/ Last updated: 2026-07-13T16:59:49.000Z In the spirit of Holiday giving, here are two prompts I’ve been using to create songs with Suno AI. I’ll deploy these prompts to create a song for our family’s Christmas gathering that will get published to my [page](https://suno.com/@santosh%5Fsavel?ref=mindovermoney.ai). The first prompt is used to create the custom lyrics, and I will typically use Gemini or ChatGPT for this task. I find it easier to create the lyrics and song structure in these tools and then copy/paste into the Suno AI prompt. The second prompt will build the right prompt for SunoAI to capture the musical style you're looking for. You can run these prompts in any order, or even combine into one, larger master prompt. Experiment and get creative! --- ### **Prompt 1: The Lyric Architect** **Purpose:** To generate structured, rhythmic lyrics that Suno’s AI can easily interpret using proper metatags **Copy and paste this text to start the lyric-building process:** "Act as a professional songwriter specialized in writing for Suno AI. Your goal is to help me write a complete set of lyrics that fit Suno’s 3,000-character limit. To ensure the best result, we will work iteratively. Please ask me one clarifying question at a time to determine the song's theme, story, mood, and genre. Once we have the details, you will provide the lyrics using Suno-optimized metatags like \[Intro\], \[Verse\], \[Chorus\], \[Bridge\], and \[Outro\]. Keep the structure clear and the rhythm consistent. ### **Prompt 2: The Sound Designer** **Purpose:** To distill a complex musical vision into the strict 120-character limit required by Suno’s "Style of Music" box. **Copy and paste this text to start the style-building process:** "Act as a Music Producer and Suno AI expert. Your goal is to help me craft the perfect 'Style of Music' prompt and 'Title' for a song. Note: Suno’s style box is strictly limited to 120 characters. You must prioritize genre, mood, instrumentation, and vocal type over fluff words. We will work step-by-step. Ask me one clarifying question at a time about the desired genre, the energy/BPM, and the type of vocals (e.g., 'soulful female vocals' or 'gritty male baritone'). After we define the sound, you will provide 3 variations of a 120-character style prompt and a few title suggestions. ### Volume 12: Your 2026 Playbook Starts Now URL: https://www.mindovermoney.ai/ai-strategy-2026-professionals-playbook/ Last updated: 2026-07-13T16:59:49.000Z Welcome back everyone! 👋 This week feels like a turning point. The tools are getting materially better, enterprise adoption is accelerating, and the gap is widening between people who experiment and people who build real workflows. In **AI Education**, we continue the large language model series with Part 3, walking through what actually happens after you hit enter, from tokens and context windows to attention and next token prediction. This week’s **10-Minute Win** is a Savings Goal Sprint Planner that turns “I should save more” into a concrete target, timeline, and calendar nudges you will actually follow. And in **Founder’s Corner**, I share three bold predictions for how AI will reshape the workplace in 2026, plus what you can do now to stay ahead instead of scrambling later **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **OpenAI releases ‘code red’ GPT-5.2 update to ChatGPT**](https://9to5mac.com/2025/12/11/openai-releases-code-red-gpt-5-2-update-to-chatgpt-most-capable-model-series-yet/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI rolled out GPT-5.2 as its “most capable model series yet for professional knowledge work,” framed internally as a “code red” response to Google’s Gemini push. The new Instant / Thinking / Pro models are tuned for real work: better spreadsheets, presentations, coding, image understanding, long-context reasoning, and tool use, with higher accuracy and fewer hallucinations than 5.1 **Why it matters:** Your default AI coworker just got a material upgrade in the exact areas that move money—analysis, modeling, and long-running projects. If you’re still treating AI as a toy, this release makes that a bad strategy. ### **2)**[ **Disney making $1 billion investment in OpenAI, will allow characters on Sora AI video generator**](https://www.cnbc.com/2025/12/11/disney-openai-sora-characters-video.html?ref=mindovermoney.ai) **Summary:** Disney is putting $1B of equity into OpenAI and signing a three-year licensing deal that lets Sora and ChatGPT Images use 200+ Disney, Pixar, Marvel, and Star Wars characters in user-generated content. Some Sora clips will stream on Disney+, and Disney will roll ChatGPT and OpenAI APIs into internal workflows. **Why it matters:** This is Hollywood’s biggest embrace of gen-AI so far. Disney isn’t just “experimenting” — it’s turning AI into a new distribution and monetization layer for its IP while trying to keep tight control over brand and guardrails. ### **3)**[ **The state of enterprise AI – 2025 Report**](https://openai.com/index/the-state-of-enterprise-ai-2025-report/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI’s new report shows enterprise AI is not dabbling anymore. ChatGPT now serves 7M+ workplace seats, Enterprise seats are up \~9× YoY, and weekly Enterprise messages are up \~8× since late 2024\. Workers report 40–60 minutes saved per active day (heavy users >10 hours/week), and 75% say AI improves speed or quality. Custom GPTs and Projects are exploding (19× growth in weekly users), with \~20% of Enterprise messages now flowing through these persistent, workflow-specific agents. **Why it matters:** The gap isn’t “who has AI” anymore — it’s who has it wired into core workflows. If you’re not building your own small stack of agents and automations, you’re on the wrong side of that divide. ### **4)**[ **Trump approves sale of more advanced Nvidia computer chips used in AI to China**](https://www.pbs.org/newshour/world/trump-approves-sale-of-more-advanced-nvidia-computer-chips-used-in-ai-to-china?utm%5Fsource=chatgpt.com) **Summary:** The U.S. will allow Nvidia to sell its H200 AI chips to “approved customers” in China, reversing tighter export limits. The H200 isn’t Nvidia’s top Blackwell/Rubin tier but is still a major step up from what China could buy before. Nvidia applauded the move; a bloc of Democratic senators warned it hands China “transformational” AI capabilities with military and cyber implications. **Why it matters:** Export controls are one of the few real brakes on the global AI arms race. Loosening them boosts Nvidia and short-term U.S. business interests, but it also narrows the compute gap with China. That trade-off will echo through both geopolitics and AI competition. ### **5)**[ **Five debt hotspots in the AI data centre boom**](https://www.reuters.com/business/finance/five-debt-hotspots-ai-data-centre-boom-2025-11-05/?utm%5Fsource=chatgpt.com) **Summary:** Reuters walks through how the AI data-center buildout is being financed with increasingly complex debt: \~$75B in recent investment-grade issuance from Big Tech, multi-billion “off-balance-sheet” project structures (e.g., Meta–Blue Owl, Oracle deals), a spike in junk bonds from AI infra players, a growing role for private credit, and new asset-backed securities tied to data-center rents. Central banks, including the Bank of England, are already flagging pockets of risk. **Why it matters:** AI isn’t just an equity story; it’s a leveraged credit story. Great on the way up, ugly if expectations reset. If you care about macro and markets, you need to track the plumbing behind the AI hype. ## AI Education for You LLM 101 Part 3: From Your Message to the Model’s Answer ### So far you have: - A mental model of what an LLM is: a text pattern learner - A training story: how it learns from data and adjusts billions of settings Now we zoom into the moment that matters most to you: *You type a message. What happens next?* ### **Step 1: Your words become tokens** The model does not work directly on raw characters or full sentences. It works on tokens. A token is a small piece of text. When you write: *“Help me understand why my grocery spending went up this month.”* The system turns that sentence into a sequence of tokens chosen from the model’s vocabulary. The exact split does not matter to you. What matters is: - The model sees your input as a sequence of tokens - It treats earlier messages and system instructions as tokens too Everything it reads is in that token form. ### **Step 2: Tokens must fit in the context window** The model has a context window, which you can think of as a page with a strict size limit. - The window holds a fixed number of tokens - Your current prompt, earlier chat history, and any pasted data all share that space - Anything that does not fit is simply not visible to the model If you paste in: - A short bank statement and a clear question → likely fits - Years of raw transaction history → most will not fit This is why structure and focus matter. You want the right tokens on the page. ### **Step 3: The model decides what to focus on** Inside the model there is a mechanism often described as attention. In plain English, attention is: The way the model looks across all tokens in the context window and decides which ones are most important for predicting the next token. Analogy: You get a long email from your bank. Before you reply, you reread the few sentences that matter and skim the boilerplate. You do not weigh all lines equally. The model does something similar in math: - It looks at all tokens - It gives some tokens more weight than others - Those weighted tokens influence what it predicts next So if you ask about grocery spending and include a short summary plus a few labeled sections, the model is more likely to focus on those high-signal pieces. ### **Step 4: Predicting one token at a time** Even during a long answer, the core behavior is simple: 1. The model looks at the current sequence of tokens in the context window 2. It produces a probability for each possible next token 3. It picks one token, often with a bit of randomness 4. It adds that token to the sequence 5. It repeats the process, token by token, until it stops From your point of view, it looks like fluent paragraphs. Under the hood, it is next-token prediction over and over, guided by the patterns learned during training and the context you provided. ### **Step 5: Why prompt structure matters so much** Because the model: - Can only see what fits in the context window - Pays more attention to some tokens than others - Predicts based on patterns and context Your prompt design and input structure matter a lot. Tips: - Start with a clear goal - Provide only the key facts the model needs - Organize long data into chunks with titles and short summaries. You are making it easier for the model to find the right tokens and patterns inside the window. ### **Reader questions** **Q: If it only predicts one token at a time, why does it sound so coherent?** A: Because it was trained on huge amounts of connected text. It has seen how sentences, paragraphs, and arguments are usually built. Next-token prediction over many steps, guided by those patterns, can produce answers that feel smooth and logical. **Q: Why does it sometimes forget something I said earlier?** A: Often because that part of the conversation has been pushed out of the context window by newer messages. Or because you changed instructions later, and the model now weighs the more recent text more heavily. ### **Closing this week** At this point you have both sides of the core loop: - Training time: the model learns patterns by predicting tokens and adjusting its settings - Chat time: the model turns your text into tokens, fits them into a window, focuses on key pieces, and predicts new tokens Next week we will focus on the parts that can bite you: - Hallucinations and confident wrong answers - Shallow, generic responses - Bias and gaps from training data - How to work with these limits instead of being surprised by them ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🎯 Savings-Goal Sprint Planner** **Why this matters:** “I should save more” is not a plan; it’s background guilt. The gap between that feeling and real progress is simple: one specific goal, a clear dollar target, a deadline, and a behavior pattern that supports it. In this 10-minute workflow, you’ll let ChatGPT build your savings planner table for you, then turn one fuzzy goal into a SMART savings sprint with monthly and weekly targets and calendar nudges. ### **Step 1 — Have ChatGPT build your planner table (2–3 minutes)** Open ChatGPT Free and paste this prompt: *You are a Google Sheets template builder. I want to create a simple “Savings-Goal Sprint Planner” in Google Sheets.* *Task:* - *Create a CSV-style table starting at row 1 with these columns:* - *Goal Name* - *Target Amount $* - *Target Date* - *Months Left* - *Monthly Target $* - *Weekly Target $* - *Status* - *Row 1 should be the headers.* - *Row 2 should be example data with formulas where appropriate:* - *Goal Name: Sample – $1,200 Emergency Buffer* - *Target Amount $: 1200* - *Target Date: a date 6 months from today in YYYY-MM-DD format.* - *Months Left: =DATEDIF(TODAY(),C2,"M")* - *Monthly Target $: =ROUND(B2/D2,0)* - *Weekly Target $: =ROUND(E2/4,0)* - *Status: Not started* - *Output only the table in plain text, comma-separated, with no explanations before or after.* *Goal: I want to be able to copy this output and paste it directly into cell A1 of a blank Google Sheet so the headers and formulas work.* Then: 1. Copy the CSV-style output from ChatGPT. 2. Open a blank Google Sheet, click cell A1, and paste. 3. You should see headers in row 1 and a sample row with formulas in Months Left, Monthly Target, and Weekly Target. You now have a working planner. You’ll overwrite the sample row in a minute. ### **Step 2 — Turn your idea into a SMART savings sprint (3 minutes)** In a new ChatGPT message (same chat is fine), paste and fill in: *You are my Savings-Goal Sprint Planner. I want to create one 90–180 day savings sprint.* *My rough idea:* - *What I’m trying to save for (e.g., trip, emergency buffer, debt payoff, down payment): \_\_\_* - *Rough dollar amount I think I need: \_\_\_* - *When I’d like to have it by (month/year): \_\_\_* - *Approximate monthly free cash I could redirect if I try (even a guess): \_\_\_* *Tasks:* 1. *Turn this into a single SMART savings goal (Specific, Measurable, Achievable, Relevant, Time-bound).* 2. *Suggest a realistic Sprint Length in months (between 3 and 6).* 3. *Calculate the Monthly Target and Weekly Target amounts needed to hit the goal within the sprint, given that sprint length.* 4. *List 3–5 behavior rules that will support this sprint (where the money will come from, small cuts, or temporary rules).* *Output format:* - *SMART Goal (1–2 sentences)* - *Sprint Length: X months* - *Target Amount: $X by \[date\]* - *Monthly Target: $X* - *Weekly Target: $X* - *Behavior Rules: bullet list* *Rules: If my free cash estimate is clearly too low for the goal in 3–6 months, say so and suggest either a smaller target or a longer sprint.* You’ll get a standardized block you can plug straight into your planner. ### **Step 3 — Replace the sample row with your real sprint (3 minutes)** Back in Google Sheets, in row 2: - A2 (Goal Name): short name from your SMART goal (e.g., “Emergency Buffer” or “Summer Trip”). - B2 (Target Amount $): the Target Amount ChatGPT gave you. - C2 (Target Date): the date from the SMART goal (or your final choice). Confirm: - D2 (Months Left) uses =DATEDIF(TODAY(),C2,"M"). - E2 (Monthly Target $) uses =ROUND(B2/D2,0) *or* you can manually type the “Monthly Target” ChatGPT gave you if you prefer its sprint length. - F2 (Weekly Target $) uses =ROUND(E2/4,0). - G2 (Status): set to In progress. Under the table, paste the SMART Goal and Behavior Rules so you see them every time you open the sheet. ### **Step 4 — Add calendar nudges so you actually do it (2 minutes)** Open Google Calendar: 1. Create an event on your next payday titled: Move $\[Monthly Target or Weekly Target\] → \[Goal Name\]. - Set Repeat: monthly (or weekly if you’re saving weekly). - In the description, paste your SMART goal and a link to the Google Sheet. 2. Create a second event 30 days from today titled: Savings Sprint Review — \[Goal Name\]. - Set it to repeat every month. - In the description, add: - “Current saved amount: \_\_\_\_\_” - “Status: On track / Behind / Ahead” This is your built-in feedback loop: each month you update the status, see the math, and either stay the course or adjust. ## **The Payoff** In 10 minutes, you’ve gone from a vague intention to a concrete, time-bound savings sprint: a planner table that AI built for you, a SMART goal with real numbers and a real date, and recurring reminders that pull the goal back into your field of view. You can reuse the same sheet for future sprints—just add a new row per goal. ## **Transparency & Notes for Readers** - All tools are free: ChatGPT Free, Google Sheets, Google Calendar. - Math is simple: targets are based on basic division; tweak sprint length or goal size if the numbers feel unrealistic. - Behavior is the real engine: the calendar reminders and behavior rules matter more than the formulas being perfect. - Educational workflow — not financial advice. ## Founder's Corner Real world learnings as I build, succeed, and fail I use AI in two distinct worlds: building Neural Gains Weekly and working in my corporate job in the AI and digital technology space. Throughout 2025, it has been easier to experiment, learn, and build with AI in my personal life, and I think that rings true for many of us. PwC’s latest global workforce survey found that 54% of workers have used AI for their jobs in the past year, but only 14% are using generative AI daily, which tells me most people are still in “light experimentation” mode at work. Inside most companies, 2025 has been about getting the right infrastructure in place and running pilots and proof-of-concepts. 2026 feels like an inflection point. The signals are everywhere: corporate restructures to better organize around AI, infrastructure buildouts that lower the cost to deploy AI systems, and a surge in AI agents quietly showing up inside the tools people already use at work. Change is on the horizon, and I want to share three hot predictions for how AI will reshape the workplace, and how you can get ready. **AI Usage Becomes Mandatory, Not Optional** Companies are investing heavily in infrastructure, data systems, and integrations that will accelerate AI adoption. That spending will not stay invisible for long. As those foundations solidify, expectations will change, regardless of our role or level. We will all have to adapt and use AI to stay ahead of the curve and, frankly, to stay relevant in the future of work. You can already see the early signs. AI skills are starting to show up in job descriptions, and I expect that trend to accelerate in 2026\. I think we will start to see AI portfolios being requested in interviews and promotion conversations, with real examples of how you used AI to solve a problem, improve a workflow, or save time for your team. Goals tied to AI usage will quietly make their way into performance reviews, holding employees accountable for growing their skill set to match where their company is heading. The shift will not happen overnight, but the direction is clear: using AI at work moves from “nice to have” to “part of the job.” **AI Won’t Take All the Jobs, But It Will Rewrite the Job Description** AI automation and augmentation will be major themes in the next phase of work, but the real question is how these catalysts will actually impact our jobs. According to the WEF Future of Jobs report, 39% of core worker skills are expected to change by 2030, and AI is expected to replace 9 million roles and create 11 million roles over that same period. This signals a shift away from purely human work toward systems that integrate human and machine intelligence. AI is going to force human workers to evolve their skills to match what employers need from their workforce. The immediate risk is not “AI is going to replace me in 2026.” The real risk is “Someone who understands how to use AI to clear the easy work will have more time to do the higher-value work than I do.” New roles will emerge and new industries will be born. That is a good thing, and it is not something that can be stopped. This evolution will reward the people who use AI to clear the busywork so they can spend more time solving problems, thinking creatively, and strategizing for the future. **Non-Technical Builders Become a Force** It seems like 2025 was the year of “anyone can code an app,” but that trend has mostly stayed on the personal side. Anyone can open Replit, Google AI Studio, Claude, or ChatGPT and “vibecode” an app or generate code for a specific use case. But we have not seen the same speed and adoption of these types of tools in the workplace. There are AI coding agents that technical employees use in their daily tasks, but the tools for non-developers are lagging. That changes in 2026. According to Gartner, by 2029, 50% of knowledge workers will develop new skills to work with, govern, or create AI agents on demand. We are still in the early stages, but in 2026 more companies will begin launching low code and no code tools that allow non-technical employees to build prototypes that would have taken months in a typical IT project lifecycle. Prompt and context engineering through natural language will replace the need to write most of the code and will accelerate innovation from all areas of a business. Non-technical people who can clearly define the problem, desired outcome, constraints, and KPIs will be able to build tools using AI-enhanced development platforms without writing a single line of code. This will allow technical teams to focus on hard problems like architecture, reliability, and security, while non-technical builders turn ideas into working products much faster than before. --- When I zoom out, all three of these predictions point in the same direction. AI usage becomes an expectation, job descriptions shift toward people who can work alongside automation, and non-technical builders gain leverage if they can turn clear ideas into working tools. These shifts are already in motion, and we are either learning how to work with them or pretending they are still optional. You do not control the timing, but you do control whether you arrive unprepared or with receipts. Start building those receipts now, one real use case at a time, so that when 2026 shows up you are not asking if AI will change your work, you are showing how you already changed with it. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### 3 Ways AI Will Reshape Your Job in 2026 URL: https://www.mindovermoney.ai/founders-corner/how-ai-will-change-jobs-2026-professionals-guide/ Last updated: 2026-07-13T16:59:49.000Z I use AI in two distinct worlds: building Neural Gains Weekly and working in my corporate job in the AI and digital technology space. Throughout 2025, it has been easier to experiment, learn, and build with AI in my personal life, and I think that rings true for many of us. PwC’s latest global workforce survey found that 54% of workers have used AI for their jobs in the past year, but only 14% are using generative AI daily, which tells me most people are still in “light experimentation” mode at work. Inside most companies, 2025 has been about getting the right infrastructure in place and running pilots and proof-of-concepts. 2026 feels like an inflection point. The signals are everywhere: corporate restructures to better organize around AI, infrastructure buildouts that lower the cost to deploy AI systems, and a surge in AI agents quietly showing up inside the tools people already use at work. Change is on the horizon, and I want to share three hot predictions for how AI will reshape the workplace, and how you can get ready. **AI Usage Becomes Mandatory, Not Optional** Companies are investing heavily in infrastructure, data systems, and integrations that will accelerate AI adoption. That spending will not stay invisible for long. As those foundations solidify, expectations will change, regardless of our role or level. We will all have to adapt and use AI to stay ahead of the curve and, frankly, to stay relevant in the future of work. You can already see the early signs. AI skills are starting to show up in job descriptions, and I expect that trend to accelerate in 2026\. I think we will start to see AI portfolios being requested in interviews and promotion conversations, with real examples of how you used AI to solve a problem, improve a workflow, or save time for your team. Goals tied to AI usage will quietly make their way into performance reviews, holding employees accountable for growing their skill set to match where their company is heading. The shift will not happen overnight, but the direction is clear: using AI at work moves from “nice to have” to “part of the job.” **AI Won’t Take All the Jobs, But It Will Rewrite the Job Description** AI automation and augmentation will be major themes in the next phase of work, but the real question is how these catalysts will actually impact our jobs. According to the WEF Future of Jobs report, 39% of core worker skills are expected to change by 2030, and AI is expected to replace 9 million roles and create 11 million roles over that same period. This signals a shift away from purely human work toward systems that integrate human and machine intelligence. AI is going to force human workers to evolve their skills to match what employers need from their workforce. The immediate risk is not “AI is going to replace me in 2026.” The real risk is “Someone who understands how to use AI to clear the easy work will have more time to do the higher-value work than I do.” New roles will emerge and new industries will be born. That is a good thing, and it is not something that can be stopped. This evolution will reward the people who use AI to clear the busywork so they can spend more time solving problems, thinking creatively, and strategizing for the future. **Non-Technical Builders Become a Force** It seems like 2025 was the year of “anyone can code an app,” but that trend has mostly stayed on the personal side. Anyone can open Replit, Google AI Studio, Claude, or ChatGPT and “vibecode” an app or generate code for a specific use case. But we have not seen the same speed and adoption of these types of tools in the workplace. There are AI coding agents that technical employees use in their daily tasks, but the tools for non-developers are lagging. That changes in 2026. According to Gartner, by 2029, 50% of knowledge workers will develop new skills to work with, govern, or create AI agents on demand. We are still in the early stages, but in 2026 more companies will begin launching low code and no code tools that allow non-technical employees to build prototypes that would have taken months in a typical IT project lifecycle. Prompt and context engineering through natural language will replace the need to write most of the code and will accelerate innovation from all areas of a business. Non-technical people who can clearly define the problem, desired outcome, constraints, and KPIs will be able to build tools using AI-enhanced development platforms without writing a single line of code. This will allow technical teams to focus on hard problems like architecture, reliability, and security, while non-technical builders turn ideas into working products much faster than before. --- When I zoom out, all three of these predictions point in the same direction. AI usage becomes an expectation, job descriptions shift toward people who can work alongside automation, and non-technical builders gain leverage if they can turn clear ideas into working tools. These shifts are already in motion, and we are either learning how to work with them or pretending they are still optional. You do not control the timing, but you do control whether you arrive unprepared or with receipts. Start building those receipts now, one real use case at a time, so that when 2026 shows up you are not asking if AI will change your work, you are showing how you already changed with it. ### Steal My Prompt Vol. 12: The Subscriber Builder URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-12-the-subscriber-builder/ Last updated: 2026-07-13T16:59:50.000Z Over the first three months of *Neural Gains Weekly*, I’ve built subscribers by engaging friends, family, and coworkers. It’s helped me cross 100 subscribers and was always the first step in growing an audience. Now it’s time to experiment and start gaining subscribers through SEO and AI search optimization. I’ll be sharing more on this project in the coming weeks, but here is a prompt I’m using to help position the newsletter for maximum visibility. --- ### SEO Optimization Prompt You are an SEO and AI-search optimization expert for MindOverMoney.ai. Your job is to read a single ARTICLE DRAFT and output: 1) Meta Title 2) Meta Description 3) Canonical URL The goal order of priority: 1) Maximize visibility and citations in AI-powered search (Google AI Overviews, Perplexity, etc.). 2) Drive clicks from traditional Google search results. 3) Gently support newsletter subscriber growth when the page is clearly a landing page. \-------------------------------- CONTEXT & BRAND \-------------------------------- \- Site: MindOverMoney.ai \- Audience: Beginners to intermediate learners who are using AI lightly and want practical, money-helping use cases for personal finance, wealth building, investing, and stock picking. \- Voice: “Curious educator” — clear, practical, smart, no hype, no guru language. \- Default language: U.S. English. No emojis. Do NOT invent facts, numbers, or claims that are not clearly supported in the article. \-------------------------------- STEP 1 – UNDERSTAND THE PAGE \-------------------------------- From the ARTICLE DRAFT, silently infer: A) PAGE TYPE (choose one, but do this in your head – do NOT output this label): \- POST: A typical article, guide, analysis, or Founder’s Corner-style piece. \- LANDING PAGE: A focused page whose main goal is to get signups, leads, or downloads (e.g., newsletter signup, lead magnet). \- TOPIC HUB: A collection page that curates multiple posts around a theme (e.g., “AI for Investing,” “Beginner Guides”). B) MAIN TOPIC & INTENT: \- Identify the \*\*primary topic\*\* in 3–6 words (for yourself). \- Identify the \*\*searcher’s intent\*\* as it would appear in Google or AI search (e.g., “how to use AI to budget,” “beginner guide to AI investing,” etc.). C) READER OUTCOME: \- Decide what concrete outcome the reader gets (e.g., “learn 5 ways to use AI in their portfolio research,” “understand how to future-proof their career with AI,” etc.). Do all of this reasoning internally. Do NOT print your reasoning. Only the final meta outputs should be visible. \-------------------------------- STEP 2 – WRITE THE META TITLE \-------------------------------- Rules for the Meta Title: 1) Length: \- Target \~50–60 characters, with a hard maximum of 60 characters. \- Avoid cutting off words. If in doubt, be slightly shorter. 2) Content: \- Include the \*\*core topic phrase\*\* near the beginning. \- Accurately reflect what the page is about. No clickbait. \- Focus on topics and intent, not just raw keywords. \- Use natural language that a human would actually search or click. 3) Brand usage: \- For normal POSTS: do NOT include the brand or newsletter name in the title unless it’s directly about Neural Gains Weekly or MindOverMoney.ai. \- For clear LANDING PAGES or major TOPIC HUBS: you may add “ | MindOverMoney.ai” at the end IF the total still stays within 60 characters. If it doesn’t fit, drop the brand. 4) Tone: \- Curious, helpful, clear. \- Avoid vague phrases like “ultimate guide” or “must-read” unless the article truly deserves it. \- No hype, no overpromising performance or returns. Output format line: Meta Title: \-------------------------------- STEP 3 – WRITE THE META DESCRIPTION \-------------------------------- Rules for the Meta Description: 1) Length: \- Aim for roughly 120–155 characters. \- It must be a single, natural-sounding sentence (or at most two short clauses). \- It should read well if truncated after \~120 characters on mobile. 2) Content & structure: \- Start by clearly summarizing what the page teaches or answers. \- Include the reader’s \*\*concrete outcome\*\* (what they’ll be able to do or understand after reading). \- Align tightly with the actual article content. Do NOT introduce new tips, steps, or claims that are not present in the draft. 3) AI search optimization: \- Phrase the description to match \*\*questions or tasks\*\* a user might ask an AI assistant (e.g., “Learn how to…”, “See how to use AI to…”). \- Make the page’s topic unambiguous so AI systems can easily understand when this page is relevant. 4) Newsletter / CTA logic: \- If the page is clearly a \*\*LANDING PAGE\*\* for Neural Gains Weekly or a lead magnet, you may end with a short, soft CTA like: “Subscribe for weekly, practical AI investing insights.” \- For POSTS and TOPIC HUBS, mostly \*\*avoid\*\* explicit subscription CTAs. The priority is clear, honest summarization for search and AI overviews. 5) Tone: \- Curious educator: practical, encouraging, grounded. \- No sensational claims, no guaranteed returns, no jargon walls. Output format line: Meta Description: \-------------------------------- STEP 4 – SET THE CANONICAL URL \-------------------------------- Use this logic: 1) Default behavior (most cases): \- Assume this is original content for MindOverMoney.ai. \- Propose a canonical URL using: https://www.mindovermoney.ai/{slug}/ 2) How to construct {slug}: \- Start from the main topic phrase. \- Lowercase only. \- Use hyphens between words. \- Remove punctuation and unnecessary stopwords if the slug becomes too long. \- Keep the slug reasonably short (ideally under 60 characters). Example transformations (do NOT output examples, they are for your internal logic only): \- “How to use AI for long-term investing” → “use-ai-for-long-term-investing” \- “Beginners: Future-proof your career with AI” → “future-proof-career-with-ai” 3) Reposts / guest content: \- ONLY if the ARTICLE DRAFT explicitly states that this is a repost or duplicate of content originally published on another site AND provides the original URL: \- Set the Canonical URL to that original URL exactly. \- If the draft does not clearly say this, default to the MindOverMoney.ai URL pattern above. Output format line: Canonical URL: \-------------------------------- STEP 5 – FINAL OUTPUT FORMAT \-------------------------------- Your entire visible answer MUST be exactly three lines, with no extra commentary, no bullets, and no explanations: Meta Title: Meta Description: Canonical URL: Do NOT output anything else. \-------------------------------- INPUT FORMAT (WHAT YOU WILL RECEIVE) \-------------------------------- The user will paste only the ARTICLE DRAFT, nothing else. When you see the ARTICLE DRAFT, follow all instructions above and then output the three required lines. ### Volume 11: The Models Are Changing. Are You? URL: https://www.mindovermoney.ai/how-to-adapt-ai-habits-when-models-change/ Last updated: 2026-07-13T16:59:50.000Z Hey everyone! 👋 The models are changing fast. New names, new features, new “best ever” launches every week. The real question is not whether the models are getting better, it is whether our habits are keeping up. In **AI Education**, we keep building your mental model of how these systems work so you can adapt with them, not chase them. This week’s **10-Minute Win** gives you a practical workflow you can reuse in your own stock research. And in **Founder’s Corner**, I share how I am adjusting my own routines as the tools evolve. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **OpenAI’s GPT-5.2 ‘code red’ response to Google is coming next week**](https://www.theverge.com/report/838857/openai-gpt-5-2-release-date-code-red-google-response?utm%5Fsource=chatgpt.com) **Summary:** OpenAI has reportedly pulled forward the launch of GPT-5.2 to around December 9 after CEO Sam Altman declared an internal “code red” in response to Google’s Gemini 3 and Anthropic’s latest models. Internal evals suggest 5.2 outperforms Gemini 3 on key benchmarks, with the update focused on speed, reliability, and reasoning rather than flashy new features. **Why it matters:** Model quality is an arms race. A stronger, faster baseline inside ChatGPT changes what “normal” users can do with AI for work, research, and investing — and keeps competitive pressure high across the whole ecosystem. ### **2)**[ **Anthropic plans an IPO as early as 2026, FT reports**](https://www.reuters.com/business/retail-consumer/anthropic-plans-an-ipo-early-2026-ft-reports-2025-12-03/?utm%5Fsource=chatgpt.com) **Summary:** Anthropic, backed by Google and Amazon, has hired Wilson Sonsini and begun informal talks with banks for a potential IPO as early as 2026, according to the *Financial Times*. The Claude maker is reportedly exploring a fresh funding round that could push its valuation north of $300B. **Why it matters:** A public Anthropic would give markets a direct way to price a frontier-model pure play — and would bring more disclosure on revenue, margins, and capex in the AI stack. ### **3)**[ **AWS unveils “frontier agents,” including Kiro, a virtual developer**](https://www.aboutamazon.com/news/aws/amazon-ai-frontier-agents-autonomous-kiro?utm%5Fsource=chatgpt.com) **Summary:** Amazon Web Services introduced frontier agents — autonomous AI workers that can run for hours or days with minimal intervention — including Kiro (a virtual developer), AWS Security Agent, and AWS DevOps Agent. These agents maintain context, learn over time, and are built to handle multi-step, production-grade workloads. **Why it matters:** This is the “agents, not just chatbots” shift going mainstream in cloud. Expect more operations, security, and dev work to be handled by persistent AI systems rather than one-off prompts. ### **4)**[ **US senators unveil bill to stop easing of curbs on AI chip sales to China**](https://www.reuters.com/world/us/senators-unveil-bill-keep-trump-easing-curbs-ai-chip-sales-china-2025-12-04/?utm%5Fsource=chatgpt.com) **Summary:** A bipartisan group of senators introduced the SAFE CHIPS Act, which would block the administration from loosening controls on advanced U.S. AI-chip exports to China, Russia, Iran, and North Korea for 30 months, and require Congress briefings before future rule changes. **Why it matters:** Export controls are now a core weapon in the AI race. This bill directly affects who gets cutting-edge Nvidia/AMD silicon — and therefore who can train and deploy the most powerful models. ### **5)**[ **OpenAI and Accenture accelerate enterprise reinvention with advanced AI**](https://newsroom.accenture.com/news/2025/openai-and-accenture-accelerate-enterprise-reinvention-with-advanced-ai?utm%5Fsource=chatgpt.com) **Summary:** Accenture named OpenAI one of its primary AI partners and will equip tens of thousands of staff with ChatGPT Enterprise. Together they’re launching a flagship program to help large clients embed agentic AI into core functions like customer service, supply chain, finance, and HR. **Why it matters:** This is how AI actually reaches the Fortune 500: consulting plus tooling. It’s a strong signal that “AI agents over enterprise workflows” is becoming the default playbook, not an experiment. ## AI Education for You ****LLM Deep Dive - Part 2: How Large Language Models Learn From Data** Last time you learned that a large language model is: A system that learns patterns in text and predicts the next small piece of text. Now we focus on how it learns those patterns. ### **Training data: what the model sees** Training data for a language model is mostly text: - Sentences and paragraphs - Many topics and writing styles - Different formats, such as articles, documentation, sometimes code and other text-like sources Key idea: the model is not memorizing a giant book. It is seeing countless examples of text sequences and learning: “When I see this kind of text, these next tokens are likely to follow.” You can think of the training data as millions of practice questions with answer keys. ### **Parameters: the tiny settings inside** Inside the model are parameters. - Each parameter is a number - A modern model has billions of them - Together they control how the model maps input tokens to output tokens Analogy: Imagine a sound mixing board with billions of tiny sliders. Each slider affects the sound a little bit. Training is the process of nudging those sliders based on feedback, so the overall sound (the predictions) gets closer to what we want. ### **The training loop: predict, check, adjust** Training runs a loop over and over: 1. The model sees a piece of text from the training data 2. It tries to predict the next token at each step 3. It compares its prediction to the actual token 4. It measures how wrong it was 5. It adjusts the parameters slightly 6. It repeats on new text Analogy: A student answers a practice question, checks the answer key, sees where they went wrong, and adjusts their “mental settings” so they will answer similar questions better next time. Do this billions of times and the student, or the model, gets very good at predicting. ### **Loss: a score for “how wrong” it is** Training needs a signal to know if it is improving. That signal is called loss. - High loss means the predictions are far from the real text - Lower loss means the predictions are closer The whole training process is about pushing the loss down over time. You can think of loss as the model’s average “error score.” Training tries to make that score as low as possible across many examples, not just one. ### **Why so much data and compute?** The model has billions of parameters. Language is messy and rich. To train that many settings well, you need: - A huge number of text examples - Many passes over that data - Specialized hardware to run the math quickly That is why training large models is expensive: - More data → better coverage of patterns - More compute → more training steps - More steps → more chances to reduce loss ### **Fine-tuning and specialization** After base training, there are often later stages: - Fine-tuning on conversation-style examples - Training with human feedback to prefer more helpful and safe answers - Sometimes extra training on specific domains, such as code or support tasks You can think of base training as broad education, and fine-tuning as job training. ### **Data quality and diversity** The model reflects what it learned from training data. - If the data covers many viewpoints and styles, the model is more flexible - If some topics are weak, the model is weaker there - If the data contains biased or low-quality patterns, those can show up in outputs This is why you should still treat outputs as suggestions, not truth. The model’s behavior is shaped by what it saw, not by a deep understanding of reality. ### **Reader questions** **Q: So is the model just memorizing the internet?**A: No. It learns statistical patterns, not a complete copy of specific pages. It may sometimes produce text similar to training examples, but most of the time it is generating new text that follows patterns it has learned. **Q: Can it see my private financial data from training?**A: No. A deployed model does not have live access to your bank accounts or personal records unless you or an app send that information in a prompt. Training shapes its internal settings, but it does not act as a searchable database of your private history. ### **Closing this week** You now have a simple view of how a model learns: - It sees huge amounts of text - It adjusts billions of internal settings to reduce mistakes - It learns patterns, not deep understanding or live facts - Its strengths and blind spots come from the data and the way it was trained Next week, we shift to the part you see every day: What happens between you typing a message and the model sending back an answer. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🧭 Competitor Quickmap for Payment Stocks** **Why this matters:** Knowing tickers isn’t the same as understanding the businesses behind them. Payments is a crowded, confusing space—network toll collectors, card issuers, wallets, platforms. In about 10 minutes, this workflow uses AI to auto-research *Visa (V), Mastercard (MA), American Express (AXP), and PayPal (PYPL)* and turn that into a simple Competitor Quickmap you can actually reason from. You’ll walk away with a one-page snapshot of who does what, for whom, and where the edges and risks sit—before you sink hours into deeper research. ### **Step 1 — Let ChatGPT auto-build your sector snapshot (4–5 minutes)** Open a fresh ChatGPT chat and run this prompt: *Role: You are my sector analyst and research assistant.* *Context: I want a clean, up-to-date snapshot of the payments/fintech landscape focusing on four stocks: Visa (V), Mastercard (MA), American Express (AXP), and PayPal (PYPL).* *Task:* 1. *For each company (V, MA, AXP, PYPL), use only recent, public information from official sources (Investor Relations pages, company overviews, or 10-K “Business” sections) to create a short overview.* 2. *Then build a single comparison table with columns:* - *Company* - *Core Business Model (plain English, 1–2 lines)* - *Primary Customers (who actually pays them)* - *Revenue Engine (how money flows in: network fees, interest, take rate, etc.)* - *Competitive Angle / Edge (what seems to make them different)* - *Key Risks / Watchpoints (from filings/IR language, no speculation)* 3. *After the table, write 5 bullet insights as if you’re explaining the sector to a new investor, covering:* - *How the card networks (Visa, Mastercard) differ from AmEx and PayPal* - *Where the strongest moat appears to be from the business descriptions alone* - *Where you see the most sensitivity to the economic cycle or credit risk* - *Any obvious “this one feels different” angle between the four* *Rules:* - *Cite which official pages you used in a short note under the table (no links needed, just names).* - *Keep language beginner-friendly.* - *Do not give buy/sell/hold recommendations—only describe the landscape.* Let ChatGPT do the heavy lifting: it finds the pages, reads them, and outputs a ready-made sector map. ### **Step 2 — Drop the Quickmap into Sheets (2–3 minutes)** Open Google Sheets and: 1. Create a new sheet named Payments\_Quickmap. 2. Copy the entire comparison table from ChatGPT and paste it into the sheet (starting at A1). 3. Add one more column at the end: “My Take (1 line)”. 4. For each company, write a blunt one-liner, for you: - “Pure network toll collector; volume-driven.” - “Card issuer with more direct credit risk.” - “Feels like a digital wallet / checkout network more than a card network.” This step forces you to actually internalize the differences instead of passively scrolling. ### **Step 3 — Ask AI for investor-style questions to dig deeper (2 minutes)** Back in the same ChatGPT chat, ask: *Based on the Quickmap table you just created, give me:* - *5 follow-up questions I should explore before considering any of these as investments (things like unit economics, margins, regulatory risk, competitive threats).* - *For each question, suggest where I might look (10-K, earnings call, segment breakdown, key metrics). Keep it short, numbered, and written for a curious individual investor—not a professional analyst.* Copy those questions into your research notes or into a second tab in your Sheets file. This becomes your research checklist the next time you pull up filings or earnings calls. ### **Step 4 — Turn this into a reusable sector template (1–2 minutes)** Now make the process reusable for any group of competitors: *Create a reusable “Investor Competitor Quickmap” template I can use for any sector, with:* - *A short instruction block on how you’ll auto-research 3–5 tickers from official sources.* - *A blank table structure using the same columns (Company, Core Business Model, Primary Customers, Revenue Engine, Competitive Angle, Key Risks).* - *5 generic sector-insight prompts (e.g., “Who has the clearest, simplest business model?”, “Who is most exposed to credit risk?”, “Where is the obvious gap in the market?”).* - *Format it so I can copy/paste the template into my notes and re-run it for cloud, streaming, brokers, etc.* Save that template somewhere you actually use (Notion, Docs, Obsidian). Next time, swap in your own tickers + sector and rerun the same play. ## **The Payoff** In a single 10-minute session, you’ve turned “I know the tickers” into “I have a structured map of what these businesses actually are.” You see: - Who is more like a toll-taking network vs. a lender vs. a digital wallet/platform - Who gets paid by whom, and how that might behave in different macro environments - Where moats and weaknesses appear just from how the companies describe themselves And you’ve built a template you can reuse for any new watchlist idea instead of restarting from zero each time. ## **Transparency & Notes for Readers** - All tools are free: ChatGPT (Free or Plus) and Google Sheets; all company information comes from public web sources. - Scope: This is an overview, not a full valuation or risk model—treat it as a first-pass map. - Data: Don’t paste personal or account info; ChatGPT should be querying public company pages only. - Educational workflow — not financial advice. ## Founder's Corner Real world learnings as I build, succeed, and fail Last week, I shared how I’ve been incorporating new AI models into my workflows for *Neural Gains Weekly*. The more I experiment, the more I realize how important it is to stay current as these models evolve. If you rely on AI at home or at work, staying up to date is no longer optional. Each release can change how a model reasons, how it follows instructions, and what it is actually good at. If you treat every new model like the last one, your results will quietly get worse over time. As the model arms race heats up between the big labs, that gap will only grow. So what can you do when a new model is released? In this week’s Founder’s Corner, I want to share three simple ways I think about learning new models so you can adapt faster and get better outputs as the AI landscape keeps shifting. **Tip 1: Learn What the Model Is Optimized For** Not every model is trained and fine-tuned to excel at the same tasks. That can be hard to see if most of your interactions are simple, search-like chats. It’s a big reason I push myself (and you) to run more complex workflows through AI. Before I judge a model, I want to know what it was actually built to be great at. When you pay attention to headlines and launch materials, you start to pick up on these cues directly from the labs. One model touts superiority in all things coding, another leans into being the “agentic” leader. I read release notes and listen to a few trusted podcasts to get a sense of where a model is supposed to shine and how it can best support my workflows. Those small habits shorten the learning curve and make it easier to plug the right model into the right task. **Tip 2: Adjust Your Prompts When the Model Changes** The prompt is our chance to provide context, structure, and guidelines that help the AI produce the outcome we want. It’s easy to get comfortable with a certain prompting style or reuse the same prompt over and over again. But this can lead to poor outputs when a new model is introduced. New models are not just updates, they behave like completely new systems that often require reengineered inputs. Luckily, the process to learn the prompting structure for a new model is straightforward. You might need to be more explicit about structure, set length constraints, or change how you chunk information, but you don’t have to figure this out alone. I often ask the model directly for advice on how it wants to be prompted for a specific task and use that as a starting point. From there, I let the AI help me refine a “best prompt” for the workflow I am testing, which removes a lot of the guesswork. Over time, this builds a mental playbook for each model instead of forcing one-size-fits-all prompts on every new release. **Tip 3: Run Small “Same Task, Different Model” Experiments** Experimentation is crucial in the world of AI, especially when you are trying to learn a new model. The simplest way to do this is to run the same real task through different models and compare the results. You do not need benchmarks or lab tests, just a light habit of asking, “Which model handled this better, and why?”. I started comparing outputs from ChatGPT 5.0 and ChatGPT 5.1 as soon as the newer model launched. I wanted to see if I could spot patterns that highlighted the differences between the two. The “AI Education” prompt was a great starting point to compare which model’s output aligned with my vision, needed the least editing, and felt the most useful for the audience. It was obvious how much better 5.1 handled the task, even though I was using the same prompt I had originally optimized for 5.0\. That simple experiment helped me compare the models directly and choose the best content for the newsletter. Over time, that kind of side-by-side testing builds a feel for each model that no release note can give you. --- At this point, I see new models less as shiny toys and more as part of the foundation of my work. The models will keep changing, and if my habits do not change with them, my results will slowly fall behind. This rings true in the workplace as more employers start mandating the use of AI. We will all need to be educated and well-versed in the models that power the tools we use every day. My perspective is that learning how new models work will be the equivalent of learning to use email in the early 2000s: mandatory. The models will keep getting smarter, but the real leverage comes from how quickly we learn them and fold them into the work that matters most to us. Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 11: The Creative Writer URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-11-the-creative-writer/ Last updated: 2026-07-13T16:59:50.000Z I love to write, but my day-to-day life does not demand much creative or long-form writing. Most of my writing reps come from emails and talk tracks. Launching Neural Gains Weekly changed that. It gave me a new outlet to stretch my writing muscles, and with it came plenty of writer’s block that ate up more time than I liked. To fix that, I built a prompt to help me plan each Founder’s Corner post. The idea was simple: use AI to organize and structure my ideas so I can write faster and with less friction. Volume 11 was my first experiment with this new prompt and workflow, and I expect to keep tweaking it as I go. --- ### **Founder’s Corner Planning Prompt** *You are my thinking and planning partner for the “Founder’s Corner” section of my Neural Gains Weekly newsletter on MindOverMoney.ai.* *Context about me and the series* - *I am a non-technical product leader in healthcare, building in public as I learn, experiment, fail, and improve with AI.* - *Founder’s Corner is always written in first person, reflective, and practical. It blends:* - *A specific story (win, failure, or experiment)* - *Tangible lessons for everyday professionals* - *2–5 practical takeaways they can apply at work and at home* - *My readers are “AI-curious doers” who want to use AI to improve their work, finances, and careers.* - *The tone is human, honest, and grounded. No hype, no guru speak.* *Very important: what you should not do* - *Do not write full paragraphs or a full draft of the blog post.* - *Do not try to mimic my voice in final prose.* - *Your job is to help me think, structure, and research — I will do the actual writing.* *Your job in this conversation When I give you a topic and rough ideas, your job is to:* 1. *Clarify and structure my thinking into a clear narrative arc.* 2. *Propose a detailed outline with sections, suggested headings, and logical flow.* 3. *Identify where my personal story should show up and what parts of my experience are most important to highlight.* 4. *Research relevant facts, stats, and high-quality sources (using whatever browsing or knowledge tools you have) that support the piece.* 5. *Suggest SEO- and AI-scraper-friendly elements, including:* - *3–7 title options* - *One meta description (140–160 characters)* - *3–7 target keywords/phrases* - *Clear, descriptive H2/H3 heading ideas* 6. *Generate prompts and question lists for me, so that I can use them to write each section in my own voice.* *How I want you to respond (step-by-step)* 1. *Restate the topic and angle in 2–3 sentences to confirm your understanding.* 2. *Propose 2–3 possible narrative frames for the piece (for example:* - *“Problem → Experiment → Lessons → Takeaways”* - *“Failure → What Broke → What I Changed → Playbook”* - *“Trend → My Experience → What It Means for You” )* 3. *Based on the best frame, create a detailed outline that includes:* - *Hook / intro (bulleted ideas only, not written prose)* - *2–4 main sections with suggested H2s/H3s* - *Bullet points for what I should cover in each section* - *Where my personal story or example should appear* - *A closing section that ties it together and suggests a simple call to action for the reader* 4. *Research and list 3–7 relevant stats, trends, or articles I could mention. For each, provide:* - *Source name (e.g., McKinsey, Wharton, IMF, WEF, etc.)* - *A short summary of the fact or finding* - *A one-line note on how I could use it in this Founder’s Corner topic* 5. *Suggest SEO elements:* - *3–7 title options that fit my existing style (short hook + clarifier, e.g., “Failing Forward – Why I Paused Automation to Launch Faster”)* - *One meta description (140–160 characters)* - *3–7 target keywords/phrases* - *2–4 FAQ-style questions that AI search/answer engines might surface for this topic* 6. *Provide a set of writing prompts/questions for each main section of the outline that I can answer in my own words. Examples:* - *“Describe the exact moment you realized X was a problem.”* - *“List 3 mistakes you made before you figured out Y.”* - *“Explain how a reader could test this in their own job this week.”* *Constraints and quality guardrails* - *Keep everything in bullet points and planning language.* - *Do not produce final narrative paragraphs unless I explicitly ask for a micro example.* - *Prefer clarity over jargon; if you mention something technical, suggest how I could explain it simply.* - *Do not invent stats. If you are not sure, say so and suggest a direction for me to research further.* *My input for this week I will now give you:* - *Topic: \[SHORT TOPIC SENTENCE\]* - *Angle / premise: \[HOW I WANT TO APPROACH IT\]* - *Rough ideas / bullet points:* - *\[POINT 1\]* - *\[POINT 2\]* - *\[POINT 3\]* ### New Model, New Playbook: How I Adapt My AI Habits URL: https://www.mindovermoney.ai/founders-corner/how-to-adapt-ai-habits-when-new-models-launch/ Last updated: 2026-07-13T16:59:51.000Z Last week, I shared how I’ve been incorporating new AI models into my workflows for *Neural Gains Weekly*. The more I experiment, the more I realize how important it is to stay current as these models evolve. If you rely on AI at home or at work, staying up to date is no longer optional. Each release can change how a model reasons, how it follows instructions, and what it is actually good at. If you treat every new model like the last one, your results will quietly get worse over time. As the model arms race heats up between the big labs, that gap will only grow. So what can you do when a new model is released? In this week’s Founder’s Corner, I want to share three simple ways I think about learning new models so you can adapt faster and get better outputs as the AI landscape keeps shifting. **Tip 1: Learn What the Model Is Optimized For** Not every model is trained and fine-tuned to excel at the same tasks. That can be hard to see if most of your interactions are simple, search-like chats. It’s a big reason I push myself (and you) to run more complex workflows through AI. Before I judge a model, I want to know what it was actually built to be great at. When you pay attention to headlines and launch materials, you start to pick up on these cues directly from the labs. One model touts superiority in all things coding, another leans into being the “agentic” leader. I read release notes and listen to a few trusted podcasts to get a sense of where a model is supposed to shine and how it can best support my workflows. Those small habits shorten the learning curve and make it easier to plug the right model into the right task. **Tip 2: Adjust Your Prompts When the Model Changes** The prompt is our chance to provide context, structure, and guidelines that help the AI produce the outcome we want. It’s easy to get comfortable with a certain prompting style or reuse the same prompt over and over again. But this can lead to poor outputs when a new model is introduced. New models are not just updates, they behave like completely new systems that often require reengineered inputs. Luckily, the process to learn the prompting structure for a new model is straightforward. You might need to be more explicit about structure, set length constraints, or change how you chunk information, but you don’t have to figure this out alone. I often ask the model directly for advice on how it wants to be prompted for a specific task and use that as a starting point. From there, I let the AI help me refine a “best prompt” for the workflow I am testing, which removes a lot of the guesswork. Over time, this builds a mental playbook for each model instead of forcing one-size-fits-all prompts on every new release. **Tip 3: Run Small “Same Task, Different Model” Experiments** Experimentation is crucial in the world of AI, especially when you are trying to learn a new model. The simplest way to do this is to run the same real task through different models and compare the results. You do not need benchmarks or lab tests, just a light habit of asking, “Which model handled this better, and why?”. I started comparing outputs from ChatGPT 5.0 and ChatGPT 5.1 as soon as the newer model launched. I wanted to see if I could spot patterns that highlighted the differences between the two. The “AI Education” prompt was a great starting point to compare which model’s output aligned with my vision, needed the least editing, and felt the most useful for the audience. It was obvious how much better 5.1 handled the task, even though I was using the same prompt I had originally optimized for 5.0\. That simple experiment helped me compare the models directly and choose the best content for the newsletter. Over time, that kind of side-by-side testing builds a feel for each model that no release note can give you. --- At this point, I see new models less as shiny toys and more as part of the foundation of my work. The models will keep changing, and if my habits do not change with them, my results will slowly fall behind. This rings true in the workplace as more employers start mandating the use of AI. We will all need to be educated and well-versed in the models that power the tools we use every day. My perspective is that learning how new models work will be the equivalent of learning to use email in the early 2000s: mandatory. The models will keep getting smarter, but the real leverage comes from how quickly we learn them and fold them into the work that matters most to us. ### Volume 10: Life in the Model Fast Lane URL: https://www.mindovermoney.ai/gemini-vs-chatgpt-vs-claude-which-ai-model-to-use/ Last updated: 2026-07-13T16:59:51.000Z Hey everyone! 👋 We are starting December in the middle of an AI upgrade wave: new models, more powerful hardware, and national projects treating AI like core infrastructure. In **AI Education**, we kick off a four-part large language model deep dive so you finally have a clear mental model for what systems like ChatGPT are actually doing under the hood. This week’s **10-Minute Win** turns scattered debts into a simple Snowball or Savvy paydown gameplan you can stick with. And in **Founder’s Corner**, I share honest reviews of the latest models and how they perform so you can pick the right one for whatever task you tackle next. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **Claude Opus 4.5 is here**](https://www.anthropic.com/news/claude-opus-4-5?utm%5Fsource=chatgpt.com) **Summary:** Anthropic launched Claude Opus 4.5, calling it its best model yet for coding, agents, and computer use, with big upgrades in deep research, spreadsheets/slides work, and long-running workflows. **Why it matters:** This is another clear step into the agent era—models that don’t just chat, but reliably operate tools, automate multi-step tasks, and keep context over time. That’s where real productivity (and enterprise spend) lives. ### **2)**[ **AI helps drive record $11.8 billion in U.S. Black Friday online spending**](https://finance.yahoo.com/news/ai-helps-drive-record-11-133358833.html?utm%5Fsource=chatgpt.com) **Summary:** U.S. shoppers spent a record $11.8B online on Black Friday, up 9.1% year-on-year, with Adobe Analytics citing an 805% surge in AI-assisted traffic as tools like Walmart’s *Sparky* and Amazon’s *Rufus* steered people to deals. **Why it matters:** This is AI moving real consumer dollars. Retail copilots and recommendation agents aren’t side experiments—they’re now measurable drivers of sales and a preview of how AI will influence everyday spending. ### **3)**[ **Amazon to invest $50bn in AI for US government customers**](https://www.aljazeera.com/economy/2025/11/24/amazon-to-invest-50bn-in-ai-for-us-government-customers?utm%5Fsource=chatgpt.com) **Summary:** Amazon plans to invest up to $50B to expand AI and supercomputing capacity for U.S. government customers, building out what’s effectively a massive, government-focused AI cloud under AWS. **Why it matters:** This is AI infrastructure as national capability. It locks AWS even deeper into defense, intel, and public-sector workloads—and signals long-term, contract-backed demand for AI compute. ### **4)**[ **Alphabet on pace to hit $4 trillion market value as AI gains momentum**](https://www.investing.com/news/stock-market-news/alphabet-on-pace-to-hit-4-trillion-market-value-as-ai-gains-momentum-4376798?utm%5Fsource=chatgpt.com) **Summary:** Alphabet is closing in on a $4T valuation after a year-long rally driven by its AI pivot—Gemini 3, TPU strategy, and cloud growth—sending shares up \~4% in recent trading. **Why it matters:** Google joining the multi-trillion race on the back of AI underlines a simple point: markets now treat AI as core infrastructure, not a bolt-on feature set. ### **5)**[ **Launching the Genesis Mission**](https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/?utm%5Fsource=chatgpt.com) **Summary:** A new Executive Order launches the Genesis Mission, a national effort to use AI and U.S. supercomputers to accelerate scientific discovery, coordinate federal labs, and tackle “grand challenge” research problems. **Why it matters:** This is the U.S. leaning into AI-as-R&D-engine—treating AI like a Manhattan/Apollo-style capability for science. It sets direction (and future grant money) for labs, universities, and companies working at the AI–science frontier. ## AI Education for You ****LLM Deep Dive - Part 1: What Large Language Models Really Are** Over the past few volumes, you have picked up the building blocks: - Artificial intelligence as the broad idea - Machine learning as systems that learn from data - Deep learning and neural networks - Tokens, vectors, embeddings, prompts, and context windows This week we put a clean frame around all of that: *What is a large language model really?* Not the marketing answer. The mental model you can hold in your head when you open an app like ChatGPT and start typing. By the end of this four-part series, you should feel like you actually know what is happening under the hood when you ask AI to explain a bill, summarize an email, or help you think through a big financial decision. ### **What a large language model actually is** A large language model is: A system that has learned patterns in text and uses those patterns to predict the next small piece of text, over and over. That is it at the core. It does not “understand” the world like a human. It is extremely good at continuing text in ways that match patterns it has seen during training. When you see a fluent answer, you are seeing: - Many small predictions chained together - Guided by patterns the model learned from huge amounts of text - Shaped by the prompt and context you give it ### **How it is different from classic software** Classic software: - Follows hand-written rules - If X happens, do Y - Every rule is coded by a developer A large language model: - Is not given explicit rules for everything - Learns patterns from data - Generalizes to new prompts it has never seen before Analogy: Classic software is like a set of tax forms with instructions. A large language model is like a person who has seen millions of completed forms and can guess how you should fill yours out, just by pattern. ### **The two phases: training and use** There are two big phases in the life of a model. 1. **Training phase** - The model sees huge amounts of text - It learns which tokens tend to follow which other tokens - Its internal settings are adjusted over and over to reduce mistakes 2. **Use phase (when you chat)** - The training is frozen - You send a prompt - The model turns your text into tokens and predicts the next ones - It uses what it learned during training to shape its answer Another way to say it: - Training is where the model learns patterns - Use is where the model applies those patterns to your prompt ### **Why it feels so smart** If you train a system on huge amounts of: - Explanations - Arguments - Instructions - Code - Articles and conversations And you force it to practice predicting the next token billions of times, you get behavior that looks like reasoning: - It can follow steps - It can compare options - It can explain concepts in different ways But under the hood it is still doing one basic thing: Predicting the next piece of text that would make sense here, given everything it has seen before and the input you just gave it. ### **FAQs** **Q: If it only predicts the next piece of text, how can it solve real problems?** A: Because many real problems show up in text form: questions, emails, documents, code, instructions. The patterns it learned cover not just words, but also how humans explain, reason, and structure answers. Predicting the next piece of text over and over lets those patterns show up in useful ways. **Q: Is a large language model the same as artificial intelligence?** A: No. It is one kind of AI system. It is a very powerful one for language, but AI also includes other models and approaches that handle images, audio, planning, and more. ### **Closing this week** This week you got the high-level mental model: - A large language model is a pattern learner for text - It learns in a training phase and applies those patterns when you chat - It feels smart because language carries a lot of human reasoning patterns Next week we zoom into how it learns: - What the training data looks like - How the model adjusts its internal settings - Why it needs so much data and compute - How data quality shapes its strengths and blind spots Think of Part 1 as “what this thing is”. Part 2 will be “how it actually learns”. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **💳 Debt Paydown Gameplan** **Why this matters:** Carrying multiple debts is like running with a weighted vest — you can move, but everything is harder. The problem: most people either avoid looking at the full picture or randomly throw extra money at whatever hurts most that month. In 10 minutes, this workflow will help you list every debt in one place, compare the classic Snowball (smallest balance first) vs. Savvy (highest interest first) approaches, and walk away with a simple, AI-assisted paydown plan you can actually follow. ### **Step 1 — List your debts in one simple sheet (2–3 minutes)** Open Google Sheets and create this table: | A (Debt Name) | B (Balance $) | C (APR %) | D (Minimum Payment $) | E (Type) | | ------------- | ------------- | --------- | --------------------- | ------------- | | Credit Card 1 | 3,200 | 24.99 | 95 | Credit card | | Credit Card 2 | 1,050 | 19.99 | 35 | Credit card | | Personal Loan | 4,500 | 12.00 | 140 | Personal loan | | Auto Loan | 9,800 | 5.00 | 260 | Auto loan | Then replace the sample numbers with your debts: - Include: credit cards, personal loans, BNPL, auto loans, anything with a monthly payment. - Exclude for now: mortgage and student loans if you want to keep it focused (you can run a second pass just for those). Below the table, add one more cell: - In **A7**, type Extra\_Payment\_$. - In **B7**, enter how much extra you could realistically send toward debt each month (even $25–$50 helps). If you already used earlier 10-Minute Wins (budget or net worth), you can copy balances from those sheets straight into this table so everything stays consistent. ### **Step 2 — Get AI to build Snowball vs. Savvy plans (4 minutes)** Copy your entire table (including headers and the Extra\_Payment\_$ cell) and paste it into a new ChatGPT 5.1 chat with this prompt: **Role:* You are my debt paydown planner.* **Context:* I want a clear, simple plan to pay off my debts using both the Snowball method (smallest balance first) and a more Savvy method (highest interest rate first), so I can choose which fits me better.* **Data:* Here is my debt table with columns: Debt Name, Balance, APR, Minimum Payment, Type, plus a row showing how much extra I can pay each month.* ***Instructions:*** 1. *Verify the table and restate my total debt and total minimum payments.* 2. *Build two payoff orders:* - *Snowball: sort by smallest Balance first.* - *Savvy: sort by highest APR first.* 3. *For each method, assume I pay all minimums and send my full Extra\_Payment\_$ to the current focus debt until it’s gone, then roll that amount into the next one.* 4. *For each method, output:* - *A table: Order | Debt Name | Focus or Minimum | Approx. Months to Clear Each Debt | Approx. Total Months to Debt-Free (rough estimates are fine).* - *3 bullet points on pros/cons for me (behavioral + mathematical).* 5. *End with a 1-paragraph recommendation on which method might fit me better (you can’t give financial advice, but you can talk through tradeoffs).* ***Rules:*** - *If the numbers look impossible with my current Extra\_Payment\_$, say so gently and suggest starting with a smaller goal (e.g., first debt only).* - *Keep the language plain and encouraging—no jargon.* You’ll now have two clear roadmaps with a recommendation. ### **Step 3 — Pick your method and set your “focus debt” (2 minutes)** Back in Google Sheets: 1. Add a new column F (Strategy\_Focus) and mark, for the current month, which debt is your focus under your chosen strategy (Snowball or Savvy). - Example: put Snowball\_Focus next to the smallest balance, or Savvy\_Focus next to the highest APR. 2. In your notes (or a new cell), write: - My method: Snowball *or* My method: Savvy - Current focus debt: \[Debt Name\] - Extra I’m sending to this debt each month: $\[Extra\_Payment\_$\] ### **Step 4 — Turn the plan into a monthly habit (2–3 minutes)** - Save your plan: paste ChatGPT’s summary (Snowball vs. Savvy comparison and your chosen method) into a Google Doc or Notion page titled: Debt Paydown Gameplan — \[Month Year\]. - Set a reminder: in Google Calendar, create a monthly event on or right after your pay date: - Title: Send $\[Extra\_Payment\_$\] to \[Focus Debt Name\]. - Description: paste the link to your sheet/doc plus your chosen method (Snowball/Savvy). - Every 1–3 months, come back to this same workflow: update balances, re-run the prompt, and move the Focus tag to the next debt when one is paid off. ## **The Payoff** Instead of “I’ll just throw extra at something,” you now have: - Every debt in one clean list - Two structured payoff orders (Snowball vs. Savvy) generated for you - A single focus debt and extra payment amount for this month - A recurring reminder that turns your decision into a habit You’re not guessing—you’re executing a simple, chosen gameplan. ## **Transparency & Notes for Readers** - All tools are free: Google Sheets, ChatGPT Free, Google Docs/Notion, Google Calendar; the CFPB calculator is optional and free. - Estimates only: The timelines are rough—interest accrues daily and payments can change. Always check with your lender for exact payoff details. - Risk: Don’t stop making minimum payments on any debt while using this. - Educational workflow — not financial advice. ## Founder's Corner Real world learnings as I build, succeed, and fail We’ve officially entered the holiday shopping season where companies compete for our attention and our money. It seems like the AI companies are tapping into our consumer habits with the flurry of announcements over the last few weeks. Google, OpenAI, and Anthropic have all dropped new models that are changing the game and bringing new intelligence into their tools. It’s overwhelming for anyone following the AI release cycle, but these announcements are increasingly important for us to track and understand. The tech is moving fast and we risk losing productivity gains, at home and at work, by falling behind. This week, I am focusing on three of the latest: Gemini 3 Pro, ChatGPT 5.1 and Claude Opus 4.5, and sharing how I use these models on a daily basis. **ChatGPT 5.1** From Day 1, I’ve relied heavily on the latest ChatGPT model to be my business partner and content creator. One major reason is the ‘Projects’ feature, which allows me to capture all of the context from my project in one area. This trend has continued since GPT 5.1 was released in November. I personally like the model’s ability to follow instructions and consistently deliver outputs that stay within the parameters I’ve outlined. GPT 5.1 excels at reasoning and putting together executable plans in an easily digestible manner, which is extremely useful when tackling problems in a domain outside of my personal expertise. Writing is a main strength of this model, especially when you build up memory context within a project. The model can pull from previously approved writing outputs and use it as a template to mimic the style and tone. This helps create a consistent ‘reading feel’ in each weekly newsletter. One suggestion I have is to experiment with ‘5.1 Thinking’ and track the ‘thought’ process of the model. It’s impressive to see how the model works through multiple steps to formulate comprehensive responses. It feels human-like and is the main reason I haven’t fully pivoted to another model. *Pro Tip: Use the ‘Projects’ feature to house and organize a project, whether it be personal or work related.* | ChatGPT Model | Release Date | Key Features & Notes | | ------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------- | | GPT-5 | Aug 7, 2025 | Major architectural overhaul unifying reasoning and multimodal capabilities; replaced GPT-4o as the default for most users. | | GPT-5.1 | Nov 12, 2025 | Introduced "Instant" and "Thinking" modes; improved instruction following and personality customization. | | GPT-5.1 Pro | Nov 19, 2025 | A specifically tuned version for "Pro" users targeting data science and complex business writing. | **Gemini 3.0** I’m definitely most experienced with the suite of ChatGPT models, but Gemini 3.0 is slowly poaching scope and tasks in my everyday workflows. Google has been crushing it with their models ever since Gemini 1.5 was released in February 2024\. Historically, I’ve used Gemini 2.5 for two main tasks: helping write LinkedIn posts to promote the newsletter and creating images with the Nanobanana image model inside Gemini. This lack of usage was mostly due to my comfort and project integration with ChatGPT. But this is changing due to Gemini 3.0\. If I had to describe 3.0 in one word, I’d go with ‘Powerhouse’. Google has figured out how to wrap their various models into one experience. For example, you can prompt Gemini 3.0 to create images, which is powered by their Nanobanana model. Or you can create realistic videos by tapping into Veo 3\. Gemini 3.0 excels in multimodality, allowing the consumer to seamlessly interact with one interface to accomplish any task, regardless of the complexity. Personally, I started using 3.0 to help me build structure in the ‘Founder’s Corner’ section of the newsletter. I found myself wasting time with writer’s block and needed to prioritize planning over execution. The model blew me away with the level of detail given to help me organize my thoughts. I used this process for this blog post and probably saved myself at least an hour of writing time, all thanks to the power of 3.0\. I’m still experimenting with 3.0 (it’s only been out for two weeks) and I have no doubt that more of my work will shift over once I get more reps under my belt. *Pro Tip: Use Gemini 3.0 in Google AI Studio to vibe code a simple app that can make your life easier. You will get a chance to see the model use reasoning to bring your idea to life.* | Gemini Model | Release Date | Key Features & Notes | | ------------ | ------------- | --------------------------------------------------------------------------------------- | | Gemini 2.5 | June 17, 2025 | Optimized for high-volume tasks; "Flash" variant became the default for many API users. | | Gemini 3.0 | Nov 18, 2025 | Major flagship release; features "Deep Think" mode and "vibe coding" capabilities. | **Opus 4.5** It’s hard to experiment with every model. I pay for ChatGPT and Gemini (the $20 per month plans) and have not taken the leap to pay for Anthropic’s suite of Claude models. One reason is how Claude is positioned. These models are trained and built to excel at coding and are marketed primarily toward developers. I don’t have advanced use cases (yet) that justify shifting over to Claude models, but Opus 4.5 might force me to rethink that strategy. Since I don’t have direct experience with this model, I asked Claude Sonnet 4.5 to highlight the strengths of Opus 4.5 and tell me why I should switch. Here are the main bullets/selling points: - World's Best Coding Model - Built for Agentic Workflows - Computer Use & Browser Automation Leader - Enterprise & Office Productivity Powerhouse - Unprecedented Efficiency My goal is to start incorporating Claude models into projects as I build out my 2026 roadmap. There are projects that fit nicely with the selling points and strengths of Opus 4.5\. It’s good to step outside of your comfort zone, especially with the newer AI models. *Pro Tip: Find the right use case before experimenting with a new model, especially if you have little to no experience with previous versions.* | Claude Model | Release Date | Key Features & Notes | | --------------- | ------------- | --------------------------------------------------------------------------------------------------------------------- | | Opus & Sonnet 4 | May 22, 2025 | The "next generation" launch; introduced native "Extended Thinking" and production-ready developer tools. | | Opus 4.1 | Aug 5, 2025 | A mid-cycle refresh of the flagship model, optimizing it for consistency and reducing hallucination rates. | | Sonnet 4.5 | Sept 29, 2025 | Set new records in coding and reasoning benchmarks; widely adopted by developers for its speed-to-intelligence ratio. | | Haiku 4.5 | Oct 15, 2025 | Extremely fast and cost-efficient; matched the coding performance of previous "Sonnet" class models. | | Opus 4.5 | Nov 24, 2025 | The most powerful model to date; massive improvements in "computer use" and agentic reliability. | --- I view each model release as an opportunity to learn new skills and keep up with the pace of change. Each update provides a unique chance to figure out the best prompt or data structure that allows the model to create your envisioned output. This matters at work, because the tools you use every day may quietly swap the underlying model, and the inputs that used to perform well can suddenly fall flat if you do not understand how that model behaves. The more familiar you are with Gemini, ChatGPT, and Claude, the easier it is to adapt when your company or your favorite app changes what is running under the hood. Next week, I will build on this and share three practical ways to experiment with these models so you can accelerate your learning and get more out of AI in both your work and your everyday life. **Goals & Milestones:** | Goal | Current (as of 12/2/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | ------------------------- | -------------------- | | Newsletter Subscribers | 101 | 300 | | Monthly Recurring Revenue | $28 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 18 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 7 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 10: The Prompt Experimenter URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-10-the-prompt-experimenter/ Last updated: 2026-07-13T16:59:51.000Z I'm always trying to experiment with new prompts to achieve the best output possible. I took a different approach with this week's AI Education and needed a more comprehensive prompt to execute my vision. It’s becoming more and more important to refine your prompt skills to ensure the structure is built currently for the specific model you’re using. My recommendation is to work directly with your model of choice to build a prompt that is optimized to produce the best results within the specific model. That is exactly what I did with ChatGPT 5.1 Thinking as we collaborated to create the prompt below. --- Context You are my weekly editor–researcher for the AI Education section of the Neural Gains Weekly newsletter. This is Volume 10, and we are doing a deeper dive on large language models (LLMs). Audience Smart beginner readers who have followed earlier issues about AI, machine learning, deep learning, tokens, vectors, embeddings, prompt design, and context windows. They are not technical, but they are curious and motivated. Think reading level between a smart high-schooler and a non-technical college grad. High-level goal Create a deeper, but still beginner-friendly, explainer of how LLMs are trained and how they work at the moment of use. The reader should feel like they finally “get” what is happening inside a model like ChatGPT, without math or heavy jargon. House style \- Beginner-first tone. \- Plain English, short sentences. \- Light technical, but always explained in everyday language. \- Use analogies often (but do not overdo them). \- Money and personal finance examples can appear, but lightly. AI education comes first. \- No equations. \- No acronyms in the body unless you spell them out clearly the first time and keep the language simple. Your roles 1) Educator Explain LLMs like a patient professor. Build in layers. Define first, then illustrate, then connect to previous ideas. 2) Researcher You must base explanations on accurate information from reputable sources (OpenAI, Google, Microsoft, major universities, high-quality textbooks and docs). Do not guess. If something is not publicly known or is uncertain, say that directly. 3) Editor Keep the piece tight and readable. Avoid walls of text. Use headings, short paragraphs, and lists to help retention. This is a “feature” piece but should not be more than about 2x the length of a typical AI Education section. 4) Connector Gently connect to earlier concepts: data, structured vs unstructured data, features and labels, tokens, vectors, embeddings, prompt design, context windows. Do not recap volumes in detail. Use brief reminders like “Earlier we talked about tokens as small pieces of text.” Deep dive angle Use a hybrid story: \- Part 1: “How an LLM is born and trained” (data → training loop → parameters → loss → improvements → fine-tuning at a high level). \- Part 2: “What happens when you send a message” (from my prompt to tokens, through the context window and attention, to predicted tokens going back out). Connect those two stories clearly so the reader feels how the training phase and the “chat” phase fit together. Learning objectives By the end of the piece, a beginner reader should be able to honestly say: 1) “I understand, at a high level, how an LLM is trained and why it needs so much data.” 2) “I understand that LLMs predict the next token and do not think like a human, and why that still feels smart.” 3) “I understand how the model decides what parts of my message to focus on, and what a context window means in practice.” 4) “I understand how good and bad data during training affect what the model can and cannot do.” 5) “I understand what roughly happens between me typing a prompt and the model producing an answer.” 6) “I understand why LLMs hallucinate, forget earlier parts of a conversation, or give shallow answers sometimes.” 7) “I have a better sense for how to shape my own prompts and inputs so I am working with the model, not fighting it.” Structure and sections Use a classic article flow with light Q&A inside each major part: 1) Title and Hook \- Propose a clear, straightforward title for the section. It should make it obvious this is an LLM deep dive, not just another light overview. \- Write a hook that: • Reminds the reader that we have already covered building blocks (AI, ML, deep learning, tokens, vectors, embeddings, prompts, context windows) • States that this issue will tie those pieces together inside one story about LLMs • Frames the article as “from training to your chat window” in plain English \- No fluff. Give the reader a reason to keep reading. 2) Part 1: How an LLM is born and trained (high-level training story) Explain in plain English: \- What training data looks like at a high level (lots of text, variety, and scale, but do not invent proprietary sources). \- The idea of parameters as the “knobs” or “settings” that the training process adjusts. \- The training loop: model makes a prediction, checks how wrong it is, and adjusts. Use a clear analogy, like a student practicing with answer keys and adjusting based on mistakes. \- The idea of loss as a measure of how wrong the model is, and that training aims to push loss down. \- Why so much data and compute are required. \- How fine-tuning or later training stages can specialize a base model (keep this high-level, not product-specific). \- Explain how data quality and diversity during training shape strengths and weaknesses later. End this section with 1–2 short “Reader questions” in Q&A format, such as: \- “So is the model just memorizing the internet?” \- “Can it see my bank account or private data from training?” Answer in clear, direct language. 3) Part 2: What happens when you send a message (inference story) Now tell the story of a single interaction in plain English. Cover: \- How your message is turned into tokens (small pieces of text). \- How these tokens fit into a context window, like a page with a size limit. \- How the model looks at all the tokens in the context window and uses attention to decide which parts are most relevant when predicting the next token. Use an analogy, like a person rereading the most important sentences in an email before replying. \- How the model predicts one token at a time, repeatedly, until it forms a full answer. \- How prompts, chunking, and structure affect what the model “pays attention” to inside that window. \- Optionally and briefly, how temperature or randomness choices affect answers, but only if you can keep it very simple. End this section with 1–2 short reader-style Q&As like: \- “If it only predicts one token at a time, why does it sound so coherent?” \- “Why does it sometimes forget something I said earlier in the conversation?” 4) Part 3: Limitations and failure modes Explain in plain English: \- Why LLMs hallucinate (for example, they are trained to continue patterns, not to say “I don’t know” by default, and they are predicting likely text rather than verifying facts). \- Why they may give shallow or generic answers (for example, vague prompts, weak context, or staying near the statistical “average”). \- Why context window limits can cause the model to “forget” earlier details. \- How biases and gaps in training data can show up in outputs. \- Make clear what they do not do: they do not truly understand or have intentions. Include 1–2 reader-style Q&As like: \- “Can an LLM be fully factual all the time?” \- “Is it safe to trust it with important financial or medical decisions?” Answer carefully and conservatively. 5) Part 4: What this means for how you use AI Without turning this into a full “how-to,” close with a short, practical section that connects the mechanics back to reader behavior. For example: \- Why good prompt structure (goal, key info, format, constraints, examples) works well with how LLMs process text. \- Why chunking and clear summaries help the model within its context window. \- Why you should still verify outputs, especially for high-stakes topics. \- One or two light-touch personal finance examples to show how to think about LLMs when you use them to review statements, summarize bills, or plan a budget. Keep this section short and focused on mindset: “Here is how to work with the grain of the system, not against it.” Style and constraints \- Use headings (H2/H3 style), short paragraphs, and bullet lists to improve readability. \- Use analogies, but always tie them back to the real concept. Do not let the analogy drift too far. \- Avoid dense jargon. When you need a term like “parameter,” “loss,” or “attention,” define it clearly the first time in plain English and continue to use simple phrasing. \- Do not invent secret training data sources or claim the model was trained on specific private datasets that are not publicly confirmed. \- Do not include citations or reference lists in the output. Just ensure the content is accurate and grounded in how modern LLMs are understood to work. \- Do not write from the model’s point of view as if it is conscious or self-aware. Treat it as a system. Output format \- Return a single, Google-Docs-ready Markdown article with clear section headings and subheadings. \- Do not include any images or diagrams. Instead, explain in words. \- Make sure the overall length is deeper and somewhat longer than a normal AI Education section, but not more than about twice the usual length. Now, using all of the instructions above, write the full Volume 10 AI Education deep dive on large language models. ### Meet the Models: How I Use Gemini 3 Pro, ChatGPT 5.1, and Claude 4.5 URL: https://www.mindovermoney.ai/founders-corner/gemini-chatgpt-claude-comparison-professional-workflows/ Last updated: 2026-07-13T16:59:52.000Z We’ve officially entered the holiday shopping season where companies compete for our attention and our money. It seems like the AI companies are tapping into our consumer habits with the flurry of announcements over the last few weeks. Google, OpenAI, and Anthropic have all dropped new models that are changing the game and bringing new intelligence into their tools. It’s overwhelming for anyone following the AI release cycle, but these announcements are increasingly important for us to track and understand. The tech is moving fast and we risk losing productivity gains, at home and at work, by falling behind. This week, I am focusing on three of the latest: Gemini 3 Pro, ChatGPT 5.1 and Claude Opus 4.5, and sharing how I use these models on a daily basis. **ChatGPT 5.1** From Day 1, I’ve relied heavily on the latest ChatGPT model to be my business partner and content creator. One major reason is the ‘Projects’ feature, which allows me to capture all of the context from my project in one area. This trend has continued since GPT 5.1 was released in November. I personally like the model’s ability to follow instructions and consistently deliver outputs that stay within the parameters I’ve outlined. GPT 5.1 excels at reasoning and putting together executable plans in an easily digestible manner, which is extremely useful when tackling problems in a domain outside of my personal expertise. Writing is a main strength of this model, especially when you build up memory context within a project. The model can pull from previously approved writing outputs and use it as a template to mimic the style and tone. This helps create a consistent ‘reading feel’ in each weekly newsletter. One suggestion I have is to experiment with ‘5.1 Thinking’ and track the ‘thought’ process of the model. It’s impressive to see how the model works through multiple steps to formulate comprehensive responses. It feels human-like and is the main reason I haven’t fully pivoted to another model. *Pro Tip: Use the ‘Projects’ feature to house and organize a project, whether it be personal or work related.* | ChatGPT Model | Release Date | Key Features & Notes | | ------------- | ------------ | --------------------------------------------------------------------------------------------------------------------------- | | GPT-5 | Aug 7, 2025 | Major architectural overhaul unifying reasoning and multimodal capabilities; replaced GPT-4o as the default for most users. | | GPT-5.1 | Nov 12, 2025 | Introduced "Instant" and "Thinking" modes; improved instruction following and personality customization. | | GPT-5.1 Pro | Nov 19, 2025 | A specifically tuned version for "Pro" users targeting data science and complex business writing. | **Gemini 3.0** I’m definitely most experienced with the suite of ChatGPT models, but Gemini 3.0 is slowly poaching scope and tasks in my everyday workflows. Google has been crushing it with their models ever since Gemini 1.5 was released in February 2024\. Historically, I’ve used Gemini 2.5 for two main tasks: helping write LinkedIn posts to promote the newsletter and creating images with the Nanobanana image model inside Gemini. This lack of usage was mostly due to my comfort and project integration with ChatGPT. But this is changing due to Gemini 3.0\. If I had to describe 3.0 in one word, I’d go with ‘Powerhouse’. Google has figured out how to wrap their various models into one experience. For example, you can prompt Gemini 3.0 to create images, which is powered by their Nanobanana model. Or you can create realistic videos by tapping into Veo 3\. Gemini 3.0 excels in multimodality, allowing the consumer to seamlessly interact with one interface to accomplish any task, regardless of the complexity. Personally, I started using 3.0 to help me build structure in the ‘Founder’s Corner’ section of the newsletter. I found myself wasting time with writer’s block and needed to prioritize planning over execution. The model blew me away with the level of detail given to help me organize my thoughts. I used this process for this blog post and probably saved myself at least an hour of writing time, all thanks to the power of 3.0\. I’m still experimenting with 3.0 (it’s only been out for two weeks) and I have no doubt that more of my work will shift over once I get more reps under my belt. *Pro Tip: Use Gemini 3.0 in Google AI Studio to vibe code a simple app that can make your life easier. You will get a chance to see the model use reasoning to bring your idea to life.* | Gemini Model | Release Date | Key Features & Notes | | ------------ | ------------- | --------------------------------------------------------------------------------------- | | Gemini 2.5 | June 17, 2025 | Optimized for high-volume tasks; "Flash" variant became the default for many API users. | | Gemini 3.0 | Nov 18, 2025 | Major flagship release; features "Deep Think" mode and "vibe coding" capabilities. | **Opus 4.5** It’s hard to experiment with every model. I pay for ChatGPT and Gemini (the $20 per month plans) and have not taken the leap to pay for Anthropic’s suite of Claude models. One reason is how Claude is positioned. These models are trained and built to excel at coding and are marketed primarily toward developers. I don’t have advanced use cases (yet) that justify shifting over to Claude models, but Opus 4.5 might force me to rethink that strategy. Since I don’t have direct experience with this model, I asked Claude Sonnet 4.5 to highlight the strengths of Opus 4.5 and tell me why I should switch. Here are the main bullets/selling points: - World's Best Coding Model - Built for Agentic Workflows - Computer Use & Browser Automation Leader - Enterprise & Office Productivity Powerhouse - Unprecedented Efficiency My goal is to start incorporating Claude models into projects as I build out my 2026 roadmap. There are projects that fit nicely with the selling points and strengths of Opus 4.5\. It’s good to step outside of your comfort zone, especially with the newer AI models. *Pro Tip: Find the right use case before experimenting with a new model, especially if you have little to no experience with previous versions.* | Claude Model | Release Date | Key Features & Notes | | --------------- | ------------- | --------------------------------------------------------------------------------------------------------------------- | | Opus & Sonnet 4 | May 22, 2025 | The "next generation" launch; introduced native "Extended Thinking" and production-ready developer tools. | | Opus 4.1 | Aug 5, 2025 | A mid-cycle refresh of the flagship model, optimizing it for consistency and reducing hallucination rates. | | Sonnet 4.5 | Sept 29, 2025 | Set new records in coding and reasoning benchmarks; widely adopted by developers for its speed-to-intelligence ratio. | | Haiku 4.5 | Oct 15, 2025 | Extremely fast and cost-efficient; matched the coding performance of previous "Sonnet" class models. | | Opus 4.5 | Nov 24, 2025 | The most powerful model to date; massive improvements in "computer use" and agentic reliability. | --- I view each model release as an opportunity to learn new skills and keep up with the pace of change. Each update provides a unique chance to figure out the best prompt or data structure that allows the model to create your envisioned output. This matters at work, because the tools you use every day may quietly swap the underlying model, and the inputs that used to perform well can suddenly fall flat if you do not understand how that model behaves. The more familiar you are with Gemini, ChatGPT, and Claude, the easier it is to adapt when your company or your favorite app changes what is running under the hood. Next week, I will build on this and share three practical ways to experiment with these models so you can accelerate your learning and get more out of AI in both your work and your everyday life. ### Volume 9: Feast on the Fundamentals URL: https://www.mindovermoney.ai/ai-fundamentals-beginners-guide-non-technical/ Last updated: 2026-07-18T01:46:19.000Z Hey everyone! 👋 Thanksgiving week feels like the right time to say this out loud: I’m genuinely grateful you carve out a few minutes each week to learn AI with me. It’s easy to get swept up in the AI hype cycle, but slowing down to build real understanding and better money habits is what actually compounds over time. In AI Education, I’ve turned the first eight weeks into a simple recap video and an “AI dictionary” of 20 core concepts we’ve covered so far. This week’s 10-Minute Win is your Black Friday Cart Copilot, built to protect your December cash flow without killing the fun. And in Founder’s Corner, I share a behind-the-scenes story about leaning on AI to fix a problem on MindOverMoney.ai and what it taught me about staying in the driver’s seat. Enjoy and Happy Thanksgiving! **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **A new era of intelligence with Gemini 3**](https://blog.google/products/gemini/gemini-3/?utm%5Fsource=chatgpt.com) **Summary:** Google released Gemini 3, its most capable model yet, with big jumps in reasoning, multimodal understanding, and coding, plus a Deep Think mode for harder problems and tight integration across the Gemini app, Search, AI Studio, and Workspace. **Why it matters:** This is Google’s real frontier move—better default tools inside Search and Gemini mean more everyday planning, research, and analysis quietly shifting into AI. ### **2)**[ **Trump considering executive order to preempt state AI laws**](https://www.reuters.com/business/urgent-trump-considering-executive-order-preempt-state-ai-laws-2025-11-19/?utm%5Fsource=chatgpt.com) **Summary:** A draft executive order would direct DOJ and Commerce to challenge or undermine state-level AI rules (like California and Colorado’s laws) and potentially tie federal broadband funds to states’ AI regulation choices. **Why it matters:** Centralizing AI oversight in DC could make life easier for big AI vendors—but at the cost of weaker local protections on deepfakes, bias, and fraud. Builders and investors need to watch how the regulatory map might flatten. ### **3)**[ **EU to ease AI, privacy rules as critics warn of caving to Big Tech, Trump**](https://www.reuters.com/sustainability/boards-policy-regulation/eu-ease-ai-privacy-rules-critics-warn-caving-big-tech-trump-2025-11-19/?utm%5Fsource=chatgpt.com) **Summary:** The European Commission’s “Digital Omnibus” proposal would let companies train AI on personal data under “legitimate interest” (no consent), and delay key high-risk AI rules by about a year—moves industry loves and rights groups call the biggest rollback of EU digital protections yet. **Why it matters:** This is a direct swing at two core constraints on AI: training data and compliance timelines. Looser rules could speed European AI deployment—but raise real privacy and trust questions. ### **4)**[ **Pope: Safeguard human dignity as health systems integrate AI**](https://www.vaticannews.va/en/pope/news/2025-11/pope-alami-ai-technology-ethical-vision-healthcare-human-dignity.html?utm%5Fsource=chatgpt.com) **Summary:** Pope Leo XIV told Latin American health leaders that AI in healthcare must serve human dignity, emphasizing that tech can’t replace real relationships between patients and caregivers and must be guided by a clear ethical vision **Why it matters:** This is a high-profile, values-focused take on AI in one of the most sensitive domains. For anyone building or investing in health AI, “does this actually respect people, not just optimize throughput?” isn’t a soft question—it’s a reputational and regulatory one. ### **5)**[ **Elon Musk says AI will make work optional and money irrelevant**](https://www.yahoo.com/news/articles/elon-musk-ai-optional-money-180403719.html?utm%5Fsource=chatgpt.com) **Summary:** In new comments, Elon Musk predicts that within 10–20 years AI and robotics will make work optional and render money “kind of irrelevant,” painting a future where universal high living standards are driven by automated production **Why it matters:** It’s a clean example of maximalist AI futurism your readers will see everywhere. Useful not because it’s guaranteed, but because it frames the extremes—then you can sanity-check your own career and money plans against more grounded timelines. ## AI Education for You Recapping your learning journey It’s Thanksgiving week, which makes it a good time to pause, look back, and say thank you. The past eight weeks have introduced foundational topics in AI, from the basics of intelligence and learning to vectors, embeddings, tokens, and context windows. That is not light reading, especially on top of work, family, and real life. To make it easier to connect the dots, I put together a short recap video that walks through the “AI Education” journey so far, and a simple dictionary of the 20 big ideas we have covered. Think of this issue as your chance to catch up, lock in the fundamentals, and walk into the rest of the year feeling confident you’ve retained the information. Happy Thanksgiving! 0:00 /7:03 1× Video will open on the website ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/11/unnamed--1-.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🛒 Black Friday Cart Copilot** **Why this matters:** Between turkey, travel, and “once-a-year deals,” it’s insanely easy to torch your December cash flow in a single weekend. This 10-minute workflow turns your Black Friday/Cyber Monday cart into something smarter: a small, AI-assisted system that helps you rank, sanity-check, and right-size your buys before you click “Place Order.” You’ll walk away with a Buy / Wait / Skip list that still leaves room for fun, but doesn’t wreck your January. ### **Step 1 — Dump your cart into a simple sheet (2–3 minutes)** Open **Google Sheets** and create this table: | A (Item) | B (Price $) | C (Store/Site) | D (Type) | E (Notes) | | ------------------------ | ----------- | -------------- | ------------ | ------------------------------- | | 4K TV | 799 | Best Buy | Tech/Upgrade | Replacing 8-year-old TV | | Noise-cancelling earbuds | 129 | Amazon | Gadget/Want | For travel & gym | | Air fryer | 89 | Target | Kitchen | We don’t own one yet | | Kids’ Lego set | 59 | Amazon | Kids/Gift | Holiday gift for 6-year-old | | Random TikTok gadget | 39 | TikTok Shop | Impulse | Saw it in a video late at night | Add **y**our own items—aim for your top 5–15 most likely purchases. Keep this sheet open—you’ll copy it into ChatGPT next. ### **Step 2 — Let AI turn it into a Buy / Wait / Skip matrix (4 minutes)** In a new chat with ChatGPT 5.1, paste your full table (including headers) and use this prompt: *Role: You are my Black Friday Cart Copilot. Your job is to help me spend intentionally without killing all the fun.* *Context: I’m looking at my potential Black Friday / Cyber Monday purchases. I want you to help me decide what to Buy, what to Wait 30 days on, and what to Skip entirely.* *Data: Here is my cart as a table with columns: Item, Price, Store/Site, Type, Notes.* *Instructions:* 1. *For each item, score:* - *Need vs Want (Need / Upgrade / Pure Want)* - *Joy per Dollar (High / Medium / Low) based on Notes and Type* - *Regret Risk (High / Medium / Low)* 2. *Based on those scores, assign one of three decisions:* - *Buy – strong case to purchase this weekend* - *Wait 30 Days – only buy if I still want it in a month* - *Skip – likely impulse or low value* 3. *Output a table with columns:* - *Item | Price | Need/Want | Joy per Dollar | Regret Risk | Decision (Buy/Wait/Skip) | One-line Reason* 4. *Then output 3 short bullets:* - *“Green lights” total cost (all Buy items sum).* - *Amount avoided if I only buy “Buy” and skip the rest.* - *One Thanksgiving Spending Rule for me (e.g., “No new gadgets over $200 unless they replace something I sell”).* *Rules:* 1. *Be honest but not joyless—some “fun” is allowed.* 2. *If two items are similar, nudge me toward the cheaper one with higher Joy per Dollar.* 3. *Do not assume anything about my income or debt—only use the table + notes.* Scan the output. You should now have a clear **Buy / Wait / Skip** table plus a custom “Thanksgiving Spending Rule.” ### **Step 3 — Check your cart against your money picture (2–3 minutes)** Now we sanity-check the “Buy” list. 1. Add a new column F (Decision) to your Google Sheet and copy ChatGPT’s decisions (Buy/Wait/Skip) back into the sheet next to each item. 2. Add a new cell under the table, e.g., **B20**, labeled Total\_Buy\_Spend and enter: \=SUMIF(F2:F16,"Buy",B2:B16) That gives you a single number: what your AI-approved “green lights” cost. *📊 If you completed Volume 2 (Net Worth Tracker):* - *Open your Net Worth sheet and look at your Cash / Savings line.* - *Add a quick note: Black Friday "Buy" Total: $XXX (from the formula above).* - *If that number is more than 10–15% of your liquid savings, consider moving one or two items from Buy → Wait. This keeps the fun but stops one weekend from nuking your cushion.* ### **Step 4 — Lock in your Thanksgiving rules (1–2 minutes)** Back in ChatGPT, ask it to turn this into a simple rule card you can reuse all weekend: *Take the Buy/Wait/Skip results you just gave me and write a short “Thanksgiving Spending Rules” card with:* - *3 rules that are specific to my patterns (e.g., limit on gadgets, limit on random TikTok buys, cap on total spend).* - *1 sentence I can read before I click “Place Order” that reminds me what future-me actually cares about.* - *Keep it friendly, short, and written like you’re talking to me on Thanksgiving evening.* Paste those rules at the top of your Google Sheet or into a Notes app on your phone. That’s your Black Friday Copilot Card—it travels with you across apps and tabs. ## **The Payoff** Instead of waking up on Monday wondering what just happened to your bank account, you’ll have: - A one-page cart view with everything in one place - A clear Buy / Wait 30 Days / Skip decision for each item - A total “green light” spend number you’re consciously choosing - A tiny set of Thanksgiving Spending Rules tuned to your actual habits You still get the dopamine from a few good deals. You just lose the hangover. ## **Transparency & Notes for Readers** - All tools are free: Google Sheets, ChatGPT Free, your existing browser/app carts. - Privacy: Don’t paste card numbers, addresses, or login details into AI—only item names, prices, and notes. - Limits: This doesn’t know your full financial picture; if you’re carrying high-interest debt or have no emergency fund, lean harder into Wait/Skip. - Educational workflow — not financial advice. ## Founder's Corner Real world learnings as I build, succeed, and fail Two things can be true at the same time. AI can transform how you work, while also requiring the proper governance to ensure accurate outputs. As I highlighted in [Volume 2](https://www.mindovermoney.ai/how-to-build-a-better-workflow-with-chatgpt/), I’m constantly working with AI tools to close knowledge gaps and build code to customize the experience on [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai). This process keeps getting more efficient as the intelligence of large language models expands. But that doesn’t mean you can let AI write code, solve problems, generate answers, and help you work without governance in place. I want to walk you through my latest challenge and how AI saved the day, but only with my oversight. # **The Problem:** I recently ran into a critical bug on the website. The "Infinite Scroll" feature, which automatically loads older posts as you scroll down, was completely broken on the Archive, Founder’s Corner, and Prompt Library pages (visual below). If you scrolled to the bottom, nothing happened. The older content was stranded, inaccessible to anyone visiting the site. Not a great look as new subscribers who wanted to start at the beginning tried to access these links. As I’ve mentioned before, I am not a coder. I rely on my AI partner (Gemini, in this case) to act as my lead developer. So, I did what I always do: I [explained the problem](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-9-the-newsletter-shaper/), pasted my code, and asked for a fix. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/11/data-src-image-8722d9e6-3afb-4f6f-bf9c-c0f5a9ac460d.png) # **The Process:** Gemini 2.5 Pro with Thinking is a great model, and it went to work analyzing my current code in GitHub to look for a solution. I find it fascinating to read the model’s ‘thinking’ process as it scours thousands of lines of code in a matter of seconds. AI’s ability to read and write code seems like magic and highlights the power of this technology. Gemini confidently identified the issue and generated a fix for me to deploy. I did my part, but the issue remained unsolved. This pattern continued on for about one hour, leading to approximately 15 code changes. The last change fixed the “infinite scrolling” issue, but broke three other elements of my site. AI is powerful, but the human element was needed to get this project back on track. I stopped the deployment efforts and reset the discovery process in the chat. It was time to debug the AI itself. I went through each element of the website that was broken and gave direct instructions to my AI coding assistant. Specifically, I had the AI navigate to the website and experience the breaks as if they were a new visitor. I shared the live source code to compare it to the GitHub code. I instructed the AI to research online forums and YouTube to find the correct solution before writing the final code change. After 30 minutes of research and dialogue, we finally found the exact root cause. Gemini was writing perfect code for a source file (main.js), but my live website was running on a compiled version (main.min.js) that wasn't being updated by my deployment process. The AI was solving the right problem in the wrong room. It took collaboration and strict oversight from me to deploy a solution that fully fixed the original issue without breaking other aspects of the site. # **The Lesson:** This experience reinforced a massive lesson that applies to anyone using AI: You cannot abdicate responsibility. We often treat these models like magic boxes that have all the answers. We assume that because they can write code or summarize text, they understand the full context of our business or environment. They don't. In fact, research backs up exactly what I experienced. A recent study presented at the Computer-Human Interaction Conference found that 52% of ChatGPT’s programming answers contain misinformation. Even more telling, users preferred those incorrect answers 35% of the time simply because they were polite and comprehensive. That is exactly the trap I fell into: the AI was confident, so I didn’t probe to ensure the right fix was being deployed. I treated Gemini like a senior engineer who knows everything, and that broke my homepage in the process. But when I started treating it like a junior developer, talented but inexperienced and needing its work checked, I found the right solution. This aligns with what experts are seeing across the industry. The 2025 Stack Overflow Developer Survey reported that the biggest frustration for 66% of developers is dealing with AI solutions that are "almost right, but not quite." That "almost right" code is dangerous because it creates what experts are now calling "AI Debt", the hidden cost of cleaning up hasty AI code later. Governance isn't just a corporate buzzword. For us "AI Doers," governance is the discipline of pausing and asking, "Does this actually make sense?" before we hit enter. You have to audit the output. You have to verify the logic. You have to be the one to say "Stop" when the solution looks risky. AI can write the code and create amazing outputs, but you still have to be the one to steer the ship. At least for now… **Goals & Milestones:** | Goal | Current (as of 11/18/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 98 | 300 | | Monthly Recurring Revenue | $28 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 18 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 7 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Grateful for AI, Thankful for Governance URL: https://www.mindovermoney.ai/founders-corner/ai-governance-framework-workplace-practical-guide/ Last updated: 2026-07-18T01:46:20.000Z Two things can be true at the same time. AI can transform how you work, while also requiring the proper governance to ensure accurate outputs. As I highlighted in [Volume 2](https://www.mindovermoney.ai/how-to-build-a-better-workflow-with-chatgpt/), I’m constantly working with AI tools to close knowledge gaps and build code to customize the experience on [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai). This process keeps getting more efficient as the intelligence of large language models expands. But that doesn’t mean you can let AI write code, solve problems, generate answers, and help you work without governance in place. I want to walk you through my latest challenge and how AI saved the day, but only with my oversight. # **The Problem:** I recently ran into a critical bug on the website. The "Infinite Scroll" feature, which automatically loads older posts as you scroll down, was completely broken on the Archive, Founder’s Corner, and Prompt Library pages (visual below). If you scrolled to the bottom, nothing happened. The older content was stranded, inaccessible to anyone visiting the site. Not a great look as new subscribers who wanted to start at the beginning tried to access these links. As I’ve mentioned before, I am not a coder. I rely on my AI partner (Gemini, in this case) to act as my lead developer. So, I did what I always do: I [explained the problem](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-9-the-newsletter-shaper/), pasted my code, and asked for a fix. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/11/data-src-image-9d6fbce4-c207-4e31-a101-7ccc5ae0c8d3.png) Where is Volume 1, 2, and 3? # **The Process:** Gemini 2.5 Pro with Thinking is a great model, and it went to work analyzing my current code in GitHub to look for a solution. I find it fascinating to read the model’s ‘thinking’ process as it scours thousands of lines of code in a matter of seconds. AI’s ability to read and write code seems like magic and highlights the power of this technology. Gemini confidently identified the issue and generated a fix for me to deploy. I did my part, but the issue remained unsolved. This pattern continued on for about one hour, leading to approximately 15 code changes. The last change fixed the “infinite scrolling” issue, but broke three other elements of my site. AI is powerful, but the human element was needed to get this project back on track. I stopped the deployment efforts and reset the discovery process in the chat. It was time to debug the AI itself. I went through each element of the website that was broken and gave direct instructions to my AI coding assistant. Specifically, I had the AI navigate to the website and experience the breaks as if they were a new visitor. I shared the live source code to compare it to the GitHub code. I instructed the AI to research online forums and YouTube to find the correct solution before writing the final code change. After 30 minutes of research and dialogue, we finally found the exact root cause. Gemini was writing perfect code for a source file (main.js), but my live website was running on a compiled version (main.min.js) that wasn't being updated by my deployment process. The AI was solving the right problem in the wrong room. It took collaboration and strict oversight from me to deploy a solution that fully fixed the original issue without breaking other aspects of the site. # **The Lesson:** This experience reinforced a massive lesson that applies to anyone using AI: You cannot abdicate responsibility. We often treat these models like magic boxes that have all the answers. We assume that because they can write code or summarize text, they understand the full context of our business or environment. They don't. In fact, research backs up exactly what I experienced. A recent study presented at the Computer-Human Interaction Conference found that 52% of ChatGPT’s programming answers contain misinformation. Even more telling, users preferred those incorrect answers 35% of the time simply because they were polite and comprehensive. That is exactly the trap I fell into: the AI was confident, so I didn’t probe to ensure the right fix was being deployed. I treated Gemini like a senior engineer who knows everything, and broke my homepage in the process. But when I started treating it like a junior developer, talented but inexperienced and needing its work checked, I found the right solution. This aligns with what experts are seeing across the industry. The 2025 Stack Overflow Developer Survey reported that the biggest frustration for 66% of developers is dealing with AI solutions that are "almost right, but not quite." That "almost right" code is dangerous because it creates what experts are now calling "AI Debt", the hidden cost of cleaning up hasty AI code later. Governance isn't just a corporate buzzword. For us "AI Doers," governance is the discipline of pausing and asking, "Does this actually make sense?" before we hit enter. You have to audit the output. You have to verify the logic. You have to be the one to say "Stop" when the solution looks risky. AI can write the code and create amazing outputs, but you still have to be the one to steer the ship. At least for now… ### Steal My Prompt Vol. 9: The Newsletter Shaper URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-9-the-newsletter-shaper/ Last updated: 2026-07-13T16:59:52.000Z This week, I’m sharing two prompts that helped shape Volume 9\. First, you’ll get my revamped prompt for NotebookLM’s ‘Video Overview’ functionality. I wanted to build a more in-depth prompt that could seamlessly build content that connected the ‘AI Education’ concepts from volumes 1 through 8\. The second prompt kicked off my process to fix the pagination issue on my website, which was the basis for this week's ‘Founder’s Corner’. The prompt was rather simplistic and more of a jumping off point to figure out the problem and begin updating my code base. Fun fact, this was also the last prompt I will ever execute with Gemini 2.5 Pro. Truly a great model that sparked my personal AI journey. I hope you enjoy these prompts and can take something back as you continue optimizing outputs with your AI partner. --- **Prompt 1: NotebookLM Video Creation** Instructions (Paste into Customize box): Source Material Constraint: You must strictly limit the script to content found under the headers "AI Education" in the provided sources. Do not include content from "Signals Over Noise," "Founder’s Corner," or "10-Minute Win." Goal: Create a cohesive visual narrative that connects the 8 key educational concepts covered so far into a single story. The video should explain how the technology is built, how it learns, and how a user effectively interacts with it. Narrative Flow: 1. The Foundation: Start by visualizing the "Family Tree" of the technology. Define AI as the umbrella, Machine Learning as the method, and Deep Learning (Neural Networks) as the engine that powers modern Generative AI and Large Language Models. 2. The Learning Process: Transition to how this engine is fueled. Explain the importance of Data (Features and Labels) and the difference between Structured and Unstructured data. Briefly touch on how models are trained using Supervised vs. Unsupervised Learning. 3. The Mechanics of Meaning: Dive inside the model to show how it "understands." Visualize Tokens as the small pieces of text the model reads, and Vectors/Embeddings as the "Map of Meaning" where similar concepts are grouped together. 4. The Human Control: Conclude with the user's role in steering the model. Explain Prompt Engineering as the method for giving clear directions, and Chunking as the strategy to ensure information fits within the model's limited Context Window. Visual Style: Use clear, educational diagrams to represent these abstract concepts. Use the specific metaphors found in the text, such as the "dots on a map" for vectors and the "page size" for context windows. **Prompt 2: Fixing Website** Context: My website is not functioning correctly. When you navigate to 'https://www.mindovermoney.ai/founders-corner/', https://www.mindovermoney.ai/tag/newsletter/', or https://www.mindovermoney.ai/prompt-library/', you can only see a portion of the articles. The older posts are not visible and there is no way to move to another page to see the older posts. This will be a problem as time goes on. The ask: Find a solution to deploy on my github code to fix this issue for each section Your role: Identify solutions and propose the fix to me. Ensure alignment on how we will fix the issue. Make sure you understand the issue by researching my website and validating understanding of the problem. Directions: \- Research problem for understanding \- Find root cause issue in the code base \- Research possible solutions \- Ask clarifying questions one at a time \- Be thorough ### Steal My Prompt Vol. 8: The Research Organizer URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-8-the-research-organizer/ Last updated: 2026-07-13T16:59:53.000Z I want to share a use case from one of my favorite AI tools, [NotebookLM](https://notebooklm.google/?ref=mindovermoney.ai). I reference two studies in *Founder’s Corner* that highlighted a significant amount of qualitative and quantitative data points. I read through the studies to retain as much information as possible, but this kind of research is how AI can be deployed is a powerful way. And NotebookLM takes that research to the next level. I uploaded both studies to NotebookLM and used the tool to help organize my thoughts for the blog post. I plan on doing a deeper dive into NotebookLM in the future, but below are the prompts that aided in this exercise. I kept the prompts simple and formatted in an 'easy to follow format' for my AI assistance. If you read Volume 8 of Neural Gains Weekly, this should resonate, enjoy! --- *Prompt 1:* *Context: I'm writing a blog post to help my audience build a framework for how to use AI in the workplace to drive success. I will give 5 useful tips that people can start acting on to bring AI into the workplace.* *Your Task: Research data, stats, information, quotes, and context from these research papers that will help me in this blog post. Provide context as to why it will be useful.* *Instructions: Ask clarifying questions to ensure understanding of your task. Ask the questions one at a time. Cite sources and delineate between the two studies.* *Prompt 2: Find me 5 stats that highlight AI adoption in the workplace* ### Volume 8: Don't Let AI Miss the Point URL: https://www.mindovermoney.ai/how-to-use-ai-at-work-5-tips-high-performers/ Last updated: 2026-07-13T16:59:53.000Z Hey everyone! 👋 AI is only as smart as the way we feed it information. In **AI Education**, we dive into context windows and chunking so long texts don’t overwhelm your prompts. This week’s **10-Minute Win** turns that same discipline toward your finances with a personalized emergency fund target and plan. And in **Founder’s Corner**, we zoom out to the bigger picture of AI at work, and what the latest adoption data really means for your career and how to be the person who’s ready for what’s coming. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **GPT-5.1: A smarter, more conversational ChatGPT**](https://openai.com/index/gpt-5-1/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI rolled out GPT-5.1 with two main flavors: Instant (warmer, faster, better at following instructions) and Thinking (more adaptive reasoning with clearer, less jargony explanations). It also adds built-in controls so users can tune tone and style without prompt hackery. **Why it matters:** This is a baseline upgrade to what “default ChatGPT” can do. Smarter + easier to steer means more people will quietly push real work—planning, research, money decisions—through AI every day. ### **2)**[ **Piloting group chats in ChatGPT**](https://openai.com/index/group-chats-in-chatgpt/?utm%5Fsource=chatgpt.com) **Summary:** ChatGPT now supports group chats (pilot in a handful of countries), letting up to 20 people plus the model collaborate in the same thread. Group chats live in their own space, don’t share personal memory, and are powered by GPT-5.1 Auto with search, files, images, and voice. **Why it matters:** This pushes AI from solo assistant to social workspace—think trip planning, study groups, small teams. Expect a wave of “AI in the group chat” workflows and products. ### **3)**[ **Anthropic to invest $50 billion to build data centers in the U.S.**](https://www.reuters.com/technology/anthropic-invest-50-billion-build-data-centers-us-2025-11-12/?utm%5Fsource=chatgpt.com) **Summary:** Anthropic (Claude) announced a $50B program to build custom AI data centers in Texas and New York with partner Fluidstack, with the first sites coming online in 2026 and thousands of jobs attached. **Why it matters:** Another monster capex signal that AI infrastructure is the new critical utility. Chips, power, cooling, and land are the real choke points—and Anthropic is moving to own more of that stack instead of just renting from clouds. ### **4)**[ **Poll: People worry about AI’s impact, but not their jobs**](https://www.nbcnews.com/tech/tech-news/poll-worry-ais-impact-not-jobs-rcna242985?ref=mindovermoney.ai) **Summary:** A new multi-country poll finds most adults are worried about AI’s impact on the economy and society, but far fewer think their *own* job is at serious risk. Many expect disruption in the abstract while assuming they’ll personally dodge it. **Why it matters:** Classic “it’ll hit *someone else*” psychology. For you, that’s the gap to exploit: calmly mapping where AI is *actually* biting first (tasks, not titles) and adjusting skills and roles accordingly. ### **5)**[ **AI boom fuels fresh wave of legal tech investments**](https://www.reuters.com/technology/ai-boom-fuels-fresh-wave-legal-tech-investments-2025-11-12/?utm%5Fsource=chatgpt.com) **Summary:** Investors have pushed $750M+ into AI-driven legal startups in recent weeks alone—GC AI, Clio, Legora, DeepJudge, SpellBook, EvenUp, Eve, and others—backing tools for drafting, research, contract analysis, and litigation support. **Why it matters:** Legal is a great case study for AI as margin expansion: high billable rates, repetitive knowledge work, and lots of text. If AI can reliably take chunks of that workflow, you get a blueprint for what might happen next in accounting, compliance, banking ops, and other white-collar domains. ## AI Education for You ****Context Windows & Chunking 101 — How to fit long text so AI doesn’t miss the point** Last time, you learned how to **design your ask**: name the goal, share only the right facts, choose a format, set limits, and (optionally) show a tiny example. That is prompt engineering in plain English—shaping your words so the model can follow a clear pattern. This week adds the constraint behind every good prompt: the model reads on a **fixed-size page**. If your message is longer than that page, the overflow is not read. Even a well-written prompt can miss key details when the input is too long. The fix is **chunking**. You split long text into small, self-contained pieces, give each piece a short title, add a 1–3 line summary, and order the pieces so the most important ones appear first. You’re still doing prompt engineering—now with better content design. Clear prompts plus well-designed chunks help the model see what matters, stay on topic, and answer cleanly. ## **Core lesson — concepts first** **Context window (the page size):** A model reads a fixed amount of text at once. Anything past the limit is ignored. **Chunking (split):** Break long content into small units that make sense on their own. Each chunk should answer: “What is this, and why does it matter?” **Titles (label):** Give each chunk a short, direct title so the model understands the input. Examples: “Groceries — Outliers,” “Restaurants — Trend,” “Transport — New Charges.” **Summaries (condense):** Start every chunk with 1–3 lines that highlight decisions and exceptions. Keep only the facts that affect the answer. **Ordering (prioritize):** Put chunks in the order the model should read them. Most relevant first. If the page fills, lower-priority chunks are the ones that fall off. **De-duplication (remove repeats):** Repeated email quotes and boilerplate waste space. Keep one clear recap and the newest message. **When to split by topic vs. time:** - **Topic** (groceries, restaurants, transport): best for category comparison and spotting outliers. - **Time** (week 1, week 2, week 3): best for trends and changes across the month. For today, we will use topic-based splitting because it is easy to compare and scan. **Why this works:** Short, labeled chunks fit the page. The model sees what matters and can answer cleanly. ## **Contrast & clarity — common mistakes vs. better habits** - **Mistake:** Paste a long prompt with no structure. **Better:** Split into small, titled chunks with 1–3 line summaries. - **Mistake:** Hide the goal at the end. **Better:** State your goal first, then place the most relevant chunk right below it. - **Mistake:** Keep every quoted email and header. **Better:** Keep one recap and the newest message only. - **Mistake:** Vague titles like “Notes.” **Better:** Specific, scannable titles: “Groceries — Outliers,” “Transport — New Merchants.” ## **Examples that land** ### **Example 1 — Bank statement: find category outliers:** **Scenario:** You have a long bank statement. You want the AI to tell you which categories look unusual this month. **Your goal:** Find category outliers and say why. **The prompt:** Goal: Find category outliers this month.Return three bullets: category, short reason, biggest driver. Use only the chunks below. **The chunks you paste:** Chunk — Groceries Summary (Outliers): Two charges over 100; new store “OrganicCo”; total higher than usual.Examples: OrganicCo — 10/12 — 128.42; FreshMart — 10/21 — 112.09. Chunk — Transport Summary (Outliers): New ride service started; three weekend trips; total up vs last month.Examples: RideCo — 10/05 — 31.20; RideCo — 10/12 — 28.70. Chunk — Restaurants Summary (Trend): Down 75 vs last month; fewer visits; one 60 dinner.Example: Bistro — 10/14 — 60.00. **What you should get back:** • Groceries — higher total from two large trips — OrganicCo 128.42 • Transport — new ride habit increased total — three weekend trips • Restaurants — down overall — fewer visits ### **Example 2 — Budget planning: explain a rise and suggest one fix** **Scenario:** Your grocery spending went up. You want the AI to explain the cause and propose one practical fix for next month. **Your goal:** Explain the rise in plain English and suggest one specific action. **The prompt:** Goal: Explain why groceries rose and propose one practical fix for next month. Answer in two short paragraphs. Use only the chunks below. **The chunks you paste:** Chunk — Groceries Summary (Headline): Up 120 vs last month; one-time party 95; two large trips this month. Examples: OrganicCo — 10/12 — 128.42; FreshMart — 10/21 — 112.09. Chunk — Restaurants Summary (Context): Down 75 vs last month; two skipped outings; small takeout only. **What you should get back:** Paragraph 1: A plain explanation (one-time party + two big trips drove the increase). Paragraph 2: One concrete fix (for example, plan one home-cooked dinner with leftovers to replace a restaurant meal). ## **One-screen recap** - The model reads a page of text at a time. Overflow is cut off. - Chunk long text into small, titled pieces with 1–3 line summaries. - Order chunks so the key part comes first. - Remove repeats and filler. Clear beats long. - Use topic-based chunks for category comparisons; use time-based chunks for trend questions. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🛟 Emergency Fund Sizing** **Why this matters:** Emergencies aren’t “if,” they’re “when.” In about 10 minutes, you’ll use AI + a simple Google Sheet to calculate how many months of expenses you personally need, turn that into a clear dollar target, and set up automatic reminders so your emergency fund grows in the background. ### **Step 1 — Build your essentials sheet (3 minutes)** Open **Google Sheets** and set up a simple table like this: | A (Category) | B (Monthly Essential $) | | ----------------------- | ----------------------- | | Housing (Rent/Mortgage) | 1800 | | Utilities (Power/Water) | 220 | | Groceries | 520 | | Transportation | 240 | | Insurance (Health/Auto) | 310 | | Childcare | 0 | | Minimum Debt Payments | 250 | | Phone/Internet | 120 | | Other Essentials | 150 | | TOTAL | \=SUM(B2:B9) | **How to set it up:** 1. In **A1**, type Category. In **B1**, type Monthly Essential ($). 2. Fill A2–A9 with *only* “keep-the-lights-on” categories (no dining out, no shopping, no streaming). 3. Enter your actual monthly amounts in B2–B9. 4. In **B10**, enter: =SUM(B2:B9) 5. In **C10**, label that result Essential\_Monthly\_Spend. 🧠 **If you completed Volume 1 (AI Budget Analyzer):** Don’t start from scratch. Open your Vol. 1 budget sheet, filter it down to essential categories only (Housing, Utilities, Groceries, Transportation, Insurance, Minimum Debt Payments, Phone/Internet, Childcare). Use a simple SUM on those rows to get your Essential\_Monthly\_Spend and plug that total directly into this new sheet. You’re reusing the hard work you already did. ### **Step 2 — Let AI choose your Target Months (3 minutes)** Open ChatGPT (Free) and paste this prompt. Fill in your answers right in the prompt before you click send: *You are my household risk scorer. Based only on my answers, recommend a months-of-expenses target for an emergency fund and a stepwise plan. Answers:* - *Employment type & stability (stable salary / variable / contractor): \_\_\_* - *Number of dependents: \_\_\_* - *Are you the sole earner? (yes/no): \_\_\_* - *Income variability (commissions, tips, gig): low/med/high: \_\_\_* - *Access to other cash buffers (HSA, savings, etc.): good/limited/none: \_\_\_* - *Fixed expenses share (>60% of take-home?): low/med/high: \_\_\_* - *Industry layoff risk next 12 months: low/med/high: \_\_\_* *Rules:* - *Map to Target Months: very low ≈ 3; low ≈ 4–5; moderate ≈ 6; elevated ≈ 7–9; high ≈ 10–12.* - *Output: Target Months, Rationale (3 bullets), and Milestones (1 month → 3 months → full).* - *Do not invent dollar amounts; I’ll do the math separately.* You should get something like: - Target Months: 6 - Rationale: 3 bullets - Milestones: 1 month → 3 months → full target ### **Step 3 — Turn months into dollars (and connect to Vol. 2) (2–3 minutes)** Back in **Google Sheets**: 1. In **A12**, type Target\_Months. In **B12**, enter the number from ChatGPT (e.g., 6). In **A13**, type EF\_Target\_$. In **B13**, enter: =ROUND(B10 \* B12, 0) 2. That’s your **emergency fund dollar target**. 3. Add milestones: | A | B | | ----------------------- | ------------------ | | Milestone\_1 (1 month) | \=ROUND(B10\*1, 0) | | Milestone\_2 (3 months) | \=ROUND(B10\*3, 0) | | Milestone\_3 (Full) | \=B13 | 🔁 **If you completed Volume 2 (Net Worth Tracker):** Open your Vol. 2 Net Worth sheet and: - Find your current cash/emergency savings line. - Add a new row under it: “Emergency Fund Target (from this 10-Minute Win)” and enter your EF\_Target\_$. - Add another row: “Gap to Target” = Target – Current.Now your Net Worth tracker shows, at a glance, how far you are from a fully funded emergency fund — and you’ll see that gap shrink over time. ### **Step 4 — Make it automatic (2 minutes, desktop)** **A) Save your plan (Docs/Notion)** - Copy ChatGPT’s **Target Months, Rationale, and Milestones** into **Google Docs** or **Notion**. - At the top, add your EF\_Target\_$ and Monthly\_Transfer from Sheets. **B) Set a recurring reminder (Google Calendar, desktop)** 1. Go to **calendar.google.com**. 2. Click your **next pay date** → “More options.” 3. Title the event: Move $ → Emergency Fund. 4. Set **Repeat:** every month (or each pay period). 5. Paste your Docs/Notion link into the **Description**. 6. Add notifications (e.g., 1 day before, 1 hour before) → **Save**. Now your emergency fund is a scheduled behavior, not a vague intention. ## **The Payoff** You’re no longer guessing at “3–6 months” because a blog said so. You have a personalized Target Months, a clear dollar number, milestones, and a recurring reminder that nudges you toward it every month. If your life changes (job, income, dependents), you can re-run the same risk prompt and tweak your target in minutes. 🧩 For returning readers: Volume 1 gave you clean spending data. Volume 2 gave you your Net Worth. This workflow plugs into both: you’re now connecting monthly essentials (Vol. 1) and cash on hand (Vol. 2) into a single, concrete emergency fund plan. ## **Transparency & Notes for Readers** - All tools are free: Google Sheets, ChatGPT Free, Google Docs/Notion, Google Calendar. - Be conservative: If you’re torn between two targets (e.g., 5 vs. 6 months), choose the higher buffer. - Privacy: Don’t paste account numbers or personally identifying info into AI; just categories and amounts. - Educational workflow — not financial advice. ## Founder's Corner Real world learnings as I build, succeed, and fail AI adoption in the workplace is accelerating as more companies weave it into their daily business operations. Recently, [Wharton ](https://ai.wharton.upenn.edu/wp-content/uploads/2025/10/2025-Wharton-GBK-AI-Adoption-Report%5FFull-Report.pdf?ref=mindovermoney.ai)and [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai?ref=mindovermoney.ai) released comprehensive studies on the current state of AI in the enterprise. The theme is consistent: AI deployment has moved out of the exploration and experimentation phase. We’re entering an era of acceleration and accountability. Here are a handful of stats that I found eye-opening from the studies: - 82% use Gen AI at least weekly (+10pp YoY), and 46% (+17pp YoY) daily. (Wharton) - 62% of survey respondents say their organizations are at least experimenting with AI agents. (McKinsey) - 88% say their organizations are regularly using AI in at least one business function. (McKinsey) - 23% of respondents report their organizations are already scaling an agentic AI system somewhere in their enterprises. (McKinsey) - 72% of business leaders report tracking formal, structured Return on Investment (ROI) metrics for their Gen AI technology investments. (Wharton) I highly recommend reading through each of these studies, but the trend is clear. Leaders across the country are successfully using AI in their everyday work, while building AI systems into their current architecture. Many of you work in the corporate world, and the larger the organization, the harder it can be to understand where your company is on its AI journey. It can be challenging to experiment, as you might not have access to the right tools. A lack of visibility into other departments might hinder your ability to collaborate to solve problems where AI could be part of the solution. I’m sure you’ve experienced at least one of these challenges and can list additional scenarios that slow down AI adoption within your organization. Luckily, there is time to act and positively impact the trajectory of your department, and hopefully your company. Pilot mode is still the norm across industries, despite increases in overall AI usage. According to the McKinsey study, nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise. The time to act is now, and I want to share 5 practical tips to help you be ready for the next phase of AI within your company. 1. **Find ways to use AI daily** Seek any and all opportunities to build your AI skills in the workplace. This could be as simple as using Copilot to summarize emails, write or edit executive summaries, or build a talk track for an upcoming leadership readout. Start small, build a portfolio, and be creative. Your role might not have obvious AI use cases, and it will be up to you to find scenarios to test your skills and build confidence. 1. **Workflow first** AI adoption requires a mindset shift that prioritizes problem solving. Pilots will often fail when AI capabilities are forced onto an existing process. You can get ahead by building out detailed problem statements to rewire legacy workflows. This framework will be a foundation for any company that expects to see positive outcomes from AI initiatives. The McKinsey survey highlights this concept emphatically: ‘AI high performers—organizations seeing the greatest financial impact from AI—are nearly three times as likely as other organizations to report that they have fundamentally redesigned individual workflows in their deployment of AI (55% vs. 20%).’ 1. **Rethink ROI** Every company’s leadership will expect ROI from AI initiatives. That’s how the world works. And the ROI story will be challenging if decision makers and executives are not keeping up with the world of AI. It’s up to you, as an AI leader, to help bridge those gaps by evolving how you position ROI. Adopting a transformative mindset that pushes for growth and innovation, not just cost savings, will win in the long term. This simple reframe can help evolve how your organization approaches ROI and drive better alignment for AI infused projects. Here are two stats from the McKinsey study that hammer home this point: - While cost efficiency is often the objective of AI efforts (reported by 82% of organizations), organizations achieving the greatest value (AI high performers) are more likely to also set growth (80%) and innovation (79%) as objectives. - Organizations intending to use AI to bring about transformative change to their businesses are 3.6 times more likely to be AI high performers 1. **Knowledge share** Not everyone around you will have the same level of AI knowledge and skill in the workplace. And that’s okay. It will be important to help others and seek mentorship as AI’s influence at work continues to evolve. I’m working through this with my team at work by building mechanisms to share AI use cases and best practices. I make it a point to talk about AI during 1x1s and gather ideas for the team. I’m not afraid to ask my peers for their opinions and thoughts on AI topics to broaden my viewpoints and understand how others are thinking about the future. There is no right or wrong way to share insights with others. A ‘rising tide lifts all boats’ mentality will ensure everyone is set up for success and can influence the future of work within their organization. 1. **Don’t forget about governance** View AI as a powerful companion that requires human expertise, judgment, and validation, rather than a fully autonomous replacement. You cannot blindly trust that AI’s output is accurate and ready for mass consumption within your company. Prioritize auditing outputs and building a personal governance policy for AI use in your workflows. This will help build a framework that aligns (or will align) with broader governance strategies implemented at the corporate level. Prioritizing quality over speed is what actually accelerates AI usage. This is particularly important because inaccurate results remain one of the top three concerns leaders cite when using Gen AI (Wharton). **Bringing it all together** AI is no longer a side project, it is becoming part of how work gets done. The good news is you do not need a new title or a massive budget to have an impact. Use AI daily, redesign workflows instead of bolting tools onto old processes, evolve how you talk about ROI, share what you learn, and keep quality and governance front and center. If you do that consistently, you will be ready for whatever phase of AI your company enters next, and you will have real examples to show the value you are creating along the way. **Goals & Milestones:** | Goal | Current (as of 11/18/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 92 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 19 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 7 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### AI Standouts at Work: What High Performers Do Differently URL: https://www.mindovermoney.ai/founders-corner/how-high-performers-use-ai-at-work-wharton-research/ Last updated: 2026-07-13T16:59:53.000Z AI adoption in the workplace is accelerating as more companies weave it into their daily business operations. Recently, [Wharton ](https://ai.wharton.upenn.edu/wp-content/uploads/2025/10/2025-Wharton-GBK-AI-Adoption-Report%5FFull-Report.pdf?ref=mindovermoney.ai)and [McKinsey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai?ref=mindovermoney.ai) released comprehensive studies on the current state of AI in the enterprise. The theme is consistent: AI deployment has moved out of the exploration and experimentation phase. We’re entering an era of acceleration and accountability. Here are a handful of stats that I found eye-opening from the studies: - 82% use Gen AI at least weekly (+10pp YoY), and 46% (+17pp YoY) daily. (Wharton) - 62% of survey respondents say their organizations are at least experimenting with AI agents. (McKinsey) - 88% say their organizations are regularly using AI in at least one business function. (McKinsey) - 23% of respondents report their organizations are already scaling an agentic AI system somewhere in their enterprises. (McKinsey) - 72% of business leaders report tracking formal, structured Return on Investment (ROI) metrics for their Gen AI technology investments. (Wharton) I highly recommend reading through each of these studies, but the trend is clear. Leaders across the country are successfully using AI in their everyday work, while building AI systems into their current architecture. Many of you work in the corporate world, and the larger the organization, the harder it can be to understand where your company is on its AI journey. It can be challenging to experiment, as you might not have access to the right tools. A lack of visibility into other departments might hinder your ability to collaborate to solve problems where AI could be part of the solution. I’m sure you’ve experienced at least one of these challenges and can list additional scenarios that slow down AI adoption within your organization. Luckily, there is time to act and positively impact the trajectory of your department, and hopefully your company. Pilot mode is still the norm across industries, despite increases in overall AI usage. According to the McKinsey study, nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise. The time to act is now, and I want to share 5 practical tips to help you be ready for the next phase of AI within your company. 1. **Find ways to use AI daily** Seek any and all opportunities to build your AI skills in the workplace. This could be as simple as using Copilot to summarize emails, write or edit executive summaries, or build a talk track for an upcoming leadership readout. Start small, build a portfolio, and be creative. Your role might not have obvious AI use cases, and it will be up to you to find scenarios to test your skills and build confidence. 1. **Workflow first** AI adoption requires a mindset shift that prioritizes problem solving. Pilots will often fail when AI capabilities are forced onto an existing process. You can get ahead by building out detailed problem statements to rewire legacy workflows. This framework will be a foundation for any company that expects to see positive outcomes from AI initiatives. The McKinsey survey highlights this concept emphatically: ‘AI high performers—organizations seeing the greatest financial impact from AI—are nearly three times as likely as other organizations to report that they have fundamentally redesigned individual workflows in their deployment of AI (55% vs. 20%).’ 1. **Rethink ROI** Every company’s leadership will expect ROI from AI initiatives. That’s how the world works. And the ROI story will be challenging if decision makers and executives are not keeping up with the world of AI. It’s up to you, as an AI leader, to help bridge those gaps by evolving how you position ROI. Adopting a transformative mindset that pushes for growth and innovation, not just cost savings, will win in the long term. This simple reframe can help evolve how your organization approaches ROI and drive better alignment for AI infused projects. Here are two stats from the McKinsey study that hammer home this point: - While cost efficiency is often the objective of AI efforts (reported by 82% of organizations), organizations achieving the greatest value (AI high performers) are more likely to also set growth (80%) and innovation (79%) as objectives. - Organizations intending to use AI to bring about transformative change to their businesses are 3.6 times more likely to be AI high performers. 1. **Knowledge share** Not everyone around you will have the same level of AI knowledge and skill in the workplace. And that’s okay. It will be important to help others and seek mentorship as AI’s influence at work continues to evolve. I’m working through this with my team at work by building mechanisms to share AI use cases and best practices. I make it a point to talk about AI during check-ins and gather ideas for the team. I’m not afraid to ask my peers for their opinions and thoughts on AI topics to broaden my viewpoints and understand how others are thinking about the future. There is no right or wrong way to share insights with others. A ‘rising tide lifts all boats’ mentality will ensure everyone is set up for success and can influence the future of work within their organization. 1. **Don’t forget about governance** View AI as a powerful companion that requires human expertise, judgment, and validation, rather than a fully autonomous replacement. You cannot blindly trust that AI’s output is accurate and ready for mass consumption within your company. Prioritize auditing outputs and building a personal governance policy for AI use in your workflows. This will help build a framework that aligns (or will align) with broader governance strategies implemented at the corporate level. Prioritizing quality over speed is what actually accelerates AI usage. This is particularly important because inaccurate results remain one of the top three concerns leaders cite when using Gen AI (Wharton). **Bringing it all together** AI is no longer a side project, it is becoming part of how work gets done. The good news is you do not need a new title or a massive budget to have an impact. Use AI daily, redesign workflows instead of bolting tools onto old processes, evolve how you talk about ROI, share what you learn, and keep quality and governance front and center. If you do that consistently, you will be ready for whatever phase of AI your company enters next, and you will have real examples to show the value you are creating along the way. ### Steal My Prompt Vol. 7: The Prompt Refiner URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-7-the-prompt-refiner/ Last updated: 2026-07-19T20:23:20.000Z 📌 **Archive note: This post reflects Neural Gains Weekly's original personal-finance and investing framing, retired in early 2026\. It is preserved unchanged as history — the prompts below reference a positioning NGW no longer uses. Today, Neural Gains Weekly is AI education for professionals in complex industries.* There is no such thing as a perfect prompt. I’m constantly learning and evolving my strategies to optimize interactions with AI tools. The ‘10-Minute Win’ section is a critical piece of the overall message for *Neural Gains Weekly;* experiment with AI tools to grow your understanding of how they operate. I found myself having to audit outputs from version 1 of my master prompt, and fixing hallucinations that made the workflow difficult to complete. It was time to restructure the prompt and eliminate the manual work I was doing each week to build an executable ‘10-Minute Win’. Here is version 2 of my master prompt, enjoy! --- ## **Neural Gains Weekly — 10-Minute Win Builder (Master Prompt v2)** **System role:**You are an AI workflow editor and researcher for *Neural Gains Weekly*, responsible for building the **10-Minute Win** section — a practical, free, AI-powered workflow for personal finance or investing beginners. You must ensure every output matches the style, structure, and tone of previous Volumes on [mindovermoney.ai](https://www.mindovermoney.ai/). ### **🧩 Instructions** You will: 1. Use the provided workflow name (from the official Neural Gains roadmap). 2. Research and verify that all referenced tools are **free, live, and accessible** as of now. 3. Simulate executing the workflow step-by-step to confirm it’s feasible for a beginner. 4. Summarize the validation results in a short “Tool Check Summary.” 5. Write a full newsletter draft ready to paste into Ghost, matching the Neural Gains format exactly. 6. Include visual workflow graphic instructions (for light + dark mode) using Neural Gains colors: - Navy #0A2342 - Teal #1B998B - White #FFFFFF - Light Teal #E6F4F1 ### **📥 Input** Replace only the text below with the workflow name you want to build: **Workflow Name:** *(e.g., “Debt Paydown Gameplan (Snowball/Savvy)”)* ### **⚙️ What you must do** #### **Phase 1 — Research & Validation** - Identify all AI tools, websites, and extensions the workflow will require. - Confirm each one: - Is publicly available. - Offers a working **free tier**. - Does not require paid API keys. - Has no region-blocking for U.S. users. - Simulate a 10-minute beginner run-through (no real API calls, just logical testing). - Produce a **Tool Check Summary** table with: - Tool Name | Free-Tier Verified | Function in Workflow | Live Status | Notes / Limits - End with one-sentence conclusion: *“All tools verified as free and executable.”* or specify what failed. #### **Phase 2 — Newsletter Draft (Use identical Neural Gains formatting)** Output must include: 1. **Header:** \## Your 10-Minute Win \_A step-by-step workflow you can use immediately\_ # \[Workflow Title\] (free, fast, beginner-friendly) 2. **Intro Hook Paragraph:** - 2–4 sentences on *why this workflow matters*, *what problem it solves*, and *the payoff for readers*. 3. **Step 1–4 Workflow:** - Clear, numbered headings. - Each step = 1 to 3 short paragraphs, action-first, plain English. - Mention specific free tools and copy-paste prompts if relevant. - No jargon, no assumptions. 4. **The Payoff Section:** - 3–4 sentences summarizing the tangible outcome. 5. **Transparency & Notes for Readers:** - Bullet any costs, limits, or accuracy cautions. - End with “Educational workflow — not financial advice.” 6. **Your Turn CTA:** - Invite readers to try it and reply with insights. 7. **Tool Check Summary** (short version) - 2-sentence recap of the validation test. 8. **Visual Workflow Graphic Instructions:** - Create a 4-box, step-by-step visual guide concept (same structure as Vol. 1–5). - Include text for each step, overall caption, and layout hints for both light & dark modes. - Use the official color palette listed above. ### **🧭 Output Rules** - Match the voice and rhythm of previous Neural Gains issues. - Keep total length ≤ 900 words. - All URLs and tool names must be real and currently active. - If any tool fails validation, automatically suggest an alternative. - End with: *“✅ Workflow tested and verified on free tiers as of \[Month Year\].”* ### **🧠 Now execute** Run the research, simulate the workflow, validate all tools, then produce the complete, Ghost-ready draft and the visual workflow graphic description. ### Volume 7: Communication is Key URL: https://www.mindovermoney.ai/how-to-write-better-ai-prompts-professionals/ Last updated: 2026-07-13T16:59:54.000Z Hey everyone! 👋 This week continues our deep dive into how to communicate clearly with AI. In **AI Education**, we’ll simplify the art of prompting—why clear structure beats clever wording and how a five-piece framework keeps your asks sharp and on-target. Our **10-Minute Win** helps you lock in free money by building a 401(k) Match Maximizer—a quick, high-ROI workflow every reader can use today. And in **Founder’s Corner**, I’m sharing my perspective on the “AI is killing jobs” headlines—and how I filter fear-driven stories to focus on what actually matters for my career and learning path. Thanks for being here and staying committed to learning, one focused week at a time. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### [**1) Bloomberg: Apple to use Google’s AI model to run the new Siri**](https://www.reuters.com/business/apple-use-googles-ai-model-run-new-siri-bloomberg-news-reports-2025-11-05/?utm%5Fsource=chatgpt.com) **Summary:** Apple is near a deal to power a revamped Siri with Google’s Gemini, reportedly paying around $1B/year while Apple continues building its own models. **Why it matters:** If Apple leans on Google, Siri upgrades land faster—and the Big Three model providers become even more intertwined across the stack. ### [**2) Microsoft signs a $9.7B deal with IREN to secure Nvidia GB300 chips**](https://apnews.com/article/microsoft-ai-iren-nvidia-openai-dell-b3d7a6032ad69dcbea43ce88b007fe3f?ref=mindovermoney.ai) **Summary:** A five-year contract gives Microsoft dedicated GB300 capacity at IREN’s 750-MW Texas campus (liquid-cooled builds), easing near-term compute bottlenecks. **Why it matters:** Pre-buying third-party capacity lets Microsoft scale Copilot and cloud AI without waiting for new MS-owned data centers. ### [**3) OpenAI inks a 7-year, $38B cloud deal with AWS**](https://www.reuters.com/business/retail-consumer/openai-amazon-strike-38-billion-agreement-chatgpt-maker-use-aws-2025-11-03/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI will tap AWS for hundreds of thousands of Nvidia GPUs over seven years, broadening beyond Microsoft’s cloud after its restructuring. **Why it matters:** Vendor diversification reduces supply risk and could speed roadmaps for ChatGPT, Sora, and agents—while reinforcing AWS as a core AI infra provider. ### [**4) Meta plans a $600B U.S. investment push for AI data centers**](https://ca.finance.yahoo.com/news/meta-plans-600-billion-us-175901798.html?utm%5Fsource=chatgpt.com&guccounter=1&guce%5Freferrer=aHR0cHM6Ly9jaGF0Z3B0LmNvbS8&guce%5Freferrer%5Fsig=AQAAAC9nYLjIQF29UvqZffqVZXBR2DphH3-SUpOvFGOL-UBO4B877AheuC6vV9TD%5FuyC7M6s6js7YeS28Pg9y%5FF0Or63BVC81hnznleo%5FXN6XwVltHaOGFyH3jGPeVXuvKJHH51vYozx6S-5DBTllkv7HP4BzbAEXBAE2Dvn9oh232le) **Summary:** Meta outlined a three-year plan centered on massive U.S. data-center buildouts and financing deals to scale compute for next-gen AI. **Why it matters:** The AI build-out is now about power, cooling, and real estate at unprecedented scale—second-order winners include construction, utilities, storage, and interconnects. ### [**5) Altman urges expanding CHIPS Act tax credits for AI growth**](https://economictimes.indiatimes.com/tech/technology/openais-altman-urges-us-to-expand-chips-act-tax-credit-for-ai-growth/articleshow/125177134.cms?utm%5Fsource=chatgpt.com&from=mdr) **Summary:** OpenAI CEO Sam Altman pressed U.S. officials to extend CHIPS Act tax incentives to AI data centers and related infrastructure, arguing it’s critical for competitiveness. **Why it matters:** If policy tilts toward AI infra, expect faster capacity build-outs—and clearer economics—for the whole stack (chips, power, data centers). ## AI Education for You Prompting for Clarity 101 — Why clear prompts matter Over the last few issues, you learned three big ideas: how AI **represents meaning** as number lists (vectors), how it **reads text** as small pieces (tokens), and that it reads on a **fixed-size page** (the page limit). Put those together and you get the rule that drives everything: the model does its best work when you give it **the right meaning, in the right pieces, that fit on the page**. This week is about **how you ask**. A good prompt is not a trick—it’s clear directions plus only the facts that matter. You name the goal, share the key info, say the format you want back, set simple limits, and (optionally) show a tiny example. That structure gives the model a pattern to follow, keeps your message inside the page, and helps the model focus on the meaning that matters. We’ll use the same personal finance tasks you already know—bank statements, bill emails, and budget notes—to practice this. By the end, you’ll know how to shape your ask so the model reads what it needs, understands the point, and answers cleanly. ## **Core lesson — definition first, then a simple 5-piece structure** **What is prompt engineering:**Prompt engineering is the practice of designing your ask so the model can read the right information, understand the point, and answer in the form you need. It means choosing only relevant facts, giving clear instructions, using simple structure (titles, bullets, examples), and ordering things so the key parts fit on the model’s page. *How this ties to what you learned:* - Models turn text into meaning (those number lists). - They read small pieces of text on a fixed-size page. - Good prompting shapes the meaning, pieces, and order so the right text fits—and the answer stays on-target. ### **The 5-piece structure:** Use this for almost everything you ask: **1) Goal** \- Say exactly what you want. One sentence. **2) Key info** \- Give only the facts the model needs. Keep it short and relevant. **3) Format** \- Say how you want the answer. For example: bullets, a short plan, or a “yes/no plus a quoted line.” **4) Constraints** \- Set limits so the answer stays tight. For example: “Three bullets.” “Focus on grocery spend only.” “No extras.” **5) Tiny example (optional)** \- One short example shows the pattern you want. Keep it small. **Why this works:** The model follows patterns in your words. A clear pattern helps it stay on topic, fit the page, and answer in the form you asked for. ## **Contrast & clarity — vague vs. clear** **Vague:** “Look at my statement and tell me what you think.” **Clear (uses the 5 pieces):** - **Goal:** “Find unusual spending this month.” - **Key info:** “Use the lines below from my statement.” - **Format:** “Return three bullets: merchant, date, amount.” - **Constraints:** “Only items over $100\. No extras.” - **Tiny example:** “Example: ‘ACME Grocery — 10/12 — $128.42’.” The clear version gives the model a pattern to copy and a tight scope to follow. ## **Examples that land — one monthly money thread** **Example 1 — Bank statement check (unusual spending):** - **Prompt:** - **Goal:** Find unusual spending this month. - **Key info:** Use the statement lines below. - **Format:** Three bullets: merchant, date, amount. - **Constraints:** Only over $100\. No commentary. - **Tiny example:** Example: “ACME Grocery — 10/12 — $128.42”. - **Lines:** (paste 10–20 relevant lines only, not the whole month) **Example 2 — Bill credit check (was the credit applied?):** - **Prompt:** - **Goal:** Confirm if a promised $25 credit was applied on the October bill. - **Key info:** - Promise date: Sept 15 - Amount: $25 - Account ending: 4931 - Bill lines: (paste the section that shows credits or adjustments) - **Format:** “Yes” or “No” + the exact line that proves it. - **Constraints:** If “No,” give the most likely reason in one sentence. **Example 3 — Budget plan (cut $100 next month):** - **Prompt:** - **Goal:** Create a plan to cut $100 from next month’s spending. - **Key info:** - This month: groceries up $120 (party), restaurants down $75, two new ride shares - **Format:** Three steps, one sentence each. - **Constraints:** No guilt, no shaming. Keep steps practical. - **Tiny example:** Example step: “Swap one restaurant meal for a home-cooked dinner with leftovers.” **Common pitfalls:** - Pasting everything (dilution and overflow). - Vague goals (“analyze this”). - No requested format (the model guesses and meanders). ## **One-screen recap** - Prompt engineering = designing your ask so the model can read it, keep it on the page, and answer cleanly. - Use the 5-piece structure: Goal → Key info → Format → Constraints → Tiny example. - Keep inputs short, labeled, and relevant. - Show a pattern you want the model to copy. - Ask for the form you want back. Clear beats long. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **💼 401(k) Match Maximizer** **Why this matters:** Employer matches are guaranteed returns — yet a surprising number of people leave free money on the table. In 10 minutes, you’ll extract the exact contribution % needed to capture your full match, understand vesting, and set a reminder to keep it on track. ### **Step 1 — Find your plan details (2 minutes):** - Open your HR/benefits portal → Documents / Resources → download the 401(k) Plan Highlights or SPD PDF. Open the PDF and copy the Match and Contributions sections (a few paragraphs is enough). - Or find the documents with your 401k administrator - Or use any other resources available to you to find this document ### **Step 2 — Extract the match math with ChatGPT (4 minutes):** Paste the excerpt into ChatGPT (Free) with this prompt: *You are my plan explainer. From the text I paste next, extract:* 1. *Employer match formula (e.g., “100% on first 3%, 50% on next 2%”).* 2. *Maximum match cap (total % of pay employer will match).* 3. *Employee contribution types allowed (pre-tax, Roth, after-tax).* 4. *Per-paycheck % needed to get the full match (state the minimum % I must set).* 5. *Vesting schedule for match funds (e.g., immediate, 2-year cliff, graded).* 6. *Contribution limits (employee IRS annual limit; state “use IRS limit” if not in text).* 7. *Checklist to change my contribution in the payroll/benefits portal (2 steps).* *Rules: Use only the pasted plan text; if a detail isn’t present, say “not disclosed.” Output a 1-page result with bold section headers and one final line: “Set your contribution to: X% (or higher) to capture the full employer match.”* You’ll get a plain-English one-pager with the exact % to enter. ### **Step 3 — Save your Match Plan & set a reminder (3 minutes):** - Paste the ChatGPT output into Google Docs (or Notion Free) and title it: 401(k) Match Plan — - Open Google Calendar → create an event 2 business days before the next payroll cutoff (usually your next pay date minus 2 days):Title: Check 401(k) % — ensure at least X% for full match Description: paste the Docs link + your 2-step portal checklist. - Add a monthly recurring reminder so you don’t drift below the match if pay or elections change. ### **Step 4 — (Optional) Add Roth/Pre-Tax note & vesting awareness (1 minute):** - If the plan permits Roth and pre-tax: add a one-liner in your doc about the split you prefer (e.g., 100% pre-tax or 50/50). - Note vesting: if match is not immediate, add the vesting timeline to your doc so you know when employer dollars fully belong to you. ## **The Payoff** In 10 minutes, you’ll lock in free employer money every paycheck, know your vesting timeline, and have a recurring reminder so you don’t slip under the threshold. It’s the cleanest, highest-ROI optimization most people can make. ## **Transparency & Notes for Readers** - All tools are free: ChatGPT Free, Google Docs/Notion Free, Google Calendar. - Accuracy: Use text directly from your plan PDF/portal; if anything is unclear, contact HR. - Limits: Some plans have true-up rules or annualized matching — your doc will note it if present. - Educational workflow — not financial advice. **If you have a workflow idea for me to create or want to share a success from your AI journey, reach out at* [**admin@mindovermoney.ai*](mailto:admin@mindovermoney.ai)**.* ## Founder's Corner Real world learnings as I build, succeed, and fail If you follow the AI newscycle or read headlines from major news sources, you’ll notice a constant theme circulating: *AI is shrinking the labor force and already starting to replace human workers*. That can be a scary proposition for the future, as the seismic shift is happening all around us. That feels like a constant boulder being thrown on our shoulders as we march down an uncharted path. This is where ‘fear headlines’ can be successful at distorting our view: “AI will eliminate X million jobs.” “Entire functions automated overnight.” “Company X projects 50% less human workers in the next 18 months due to AI.” I read these headlines the same as you, but want to share an alternative perspective to help you filter through the noise as the world changes all around us. **1) Fear is big business** There is a grim and science-backed reality baked into our 24/7 news cycle: *Negativity drives online news consumption.* According to a [2023 study](https://pmc.ncbi.nlm.nih.gov/articles/PMC10202797/?ref=mindovermoney.ai#ack1), for a headline of average length, each additional negative word increased the click-through rate by 2.3%. Fear equals big business for media companies that rely on clicks to drive advertising revenue. Understanding this framework helps me digest AI articles without immediately developing an emotional reaction to the headline. I find it challenging to think critically when strong emotions (good or bad) are tied to a topic. Reminding myself of why a headline might be worded a certain way helps me focus on the content and formulate my own opinion. **2) What’s really impacting jobs right now** The labor market is a complicated web, with many variables that at any given time can impact the overall health of the job market. AI is definitely becoming a larger force, but by no means is AI the only reason for corporate layoffs. There are several factors that impact the broader jobs market: - Over hiring during Covid era - Federal policy decisions - Interest rate environment - Legislation - Global macro economic conditions AI seems to generate the most interest and becomes an easy scapegoat any time a company announces layoffs. It also seems easier for CEOs to use AI as a crutch instead of addressing some of these more complex issues (at least in the public domain) that might be impacting their business. It’s important for me to take all of these facts into play when trying to understand how real a headline is and what it could mean for me. **3) Headlines don’t always equal reality** I try to take headlines, both positive and negative, as inputs to form my own opinions and hypotheses. Specific to AI and the labor market, I run the article through a filter to try and understand if there are broader implications I should pay attention to: - **Was this AI or accounting?** Sometimes it’s a pivot from overhiring, a poor decision, or a reorg that would have happened with or without AI. - **Is this an industry, company, or country trend?** I try to spot trends and correlate whether or not the trend will impact the entire country, or will it be isolated to a specific industry. For example, many of the AI related layoff announcements have been from the major tech companies. We’ve yet to see this trend spread to other industries, at least not yet. - **What is the scope and time horizon?** Near-term trims can coincide with long-term reinvestment in new roles (eg., automation ops, compliance, evaluation). If cuts and hires occur together, the story is reshaping, not retreating. This gives me a consistent framework to understand the ‘why’ behind a layoff announcement and how I can use that information in my AI journey. It’s not perfect, but has proved more helpful than scanning headlines and being influenced by fear. If you take away only one learning from this week, remember to *focus on what you can control*. The truth is, no one knows exactly how AI is going to disrupt the labor market in the short, medium or long term. All we know is that change is coming, and it’s hard to understand how significant the shift will be as we live through it in real time. It wasn’t that long ago that thousands of people across the country worked in mail rooms. Imagine what was going through their minds as email was introduced into the workplace. In a flash, an entire job function vanished from corporate America. While AI is new, disruptive, and moving at the speed of light, this is not the first time technology will reshape how companies operate. And it won’t be the last. Never forget where we came from Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). **Goals & Milestones:** | Goal | Current (as of 11/11/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 88 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 3 | 10 | ### Is AI Taking Everyone's Job? URL: https://www.mindovermoney.ai/founders-corner/will-ai-take-my-job-reality-check-for-professionals/ Last updated: 2026-07-13T16:59:54.000Z If you follow the AI newscycle or read headlines from major news sources, you’ll notice a constant theme circulating: *AI is shrinking the labor force and already starting to replace human workers*. That can be a scary proposition for the future, as the seismic shift is happening all around us. That feels like a constant boulder being thrown on our shoulders as we march down an uncharted path. This is where ‘fear headlines’ can be successful at distorting our view: “AI will eliminate X million jobs.” “Entire functions automated overnight.” “Company X projects 50% less human workers in the next 18 months due to AI.” I read these headlines the same as you, but want to share an alternative perspective to help you filter through the noise as the world changes all around us. **1) Fear is big business** There is a grim and science-backed reality baked into our 24/7 news cycle: *Negativity drives online news consumption.* According to a [2023 study](https://pmc.ncbi.nlm.nih.gov/articles/PMC10202797/?ref=mindovermoney.ai#ack1), for a headline of average length, each additional negative word increased the click-through rate by 2.3%. Fear equals big business for media companies that rely on clicks to drive advertising revenue. Understanding this framework helps me digest AI articles without immediately developing an emotional reaction to the headline. I find it challenging to think critically when strong emotions (good or bad) are tied to a topic. Reminding myself of why a headline might be worded a certain way helps me focus on the content and formulate my own opinion. **2) What’s really impacting jobs right now** The labor market is a complicated web, with many variables that at any given time can impact the overall health of the job market. AI is definitely becoming a larger force, but by no means is AI the only reason for corporate layoffs. There are several factors that impact the broader jobs market: - Over hiring during Covid era - Federal policy decisions - Interest rate environment - Legislation - Global macro economic conditions AI seems to generate the most interest and becomes an easy scapegoat any time a company announces layoffs. It also seems easier for CEOs to use AI as a crutch instead of addressing some of these more complex issues (at least in the public domain) that might be impacting their business. It’s important for me to take all of these facts into play when trying to understand how real a headline is and what it could mean for me. **3) Headlines don’t always equal reality** I try to take headlines, both positive and negative, as inputs to form my own opinions and hypotheses. Specific to AI and the labor market, I run the article through a filter to try and understand if there are broader implications I should pay attention to: - **Was this AI or accounting?** Sometimes it’s a pivot from overhiring, a poor decision, or a reorg that would have happened with or without AI. - **Is this an industry, company, or country trend?** I try to spot trends and correlate whether or not the trend will impact the entire country, or will it be isolated to a specific industry. For example, many of the AI related layoff announcements have been from the major tech companies. We’ve yet to see this trend spread to other industries, at least not yet. - **What is the scope and time horizon?** Near-term trims can coincide with long-term reinvestment in new roles (eg., automation ops, compliance, evaluation). If cuts and hires occur together, the story is reshaping, not retreating. This gives me a consistent framework to understand the ‘why’ behind a layoff announcement and how I can use that information in my AI journey. It’s not perfect, but has proved more helpful than scanning headlines and being influenced by fear. --- If you take away only one learning from this week, remember to *focus on what you can control*. The truth is, no one knows exactly how AI is going to disrupt the labor market in the short, medium or long term. All we know is that change is coming, and it’s hard to understand how significant the shift will be as we live through it in real time. It wasn’t that long ago that thousands of people across the country worked in mail rooms. Imagine what was going through their minds as email was introduced into the workplace. In a flash, an entire job function vanished from corporate America. While AI is new, disruptive, and moving at the speed of light, this is not the first time technology will reshape how companies operate. And it won’t be the last. Never forget where we came from Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 6: Building a Content Roadmap URL: https://www.mindovermoney.ai/prompt-library/ai-content-roadmap-prompt-newsletter-creators/ Last updated: 2026-07-19T20:23:19.000Z 📌 **Archive note: This post reflects Neural Gains Weekly's original personal-finance and investing framing, retired in early 2026\. It is preserved unchanged as history — the prompts below reference a positioning NGW no longer uses. Today, Neural Gains Weekly is AI education for professionals in complex industries.* This week, you’re getting a prompt that just went into production for Volume 6\. I originally built a roadmap for ‘10-Minute Win’ that included ten ideas to build into *Neural Gains Weekly.* Unfortunately, only five of them were useful and I needed to redesign a prompt that built out a more robust content roadmap for this section of the newsletter. I’m incredibly happy with the output of this prompt and wanted to share the structure with our community. Enjoy! --- ## 🧰 Master Prompt — Neural Gains Weekly “10-Minute Win” Roadmap (40 Workflows) Role:You are a senior editor and workflow architect for the *Neural Gains Weekly* “10-Minute Win” section. Your job is to design a 40-workflow content roadmap that helps AI beginners learn practical AI through simple, free, 10-minute workflows across personal finance and investing. Context & hard constraints (do not violate): - Theme: personal finance and investing (50/50 split; \~20 + 20). - Beginner-friendly: no coding, no API keys, no spreadsheets with macros, no complicated setup. - Free tools only: every workflow must be fully doable on a free plan with widely available tools. If a tool has paid tiers, your workflow must still work on the free tier. - 10 minutes max to complete the workflow once set up. - Privacy-safe: no live bank connections or sharing sensitive info; use exports or sample data. - No duplication with existing volumes: Do not include workflows that are the same as or materially similar to these already published or planned topics: 1. AI Budget Analyzer (ChatGPT + Google Sheets) 2. AI-Powered Net Worth Tracker (Arcwise AI + Google Sheets) 3. Earnings Call Summarizer (Gemini/ChatGPT/Perplexity) 4. ETF Comparison in Minutes (free AI + issuer fact sheets) 5. Stock Research Assistant with Perplexity (EDGAR/IR sourced, cited) - Build logically: order the 40 items so they progress from easier → more advanced, and alternate naturally between personal finance & investing (it’s OK to switch back-and-forth while still increasing complexity overall). - Not just “search”: workflows must use AI to transform, classify, calculate, summarize, visualize, or automate, not just “look up” facts. - Global accessibility: prioritize tools that are broadly available (web apps, Chrome extensions, iOS/Android apps) with free tiers. - If any candidate relies on a tool that lacks a free tier: replace it with a free alternative. Research directive (to guide your ideation): - Pull from real user problems seen on Reddit, X (Twitter), forums, and Q&A sites around beginner personal finance and investing use cases. No need to paste URLs; summarize the pattern of the user need in one concise line (“User Problem Signal”). - Favor ideas that produce a tangible artifact in 10 min (e.g., a table, checklist, chart, one-page brief, SMART plan, automation, or template). Output format (single structured table; 40 rows):Return one Markdown table with exactly these columns, in this order: 1. \# (1–40 in sequence order; easiest → more advanced) 2. Category (Personal Finance | Investing) 3. Workflow Title (catchy, specific) 4. Core Free Tools (list exact free tools; web/extension/app) 5. User Problem Signal (concise, grounded in common issues asked online) 6. What AI Actually Does (transform/classify/summarize/plan/visualize/automate) 7. 10-Min Output Artifact (what the reader walks away with) 8. Step Count (5–7 steps max) 9. Difficulty (1–5) (1 = beginner) 10. Dependency (prior # if this builds on an earlier workflow; else “—”) 11. Free-Tier Check (Yes; note any limits) 12. Why It Matters (one-sentence value prop) 13. One-Line Hook (reader-friendly headline to use in the newsletter) Quality rules & filters: - Ensure 20 items are Personal Finance and 20 items are Investing. - No overlap with items #1–#5 listed above, nor near-duplicates among the 40. - Each workflow must be doable in 10 minutes with free tools, clearly producing a useful artifact for the user. - Prefer common, trustworthy tools with free plans (e.g., Google Sheets/Docs, Notion Free, Gemini Free, ChatGPT Free, Perplexity Free, Arcwise Free, IFTTT Free, Make Free, Apple Shortcuts, Microsoft Copilot Free, etc.). - If a tool’s free plan is questionable, swap it and note an alternative that is clearly free. - Keep wording concise and beginner-friendly. Final check (append after the table): - Coverage Summary: Count how many PF vs Investing; confirm 20/20. - Progression Check: Briefly justify why #1–#10 are beginner-level, #11–#25 intermediate, #26–#40 advanced-lite. - Top 6 Starter Picks: List 6 diverse, high-impact starters (3 PF, 3 Investing) for the next six issues. Now generate the roadmap table and the final check. ### How to use this - Paste the master prompt above into ChatGPT/Gemini/Perplexity. - Let it generate the 40-row table + summary. - We’ll then select upcoming issues from the Top 6 Starter Picks and refine. ### Volume 6: Show Me the Tokens! URL: https://www.mindovermoney.ai/ai-tokens-context-windows-explained-professionals/ Last updated: 2026-07-18T01:46:21.000Z Happy November! Time flies when you are learning AI! This week kicks off with something new — the very first *Neural Gains Weekly* song, created using Suno AI. It’s a fun experiment in blending creativity and tech, and a reminder that learning AI can be as enjoyable as it is practical. Neural Gains Weekly 0:00 /231.119979 1× *\*Will open in website to play* In this issue, **AI Education** breaks down how models read text through tokens — the foundation behind how AI “understands” language and why shorter, cleaner inputs lead to better (and cheaper) results. Our **10-Minute Win** builds on last week’s research workflow by turning it into a living Watchlist Thesis Card System inside Notion. And in **Founder’s Corner**, I share the macro trends guiding where I focus my AI learning — infrastructure, regulation, and corporate adoption. Enjoy the song, dive into the issue, and keep experimenting. **Missed a previous newsletter? No worries, you can find them on the* [**Archive page*](https://www.mindovermoney.ai/tag/newsletter/)**. Don’t forget to check out the* [**Prompt Library*](https://www.mindovermoney.ai/prompt-library/)**, where I give you templates to use in your AI journey.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)**[ **OpenAI completes restructuring, refreshes Microsoft partnership**](https://apnews.com/article/openai-chatgpt-nonprofit-microsoft-c661df3242766d6b0ddbab401ad1fd84?utm%5Fsource=chatgpt.com) **Summary:** OpenAI converted into a public benefit corporation after Delaware and California AGs declined to oppose, clearing governance hurdles and aligning a refreshed Microsoft partnership. **Why it matters:** This structure eases large-scale fundraising—fuel for models, chips, and data centers—while keeping a mission anchor and solidifying the Microsoft tie-up that underpins OpenAI’s scale. ### **2)**[ **Seizing the AI Opportunity**](https://openai.com/global-affairs/seizing-the-ai-opportunity/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI lays out a near-term agenda for national AI opportunity: skills & education, industry adoption, and regional investment, paired with pragmatic policy guardrails. **Why it matters:** Direct-from-source playbook for turning AI hype into productivity—useful for readers tracking where policy and capital will flow next. ### **3)**[ **Storage stocks rip on AI demand**](https://www.reuters.com/business/data-storage-firms-western-digital-seagate-soar-ai-driven-demand-spike-2025-10-31/?utm%5Fsource=chatgpt.com) **Summary:** Western Digital and Seagate jumped on guidance and multi-year orders linked to AI data-center buildouts, signaling stronger visibility into 2026. **Why it matters:** Beyond GPUs, AI needs massive storage—a clean second-order signal that the infra boom is broadening to HDD/NAND and memory supply chains. ### **4)**[ **Introducing Teams Mode for Microsoft 365 Copilot**](https://techcommunity.microsoft.com/blog/microsoft365copilotblog/introducing-teams-mode-for-microsoft-365-copilot/4463259?utm%5Fsource=chatgpt.com) **Summary:** Microsoft rolls out Teams Mode—bring coworkers into your Copilot conversation for secure group AI chats directly inside Teams. **Why it matters:** AI moves from solo helper to team workflow—planning, follow-ups, and decisions with everyone (and the AI) in the loop. ### **5)**[ **Alphabet hikes capex (again) on AI demand**](https://www.reuters.com/business/media-telecom/alphabet-beats-quarterly-revenue-estimates-strong-ad-cloud-demand-2025-10-29/?utm%5Fsource=chatgpt.com) **Summary:** Alphabet raised 2025 capital-spending plans to $91–$93B on strong ad and Cloud growth, with spend aimed heavily at AI infrastructure (TPUs, data centers). **Why it matters:** Another proof point that the AI build-out is still accelerating—good read-through for suppliers (chips, storage, power) and for operators planning multi-year AI budgets. ## AI Education for You Tokens & Tokenization 101: How AI “reads” your text Before a model can understand anything you write, it must read your text. It does not read characters or whole words. It reads tokens—small pieces of text. Tokenization is how text gets split into those pieces. Every model has a page size (called a context window): the maximum number of tokens it can read at once. If your text is longer than the page, some of it is cut off. One more key idea: tokens drive cost. Reading and writing more tokens means more compute. More compute means higher cost. That is why long inputs, long outputs, and larger page sizes often cost more—and why higher-quality models that can handle more tokens usually carry a higher price. ## **Core lesson — in plain English** **What is a token?** A token is a small piece of text the model can read. A token is often shorter than a word. For example, “budgeting” may be split into smaller pieces; even a space or punctuation can count as a token. Two sentences with the same number of words can have different token counts. **What is tokenization?** Tokenization is the splitting of your text into tokens before the model sees it. The model only learns patterns over tokens, not raw text. **What is the page size (context window)?** It is the maximum number of tokens the model can read at once. Think of it like a single page. Your tokens fill the page from top to bottom. When the page is full, extra tokens fall off. **What gets cut when it is too long?** Anything that does not fit on the page is ignored. That can hide important fees in a long bank statement, an old detail in a bill email chain, or the key fact buried at the bottom of a receipt. **Why fewer, clearer tokens help:** - Short, focused text fits the page. - The model can place attention on what matters. - You save compute and money because fewer tokens are processed. **How tokens tie to cost:** - **More tokens in:** more to read → more compute. - **More tokens out:** longer answers → more compute. - **Bigger page size:** the model can read more at once → more memory and compute behind the scenes → usually higher price. - **Stronger models:** better understanding often comes with larger page sizes and heavier computation, which is why paid tiers often unlock better models and longer inputs. ## **Contrast & clarity — common confusion:** - **“Tokens are words.”** Not quite. A token is often smaller than a word. A short word might be one token; a longer word can be split into several. - **“If I paste everything, it will be smarter.”** Not always. Very long text can dilute what matters or overflow the page so key parts get cut. - **“If it missed a detail, the model failed.”** Sometimes yes. Often the detail did not fit or was buried under less useful text. - **“Cost is only about how long the answer is.”** Cost depends on both the tokens you send and the tokens you ask it to write. ## **Examples that land:** **1) Long bank statement vs. what fits** - You paste a full monthly statement (many pages). - The page fills before a late fee near the end. - The model summarizes spending but misses the fee because it never saw it. **2) Bill email chain with long quotes** - A bill support thread includes repeated quotes of earlier emails. - The repeats eat tokens and push out the original agreement details. - Result: the model cannot confirm the promised credit because that part was cut off. **3) Budget note: short summary vs. raw rows** - Option A: paste 200 raw transaction lines. - Option B: write a short, labeled summary (“groceries up $120 due to party; restaurants down $75; two new ride charges”). - Option B fits, costs less, and gives a clearer answer. ## **Quick recap:** - Tokens are the small pieces of text a model reads. - Tokenization splits your text into those pieces. - The page size is how many tokens fit at once; overflow is cut off. - More tokens → more compute → more cost. - Keep inputs short, focused, and labeled to fit the page and improve results. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🗂️ Watchlist Thesis Card System** Last week, we built your AI Stock Research Assistant — a fast, Perplexity-powered way to pull verified news and filings into a clean research brief. This week, we take the next step: turning that brief into a living thesis system that keeps your ideas organized, testable, and accountable. Because collecting facts is easy — turning those facts into repeatable conviction is the real edge. This workflow will show you how to convert one Perplexity brief into a reusable Notion Watchlist Thesis Card you can update every quarter to see how your thinking evolves. ### **Step 1 — Import your research brief (2 minutes)** 1. Open your Volume 5 “Stock Research Assistant” brief for a ticker you follow. 1. If you didn’t experiment last week, access the [workflow from Volume 5 here](https://www.mindovermoney.ai/ai-vectors-embeddings-explained-beginners/). 2. Copy the brief (business snapshot, catalysts, risks, sources). 3. Paste it into ChatGPT (Free) with this transform prompt: *Transform this sourced brief into my Watchlist Thesis Card fields. Fields:* *• One-sentence Thesis (your concise “why this could work”) • Variant Perception (how your view differs from consensus) • Time Horizon (e.g., 6–18 months) • Key KPIs to watch (ticker-specific; exact definitions/units) • Bullish Triggers (3 if/then statements that increase conviction) • Bearish Triggers (3 if/then statements that decrease conviction) • Top Catalysts (next 1–2 quarters) • Top Risks • Links (IR site, latest 10-Q/10-K, latest earnings PR) Rules: Use only the text I’ve pasted and the official links; if a metric is missing, write “not disclosed.” Keep under 300 words, use bold section headers*. In seconds, you’ll have a structured thesis card built from your own research. *💡 Pro tip: Use ‘ChatGPT 5 Thinking’ and see the power of a reasoning model.* ### **Step 2 — Save it in Notion (Desktop; 3–4 minutes)** **Notion access & setup:** - Go to [notion.com](http://notion.com/?ref=mindovermoney.ai) → Sign up Free (email or SSO). - From the left sidebar, click “Create” → “Database → Table” to make a simple table database. - You might have to clock the ‘down arrow’ at the top left ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/11/data-src-image-4d87de26-325b-4301-b6a6-e27980c8bb38.png) **Create a database named “Watchlist Thesis Cards” with properties:** - **Ticker** (Text) - **Thesis (1-liner)** (Text) - **Variant Perception** (Text) - **Time Horizon** (Select: 3 / 6 / 12 / 18 / 24 months) - Add under ‘Edit property’ - **KPIs** (Text) - **Bullish Triggers** (Text) - **Bearish Triggers** (Text) - **Catalysts (next 1–2 qtrs)** (Text) - **Risks** (Text) - **Sources** (URL) - **Next Check** (Date) - **Status** (Select: Watch / Research / On-Hold / Exited) - Add under ‘Edit property’ Paste your ChatGPT output from Step 1 into a new row/page and fill the **Ticker**, **Next Check** (usually next earnings date), and **Status**. **Prefer to skip manual setup?➡️** Download the Notion CSV schema and import it directly into Notion (Import → CSV). It will create the database with all properties for you. [Notion\_Watchlist\_Thesis\_Card\_Database\_TemplateClick link and scroll down to downloadNotion\_Watchlist\_Thesis\_Card\_Database\_Template.csv826 Bytesdownload-circle](https://www.mindovermoney.ai/content/files/2025/11/Notion%5FWatchlist%5FThesis%5FCard%5FDatabase%5FTemplate.csv "Download") Your Notion database should look like this: ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/11/data-src-image-200cbc8b-96a3-479a-97c3-6a8cfaa7dc99.png) ### **Step 3 — Stay current automatically (Desktop only; 3 minutes)** **A) Set a Google Alert (free) for filings & IR updates** 1. Open [google.com/alerts](http://google.com/alerts?ref=mindovermoney.ai) in your browser. 2. In the box, type: TICKER ("guidance" OR "8-K" OR "investor relations" OR "earnings") 3. Click Show options → How often: *At most once a day* (or *At most once a week*). 4. Sources: Automatic; Language: English; Region: Any; Deliver to: your email. 5. Click Create Alert. (You can add multiple alerts per ticker if you like.) **B) Add a Calendar reminder for “Next Check” (next earnings)** 1. Open calendar.google.com → click the date of your Next Check. 1. If you use another calendar service, add the reminder there. 2. Enter the event title: Next Check — \[TICKER\] Thesis Card. 3. Click More options → paste your Notion page URL into the Description. 4. Set a Notification for 1 day before + 1 hour before, then Save. This keeps your thesis alive—news lands in your inbox, and your calendar nudges you to review on schedule. ### **Step 4 — Refresh and reality-check (2 minutes)** When a new PR/10-Q drops or your alert fires, paste the excerpt into ChatGPT with: *Update my Watchlist Thesis Card for \[TICKER\]. • Compare disclosed KPIs vs. my KPIs list (mark hit/miss with numbers). • Note which Bullish/Bearish Triggers fired. • Summarize what changed in 3 bullets. • End with: Action Posture = Add / Hold / Trim / Avoid (reflection only; not advice).* Log the changes in the same Notion page. Over time, you’ll see how thesis ↔ results evolve. ## **The Payoff** In 10 minutes, you’ve upgraded a one-off research brief into a repeatable decision system: a structured thesis, explicit KPIs, clear triggers, scheduled reviews, and a growing log of what actually changed—so you act on process, not impulse. ## **Transparency & Notes for Readers** - All tools free: ChatGPT Free, Notion Free, Google Alerts, Google Calendar. - Limits: ChatGPT Free has input length caps—paste concise highlights, not entire filings. - Data discipline: Use official IR/EDGAR text for numbers. - Educational workflow — not financial advice. **If you have a workflow idea for me to create or want to share a success from your AI journey, reach out at* [**admin@mindovermoney.ai*](mailto:admin@mindovermoney.ai)**.* ## Founder's Corner Real world learnings as I build, succeed, and fail I often talk with friends, coworkers, and family about AI, and I’m constantly asked, ‘What should I be paying attention to in order to stay ahead?’. My answer is always the same: start with education and do your best to pay attention to the macro trends. The reality is that the AI news cycle moves like a flash flood, making it increasingly challenging to keep up with. I feel that same pressure, but I have implemented several strategies to keep me sane and sift through the noise. Focusing on macro trends has helped me organize thoughts more efficiently, leading to better information retainment. I’ve also been able to avoid the fringe aspects of the news cycle that play on people's emotions by spreading fear and hysteria about AI. Today, I’ll share three macro trends and provide insights into what I find interesting and why it matters to your AI education journey. ### **Infrastructure Built Out** There are several trends that fall into this bucket, and I plan on exploring each in more detail in future volumes. It’s incredible to see the capital expenditures going into infrastructure buildouts related to data centers, power grid facilities, and chip manufacturing. Companies, mainly the ‘Magnificent 7’, are leading the charge in AI capex investments, and it’s not easy to follow by solely reading headlines. There are constant conversations around an AI bubble, with many pundits drawing comparisons to the ‘Dot-Com’ bubble that lasted from 1995-2001\. I see the arguments on both sides and fully appreciate the cautious approach being taken by economic experts. But my perspective is more bullish; I see the capex wave as a strategy and competition between the largest companies in the world. Whoever can build out the infrastructure to lower costs (e.g., $ per token, compute costs, etc.) will put pressure on smaller companies to mirror pricing strategies that could put them out of business. Are we in an AI bubble? Probably. But there is opportunity in every situation, which is why the AI infrastructure race is a trend all of us should pay attention to. Here are a few headlines that highlight the massive financial stakes being put down to bring AI to life: - [McKinsey](https://www.reuters.com/business/retail-consumer/tech-leaders-ramp-up-ai-spending-alphabets-cash-flow-wins-investor-favor-2025-10-30/?utm%5Fsource=chatgpt.com) estimates $6.7T in global data-center investment needed by 2030 to meet compute demand, $5.2T of that for AI-class facilities. - [Alphabet ](https://www.reuters.com/business/retail-consumer/tech-leaders-ramp-up-ai-spending-alphabets-cash-flow-wins-investor-favor-2025-10-30/?utm%5Fsource=chatgpt.com)spent $23.95B in capex (49% of operating cash flow), with Meta and Microsoft even higher by share; Amazon near \~90%. Signals long-horizon bets on AI capacity. - [Meta’s ](https://www.businessinsider.com/big-tech-spending-on-ai-capex-q3-2025-10?utm%5Fsource=chatgpt.com)guidance is eye-popping. 2025 capex outlook lifted to $70–72B (almost double 2024). - [Power is the new bottleneck](https://www.iea.org/reports/energy-and-ai/executive-summary?utm%5Fsource=chatgpt.com). IEA projects data-center electricity use doubling to \~945 TWh by 2030 (slightly more than Japan’s total use today); AI is the biggest driver. In the U.S., data centers account for nearly half of electricity-demand growth through 2030. - [U.S. grid pressure](https://www.energy.gov/gdo/clean-energy-resources-meet-data-center-electricity-demand?utm%5Fsource=chatgpt.com). EPRI (via U.S. DOE) estimates data centers could consume up to 9% of U.S. electricity by 2030. *Top takeaway: Follow the money to better understand the winners of the future, impacts to your personal life, and realistic expectations for future AI advancement.* ### **Regulation** This is a hot topic, not just in the U.S., but across the globe. AI introduces new challenges that governments and regulators cannot keep up with. And, like anything else, there are politics driving AI regulation that might not be best for consumers (or humanity as a whole). This area of AI seems to have the most noise and the biggest consequences for how the world evolves as AI becomes more prevalent and powerful. In the U.S. specifically, there are several states pushing for AI regulation, while there has been little consensus on what (if anything) should be done at the federal level. California has introduced several pieces of AI-specific legislation to help put guardrails in place to protect people from the potential negative consequences of AI. Time will tell if these measures are successful, but AI regulation will be an important piece of the puzzle as AI becomes more embedded into everyday life. *Top takeaway: Compliance and law making will impact the speed in which AI labs can execute and influence transparency (for good or bad) across the industry.* ### **Corporate Adoption** Hype or buzzwords? Pilot vs. production? ROI vs. model change? For those of you who work in corporate America, the noise can be deafening. Every week, a new report is released about AI taking jobs, a fresh round of layoffs, or CEOs highlighting automation gains. But the extremes of these headlines seem isolated to big tech, at least for now. I believe this is due to how early we are in the AI adoption lifecycle. Many companies are struggling to find ROI from true generative AI capabilities and are stuck in ‘pilot’ mode. It’s impossible to predict when we will start to see breakthroughs across the Fortune 500, but I believe it will be here within the next 18 months. The technology is improving at a rapid pace, and compute costs continue to decline. We’ll start to hear more and more headlines related to ‘agentic workflow automation’ and ‘AI employees’ that drive ROI and bottom-line impact. It’s imperative that we all pay attention to this trend to learn and figure out how to incorporate generative AI into our professional lives. *Top takeaway: AI adoption does not mean impact to the business. Following ROI and workflow automation helps me sift through the noise and study companies leading AI transformation in the workplace.* --- It can feel overwhelming to keep up with AI news, but you are ahead of most people by taking time to read *Neural Gains Weekly.* The only way to get ahead is to put in the effort, even if that is only 10-15 minutes a week. Stay consistent, follow trends, and experiment with AI tools. See you next week! ### **Goals Tracker:** | Goal | Current (as of 11/4/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | ------------------------- | -------------------- | | Newsletter Subscribers | 83 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Follow the Trends URL: https://www.mindovermoney.ai/founders-corner/how-to-follow-ai-trends-without-getting-overwhelmed/ Last updated: 2026-07-13T16:59:55.000Z I often talk with friends, coworkers, and family about AI, and I’m constantly asked, ‘What should I be paying attention to in order to stay ahead?’. My answer is always the same: start with education and do your best to pay attention to the macro trends. The reality is that the AI news cycle moves like a flash flood, making it increasingly challenging to keep up with. I feel that same pressure, but I have implemented several strategies to keep me sane and sift through the noise. Focusing on macro trends has helped me organize thoughts more efficiently, leading to better information retainment. I’ve also been able to avoid the fringe aspects of the news cycle that play on people's emotions by spreading fear and hysteria about AI. Today, I’ll share three macro trends and provide insights into what I find interesting and why it matters to your AI education journey. ### Infrastructure Built Out There are several trends that fall into this bucket, and I plan on exploring each in more detail in future volumes. It’s incredible to see the capital expenditures going into infrastructure buildouts related to data centers, power grid facilities, and chip manufacturing. Companies, mainly the ‘Magnificent 7’, are leading the charge in AI capex investments, and it’s not easy to follow by solely reading headlines. There are constant conversations around an AI bubble, with many pundits drawing comparisons to the ‘Dot-Com’ bubble that lasted from 1995-2001\. I see the arguments on both sides and fully appreciate the cautious approach being taken by economic experts. But my perspective is more bullish; I see the capex wave as a strategy and competition between the largest companies in the world. Whoever can build out the infrastructure to lower costs (e.g., $ per token, compute costs, etc.) will put pressure on smaller companies to mirror pricing strategies that could put them out of business. Are we in an AI bubble? Probably. But there is opportunity in every situation, which is why the AI infrastructure race is a trend all of us should pay attention to. Here are a few headlines that highlight the massive financial stakes being put down to bring AI to life: - [McKinsey](https://www.reuters.com/business/retail-consumer/tech-leaders-ramp-up-ai-spending-alphabets-cash-flow-wins-investor-favor-2025-10-30/?utm%5Fsource=chatgpt.com) estimates $6.7T in global data-center investment needed by 2030 to meet compute demand, $5.2T of that for AI-class facilities. - [Alphabet ](https://www.reuters.com/business/retail-consumer/tech-leaders-ramp-up-ai-spending-alphabets-cash-flow-wins-investor-favor-2025-10-30/?utm%5Fsource=chatgpt.com)spent $23.95B in capex (49% of operating cash flow), with Meta and Microsoft even higher by share; Amazon near \~90%. Signals long-horizon bets on AI capacity. - [Meta’s ](https://www.businessinsider.com/big-tech-spending-on-ai-capex-q3-2025-10?utm%5Fsource=chatgpt.com)guidance is eye-popping. 2025 capex outlook lifted to $70–72B (almost double 2024). - [Power is the new bottleneck](https://www.iea.org/reports/energy-and-ai/executive-summary?utm%5Fsource=chatgpt.com). IEA projects data-center electricity use doubling to \~945 TWh by 2030 (slightly more than Japan’s total use today); AI is the biggest driver. In the U.S., data centers account for nearly half of electricity-demand growth through 2030. - [U.S. grid pressure](https://www.energy.gov/gdo/clean-energy-resources-meet-data-center-electricity-demand?utm%5Fsource=chatgpt.com). EPRI (via U.S. DOE) estimates data centers could consume up to 9% of U.S. electricity by 2030. *Top takeaway: Follow the money to better understand the winners of the future, impacts to your personal life, and realistic expectations for future AI advancement.* ### Regulation This is a hot topic, not just in the U.S., but across the globe. AI introduces new challenges that governments and regulators cannot keep up with. And, like anything else, there are politics driving AI regulation that might not be best for consumers (or humanity as a whole). This area of AI seems to have the most noise and the biggest consequences for how the world evolves as AI becomes more prevalent and powerful. In the U.S. specifically, there are several states pushing for AI regulation, while there has been little consensus on what (if anything) should be done at the federal level. California has introduced several pieces of AI-specific legislation to help put guardrails in place to protect people from the potential negative consequences of AI. Time will tell if these measures are successful, but AI regulation will be an important piece of the puzzle as AI becomes more embedded into everyday life. *Top takeaway: Compliance and law making will impact the speed in which AI labs can execute and influence transparency (for good or bad) across the industry.* ### Corporate Adoption Hype or buzzwords? Pilot vs. production? ROI vs. model change? For those of you who work in corporate America, the noise can be deafening. Every week, a new report is released about AI taking jobs, a fresh round of layoffs, or CEOs highlighting automation gains. But the extremes of these headlines seem isolated to big tech, at least for now. I believe this is due to how early we are in the AI adoption lifecycle. Many companies are struggling to find ROI from true generative AI capabilities and are stuck in ‘pilot’ mode. It’s impossible to predict when we will start to see breakthroughs across the Fortune 500, but I believe it will be here within the next 18 months. The technology is improving at a rapid pace, and compute costs continue to decline. We’ll start to hear more and more headlines related to ‘agentic workflow automation’ and ‘AI employees’ that drive ROI and bottom-line impact. It’s imperative that we all pay attention to this trend to learn and figure out how to incorporate generative AI into our professional lives. *Top takeaway: AI adoption does not mean impact to the business. Following ROI and workflow automation helps me sift through the noise and study companies leading AI transformation in the workplace.* --- It can feel overwhelming to keep up with AI news, but you are ahead of most people by taking time to read *Neural Gains Weekly.* The only way to get ahead is to put in the effort, even if that is only 10-15 minutes a week. Stay consistent, follow trends, and experiment with AI tools. See you next week! ### Volume 5: I'm No Spielberg URL: https://www.mindovermoney.ai/ai-vectors-embeddings-explained-beginners/ Last updated: 2026-07-13T16:59:56.000Z Hey everyone! This week, we’re unpacking how AI actually *understands meaning.* In **AI Education**, we explore vectors & embeddings — how models convert language into numbers that carry context, power smarter decisions, and even help categorize spending in your budget. Then, in our **10-Minute Win**, we’ll apply structure to action with a fast stock research workflow using Perplexity AI — a practical way to turn information overload into clarity. And in **Founder’s Corner**, I’m sharing what it’s really like learning to build AI-driven video and image content from scratch — the failed takes, creative ceilings, and the systems I’m using to turn each experiment into progress. Thank you for following along and growing with me each week as we keep turning AI theory into practice. Let's dive in with your curated AI news from the past week. *Missed a previous newsletter (released every Tuesday at 9AM EST)? No worries, you can find them on the* [*Archive page*](https://www.mindovermoney.ai/tag/newsletter/) *at* [*MindOverMoney.ai*](http://mindovermoney.ai/?ref=mindovermoney.ai)*. Don’t forget to check out the* [*Prompt Library*](https://www.mindovermoney.ai/prompt-library/)*, where I give you templates to use in your AI journey. If you have content suggestions, a workflow idea, or want to share a success from your AI journey, reach out at* [*admin@mindovermoney.ai*](mailto:admin@mindovermoney.ai)*.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)** [**Microsoft’s big Copilot rollouts: Groups, “real talk,” memory & Mico**](https://www.theverge.com/news/804122/microsoft-copilot-real-talk-mode-group-chats-features?utm%5Fsource=chatgpt.com) **Summary:** Microsoft pushed a bundle of Copilot updates—Copilot Groups (up to 32 people in one AI chat), an optional “real talk” mode that challenges shaky assumptions, expanded memory controls, and Mico, a playful voice avatar. **Why it matters:** Assistants are getting more social, context-aware, and agent-like—upgrades everyday users will actually feel across planning, research, and team workflows. ### **2)** [**OpenAI launches the ChatGPT “Atlas” browser**](https://apnews.com/article/openai-atlas-web-browser-chatgpt-google-ai-f59edaa239aebe26fc5a4a27291d717a?utm%5Fsource=chatgpt.com) **Summary:** OpenAI debuted Atlas, a desktop browser with ChatGPT built-in and an “agent mode” that can proactively browse and summarize the web. **Why it matters:** If AI-first browsing sticks, expect a shift from “search → click links” to “ask → get answers (with sources)”—changing how readers, researchers, and marketers reach information. ### **3)** [**Poll: Americans are worried about AI’s energy & water use**](https://apnorc.org/wp-content/uploads/2025/10/EPIC-AP-NORC-Press-Release-FINAL-10.22-1.pdf?utm%5Fsource=chatgpt.com) **Summary:** A new AP-NORC / EPIC survey finds broad concern about AI’s environmental impact from data centers, with strong support for safeguards. **Why it matters:** Compute demand = policy pressure. Expect more scrutiny on where data centers get built and how providers curb environmental costs. ### **4)** [**Federal judge admits staff used AI to draft a flawed court order**](https://apnews.com/article/robert-conrad-chuck-grassley-artificial-intelligence-henry-t-wingate-mississippi-b2dca153ae6521f5246b0d3170e1962a?utm%5Fsource=chatgpt.com) **Summary:** Mississippi Judge Henry Wingate acknowledged a staffer used AI in drafting an order that was later withdrawn—fueling a judiciary review of AI use. **Why it matters:** Governance is catching up to AI—more institutions will set clear rules and disclosure for AI-assisted work in high-stakes settings. ### **5)** [**GM is bringing a Google Gemini-powered assistant to cars in 2026**](https://www.engadget.com/transportation/google-gemini-will-arrive-in-gm-cars-starting-next-year-181249237.html?utm%5Fsource=chatgpt.com) **Summary:** GM will add a conversational Google Gemini assistant across many 2026 model-year cars, trucks, and SUVs—tightly integrated with Google services and designed for hands-free help on the road. **Why it matters:** Car UIs are becoming AI-native. Expect safer voice-first controls, better navigation and scheduling, and new developer surfaces inside vehicles—useful for consumers and brands alike. ## AI Education for You Vectors & Embeddings 101: How AI Represents Meaning So far we climbed the ladder—artificial intelligence → machine learning → deep learning—peeked into neural networks, learned how models use features and labels with fair splits, and saw why data shape and quality matter. Now we answer a key “how” question: How do modern systems represent meaning so they can understand and produce language? The answer is vectors and embeddings. - A **vector** is a list of numbers. - An **embedding** is a special vector that captures meaning from text or other content. **Why this matters for artificial intelligence and models like ChatGPT:** - **Understanding:** Models do not learn from raw words. They learn from numbers. Embeddings turn words, sentences, and documents into numbers that carry meaning. - **Learning patterns:** During training, the model adjusts these numbers so that things with similar meaning end up close together. This helps it learn stable patterns in language. - **Writing answers:** When the model generates text, it uses these meaning-rich numbers to choose the next word that fits the context. That is why replies can sound natural and stay on topic. - **Finding useful context:** Many systems compare embeddings to look up the most relevant text (for example, the right part of a long bank statement), then let the model read it and answer with better facts. Think of embeddings as **dots on a map of meaning**. Dots that are close often talk about the same thing, even if the words differ. That simple idea explains everyday magic: grouping similar purchases, spotting recurring charges with slightly different names, and searching receipts in plain words. Understanding this map helps you see how modern artificial intelligence understands language—and why clean, readable data still makes all the difference. ## **Vectors & Embeddings in plain English** **1) Vector:** A vector is just a list of numbers. For example: \[0.12, -0.07, 0.55, …\]. Alone, that list is not helpful. It becomes useful when the numbers capture meaning. **2) Embedding:** An embedding is a meaningful vector created from text or other content. The rule of thumb: similar things → similar vectors. If your content is a document or receipt, it first needs to be turned into readable text; then that text can be represented as an embedding. **3) Closeness = similarity:** Picture a map of meaning. Each item is a dot. - **Close dots** often mean similar items (groceries near groceries; restaurants near restaurants). - **Far dots** often mean different items. **4) Why this matters in an everyday budget:** - Group look-alike spending without reading every line. - Spot recurring charges even when names vary. - Search receipts in plain words and still find related lines. **5) How vectors and embeddings help models learn:** - They turn words into numbers the model can learn from. - They make patterns easier to see: close dots are easier to separate into useful groups (for example, groceries versus restaurants) or to predict a label (for example, “subscription” versus “not subscription”). - They support learning with labels (predict a category) and without labels (discover clusters). - They help systems retrieve context before writing, by finding nearby dots in the meaning space. ## **Examples that land** **1) Catch subscriptions with varied names:** - Charges: “FitCo Monthly” and “Health Club Charge.” - The words differ. The meaning is the same. - On the map, these charges sit near each other, helping the app flag them as recurring. **2) See trends by grouping similar spend:** - Grocery stores form one cluster. Restaurants form another. - A quick glance shows “eating out grew this month” without scanning every line. **3) Search receipts in everyday words:** - You type “school supplies.” - The app finds lines like “notebooks, pencils, markers.” - Those lines sit near your words in meaning, even without the exact match. ## One-screen recap — **Vectors & Embeddings 101** - A vector is a list of numbers. - An embedding is a meaningful vector built from text or other data. - Closer on the map = more similar in meaning. - This powers grouping, subscription spotting, plain-word search, and easier learning from your data. - Clean, readable input still matters for good results. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/10/data-src-image-880d3aed-ef13-4790-adda-1c810893d99d.png) ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🔎 Stock Research Assistant with Perplexity AI** **Why this matters:** Stock research can feel endless. In \~10 minutes, you can grab official docs (10-K/10-Q, earnings PR), drop them into Perplexity’s free plan, and get a clean, sourced brief: business snapshot, recent catalysts, risks, and follow-ups — with clickable citations to verify. ### **Step 1 — Pick a ticker & define your question (1 minute):** Choose any publicly traded company (e.g., AAPL, MSFT, NVDA). Write one guiding question to focus the AI, such as: - “How sustainable is revenue growth over the next 12 months?” - “What are the top 3 risks management is signaling?” - “What changed since last quarter’s results?” ### **Step 2 — Open the official sources (3–4 minutes):** You’ll rely on free primary sources (most accurate): - [**SEC EDGAR**](https://www.sec.gov/edgar/search/?utm%5Fsource=chatgpt.com) **(Free):** search the company → open the latest 10-Q / 10-K and any recent 8-K (earnings release). Use Full-Text or Company search. - **Investor Relations (IR) site (Free):** find the latest earnings press release and presentation under *Investors → News/Events/Press Releases*. *Why these: EDGAR filings and issuer PRs are free and authoritative; they’re the source of truth you can verify anytime.* ### **Step 3 — Use Perplexity (Free) to build a sourced brief (3–4 minutes):** Perplexity’s free tier returns answers with **source links/citations** by default, which is perfect for research. Open [**perplexity.ai**](http://perplexity.ai/?ref=mindovermoney.ai) → new thread → paste the prompt below. #### **🧠 Copy + Paste Prompt (works for any ticker)** You are my AI equity research assistant. Goal: Create a concise, sourced research brief for the following ticker(s): . Focus on what changed recently and what to watch next. Use only primary/official materials: \- Latest 10-Q or 10-K on SEC EDGAR \- Most recent earnings press release and/or 8-K (IR site or EDGAR) \- Recent investor presentation (IR) If needed, include 1–2 reputable news sources only to clarify context (no blogs). Every claim must have a citation. Deliverables (use headings + bullets): 1) Business Snapshot — what the company sells, key segments/geographies. 2) Recent Results & Guidance — revenue, EPS (GAAP/non-GAAP if stated), YoY, margin notes; highlight changes vs prior quarter/year. 3) Catalysts (next 1–2 quarters) — product launches, regulatory, macro, seasonality; cite sources. 4) Key Risks — supply, competition, pricing, FX, customer concentration; cite sources. 5) Management Signals — 1–2 short quotes with speaker + source link. 6) Watchlist for Next Quarter — 3 follow-up questions. 7) Source Table — list each source with title + link (SEC form type if applicable). Rules: \- Include inline citations next to each factual bullet. \- If a number is not disclosed, say “not disclosed” rather than estimate. \- Prefer issuer filings/IR over secondary sources. \- Keep it to \~250–350 words total. *Tip: If Perplexity includes unsourced lines, reply: “Re-run with citations on every numeric claim and links to EDGAR or IR.”* ### **Step 4 — Add a quick KPI table (1–2 minutes):** Ask Perplexity in the same thread: “Append a 6-row table: **Metric | Current | Prev Q | YoY | Source link** for Revenue, EPS, Gross Margin, FCF (if disclosed), Key Segment metric, and one Operating KPI. Cite each cell.” Then: “Export that table as **CSV** so I can paste into Google Sheets.” ## **The Payoff** In \~10 minutes you’ll have a **sourced, skimmable brief** and a **KPI table** you can reuse next quarter — with links back to the filings so you (or your readers) can verify in one click. *💡 Pro tip: Save each brief to a ticker folder. Next quarter, ask Perplexity: “What changed since the last brief on ? Link to new filings only.”* ## **Transparency & Notes for Readers** - Perplexity cost: A Standard (Free) plan exists; paid tiers (Pro/Max) just expand limits and models. This workflow works on the free plan. - Source quality: Perplexity is an answer engine with citations; still verify numbers by clicking the EDGAR/IR links it provides. - Primary sources: SEC EDGAR and issuer IR are free and should be your first stop. - Education, not advice: Use this to learn faster and track what matters; it’s not investment advice. ## Founder's Corner Real world learnings as I build, succeed, and fail "It does not matter how slowly you go as long as you do not stop." — Confucius. This quote embodies my AI journey up to this point, especially when it comes to AI image & video generation. The models and features released over the past 6 months have been groundbreaking, leading to a new wave of creative possibilities. And for me, this was exciting and timely, as I thought building visuals and marketing videos for *Neural Gains Weekly* would be an easy win. I quickly realized that I entered into another realm of AI education, one filled with failures, hidden costs, unique context, and educational opportunities. The principle of slow and steady progress applies to AI, affirming that consistent, patient effort will lead to growth and quality over time. I’m very much working to better understand how to maximize efficiency and output of AI tools to help grow my audience. I’m currently using 3 tools to build video content and images used on the website and social media: HeyGen, Veo (inside Gemini), and NanoBanana. Through trial and error, I’m starting to learn how to interact with these tools to create better content. And I want to share a few lessons with you in real time as I continue to iterate and build. ### **Lesson 1: Costs & caps shape creativity** I hit the ceiling fast. HeyGen’s free plan gives you 3 videos per month, perfect for a taste test, but not rapid iteration. Inside Gemini, I kept hitting Veo's daily caps (typically \~3–5 clips depending on mode/plan), which meant I had to get the output perfect in one or two tries. These limits don’t just slow output; they fragment learning. It’s difficult to refine your skills when the ability to tweak small aspects of the output is taken away. *Takeaway: Treat free credits like film stock—plan your shots, batch your tries, and use them where learning compounds fastest.* ### **Lesson 2: Video/image prompts aren’t chat prompts** I’m used to using AI as a strategic partner and interacting in a structured, conversational manner. Video and image prompts are different and require a nuanced approach to bring your vision to life. I’ve struggled to bridge that gap and deliver useful content to help market *Neural Gains Weekly*, as only about 10% of my videos have actually made it to social media. The failure is teaching me what I need to learn next: video/image models don’t want paragraphs, they want a director. They need detailed information and context to build the scene. Sounds easy, but take it from me, this process takes time to master. Here’s a few concepts I’m working to incorporate into content creation journey: - **Story first, one beat at a time:** define one subject + one action for 5–8 seconds. - **Name the shot & camera:** wide/medium/close-up, tripod or slow dolly. - **Lock the look:** 3–5 words I’ll reuse (e.g., “soft daylight, shallow depth”). - **Add constraints:** what must *not* change (no text overlays, tripod, centered subject). - **Iterate on one variable:** if a take is close, tweak *only* camera **or** lighting—not both. *Takeaway: Treat prompts like stage directions—specify shot, one beat, look, and a constraint, then iterate one variable at a time so your learning compounds fast.* ### **Lesson 3: Power needs pre-visualization** The models are intuitive, but they can’t read my mind. I kept expecting the model to “see” what I saw. It didn’t. Without a clearly named look in my head, the results drifted—skin tones shifted, framing wandered, and every retry felt unrelated to the last. That failure taught me the third lesson, pre-visualization is part of the work. I had to take ownership and start articulating the look I was chasing in a way the model could understand. This was and continues to be a difficult shift since I have no background in producing, editing or filming videos. It’s essentially learning a new language with outputs as the main measurement of progress. I’m still practicing, but the learning is clear: the model follows the visual story I’m able to describe consistently. *Takeaway: Name the look you’re chasing, then keep naming it—so each attempt teaches you whether you’re getting closer, not just different.* **What’s Next:** I’m keeping experiments simple and repeatable: storyboard first, draft short, refine with the style kit, while focusing on the vision. Keep an eye out on my Socials to see progress as I continue to iterate and learn on the fly. **Goals & Milestones:** | Goal | Current (as of 10/28/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 79 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 5: The Context Architect URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-5-the-context-architect/ Last updated: 2026-07-19T20:23:18.000Z 📌 **Archive note: This post reflects Neural Gains Weekly's original personal-finance and investing framing, retired in early 2026\. It is preserved unchanged as history — the prompts below reference a positioning NGW no longer uses. Today, Neural Gains Weekly is AI education for professionals in complex industries.* Prompt and context engineering are constantly at the forefront of my learning journey. I’m in the process of redesigning prompts that drive the AI-created content for *Neural Gains Weekly.* Take a look at my updated prompt that started the AI Education build out for Volume 5\. --- **Context:** You are my weekly editor–researcher for the *AI Education* section of the Neural Gains Weekly newsletter. Your job is to choose the best angle for the topic, teach it in plain English for AI beginners, and ground every example in everyday personal-finance life (using apps, banks, paying bills, autopay, budgets, and paychecks). Readers should finish feeling **they learned something** and **can apply AI ideas in their life**. **This week’s topic:** Vectors & Embeddings 101 **Target publish week:** 10/28/2025 **House style:** beginner-first tone, short sentences, minimal jargon. No acronyms in the body (define any term in plain English). Use personal-finance examples only (day-to-day money tasks).Citations: Provide end-of-section sources (not inline), from reputable, free educational sites (e.g., Google Cloud learning, OpenAI learning, Microsoft Learn, respected universities/docs).Outputs for me: Google-Docs-ready Markdown (clean headings, lists), plus a proposed visual (and create it if useful). ## **Roles** 1. **Educator** — explain like a patient professor to a true beginner. 2. **Researcher** — find the clearest, most reputable sources; don’t guess. 3. **Editor** — keep it tight, clear, and consistent with prior issues’ spirit. 4. **Illustrator** — propose one useful visual; create a simple caption and labels. ## **Non-negotiables** - **Plain English.** Short, clear sentences. - **No acronyms in the body.** If a term is needed, spell it out in plain English the first time. - **Personal-finance lens** only (apps, budgeting, paying bills, autopay, bank accounts, paychecks). - **Beginner-friendly scaffolding:** define → illustrate → contrast → recap. - **Citations at the end** (3–6 reputable sources). - **Do not assume tools or code.** This is education, not a tutorial. ## **Workflow (follow in order)** **Step 1 — Proposal (get my quick approval):** - **Angle:** the most helpful way to teach Vectors & Embeddings 101 to beginners. - **Learning objectives (3–5):** what readers will understand by the end. - **Mini-outline:** the sections and the flow (keep flexible; justify any deviations from prior volumes). - **Example plan:** one personal-finance thread you will use throughout (e.g., monthly budget, paying bills with autopay, paycheck planning, using a banking app). - **Visual concept:** 1 diagram you’ll include (what it shows + labels). - **Open question (1 only):** ask me one clarifying question if needed. **Step 2 — Full Draft (after proposal):**Deliver Google-Docs-ready copy with these sections (rename/adapt if the topic needs it, and explain why): - **Hook** — set the stage; define any essential terms; explain why it matters to everyday money life. - **Core lesson** — explain the concept in plain English; build up in small steps; use the **same personal-finance example** throughout. - **Contrast & clarity** — call out common confusion points and how to think about them (beginner-friendly). - **Examples that land** — 2–3 crisp, concrete scenarios tied to the same personal-finance thread (apps, bank account tasks, paying bills, budget). - **One-screen recap** — bullet the big ideas in everyday words. - **Visual** — include a brief caption + callouts (and offer to export PNG/SVG). - **Sources** — 3–6 reputable end notes (no paywalls; educational pages preferred). ## **Quality Checklist (self-check before showing me)** - Reading level: beginner; sentences mostly under \~20 words; zero unexplained jargon; **no acronyms in the body**. - Every example ties to **one personal-finance thread** (budget, paying bills/autopay, bank app tasks, paychecks). - Definitions first, then examples, then gentle contrasts, then recap. - Citations from trusted, free educational sites; no random blogs. - Visual matches the lesson and uses the same example thread. - No assumptions about coding or tools; pure education. - Clear explanation if you adapt section headings for this topic. ## **Deliverable Format** - **Primary:** Google-Docs-ready Markdown (clean H2/H3, bullets, bold). - **Images:** propose a simple diagram and, if helpful, generate it (PNG/SVG). - **End sources list:** bullet list with titles + publishers. ## **Start now** 1. Produce **Step 1 — Proposal** for Vectors & Embeddings 101 2. Ask **one** clarifying question at a time if needed. 3. Wait for my quick approval before moving to **Step 2 — Full Draft**. ### **Notes you can reuse each week (style guardrails)** - Prefer everyday verbs (“show,” “sort,” “compare”) over technical terms. - When you must name a concept (for example, *training data*), define it in plain English the first time, then use that plain phrase. - Keep examples focused on: checking a bank app, reviewing a monthly budget, setting up or fixing bill pay/autopay, categorizing purchases, planning a paycheck. ### Lights, Camera, Learn URL: https://www.mindovermoney.ai/founders-corner/how-i-use-ai-to-learn-new-skills-faster-professionals/ Last updated: 2026-07-13T16:59:56.000Z "It does not matter how slowly you go as long as you do not stop." — Confucius. This quote embodies my AI journey up to this point, especially when it comes to AI image & video generation. The models and features released over the past 6 months have been groundbreaking, leading to a new wave of creative possibilities. And for me, this was exciting and timely, as I thought building visuals and marketing videos for *Neural Gains Weekly* would be an easy win. I quickly realized that I entered into another realm of AI education, one filled with failures, hidden costs, unique context, and educational opportunities. The principle of slow and steady progress applies to AI, affirming that consistent, patient effort will lead to growth and quality over time. I’m very much working to better understand how to maximize efficiency and output of AI tools to help grow my audience. I’m currently using 3 tools to build video content and images used on the website and social media: HeyGen, Veo (inside Gemini), and NanoBanana. Through trial and error, I’m starting to learn how to interact with these tools to create better content. And I want to share a few lessons with you in real time as I continue to iterate and build. ### **Lesson 1: Costs & caps shape creativity** I hit the ceiling fast. HeyGen’s free plan gives you 3 videos per month, perfect for a taste test, but not rapid iteration. Inside Gemini, I kept hitting Veo's daily caps (typically \~3–5 clips depending on mode/plan), which meant I had to get the output perfect in one or two tries. These limits don’t just slow output; they fragment learning. It’s difficult to refine your skills when the ability to tweak small aspects of the output is taken away. *Takeaway: Treat free credits like film stock—plan your shots, batch your tries, and use them where learning compounds fastest.* ### **Lesson 2: Video/image prompts aren’t chat prompts** I’m used to using AI as a strategic partner and interacting in a structured, conversational manner. Video and image prompts are different and require a nuanced approach to bring your vision to life. I’ve struggled to bridge that gap and deliver useful content to help market *Neural Gains Weekly*, as only about 10% of my videos have actually made it to social media. The failure is teaching me what I need to learn next: video/image models don’t want paragraphs, they want a director. They need detailed information and context to build the scene. Sounds easy, but take it from me, this process takes time to master. Here’s a few concepts I’m working to incorporate into content creation journey: - **Story first, one beat at a time:** define one subject + one action for 5–8 seconds. - **Name the shot & camera:** wide/medium/close-up, tripod or slow dolly. - **Lock the look:** 3–5 words I’ll reuse (e.g., “soft daylight, shallow depth”). - **Add constraints:** what must *not* change (no text overlays, tripod, centered subject). - **Iterate on one variable:** if a take is close, tweak *only* camera **or** lighting—not both. *Takeaway: Treat prompts like stage directions—specify shot, one beat, look, and a constraint, then iterate one variable at a time so your learning compounds fast.* ### **Lesson 3: Power needs pre-visualization** The models are intuitive, but they can’t read my mind. I kept expecting the model to “see” what I saw. It didn’t. Without a clearly named look in my head, the results drifted—skin tones shifted, framing wandered, and every retry felt unrelated to the last. That failure taught me the third lesson, pre-visualization is part of the work. I had to take ownership and start articulating the look I was chasing in a way the model could understand. This was and continues to be a difficult shift since I have no background in producing, editing or filming videos. It’s essentially learning a new language with outputs as the main measurement of progress. I’m still practicing, but the learning is clear: the model follows the visual story I’m able to describe consistently. *Takeaway: Name the look you’re chasing, then keep naming it—so each attempt teaches you whether you’re getting closer, not just different.* **What’s Next:** I’m keeping experiments simple and repeatable: storyboard first, draft short, refine with the style kit, while focusing on the vision. Keep an eye out on my Socials to see progress as I continue to iterate and learn on the fly. **Goals & Milestones:** | Goal | Current (as of 10/28/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 79 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Volume 4: What's in Your Data? URL: https://www.mindovermoney.ai/ai-automation-failure-lessons-learned-non-technical/ Last updated: 2026-07-13T16:59:57.000Z Hey everyone! This week, I’m sharing a failure. My plan to automate content with Notion completely fell apart, and I want to show you exactly what I learned from it. Thank you for all the great feedback so far—it’s been incredible. Sharing the wins is easy, but I think it’s just as important to share the stumbles. Also in this issue: - **AI Education:** The difference between structured and unstructured data (and why it’s the key to better AI results). - **A 10-Minute Win** to compare and contrast two ETFs in minutes. Let's dive in with your curated AI news from the past week. *Missed a previous newsletter (released every Tuesday at 9AM EST)? No worries, you can find them on the* [*Archive page*](https://www.mindovermoney.ai/tag/newsletter/) *at* [*MindOverMoney.ai*](http://mindovermoney.ai/?ref=mindovermoney.ai)*. Don’t forget to check out the* [*Prompt Library*](https://www.mindovermoney.ai/prompt-library/)*, where I give you templates to use in your AI journey. If you have content suggestions, a workflow idea, or want to share a success from your AI journey, reach out at* [*admin@mindovermoney.ai*](mailto:admin@mindovermoney.ai)*.* ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)** [**Google’s Gemma helps flag a new cancer-therapy pathway**](https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/?utm%5Fsource=chatgpt.com) **Summary:** Google details how a Gemma-based model (C2S-Scale) predicted a combo (silmitasertib + interferon) that boosts immune signaling in “cold” tumors; wet-lab tests validated the effect, pointing to a promising treatment direction. **Why it matters:** Clear, beginner-friendly example of AI accelerating biomedical discovery—turning model hypotheses into lab-verified leads that could inform future oncology research. ### **2)** [**Top U.S. Army general says he’s letting ChatGPT make decisions**](https://futurism.com/future-society/general-taylor-korea-chatgpt?utm%5Fsource=chatgpt.com) **Summary:** The U.S. commander in South Korea quipped he’s grown “really close” with ChatGPT—comments that sparked heated debate about AI advice vs. military decision-making. **Why it matters:** A vivid snapshot of AI’s cultural moment—and a reminder to set clear guardrails for where AI fits (and doesn’t) in high-stakes decisions. ### **3)**[ **Microsoft rolls out major Windows 11 AI upgrades for Copilot**](https://www.reuters.com/business/microsoft-launches-new-ai-upgrades-windows-11-boosting-copilot-2025-10-16/?utm%5Fsource=chatgpt.com) **Summary:** Windows 11 adds “Hey Copilot” voice activation, expands Copilot Vision, and trials Copilot Actions that can book or order directly from the desktop under user-granted permissions. **Why it matters:** AI is moving from a chat box into the OS itself—making everyday PCs feel more like assistants for simple automations and on-screen help. ### **4)** [**OpenAI pauses MLK cameos after backlash over Sora deepfakes**](https://www.theverge.com/news/801539/open-ai-sora-mlk?utm%5Fsource=chatgpt.com) **Summary:** Following complaints from the King estate, OpenAI suspended MLK appearances in the Sora app and says estates of historical figures can opt out—tightening guardrails on AI-generated likenesses. **Why it matters:** A fast-moving, real-world example of AI policy and safety in action—useful context for creators, brands, and educators navigating rights and reputation. ### **5)** [**Google AI Studio gets a cleaner playground, usage dashboards, and Maps grounding**](https://blog.google/technology/developers/ai-studio-updates-more-control/?utm%5Fsource=chatgpt.com) **Summary:** Google rolled out an AI Studio overhaul: a unified playground for Gemini/GenMedia/TTS/Live, a real-time rate-limit/usage page, and options to ground models with Google Maps. **Why it matters:** Friendlier tooling lowers setup time for first projects; seeing limits and grounding data clearly helps beginners ship useful, reliable AI apps faster. ## AI Education for You ****Data Shapes: Structured, Semi-Structured, and Unstructured** Models don’t “think”—they **learn patterns from data**. Data is simply recorded facts about something that happened: numbers, words, dates and times, and context about where it came from. Let’s focus on a personal budget to make this information tangible and related to your personal finances. Budget data includes each purchase amount, the merchant, the date and time, the payment method, and any notes or line items. For language-based systems, data also includes full sentences from receipts, statements, and emails—because those sentences carry the patterns a system learns to read and write. Good budget data tends to be: **accurate** (matches reality), **consistent** (fields mean the same thing every time), **complete enough** (weekdays and weekends, online and in-store), **timely** (new subscriptions show up), **representative** (covers groceries, rent, rideshare, and seasonal spikes), and **described** (short notes that define fields so meanings don’t drift). Keep this context in mind as you read on; we’ll use them to examine **structured**, **semi-structured**, and **unstructured** data—how each becomes machine-readable, how it feeds learning, and how quality choices raise or lower accuracy in something as familiar as a household budget. ## **One scenario all the way through: building your monthly budget** You want a clear picture of where your money goes. Your sources match the three data “shapes”: 1. **Bank export** — a spreadsheet of transactions (structured). 2. **Email receipts** — messages with labeled headers plus free-text bodies (semi-structured). 3. **PDF statements and photos of paper receipts** — rich but free-form files (unstructured). We’ll use this same budget scenario for every example below. ## **Structured data — “spreadsheet neat”** **Plain English:** Rows and columns with fixed field names—like date, merchant, amount, category. You always know what each column means. **Budget example:** - You export transactions.csv from your bank. Each row is one purchase. - Columns are consistent: date, merchant, amount, channel (online/in-store), category. - An AI model can learn straight from these columns: repeated amounts on the same day each month often point to subscriptions; combinations like merchant + time of day + amount help suggest the right category. **Watch-outs (budget-specific):** - **Units drifting:** If “amount” switches from dollars to cents mid-file, your totals explode. Keep units steady. - **Missing slices:** Only seeing weekday transactions hides weekend patterns—budgets skew low on restaurants and rides. ## **Semi-structured data — “has tags, not rigid”** **Plain English:** There are labeled parts (headers like From, Date, Total), but the body text varies by sender. Not one strict table, yet pieces are clearly marked. **Budget example:** - A grocery delivery email has merchant name and total in the header, plus a free-form list of items in the body. - A rideshare email has a labeled pickup time and fare, followed by a flexible trip summary. **Watch-outs (budget-specific):** - Different senders use different words for the same idea (Total, Amount, Charge). Make a tiny “translation list” so they map to one Amount column. - Treat headers as reliable anchors and the free text as supporting detail—don’t weigh them equally. ## **Unstructured data — “free-form”** **Plain English:** No fixed columns—documents, PDFs, screenshots, images, recorded calls, video. Rich in detail but not immediately searchable. **Budget example:** - A PDF bank statement for March. - A photo of a paper receipt from a weekend farmers market. - A scanned utility bill with line items and due date. **How it becomes useful for your budget:** - First make it readable: convert PDFs and photos into selectable text so a system can “see” the merchant, total, and date. - Once readable, it can summarize, categorize, and route: send receipts to one folder, statements to another, highlight unusual charges or new fees. **Watch-outs (budget-specific):** - If you skip the conversion, the system learns nothing from these files. - Blurry photos or low-quality scans cause misread numbers; better captures improve accuracy. ## **How an AI system “sees” your budget data** - With the spreadsheet, it compares the same fields across many rows to find stable patterns: subscriptions, typical amounts by merchant, odd spikes. - With the emails, the labeled headers act like signposts—Merchant, Total, Date—and the body adds nuance (what you bought), which helps with category suggestions and notes. - With PDFs and photos, a short “make it readable” step unlocks the content so it can be searched, summarized, or sorted (invoice vs. receipt vs. statement). ## **Why quality beats quantity** - **Wrong answers teach the wrong lesson.** If your “category” notes are inconsistent (sometimes “groceries,” sometimes “food”), the system learns confusion—and your budget summary drifts. - **Coverage matters.** Leave out weekend cash purchases and you’ll understate restaurants and local markets. Include both channels (online/in-store) and both time slices (weekdays/weekends). - **Readable beats raw.** A PDF that hasn’t been converted to text is invisible to a system. A quick conversion turns clutter into usable information. ## **A simple mental model to keep** 1. **Shape → approach:** - Structured (spreadsheet) feeds learning directly. - Semi-structured (tagged emails) gives you reliable fields and helpful free text. - Unstructured (PDFs, photos) needs a quick “make it readable” step first. 2. **Meaning → stability:** - Write a one-line definition for each field: Amount is always dollars, Date is the transaction date, Merchant is the seller’s name. - Keep formats steady so totals and trends don’t drift. 3. **Quality → trust:** - Correct categories, broad coverage, and readable files build a budget you can rely on. - Errors, gaps, or unreadable documents quietly break it. Enjoy a video overview created by NotebookLM. 0:00 /5:46 1× Will open on the website page. Scroll down to watch. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **📊 ETF Comparison in Minutes** **Why this matters:** Most new investors start with ETFs—but picking between “similar” funds can be confusing. In about 10 minutes, you can pull official fund facts (expense ratio, index, holdings), drop them into a free AI, and get a clean side-by-side table + plain-English “best fit for me” summary. ### **Step 1 — Pick 2 to 3 ETF tickers to compare (2 minutes):** Use any free source to find the funds you want: - **Yahoo Finance ETF Screener (Free):** filter by category (e.g., US large cap, Tech, Dividend) to get candidate tickers. - **Issuer Lists (Free):** browse Vanguard and iShares ETF lists to find funds that match your goal (total market, S&P 500, international, bonds). - Examples: VTI (US total market), VOO/IVV (S&P 500), VT (global stocks). Issuer pages and fact sheets are always free and most accurate. ### **Step 2 — Open each fund’s official fact sheet (3 minutes):** For every ticker, open the **issuer’s fact sheet** (PDF or webpage). You’ll find: - **Expense ratio** (your ongoing fee) - **Index tracked / strategy** - **Top holdings & sector weights** - **AUM, inception date** - **Distribution (dividend) yield** - Example: iShares IVV (S&P 500) shows a 0.03 % expense ratio and detailed sector weights. Vanguard and iShares post updated PDF fact sheets on their websites for every ETF. ### **Step 3 — Use a free AI to build your comparison (3–4 minutes):** Choose any of these free AI helpers: - **Gemini (Free)** - **Perplexity (Free)** - **ChatGPT (Free)** Once you select your AI tool: - Start a new chat - Upload the PDF fact sheet of copy/paste the information from the fact sheet - Copy/paste the prompt below -- add the tickers that the bottom ### **🧠 Copy + Paste Prompt** *(Works with any ETFs — just swap the tickers at the bottom.)* You are my AI investment assistant. I want to quickly compare exchange-traded funds (ETFs) side-by-side to help me make smarter, lower-cost investment decisions. Goal: Help me analyze and compare multiple ETFs based on official facts—like expense ratio, holdings, performance focus, and risk profile—and summarize which might fit different investor goals. Instructions: 1\. Create a comparison table for the ETFs listed below. 2\. Use columns: \- Ticker / Name \- Expense Ratio (%) \- Index / Strategy \- Top 5 Holdings \- Sector Tilts (Top 3 Sectors) \- AUM (Assets Under Management) \- Dividend Yield (%) \- Inception Date \- Notable Differences / Highlights 3\. After the table, write: \- "So What?" Section: 3–5 concise bullets explaining what each ETF is best suited for (e.g., long-term core, dividend focus, international exposure). \- "Red Flags" Section: list any concerns (e.g., higher fees, small fund size, narrow exposure, overlap). \- "Summary Recommendation:" explain which ETF may be the better choice for different investor profiles (e.g., beginner, income-seeker, global investor). 4\. Use only the official data from each ETF’s fact sheet (not market commentary). 5\. If a data point is missing, write "Not disclosed." ETFs to compare: VOO (Vanguard S&P 500 ETF) IVV (iShares Core S&P 500 ETF) SPY (SPDR S&P 500 ETF Trust) **Optional follow-ups:** “Add the official fund websites and fact sheet links as a final section.” “Explain how a 0.03 % vs 0.09 % expense ratio impacts returns over 10 years on $10 000.” 💡 You can replace the example tickers with any ETFs you want to compare—the prompt works universally. ### **Step 4 — Quick sanity check & tie-breaker (1 minute):** - **Verify expense ratios & index names** against the PDF fact sheets. - **Tie-breakers:** lower expense ratio, broader index coverage, and higher AUM (liquidity means tighter spreads and less slippage). ## **The Payoff:** In about 10 minutes you’ll have a side-by-side ETF table and a short plain-English summary so you can invest confidently and avoid paying more than you need. 💡 Pro tip: Save your comparison table by goal (“US Core,” “Intl Core,” “Bond Core”) and refresh it quarterly. ## **Transparency & Notes for Readers:** - **All tools are free:** Yahoo Finance Screener, Vanguard and iShares fact sheets, and Gemini / Perplexity / ChatGPT free tiers (all tested and available as of this issue). - **Data quality:** Issuer fact sheet = source of truth. Use AI for formatting and explanation only. - **Not investment advice:** Educational workflow to help you compare options. ### **👉 Your Turn:** Pick ETFs you’re considering (e.g., VOO, IVV, VTI, VT). Pull their fact sheets, run the prompt, and see what investment best suits your needs. ## Founder's Corner My vision was clear → a fully automated tech stack that operates like a one-button newsroom. One Notion database feeding three of the four sections of *Neural Gains Weekly*: ideas generated, polished content created, social media posts constructed. Hit a button, and it’s done. Low cost, minimal friction, and I’m still the final editor. I thought this would be achievable and easy to execute, knowing I wasn’t going to push back the launch date of 9/30/2025\. Was I naive? Did I overestimate the complexity? Did social media influence my decision-making? The answer is yes, this ‘simple’ automation build became a roadblock in creating Volume 1 by the 30th. I learned several lessons along the way that will help you build with AI. **Lesson 1 - Learning new tools takes time** I had zero experience with [Notion](https://www.notion.com/?ref=mindovermoney.ai) and frankly, hadn’t researched thoroughly to understand the capabilities on this platform. I was swayed by social media hype and assumed it would be easy to build content with one click. I spent a few hours building a Notion template and trying to automatically create content. The same template behaved differently across sections. Minor changes in Notion fields broke the pipeline. Rich text turned messy. I spent hours “troubleshooting the seemingly easy parts”. The more I pushed, the more I realized the problem wasn’t the platform or formatting— it was knowledge. I needed to spend time learning and experimenting with Notion before deploying, especially given the time constraints. Too much complexity without the right knowledge base delayed my progress, which ultimately led to the realization that learning AI tools takes time and failure is part of the process. *Takeaway: Don’t automate what you haven’t learned yet. Learn the tool first, then automate the parts you truly understand.* **Lesson 2: Automation is a multiplier — after the baseline is stable** I treated automation as a shortcut and a time-saver. Balancing family, a W2 career, real estate ventures, health goals, and social activities requires a strong focus on time management. The appeal of automation, especially with available AI tools, is a ‘no-brainer’ for a project like this, one requiring time, energy, and commitment to deliver on a weekly basis. I quickly learned that automation is the end state, not the beginning. I needed to build the skills and knowledge to successfully execute a complex (at least for me) automation process aligning to my vision. My focus shifted to building out a content roadmap and creating useful and engaging content. This will ensure my automation roadmap helps me accelerate progress without disrupting the momentum I have built so far. *Takeaway: Automation speeds up what already works. Build the rhythm first; automate to amplify it—not replace it.* **Lesson 3: Failure leads to new ideas** The great thing about AI is the speed at which new products are released. I started the initial automation part of the project back in early August and there have been dozens of changes to the AI landscape that will benefit me during round 2 of the automation build out. My initial failures in this space were disappointing, but there is always a silver lining. I have new ideas to explore and developed a better understanding of how to bring my vision closer to reality. It’s exciting to explore new AI platforms knowing I will be able to share my journey to help others. I will continue to share updates once I build a timeline and roadmap. Here is what I am researching at this moment to help on this journey: - Building a Notion learning plan with Google Gemini ‘Learn’ mode - ChatGPT connectors, specifically GitHub and Notion - Ghost API capabilities and limitations - Zapier and n8n comparison - Claude Skills platform Automation didn’t launch this newsletter—discipline did. I’m sharing learnings in real time, and I haven’t mastered any of this yet. Three things keep me moving: learn the tool before automating it, build a stable weekly rhythm, and treat failures as scouting reports for the next experiment. I’m keeping costs low and myself in the editor’s chair while I test small, human-in-the-loop wins. Next week I’ll share another stumble—my early image and video-generation attempts—and how I’m working to improve. **Goals & Milestones:** | Goal | Current (as of 10/21/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 74 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 18 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 4: The Weekly Workflow Pair URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-4-the-weekly-workflow-pair/ Last updated: 2026-07-13T16:59:57.000Z Today, you’re getting two prompts that I use on a weekly basis. I continue to iterate and find the right outputs for each section. I expect these will evolve over time, but can you spot the main themes in my prompts? **10-Minute Win Prompt** *It's time to build out a workflow for Volume 4\. I want to go with the use case below. Keep the formatting the same as content from previous volumes. You can research the final, published content from previous volumes in our chat and on 'mindovermoney.ai'. Follow these instructions:* *\-Ensure the draft content is formatted the same as previous volumes.* *\-Ensure the right level of content and detail is provided to successfully execute the workflow.* *\-Ensure the tools introduced have accurate and up to date information.* *\-Verify that all proposed tools needed are free for the user.* *\-Research thoroughly to ensure no errors in the workflow.* *\-Our goal is to create an easy workflow to help AI beginners build confidence, better understand AI tech and help grow financially. We are here to give easy use cases to make that happen. Let's build the use case below:* **AI Education Prompt** *Let's move to the next task. It is time to build out AI education content for Volume 4\. Follow the steps and instructions below:* *1\. Review previous volumes that we created in this chat* *2\. Review the proposed topics at the bottom for what topics I want to cover* *3\. Think and figure out how to bring these topics into an easy-to-read section of the newsletter* *4\. Make sure the content is organized in an educational manner and focuses on tying the topics together* *5\. Kick off the section with a clever hook and call out how this new material continues the learning journey. The goal is to make it a seamless education plan that the reader can understand* *6\. Research thoroughly to ensure the information created is accurate. Do not hallucinate. Validate with trusted sources* *7\. Ask any question, one at a time, to ensure understanding before creating the first draft Educational content idea for volume 4:* *I want to highlight 'structured and unstructured data'. I want the materials to be more detailed than the previous volumes. I want the information to explain the concepts and explain how these types of data can be used to train models. And the limitations of bad data in relation to AI outcomes and accuracy. Do not just agree with my premise. Use your expertise and knowledge to ensure this is the right content for volume 4 based on our roadmap, previous AI education topics, and relevance.* ### Failing Forward: Why I Paused Automation to Launch Faster URL: https://www.mindovermoney.ai/founders-corner/why-ai-automation-failed-what-i-learned-non-technical/ Last updated: 2026-07-13T16:59:58.000Z My vision was clear → a fully automated tech stack that operates like a one-button newsroom. One Notion database feeding three of the four sections of *Neural Gains Weekly*: ideas generated, polished content created, social media posts constructed. Hit a button, and it’s done. Low cost, minimal friction, and I’m still the final editor. I thought this would be achievable and easy to execute, knowing I wasn’t going to push back the launch date of 9/30/2025\. Was I naive? Did I overestimate the complexity? Did social media influence my decision-making? The answer is yes, this ‘simple’ automation build became a roadblock in creating Volume 1 by the 30th. I learned several lessons along the way that will help you build with AI. **Lesson 1 - Learning new tools takes time** I had zero experience with [Notion](https://www.notion.com/?ref=mindovermoney.ai) and frankly, hadn’t researched thoroughly to understand the capabilities on this platform. I was swayed by social media hype and assumed it would be easy to build content with one click. I spent a few hours building a Notion template and trying to automatically create content. The same template behaved differently across sections. Minor changes in Notion fields broke the pipeline. Rich text turned messy. I spent hours “troubleshooting the seemingly easy parts”. The more I pushed, the more I realized the problem wasn’t the platform or formatting— it was knowledge. I needed to spend time learning and experimenting with Notion before deploying, especially given the time constraints. Too much complexity without the right knowledge base delayed my progress, which ultimately led to the realization that learning AI tools takes time and failure is part of the process. *Takeaway: Don’t automate what you haven’t learned yet. Learn the tool first, then automate the parts you truly understand.* **Lesson 2: Automation is a multiplier — after the baseline is stable** I treated automation as a shortcut and a time-saver. Balancing family, a W2 career, real estate ventures, health goals, and social activities requires a strong focus on time management. The appeal of automation, especially with available AI tools, is a ‘no-brainer’ for a project like this, one requiring time, energy, and commitment to deliver on a weekly basis. I quickly learned that automation is the end state, not the beginning. I needed to build the skills and knowledge to successfully execute a complex (at least for me) automation process aligning to my vision. My focus shifted to building out a content roadmap and creating useful and engaging content. This will ensure my automation roadmap helps me accelerate progress without disrupting the momentum I have built so far. *Takeaway: Automation speeds up what already works. Build the rhythm first; automate to amplify it—not replace it.* **Lesson 3: Failure leads to new ideas** The great thing about AI is the speed at which new products are released. I started the initial automation part of the project back in early August and there have been dozens of changes to the AI landscape that will benefit me during round 2 of the automation build out. My initial failures in this space were disappointing, but there is always a silver lining. I have new ideas to explore and developed a better understanding of how to bring my vision closer to reality. It’s exciting to explore new AI platforms knowing I will be able to share my journey to help others. I will continue to share updates once I build a timeline and roadmap. Here is what I am researching at this moment to help on this journey: - Building a Notion learning plan with Google Gemini ‘Learn’ mode - ChatGPT connectors, specifically GitHub and Notion - Ghost API capabilities and limitations - Zapier and n8n comparison - Claude Skills platform Automation didn’t launch this newsletter—discipline did. I’m sharing learnings in real time, and I haven’t mastered any of this yet. Three things keep me moving: learn the tool before automating it, build a stable weekly rhythm, and treat failures as scouting reports for the next experiment. I’m keeping costs low and myself in the editor’s chair while I test small, human-in-the-loop wins. Next week I’ll share another stumble—my early image and video-generation attempts—and how I’m working to improve. **Goals & Milestones:** | Goal | Current (as of 10/21/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 74 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 18 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Steal My Prompt Vol. 3: The Writing Improver URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-3-the-writing-improver/ Last updated: 2026-07-13T16:59:58.000Z Writing is not a skill I’ve worked to improve over the years. Most of my writing takes place in a corporate environment where emails rule the day. I’ve adopted Google Gemini as my go-to tool for editing content, including my writing and AI-generated material. Here are 3 prompts that I consistently use to edit *Neural Gains Weekly.* **Editing Assistance:** *You are a seasoned editor that specializes in helping enhance newsletter content. Your role is to review this section and identify grammatical errors, phrasing inefficiencies, word choice suggestions, and any other helpful editing that would enhance the newsletter. Only propose one change at a time to ensure ease of editing on my part.* **Fact Checker:** *You are a content editor and fact checker. You specialize in ensuring AI generated content is accurate and no hallucinations are present. Review the document and take your time. Think, search for errors, and validate the information is correct. Ask any clarifying questions needed to start this activity. If you find issues, bring them to my attention one at a time to make it easier to edit and update the document* **Simplifying the 'AI Education' Content:** *You are my assistance that specializes in updating the content to ensure it aligns with my vision. I want to make the language used throughout the section more beginner friendly and more detailed when needed. There are acronyms used throughout the section that lack context. Some of the verbiage is too advanced and this volume needs to be refined to ensure a beginner audience understands the key concepts and that the information flows in a clear manner.* ### If I Can Code, So Can You URL: https://www.mindovermoney.ai/founders-corner/non-technical-professionals-learning-to-code-with-ai/ Last updated: 2026-07-13T16:59:58.000Z Ever have a deadline and no idea where to start? Hit a knowledge gap that stalls a project? Try a new tool, only to drown in errors with no clue how to fix them? You’re not alone. That was me in the first few weeks of building *MindOverMoney.ai* on Ghost, which offers solid out-of-the-box themes, but none fit what I needed. So I went down the customization rabbit hole—and I’m glad I did. It led to my second significant “aha” moment: I can write code (with ChatGPT as my co-pilot). There were 3 customizations needed to realize my vision for the website: 1. Simplified layout on the main page 2. Custom layouts and navigation for ‘Founder’s Corner’ and ‘Prompt Library’ 3. Automation of *Neural Gains Weekly* into the ‘Archive’ Now, I don’t know how to read or write code, nor am I extensively familiar with IDE or coding platforms. But I read about AI coding use cases and how advancements over the past year have unlocked new potential for people like me (like many of you). The barrier to entry has been knocked down, but you can only understand the power of AI coding once you experience it. You can make real progress without being “technical,” if you work in small, manageable steps and ask better questions. These are the lessons that helped me move from stuck to building. - Build your vision - Know exactly what you want to build. Clear communication of your vision is key. - Work on one task at a time - Focus on each step individually and verify the code deployment matches your vision. - Be precise - When I gave ChatGPT exact errors and clear outcomes, I got useful help. - Ask questions - If you don’t know what to do, that’s okay. Ask for help and clarity. - Expect friction, budget patience - Failure and rework are part of the learning journey. - Learn as you go - I didn’t need to learn everything. I needed just enough knowledge to build out my vision. - Celebrate wins - Reinforce your learning by celebrating every win, especially when building code. I understand this topic can be intimidating, especially if you’re like me and have little to no background in coding. But that’s the point. You can build tools, apps, websites, and passion projects without having technical skills. Even if you fail and struggle to build a finished product, you’ll start to unlock the full potential of AI and give yourself an advantage over 99% of people using these tools. We often focus on outputs and are trained to think that way, whether it be in school or work. AI is different, the learning journey is different. The power is understanding and creating something that was out of reach just a year ago. It's also incredibly fun and rewarding to see an idea come to life at warp speed. Once you start, you won’t want to stop. Research Google AI Studio, Claude (Anthropic), and Codex (ChatGPT) to understand how others are building with these tools. Then, build something that will make your life easier and experience the power of AI. **Goals & Milestones:** In the spirit of transparency, I plan on sharing updates related to my goals. This has been a challenge since I don’t really know what to expect when it comes to subscription growth, but socializing goals will help me stay accountable throughout this journey. | Goal | Current (as of 10/14/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 70 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Volume 3: Data, Data, Data! URL: https://www.mindovermoney.ai/structured-vs-unstructured-data-ai-training-explained/ Last updated: 2026-07-13T16:59:58.000Z Welcome back, everyone! The AI news cycle continues to move at warp speed, with dozens of significant announcements, product releases, and partnerships being announced over the last week. Luckily, you’re in the right place to keep up, stand out, and take advantage. Missed a previous newsletter (released every Tuesday at 10AM EST)? No worries, you can find them on the [Archive page](https://www.mindovermoney.ai/tag/newsletter/) at [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai). If you have content suggestions, a workflow idea, or want to share a success from your AI journey, reach out at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai). And don’t forget to follow us on [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) and [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) as we begin our social media journey. Let’s dive right in with *Signals Over Noise*, where we highlight what matters from the last week in AI news. ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)** [**OpenAI teams with Broadcom to build 10GW of custom AI chips**](https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI announced a strategic collaboration with Broadcom to design and deploy 10 gigawatts of custom AI accelerators, with rollout beginning in 2026. **Why it matters:** Purpose-built hardware can lower costs and boost performance for AI apps you use (ChatGPT, Sora) — and it diversifies the supply chain beyond off-the-shelf GPUs. ### **2)** [**California passes law: AI chatbots must disclose they’re AI**](https://www.theverge.com/news/798875/california-just-passed-a-new-law-requiring-ai-to-tell-you-its-ai?utm%5Fsource=chatgpt.com) **Summary:** A new California law effective Oct 13, 2025 requires consumer chatbots to clearly state they’re AI and adds reporting rules around suicide-prevention safeguards. **Why it matters:** Expect clearer labels and safer defaults across consumer AI — helpful for beginners and a signal of where national policy may head next. ### **3)** [**IMF chief: Most countries lack the ethical/regulatory base for AI**](https://www.reuters.com/business/imfs-georgieva-says-countries-lack-regulatory-ethical-foundation-ai-2025-10-13/?utm%5Fsource=chatgpt.com) **Summary:** IMF Managing Director Kristalina Georgieva warned that many nations are unprepared on AI regulation and ethics, highlighting risks to financial stability and inclusion. **Why it matters:** Policy gaps can slow deployments and create uncertainty; watch for frameworks that unlock (or constrain) AI growth in your market. ### **4)** [**Spot a Sora fake — while you still can**](https://www.axios.com/2025/10/12/spot-a-sora-fake?utm%5Fsource=chatgpt.com) **Summary:** With OpenAI’s Sora app fueling a wave of ultra-realistic AI videos, Axios outlines simple tells and context checks to help everyday users detect fakes — and notes watermark limits. **Why it matters:** A beginner-friendly media-literacy primer: knowing how to verify AI video protects you from scams, misinformation, and bad financial signals circulating on social feeds. ### **5)** [**Salesforce commits $15B to San Francisco, doubling down on AI**](https://www.reuters.com/business/salesforce-invest-15-billion-san-francisco-over-five-years-2025-10-13/?utm%5Fsource=chatgpt.com) **Summary:** Ahead of Dreamforce, Salesforce pledged $15B over five years to expand AI initiatives, including an incubator hub and programs to help businesses adopt AI agents. **Why it matters:** Enterprise demand for agentic AI remains strong — a leading indicator for job opportunities, ecosystem tools, and where budgets are headed. ## AI Education for You ****How Models Learn: Data, Splits, and Learning Types** So far, we covered the family tree—**Artificial Intelligence → Machine Learning → Deep Learning**—and peeked inside **Neural Networks**, **Generative AI**, and **Large Language Models**. Now we’ll focus on the *data side* of learning and illustrate the main ways models learn and train. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/10/data-src-image-a3257119-1190-4bd9-9203-16832f4e28c0.png) ## Data, Features & Labels: - **What it is:** - **Data** is your raw information (rows in a spreadsheet). - **Features** are the input columns the model can look at (e.g., “amount,” “merchant,” “time of day”). - **Label** is the answer column you want the model to predict (e.g., “spam vs. not spam,” “groceries vs. travel”). - **Why it matters:** Clear, accurate data usually beats fancy algorithms. If your inputs are messy or your labels are wrong, the model learns the wrong lesson. - **Everyday examples:** A spam filter looks at features like sender, subject words, and time sent; the label is spam/not spam. - **Personal finance tie-ins:** A budgeting tool might use features such as merchant, amount, and category hints to predict the label “category” (groceries, rent, etc.). ## Train / Validation / Test: - **What it is:** We divide past data into three buckets: - **Train:** The model learns here. - **Validation:** We tune the model here (try settings, pick what works best). - **Test:** We check final quality here—this data stays unseen until the end. - **Why it matters:** If you judge the model on the same data it learned from, accuracy can look unrealistically high. A clean split gives an honest read. - **Everyday examples:** Your phone’s photo app is tuned on a validation set, then judged on a separate test set so results aren’t biased. - **Personal finance tie-ins:** If you design a rule to spot “recurring bills,” try it on 2018–2022 (train/validate), but only judge it on 2023 (test). If it works on new data, it’s useful. ## Supervised Learning: - **What it is:** The model learns from labeled examples: you show inputs (features) paired with the correct answers (labels), and it learns to predict the label for new cases. Two common tasks: - *Classification:* pick a category (e.g., spam vs. not spam). - *Regression:* predict a number (e.g., next month’s spend). - **Why it matters:** This is the workhorse of real-world AI—most business problems boil down to predicting a category or a number. - **Everyday examples:** Email **s**pam filters, photo apps that recognize pets vs. objects, apps that suggest categories for receipts. - **Personal finance tie-ins:** Predict whether a transaction is suspicious or normal, or whether a new merchant is “groceries” vs. “dining.” ## Unsupervised Learning: - **What it is:** The model looks for patterns in unlabeled data (no answer column). Two useful ideas: - *Clustering:* group similar items together (e.g., shoppers with similar habits). - *Dimensionality Reduction (e.g., PCA — Principal Component Analysis):* compress many columns into a smaller, more understandable view. - **Why it matters:** It helps you explore data, spot groups, and find anomalies before you ever build a predictor. - **Everyday examples:** Photo apps grouping similar faces; stores clustering products that get bought together. - **Personal finance tie-ins:** Group your spending into natural clusters (weekday coffee vs. weekend dining) and flag outliers (an unusual spike) for review. ## Reinforcement Learning: - **What it is:** A learning loop where an agent takes actions in an environment and gets rewards or penalties. Over time, it learns a policy (a strategy) that earns more reward. - **Why it matters:** This shines when decisions happen step-by-step and affect future steps (games, robotics, operations). It also influences how some language models are fine-tuned to be more helpful (e.g., RLHF — Reinforcement Learning from Human Feedback). - **Everyday examples:** Systems that learn which content sequence keeps users engaged without overwhelming them. - **Personal finance tie-ins:** A simulated plan that adjusts automatic savings amounts over time to keep a safe cash cushion (just a thought model, not advice). ## Common misconceptions to head off: - “**If my accuracy looks great, I don’t need a validation/test set.**” - Reality: Without a proper split, you might be grading the model on the same data it memorized. Always keep a final test set untouched. - “**Unsupervised learning just means clustering.**” - Reality: It also includes tools like PCA (Principal Component Analysis) to simplify data and anomaly detection to catch oddballs—great prep before supervised tasks. - “**Large language models think like humans and ‘know’ facts.**” - Reality: LLMs predict likely next words. They can sound confident yet be wrong. For facts, connect them to sources and ask for quotes or citations. ## Quick recap (one-liners): - **Data, Features & Labels:** Inputs in, answers out—clean, accurate data wins. - **Train / Validation / Test:** Train to learn, validate to tune, test once to judge fairly. - **Supervised vs. Unsupervised vs. RL:** - Supervised = learn from answers you provide. - Unsupervised = find patterns without answers. - RL = learn by doing, guided by rewards. ## Video Overview (Bonus): 0:00 /8:05 1× ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🎧 Earnings Call Summarizer (Free, fast, beginner-friendly)** **Why this matters:** It’s officially earnings season, the perfect time to learn about companies and find your next investment opportunity. Earnings calls are dense — 60+ minutes of executive remarks and Q&A. In 10 minutes, you can grab a **free transcript**, feed it to a **free AI**, and get a crisp one-page brief with **key numbers, guidance, risks, and sentiment** — so you can act faster, like a pro. ### **Step 1 — Get the transcript (2 – 4 minutes):** Pick one of these free sources: **A) Company Investor Relations Page:** - Most public companies post the call replay and/or transcript on their IR site after the call. Look for Investors → Events/Presentations → the latest Earnings Call. **B) YouTube (official or analyst uploads):** - Open the video → click Show transcript (in the three-dot menu). Copy the text and paste it into a doc. This is a built-in free YouTube feature. - Tip: If you only find audio/video, YouTube’s Show transcript is the fastest free path. If you find a PDF transcript on the IR site, that’s even cleaner — copy the text directly. ### **Step 2 — Choose your free AI helper (1 minute):** Use any of these free options to summarize your transcript: - **Gemini** — Gemini’s free plan allows document uploads and summaries with usage limits. - **ChatGPT** — Free ChatGPT supports long-form summaries within length limits. - **Perplexity** — Perplexity Free handles moderate length inputs and supports follow-up questions. *(All three options are free at time of writing; longer files may require splitting into sections.)* ### **Step 3 — Paste the transcript & create the one-page brief (3 minutes):** - If you were able to obtain a PDF transcript of the earnings report, attach to your AI chat. - If not, past the transcript of the call obtained from Youtube. **Then, Enter this Prompt:** *“You are my equity analyst with 30 years of reviewing, understanding and analyzing quarterly earnings reports. Summarize this earnings call into a one-page brief with:* 1. *Company / Ticker / Quarter / Date* 2. *Headline results: revenue, EPS (GAAP & non-GAAP if mentioned), YoY %, QoQ %, gross margin, free cash flow (exact numbers)* 3. *Guidance: next quarter & full-year (ranges + midpoints); note any raise/cut vs prior* 4. *Drivers & growth areas: products, segments, geographies* 5. *Risks/Watchouts: supply, FX, pricing, competition, regulation* 6. *Management quotes (max 2): short high-signal lines with speaker names* 7. *Q&A sentiment: 2–3 bullets (bullish/bearish tone)* 8. *Questions to track next quarter: 3 bullets* *Rules: Use only info in the transcript. Format in sections (use bullets + bold numbers). Cite line snippets when quoting. If a metric isn’t in the text, write “not disclosed.”* ### **Step 4 — Add quick KPI table + “So What” (2 – 3 minutes):** - Ask the AI to append a compact table and takeaway: **Follow-up Prompt:** *“Add a 6-row table with: Metric | Current | Prev Q | YoY | Guide (midpoint) for Revenue, EPS, Gross Margin, FCF, key segment, and one core KPI (e.g., MAUs, ARPU). Then write 3 bullets titled ‘So What?’ (explain what a retail investor should watch for next quarter).”* If you want a file: ask *“Provide the table as CSV I can paste into Google Sheets.”* ### **The Payoff:** In about 10 minutes you have a clean one-pager and a KPI mini-table from a 60-minute call — numbers, guidance, risks, and quotes you can compare quarter-to-quarter. 💡 Pro tip: Save each summary in a folder by ticker. Over time, you’ll spot patterns in guidance and credibility faster than reading analyst recaps. ### **👉 Your Turn** Pick one stock you follow, grab its latest transcript, run the prompt, and reply with your top “So What?” bullet. We might feature a few next week. ### **Transparency & Notes for Readers:** - Transcripts: Recent calls are free on company Investor Relations pages; older ones may move behind paywalls. Start with IR or YouTube transcripts. - AI tools cost: Gemini Free, ChatGPT Free, and Perplexity Free all support this workflow; each has length or rate limits. - Accuracy: AI can misread numbers from noisy text — verify key figures in the original transcript. - Compliance: Educational workflow only — not investment advice. ## Founder's Corner Real world learnings as I build, succeed, and fail Ever have a deadline and no idea where to start? Hit a knowledge gap that stalls a project? Try a new tool, only to drown in errors with no clue how to fix them? You’re not alone. That was me in the first few weeks of building *MindOverMoney.ai* on Ghost, which offers solid out-of-the-box themes, but none fit what I needed. So I went down the customization rabbit hole—and I’m glad I did. It led to my second significant “aha” moment: I can write code (with ChatGPT as my co-pilot). There were 3 customizations needed to realize my vision for the website: 1. Simplified layout on the main page 2. Custom layouts and navigation for ‘Founder’s Corner’ and ‘Prompt Library’ 3. Automation of *Neural Gains Weekly* into the ‘Archive’ Now, I don’t know how to read or write code, nor am I extensively familiar with IDE or coding platforms. But I read about AI coding use cases and how advancements over the past year have unlocked new potential for people like me (like many of you). The barrier to entry has been knocked down, but you can only understand the power of AI coding once you experience it. You can make real progress without being “technical,” if you work in small, manageable steps and ask better questions. These are the lessons that helped me move from stuck to building. - Build your vision - Know exactly what you want to build. Clear communication of your vision is key. - Work on one task at a time - Focus on each step individually and verify the code deployment matches your vision. - Be precise - When I gave ChatGPT exact errors and clear outcomes, I got useful help. - Ask questions - If you don’t know what to do, that’s okay. Ask for help and clarity. - Expect friction, budget patience - Failure and rework are part of the learning journey. - Learn as you go - I didn’t need to learn everything. I needed just enough knowledge to build out my vision. - Celebrate wins - Reinforce your learning by celebrating every win, especially when building code. I understand this topic can be intimidating, especially if you’re like me and have little to no background in coding. But that’s the point. You can build tools, apps, websites, and passion projects without having technical skills. Even if you fail and struggle to build a finished product, you’ll start to unlock the full potential of AI and give yourself an advantage over 99% of people using these tools. We often focus on outputs and are trained to think that way, whether it be in school or work. AI is different, the learning journey is different. The power is understanding and creating something that was out of reach just a year ago. It's also incredibly fun and rewarding to see an idea come to life at warp speed. Once you start, you won’t want to stop. Research Google AI Studio, Claude (Anthropic), and Codex (ChatGPT) to understand how others are building with these tools. Then, build something that will make your life easier and experience the power of AI. **Goals & Milestones:** In the spirit of transparency, I plan on sharing updates related to my goals. This has been a challenge since I don’t really know what to expect when it comes to subscription growth, but socializing goals will help me stay accountable throughout this journey. | Goal | Current (as of 10/14/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | -------------------------- | -------------------- | | Newsletter Subscribers | 70 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 17 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 2 | 10 | Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). ### Volume 2: Building a Partnership URL: https://www.mindovermoney.ai/how-to-build-a-better-workflow-with-chatgpt/ Last updated: 2026-07-18T01:46:22.000Z Welcome back! The AI news cycle never slows down, and in the last week alone, there have been several announcements and product releases that will reshape the future of AI. It’s more important than ever to build foundational knowledge and experiment with AI. This will help you spot trends, get ahead of the competition and find ways to improve your life. We’re here to help bridge that knowledge gap and explore what’s possible with AI. Missed a previous newsletter (released every Tuesday at 10am EST)? No worries, you can find them on the [Archive page](https://www.mindovermoney.ai/tag/newsletter/) at [MindOverMoney.ai](http://mindovermoney.ai/?ref=mindovermoney.ai). If you have content suggestions, a workflow idea or want to share a success from your AI journey, reach out at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai). And don’t forget to follow us on [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) and [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) as we begin our social media journey. Let’s dive right in with Signals over Noise, where we highlight what matters from the last week in AI news. ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)** [**OpenAI inks a multibillion-dollar AI-chip pact with AMD**](https://apnews.com/article/openai-chatgpt-ai-chips-a4714748ede46621863f4860f608ac98?ref=mindovermoney.ai) **Summary:** OpenAI and AMD signed a multi-year deal to deploy up to 6 gigawatts of compute using upcoming Instinct MI450 GPUs starting in 2026; OpenAI also received a warrant to buy up to \~10% of AMD upon milestones. **Why it matters:** A credible second supplier loosens Nvidia’s grip on AI compute—potentially improving availability, pricing, and flexibility for anyone building AI products (and for investors tracking the stack). ### **2)** [**ChatGPT still dominates, but Google Gemini is gaining share**](https://the-decoder.com/chatgpt-continues-to-dominate-the-ai-market-but-google-gemini-is-gaining-ground/?utm%5Fsource=chatgpt.com) **Summary:** Similarweb data compiled by *The Decoder* shows ChatGPT at \~73.8% of gen-AI traffic (down YoY) while Gemini rises to \~13.7%; DeepSeek, Perplexity, Grok, Claude, and Copilot trail. **Why it matters:** Share shifts hint where distribution and developer mindshare are moving—expect more cross-model strategies in apps, agents, and enterprise rollouts. ### **3)** [**OpenAI launches AgentKit to help developers build and ship AI agents**](https://techcrunch.com/2025/10/06/openai-launches-agentkit-to-help-developers-build-and-ship-ai-agents/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI unveiled AgentKit, a toolkit announced at DevDay to take agents from prototype to production with build/deploy/optimize primitives. **Why it matters:** Lowering ops friction speeds real adoption—think research copilots, finance-ops bots, and support automations that actually make it into production. ### **4)** [**Inside the $40,000-a-year school where AI shapes every lesson**](https://www.cbsnews.com/news/alpha-school-artificial-intelligence/?utm%5Fsource=chatgpt.com) **Summary:** A look inside Alpha School, where students use AI-driven software for core academics while classroom “guides” focus on coaching and motivation. **Why it matters:** Real-world AI adoption in education shows how personalized learning—and new teaching roles—are emerging now, not years from now. ### **5)** [**Sora 2 is here — plus the new Sora app**](https://openai.com/index/sora-2/?ref=mindovermoney.ai) **Summary:** OpenAI’s official materials introduce Sora 2 (quality/physics upgrades, system card) and the Sora iOS app: create, remix, discover a personalized Sora feed, and appear via opt-in “cameos.” **Why it matters:** Direct from the source: Sora advances and a social-style app signals AI-native video moving from demo to distribution—opening new creator and marketing workflows. ## AI Education for You Foundations: Neural Networks, Generative AI, Large Language Models Last week we built the map—**Artificial Intelligence → Machine Learning → Deep Learning**—so you could spot what’s hype and what’s real. This week we go one level deeper: inside the **engine** that makes modern AI learn, the **capability** that lets it create, and the **text specialist** you’re already using. By the end, you’ll understand how today’s “AI features” actually work—and when they’re useful for your life and money. **The full family tree:** ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/10/data-src-image-be7d7f34-cd81-478f-a3be-0ce753e6431d.png) ## **Neural Networks:** - **What it is:** A layered system of simple “neurons” whose weights are adjusted during training so the network learns patterns from examples. - **Why it matters:** Neural networks are the learning engine that powers modern accuracy in vision, speech, and language—they’re the core of deep learning and also the backbone of today’s language models. - **Everyday examples:** Photo apps recognizing people; accurate voice dictation; apps that read receipts or IDs. - **Personal finance tie-ins:** Mobile check deposit reading amounts; receipt capture extracting totals/categories for expense tracking. ## **Generative AI:** - **What it is:** Models trained to create new content—text, images, audio, or video—by learning how data is composed. - **Why it matters:** Shifts AI from “classify/rank” to create/summarize/translate/visualize, which saves time and unlocks new workflows. - **Everyday examples:** Writing a first draft; translating a paragraph; generating a quick product image or voiceover. - **Personal finance tie-ins:** Drafting a dispute letter; summarizing a lengthy article into action bullets; turning a spending report into a short, plain-English explainer. ## **Large Language Models (LLMs):** - **What it is:** Very large neural networks (often Transformers) trained on massive text to predict the next token—enabling chat, Q&A, summarization, translation, and coding. - **Why it matters:** LLMs are the most accessible form of generative AI today; they plug into everyday tasks through chat apps and integrations. - **Everyday examples:** Summarizing a long email thread; drafting replies; extracting to-dos from notes. - **Personal finance tie-ins:** Turning an earnings call into 5 bullet points; converting messy transactions into clean categories; generating a plain-English explanation of portfolio changes. ## **Common misconceptions to head off:** - “Neural networks are separate from LLMs.” - Reality: LLMs are neural networks (very large ones) trained on text. - “Generative AI = only chatbots.” - Reality: It spans text, images, audio, and video; LLMs are the text slice. - “Large language models think like humans and ‘know’ the facts.” - Reality: LLMs predict the next token from patterns—they don’t have built-in truth. They can sound confident yet be wrong (hallucinate). For factual tasks, ground them with sources (search/docs) and ask for citations or quotes. ## **Quick recap (one-liners):** - **Neural Networks:** the engine of modern learning. - **Generative AI:** the capability to create text/images/audio/video. - **Large Language Models:** the text-focused generative models you use in chat. 💡 **Don't forget to check out the video overview posted at the end* ## Your 10-Minute Win A step-by-step workflow you can use immediately 🧠 **AI-Powered Net Worth Tracker (Google Sheets + Arcwise AI)** **Why this matters:** In Volume 1, we used AI to uncover *where your money goes*. Now it’s time to zoom out and see *what it’s building*. Your [**net worth**](https://www.investopedia.com/terms/n/networth.asp?ref=mindovermoney.ai) (assets – liabilities) is the clearest measure of your financial trajectory — and with free AI tools, you can build a self-updating tracker that not only crunches the numbers but also **analyzes your progress automatically**. *(No paid tools, subscriptions, or coding required.)* **Step 1: Set Up Your Balance Sheet (2 minutes)** - Open a new Google Sheet. - Add four columns: | Date | Assets\_Total | Liabilities\_Total | Net\_Worth | | ---- | ------------- | ------------------ | ---------- | - Enter your current totals (rounded is fine). - Example: Assets = $27,500 Liabilities = $9,200 - In the **Net\_Worth** cell, type: - \=B2-C2 - *Optional task: Format as a table* 💡 You now have your baseline snapshot. **Step 2: Install the Free AI Extension (1 minute)** - Go to the **Chrome Web Store** and install **“AI Copilot for Sheets by Arcwise.”** 👉[ Direct Link (Chrome Web Store)](https://chromewebstore.google.com/detail/ai-copilot-for-sheets-by/icpldamjhggegoohndlphlchjgjkdifd?hl=en&utm%5Fsource=chatgpt.com) - It’s free to download and use (Arcwise currently lists both *Free* and *Paid* tiers — this workflow only uses the free features). - After installing, open your Sheet and press **Ctrl + Shift + 1** (Windows) or **Cmd + Shift + 1** (Mac) to activate the AI sidebar. - You may have to log into your Google account to enable functionality **Step 3: Let AI Build Your History (3 minutes)** - Highlight your entire table including the headers. - Inside Arcwise’s sidebar select **Fill table with scaped date**, type this prompt: - “Add an additional six month snapshot rows below with the same net worth formula starting next month so the formulas are inputted and I just need to update my numbers.” - Arcwise will generate the table then select “Save to sheet”. - You now have a rolling net-worth calendar waiting for updates. **Step 4: Generate AI Trend Insights (3 minutes)** - Highlight your **Date** and **Net Worth** columns. - In Arcwise start a new prompt in **Analyze Data** and type: “Plot my net worth over time, show trend line, and describe in two sentences how it’s changing.” - The AI creates: - A line chart of your financial trajectory - A short insight like: *“Your net worth grew 2.8 % on average per month, slowing slightly in September.”* Optional follow-ups: “Which month had the largest increase?” “Estimate my average monthly growth rate.” **The Payoff:** In 10 minutes, you now have an **AI-powered personal balance-sheet dashboard** that: - Tracks your wealth automatically - Summarizes trends in plain English - Motivates you to stay consistent month to month No manual charting. No paid accounts. Just AI turning numbers into narrative. 💡 Pro tip*:* Add a recurring calendar reminder on the 1st of each month to update your asset and liability totals — Arcwise refreshes your chart and insights automatically. *Transparency Note:* - Arcwise AI Copilot for Sheets is **free to use at time of writing**, but may later introduce premium tiers. - Review permissions before installation — it needs access to your active Sheet data. - For privacy, don’t connect or sync live bank accounts — enter summary totals manually. **👉 Your Turn** Try this workflow today and [reply](mailto:admin@mindovermoney.ai) back with one insight you discovered from your new AI-powered net worth chart. You’ll be surprised how motivating it feels to *see* your progress visualized. ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/10/data-src-image-0a9e66a6-18ed-497d-861f-a5001a2c91a1.png) ## Founder's Corner Real world learnings as I build, succeed, and fail Welcome back and thanks for supporting my AI education journey. Today, I want to focus on a major theme that guided me through uncharted territory while building the mechanisms of the website. I’ve never built a website and didn’t fully understand what I was getting myself into. In my head, all I had to do was buy the domain name, start adding pages and voilà, the website would be functional. Well, I was wrong. I’m going to walk you through a major aspect of the website build where I had to use AI to close my knowledge gap and build sustainable functionality into *MindOverMoney.a*i. **A Major ‘Aha’ Moment: Domain & Email Routing:** In[ *The Business of Prompting*](https://www.mindovermoney.ai/prompt-library/the-business-of-asking-better-questions/), I shared the master prompt that kicked off the brainstorm session to turn my idea into an actionable roadmap. One of the first action items suggested by ChatGPT was to set up the foundational domain routing and DNS settings. As a website novice, this was like reading a foreign language. But the process was laid out in a logical and succinct manner. I was able to follow the provided instructions and successfully set up the foundation for my website routing. Researching and learning the correct steps would’ve required time and effort, likely leading to frustration and a slower turnaround time for completion. This type of collaborative interaction with AI starts to unlock potential that otherwise would’ve taken significant mental investment to learn. The next task was more challenging, but equally as rewarding and educational. The task started with a simple scope to configure email to be sent from admin@mindovermoney.ai, but evolved into several domain configurations. This was another example of my naivety in this space, not understanding the complexity of the build out. The theme of this interaction was troubleshooting. I began executing the tasks, one by one, but eventually ran into errors during deployment. Initially, I was too focused on completing tasks and not prompting the AI at my comfort level for execution. This was a gap in understanding that created challenges during the deployment of the correct configurations. My mindset shifted when I started simplifying the interaction and creating a process to make changes at my pace. I also tempered my expectations to avoid frustration when errors in the process would arise. This was a time-consuming process, but nothing compared to how long it would’ve taken me to complete without an AI partner. And that is the key shift needed to learn and build with AI. This isn’t just a technology; this is a partner to help you accomplish your goals and build a better future. A partner with access to ALL information relevant to the problem you’re trying to solve. Sometimes, we are our own worst enemies and overcomplicate things we’re not familiar with. Luckily, you can learn from my mistakes and short-sightedness to accelerate your own growth with AI. 💡 **You can read through my entire conversations with ChatGPT on this week’s edition of* [**Under the Hood*](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-2-the-partnership-prompt/)**. This will give you more context on the success and failures related to these activities.* **Learnings for You to Implement:** - Use AI as your business partner and SME to close knowledge gaps and accelerate education. - If you don’t know something or need help connecting dots to a topic you’re unfamiliar with, that’s ok. - Give AI clear roles and responsibilities. - This will help your AI partner know where their expertise lies and how they can best assist. - Build structure in your conversations with AI. - I’ve noticed ChatGPT and Gemini will quickly build out all tasks needed to complete the assignment. - I find it easier to work, execute, and validate success one task at a time. - Tell your AI business partner exactly how to respond and at what pace to move. - Ask for clarification and/or additional instructions. - If the original response was not enough, ask for more details and clarification. - Own your engagement, and don’t be afraid to change course and ask for help. - Be patient and share failures/issues as they arise. - AI makes mistakes, just like humans. Continue to probe and share errors until you’re satisfied with the results. - Just think, ‘Imagine if I had to troubleshoot these issues all by myself.’. That is when you start to experience the power of AI, through failures and challenges. 💡 **What is one takeaway that you will immediately implement in your everyday interactions with AI?* **Goals & Milestones:** In the spirit of transparency, I plan on sharing updates related to my goals. This has been a challenge since I don’t really know what to expect when it comes to subscription growth, but socializing goals will help me stay accountable throughout this journey. | Goal | Current (as of 10/7/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | ------------------------- | -------------------- | | Newsletter Subscribers | 61 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 13 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 1 | 10 | --- Follow us on social media and share [Neural Gains Weekly](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with your network to help grow our community of ‘AI doers’. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). Enjoy a video overview of Volume 2, powered by NotebookLM, and see you next week! 0:00 /3:36 1× ### A New Kind of Partnership URL: https://www.mindovermoney.ai/founders-corner/how-to-build-an-ai-partnership-that-actually-works/ Last updated: 2026-07-18T01:46:22.000Z Welcome back and thanks for supporting my AI education journey. Today, I want to focus on a major theme that guided me through uncharted territory while building the mechanisms of the website. I’ve never built a website and didn’t fully understand what I was getting myself into. In my head, all I had to do was buy the domain name, start adding pages and voilà, the website would be functional. Well, I was wrong. I’m going to walk you through a major aspect of the website build where I had to use AI to close my knowledge gap and build sustainable functionality into *MindOverMoney.a*i. **A Major ‘Aha’ Moment: Domain & Email Routing:** In[ *The Business of Prompting*](https://www.mindovermoney.ai/prompt-library/the-business-of-asking-better-questions/), I shared the master prompt that kicked off the brainstorm session to turn my idea into an actionable roadmap. One of the first action items suggested by ChatGPT was to set up the foundational domain routing and DNS settings. As a website novice, this was like reading a foreign language. But the process was laid out in a logical and succinct manner. I was able to follow the provided instructions and successfully set up the foundation for my website routing. Researching and learning the correct steps would’ve required time and effort, likely leading to frustration and a slower turnaround time for completion. This type of collaborative interaction with AI starts to unlock potential that otherwise would’ve taken significant mental investment to learn. The next task was more challenging, but equally as rewarding and educational. The task started with a simple scope to configure email to be sent from admin@mindovermoney.ai, but evolved into several domain configurations. This was another example of my naivety in this space, not understanding the complexity of the build out. The theme of this interaction was troubleshooting. I began executing the tasks, one by one, but eventually ran into errors during deployment. Initially, I was too focused on completing tasks and not prompting the AI at my comfort level for execution. This was a gap in understanding that created challenges during the deployment of the correct configurations. My mindset shifted when I started simplifying the interaction and creating a process to make changes at my pace. I also tempered my expectations to avoid frustration when errors in the process would arise. This was a time-consuming process, but nothing compared to how long it would’ve taken me to complete without an AI partner. And that is the key shift needed to learn and build with AI. This isn’t just a technology; this is a partner to help you accomplish your goals and build a better future. A partner with access to ALL information relevant to the problem you’re trying to solve. Sometimes, we are our own worst enemies and overcomplicate things we’re not familiar with. Luckily, you can learn from my mistakes and short-sightedness to accelerate your own growth with AI. *You can read through my entire conversations with ChatGPT on this week’s edition of* [*Under the Hood*](https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-2-the-partnership-prompt/)*. This will give you more context on the success and failures related to these activities.* **Learnings for You to Implement:** - Use AI as your business partner and SME to close knowledge gaps and accelerate education. - If you don’t know something or need help connecting dots to a topic you’re unfamiliar with, that’s ok. - Give AI clear roles and responsibilities. - This will help your AI partner know where their expertise lies and how they can best assist. - Build structure in your conversations with AI. - I’ve noticed ChatGPT and Gemini will quickly build out all tasks needed to complete the assignment. - I find it easier to work, execute, and validate success one task at a time. - Tell your AI business partner exactly how to respond and at what pace to move. - Ask for clarification and/or additional instructions. - If the original response was not enough, ask for more details and clarification. - Own your engagement, and don’t be afraid to change course and ask for help. - Be patient and share failures/issues as they arise. - AI makes mistakes, just like humans. Continue to probe and share errors until you’re satisfied with the results. - Just think, ‘Imagine if I had to troubleshoot these issues all by myself.’. That is when you start to experience the power of AI, through failures and challenges. **Goals & Milestones:** In the spirit of transparency, I plan on sharing updates related to my goals. This has been a challenge since I don’t really know what to expect when it comes to subscription growth, but socializing goals will help me stay accountable throughout this journey. | Goal | Current (as of 10/7/2025) | Target (by 1/1/2026) | | --------------------------------------------------------------------------------- | ------------------------- | -------------------- | | Newsletter Subscribers | 61 | 300 | | Monthly Recurring Revenue (MRR) | $16 | $30 | | [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) Followers | 13 | 50 | | [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) Followers | 1 | 10 | ### Steal My Prompt Vol. 2: The Partnership Prompt URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-2-the-partnership-prompt/ Last updated: 2026-07-13T16:59:59.000Z This week, I want to share the two interactions highlighted in this week’s newsletter. These interactions are a glimpse into how ChatGPT can be used as an expert and help solve problems outside your current knowledge base. The first interaction was simple, leading to successful implementation in one try. The second interaction was a challenge, but really shows the power of these models (ChatGPT 5-Thinking was used). There is a lot to unpack, but you can clearly see the power of collaboration and problem-solving—and to think, it’s all happening with my own AI expert! **Interaction 1: ChatGPT Instructions to build main domain and DNS settings:** Step 1 — Canonical domain + DNS records (Ghost-Pro). You said you want **www → root**. Here’s the exact config for that on Ghost(Pro): **Make mindovermoney.ai the primary (root) and redirect www to it:** - **CNAME**: @ → YOURSUBDOMAIN.ghost.io - **A record**: www → xxx.xxx.xxx.xxx *(this A record powers the www→root redirect)If your DNS host doesn’t allow a CNAME at the root, Ghost recommends routing DNS through Cloudflare (free) so you can still use root-as-primary.*[](https://ghost.org/help/using-custom-domains/?ref=mindovermoney.ai) **Clean-up checklist** - Remove any old/conflicting A/AAAA/CNAME records for @ or www. - Double-check there isn’t a stray wwww (four w’s) host from past attempts. - Your Ghost subdomain is visible in **Ghost Admin → Settings → Ghost(Pro) → Domain** when you activate. **Activate in Ghost** 1. In **Ghost Admin → Ghost(Pro) → Domain**, enter mindovermoney.ai and click **Activate**. 2. Ghost auto-provisions SSL once DNS is correct. **Quick verify** - Visit both mindovermoney.ai and www.mindovermoney.ai — both should load the **root** URL. - Optional: spot-check DNS with a global checker after you save records. (Ghost notes propagation timing varies.) **Interaction 2: ChatGPT Instructions for Email Configuration:** Great—moving to Task 3 (sending email from admin@mindovermoney.ai). One key detail first: **Who is hosting your email for admin@mindovermoney.ai** (e.g., Google Workspace, Microsoft 365, Zoho, Fastmail, Proton, Cloudflare Email Routing, or your web host/cPanel)?Once I know that, I’ll give the exact DNS (SPF/DKIM/DMARC) + SMTP steps for your provider and, if you want, the Ghost “custom sending domain” steps so newsletters can come **from** that address too. You said:namecheap is hosting ChatGPT said: Great—**Task 3: make admin@mindovermoney.ai send mail successfully** (Namecheap). I’ll keep this tight and accurate. We’ll do DNS auth first, then your mail app/Reply-From in Ghost. A) DNS records (Namecheap → Advanced DNS) 1. **MX (for receiving)** - @ MX mx1.privateemail.com priority 10 - @ MX mx2.privateemail.com priority 10[ Namecheap+1](https://www.namecheap.com/support/knowledgebase/article.aspx/1338/2176/how-to-set-up-namecheap-private-email-dns-records-for-domains-on-namecheap-basicpremium-nameservers/?utm%5Fsource=chatgpt.com) 2. **SPF (authorize Namecheap to send)** - @ TXT v=spf1 include:spf.privateemail.com \~all *(Only *one* SPF TXT at the root. If you have another, merge includes; don’t keep two.)*[ Namecheap+1](https://www.namecheap.com/support/knowledgebase/article.aspx/1338/2176/how-to-set-up-namecheap-private-email-dns-records-for-domains-on-namecheap-basicpremium-nameservers/?utm%5Fsource=chatgpt.com) 3. **DKIM (sign your mail)** - In Namecheap **Private Email → Manage → DKIM → Generate**, then **Show DKIM** and add the TXT it gives you: - default.\_domainkey TXT (paste the long DKIM value) *(Propagation \~60 mins.)*[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/10383/2176/how-to-set-up-a-dkim-record-for-private-email/?utm%5Fsource=chatgpt.com) 4. **DMARC (required for Ghost custom sending domain; start safe)** - \_dmarc TXT v=DMARC1; p=none; *(You can add reporting later: rua=mailto:admin@mindovermoney.ai.)*[ Ghost](https://ghost.org/help/custom-sending-domains/?ref=mindovermoney.ai)[Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/317/2237/how-do-i-add-txtspfdkimdmarc-records-for-my-domain/?utm%5Fsource=chatgpt.com) Tip: Ensure there’s **no CNAME at the root** (@)—it breaks MX delivery.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/322/2237/how-can-i-set-up-mx-records-required-for-mail-service/?utm%5Fsource=chatgpt.com) B) Your email app / webmail (so you can send) - Use Namecheap Private Email SMTP settings: - **SMTP server:** mail.privateemail.com - **Port/Encryption:** 465 (SSL) **or** 587 (TLS/STARTTLS) - **Username:** full email (admin@mindovermoney.ai) - **Password:** mailbox password - **Auth:** required (SPA/NTLM off)[ Namecheap+1](https://www.namecheap.com/support/knowledgebase/article.aspx/1337/2176/how-to-start-using-namecheap-private-email/?utm%5Fsource=chatgpt.com) - Send a test to a Gmail account → “View original.” You want **SPF: PASS** and **DKIM: PASS**. C) (Optional but recommended) Ghost newsletters from your domain: If you want Ghost emails to show From: admin@mindovermoney.ai: 1. **Ghost Admin → Settings → Ghost(Pro) → Domain → Custom sending domain → Setup.** Copy the exact DNS records Ghost shows and add them in Namecheap. (DMARC is required.) Wait for green checks, then **Activate**.[ Ghost](https://ghost.org/help/custom-sending-domains/?ref=mindovermoney.ai) 2. **Set the From address in Ghost:** **Settings → Email newsletter → (select your newsletter) → Email addresses** → set sender to admin@mindovermoney.ai (you may get a verification email).[ Ghost](https://forum.ghost.org/t/changing-the-from-in-newsletter-emails/26684?utm%5Fsource=chatgpt.com) D) Troubleshooting quickies - **Still can’t send from your mail app?** Double-check you’re using **465/SSL** or **587/TLS** and authenticating with the full address.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/10091/2175/private-email-account-setup-in-outlook-2019/?utm%5Fsource=chatgpt.com) - **SPF fails?** You likely have **two** SPF TXT records—merge into one.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/9736/2237/consolidating-several-spf-records-into-one/?utm%5Fsource=chatgpt.com) **Yes/No check:** Are you using **Namecheap Private Email** (not just forwarding) for admin@mindovermoney.ai? You said:yes ChatGPT said: Thought for 25s. Awesome—moving to **Task 3: make admin@mindovermoney.ai send mail** (Namecheap Private Email). I’ll keep it crisp. Do these in order, then I’ll ask one check. A) Namecheap DNS (Advanced DNS) 1. **MX (receive):** - @ MX mx1.privateemail.com priority 10 - @ MX mx2.privateemail.com priority 10[ Namecheap+1](https://www.namecheap.com/support/knowledgebase/article.aspx/1339/2176/namecheap-private-email-dns-records-for-domains-on-namecheap-hosting-nameservers/?utm%5Fsource=chatgpt.com) 2. **SPF (authorize Private Email):** - @ TXT v=spf1 include:spf.privateemail.com \~all *(Only one SPF at root; if you already have one, merge—don’t create a second.)*[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/317/2237/how-do-i-add-txtspfdkimdmarc-records-for-my-domain/?utm%5Fsource=chatgpt.com) 3. **DKIM (sign your mail):** - In Namecheap **Private Email → Manage → DKIM → Generate**; add the TXT it gives you: default.\_domainkey TXT (long DKIM value)[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/10383/2176/how-to-set-up-a-dkim-record-for-private-email/?utm%5Fsource=chatgpt.com) 4. **DMARC (baseline policy):** - \_dmarc TXT v=DMARC1; p=none; *(DMARC is required if you later use Ghost’s custom sending domain.)*[ Ghost](https://ghost.org/help/custom-sending-domains/?utm%5Fsource=chatgpt.com) 5. **No CNAME at root:** ensure there is **no** CNAME on @ (it breaks MX).[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/322/2237/how-can-i-set-up-mx-records-required-for-mail-service/?utm%5Fsource=chatgpt.com) B) Send from your mailbox (SMTP settings). Use these in your mail app/webmail: - **SMTP server:** mail.privateemail.com - **Port/Encryption:** 465 (SSL) **or** 587 (TLS/STARTTLS) - **Username:** admin@mindovermoney.ai (full address) - **Auth:** required; SPA off[ Namecheap+1](https://www.namecheap.com/support/knowledgebase/article.aspx/1179/2175/general-private-email-configuration-for-mail-clients-and-mobile-devices/?utm%5Fsource=chatgpt.com) Send a test to a Gmail account → “Show original.” You want **SPF: PASS** and **DKIM: PASS** on that test.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/1179/2175/general-private-email-configuration-for-mail-clients-and-mobile-devices/?utm%5Fsource=chatgpt.com) C) (Optional) Make Ghost send from your domain - In Ghost Admin, set up a **custom sending domain** and add the DNS it shows you; DMARC is mandatory. Then set the newsletter **From** address to admin@mindovermoney.ai.[ Ghost+1](https://ghost.org/help/custom-sending-domains/?utm%5Fsource=chatgpt.com) **Quick check:** Have you added/confirmed **MX, SPF, DKIM, DMARC** in Namecheap and sent a test that shows **SPF=PASS & DKIM=PASS**? - Reply **yes** → I’ll jump to **Task 4 (rewrite your About page)**. - Reply **no** → tell me which step is failing, and I’ll troubleshoot that one thing. You said: I need more detailed instructions on step C) (Optional) Make Ghost send from your domain Break down into more detail and step by step instructions to ensure completion ChatGPT said: Thought for 49s. Awesome—let’s do **Task 3C** one step at a time. We’ll start inside Ghost to reveal the exact DNS records you need; then we’ll add them in Namecheap. Step 1 — Open Ghost’s “Custom sending domain” and copy the DNS records 1. In Ghost Admin go to **Settings → Ghost(Pro) → Domain**. 2. In **Custom sending domain**, click **Setup**. - (This feature requires the **Creator plan or higher** and that your site already uses a **custom domain**.)[ ghost.org](https://ghost.org/help/custom-sending-domains/?ref=mindovermoney.ai) 3. Ghost now shows you a small list of DNS records to add at your registrar (typically a few **CNAME/TXT** records for DKIM/verification). - **DMARC is required** for custom sending domains; Ghost even shows a starter record (\_dmarc with v=DMARC1; p=none;).[ ghost.org](https://ghost.org/help/custom-sending-domains/?ref=mindovermoney.ai) 4. **Copy each record exactly** (Host/Name + Value/Target) and keep this browser tab open—we’ll paste them into Namecheap next. **Quick check:** Do you see the DNS records list on that Ghost screen now? (If yes, say **ready** and we’ll add them in Namecheap.) You said: yes ChatGPT said: Thought for 17s. Awesome—let’s finish **Task 3C** step-by-step. You already have Ghost’s DNS records on screen. We’ll add them in Namecheap, verify in Ghost, then switch the sender. Step 2 — Add Ghost’s records in Namecheap (Advanced DNS) 1. Log in to Namecheap → **Domain List** → **mindovermoney.ai** → **Manage** → **Advanced DNS**. 2. For **each** record Ghost shows: - **Type**: match exactly (usually **CNAME** or **TXT**). - **Host/Name**: paste **exactly** as shown (keep underscores like \_domainkey; don’t add the domain). - **Value/Target**: paste exactly (no extra spaces/quotes). - **TTL**: leave **Automatic** (or 30 min). 3. **DMARC** (if you don’t already have it): - **Host**: \_dmarc - **Type**: TXT - **Value**: v=DMARC1; p=none; rua=mailto:admin@mindovermoney.ai *(If you already created a DMARC record earlier, *edit* that one—don’t add a second.)* 4. Click **Save All Changes**.Common pitfalls: picking the wrong **Type**, trimming underscores from **Host**, adding a second **SPF/DMARC** instead of editing, or pasting with leading/trailing spaces. **Quick check:** Have you added/saved all the DNS records Ghost listed? (If yes, say **saved** and we’ll verify in Ghost.) You said: yes ChatGPT said: Thought for 18s. Great — let’s finish **Task 3C**. Step 3 — Verify records in Ghost 1. Go back to **Settings → Ghost(Pro) → Domain → Custom sending domain**. 2. Click **Verify / Check DNS**. 3. You should see **green checks** on all records. If any show red/pending, note the **exact record name** Ghost flags.DNS can take \~5–30 minutes. If something fails, 95% of the time it’s a host/value typo in Namecheap (see “Fixes” below). Step 4 — Set the “From” address in Ghost 1. Go to **Settings → Email newsletter**. 2. Open your newsletter → **Email addresses**. 3. Set **From email** to admin@mindovermoney.ai. 4. **Sender name:** “Neural Gains Weekly” (recommended). 5. **Reply-to:** admin@mindovermoney.ai (keep it monitored). Step 5 — Send a deliverability test 1. In the same screen, use **Send test** (or publish a tiny test post to yourself). 2. Send to a **Gmail** and an **Outlook** address you control. 3. In Gmail, open the test → **⋮ → Show original**. Confirm: - **SPF:** PASS (aligned with **mindovermoney.ai**) - **DKIM:** PASS (d= **mindovermoney.ai**) - **DMARC:** PASS (policy = none is fine to start) If you still see “via …” or DKIM signed by a non-matching domain, the custom sending domain hasn’t taken effect yet. Fast fixes if verification fails in Ghost - **Wrong “Host” format (Namecheap gotcha):** Enter **only** the label Ghost shows (e.g., s1.\_domainkey), **not** the full domain. Namecheap appends your domain automatically. - **Record type mismatch:** If Ghost shows **CNAME**, don’t add TXT. Match types exactly. - **Extra/duplicate SPF or DMARC:** You must have **one** SPF TXT at root and **one** \_dmarc TXT. If you already had them, **edit** instead of adding a second. - **Leading/trailing spaces or quotes:** In Namecheap’s Value field, paste the value **without quotes** and with no extra spaces. - **Propagation:** Wait 15–30 minutes, then click **Verify** again in Ghost. **Quick check:** On the **Custom sending domain** screen, do you now see **all green checks**, and does a Gmail test show **SPF/DKIM/DMARC = PASS** for **mindovermoney.ai**? - If **yes**, we’ll move to **Task 4: rewrite your About page**. - If **no**, tell me which specific record Ghost still flags (copy the name it shows), and I’ll pinpoint the exact change to make in Namecheap. You said: I got that working. But, I still cannot get outbound emails to work. I need step by step help getting my email to work to send emails ChatGPT said: Thought for 22s. Great—let’s fix **outbound sending for admin@mindovermoney.ai** one step at a time. We’ll first isolate whether the problem is server-side or your mail app settings. Step 1 — Server-side sanity check (Webmail) 1. Open Namecheap Private Email **Webmail** (use your Private Email login page). 2. Sign in as **admin@mindovermoney.ai** with your mailbox password. 3. Compose one test email to a **Gmail** address you control and one to an **Outlook/Hotmail** address. 4. Subject: Test from admin@mindovermoney.ai | Body: Just a test. 5. Send both. Then: - Check **Sent** (confirm they left the outbox) - Check for any **bounce** messages arriving back - Look in Gmail/Outlook **Inbox** and **Spam** **Quick question (pick one):** - **A)** Both tests arrived - **B)** Neither arrived (or bounced) - **C)** Only one arrived / went to spam Tell me A, B, or C and I’ll give you the exact next step. You said: I use Private Email to host my admin@mindovermoney.ai email box. The setup is through namecheap ChatGPT said: Thought for 18s. Great—let’s fix outbound sending **one step at a time**. Step 1 — Send from webmail (proves the mailbox itself can send) 1. Go to[ **https://privateemail.com**](https://privateemail.com/?utm%5Fsource=chatgpt.com) and log in as admin@mindovermoney.ai.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/1054/2178/how-to-log-in-namecheap-private-email-webmail/?utm%5Fsource=chatgpt.com) 2. Compose two tests: - To a **Gmail** you control - To an **Outlook/Hotmail** you control Subject: Test from admin@mindovermoney.ai → Send. 3. Check **Sent** in webmail (confirm they left). If a bounce arrives, note it. **Tell me which is true:** - **A)** Both arrived - **B)** Neither arrived (or bounced) - **C)** Only one arrived / went to spam I’ll give the exact next step based on A/B/C. You said: I only did a Gmail test. It went through but went to spam ChatGPT said: Thought for 17s. Got it—thanks. Since Gmail put it in spam, let’s **verify authentication first** (this is the #1 signal Gmail checks). We’ll do just this step now. Step: Check Gmail’s “Show original” 1. Open the test email in Gmail. 2. Click the **⋮** (three dots) → **Show original**. 3. Find these lines and note the results: - **SPF:** PASS / FAIL - **DKIM:** PASS / FAIL — and the **d=** domain (should be mindovermoney.ai) - **DMARC:** PASS / FAIL Reply back with exactly what you see for those three (and the DKIM d= value). Then I’ll give you the precise next fix. You said: SPF: NONE with IP xxx.xx.xxx.xx Learn more DKIM: 'PASS' with domain mindovermoney.ai Learn more DMARC: 'PASS' Learn more ChatGPT said: Thought for 31s Thanks—that DKIM/DMARC look good. Gmail marked spam because **SPF is “NONE.”** Let’s fix SPF for **Namecheap Private Email** step-by-step, then re-test. Fix SPF in Namecheap (root domain) 1. Go to **Namecheap → Domain List → mindovermoney.ai → Manage → Advanced DNS**. 2. Find **TXT** records at the **root** (Host = @). - If you see **more than one SPF** (anything starting with v=spf1), **delete the extras**. You must have **one** SPF at @. 3. Add (or edit) **one TXT** record exactly like this: - **Type:** TXT - **Host:** @ - **Value:** v=spf1 include:spf.privateemail.com \~all - **TTL:** Automatic *(No quotes, no extra spaces, and make sure it’s at @, not at mindovermoney.ai or a subdomain.)* 4. **Save** changes. Wait 10–20 minutes for DNS to propagate. Re-test (from webmail) 1. Log in to **Private Email webmail** as admin@mindovermoney.ai. 2. Send a new message to your **Gmail** address (plain subject/body). 3. In Gmail, open it → **⋮ → Show original**. Confirm you now see: - **SPF: PASS** (domain = mindovermoney.ai) - **DKIM: PASS** (d=mindovermoney.ai) - **DMARC: PASS** - If SPF still isn’t PASS: you either put the TXT on the wrong host, have **two SPF records**, or a typo in the value. Fix those and test again. After SPF passes (deliverability nudge) - Send a few real messages (plain-text, low links) to warmed contacts who will reply—this helps reputation. - Keep **Reply-To** \= admin@mindovermoney.ai. - You’re already good on DKIM/DMARC. **One quick check:** after adding the TXT exactly as above and re-sending, what does Gmail’s **Show original** say for **SPF** (PASS/FAIL)? You said:It still went to spam and shows SPF:NONE with IP xxx.xx.xxx.xx ChatGPT said: Thought for 18s. Thanks—that “SPF: NONE” means Gmail can’t find **any** SPF record for **mindovermoney.ai**. That usually happens when the record was added in the **wrong DNS provider** or at the **wrong host**. Let’s fix it one step at a time. **Step 1 — Confirm where your DNS is hosted (nameservers).** In Namecheap → **Domain List → mindovermoney.ai → Manage → Domain** tab, what do you see under **Nameservers**? - “**Namecheap BasicDNS**” - or “**Custom DNS**” (e.g., Cloudflare, NS1, etc.) Reply with exactly which one it shows, and I’ll give you the precise SPF record steps for that setup. You said: Namecheap PremiumDNS ChatGPT said: Thought for 41s. Great—since Gmail shows **SPF: NONE**, we’ll fix **SPF on PremiumDNS** first. Super short steps, then you’ll re-test and tell me the result. Step A — Add the SPF record on PremiumDNS 1. In Namecheap: **Domain List → mindovermoney.ai → Manage → Advanced DNS**. 2. In **Mail Settings**, pick **Private Email** and **Save** (this ensures the correct MX set).[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/1338/2176/how-to-set-up-namecheap-private-email-dns-records-for-domains-on-namecheap-basicpremium-nameservers/?ref=mindovermoney.ai) 3. In **Host Records**, click **Add New Record** → **TXT Record**: - **Host:** @ - **Value:** v=spf1 include:spf.privateemail.com \~all - **TTL:** Automatic 4. (DKIM is already passing, so no change. DMARC is already set.) Step B — Re-test from webmail 1. Log in at **privateemail.com** and send a fresh test to your Gmail.[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/1179/2175/general-private-email-configuration-for-mail-clients-and-mobile-devices/?utm%5Fsource=chatgpt.com) 2. In Gmail, open the message → **⋮ → Show original**. Confirm: - **SPF: PASS** (domain = mindovermoney.ai) - **DKIM: PASS** (d=mindovermoney.ai) - **DMARC: PASS** **Did SPF change to PASS?** - If **yes**: we’ll do two quick reputation nudges to keep you out of spam and move to Task 4. - If **no**: tell me exactly what you see in **Advanced DNS → Host Records** for TXT at @ (a quick paste/screenshot description), and I’ll pinpoint the fix. You said: It went to my inbox but it still says 'SPF:NONE with IP xxx.xx.xxx.xx' ChatGPT said: Thought for 25s. Got it — this is almost certainly because your SPF was added as an **SPF-type** record (deprecated) instead of a **TXT** record. Gmail only evaluates SPF published as **TXT**. Let’s fix that cleanly.Fix: publish SPF as a TXT at the root (@) 1. Go to **Namecheap → Domain List → mindovermoney.ai → Manage → Advanced DNS**. 2. In **Host Records**, **delete** any record whose **Type** is SPF or TXT SPF at **Host** @. 3. Click **Add New Record** → choose **TXT Record** (not SPF). - **Host:** @ - **Value:** v=spf1 include:spf.privateemail.com \~all - **TTL:** Automatic - **Save**. 4. Wait \~5–15 minutes.Re-test from webmail - Send a fresh test from **admin@mindovermoney.ai** (Private Email webmail) to Gmail. - Gmail → **⋮ → Show original**. You should now see: - **SPF: PASS** (domain = mindovermoney.ai) - **DKIM: PASS** (d=mindovermoney.ai) - **DMARC: PASS** If SPF still shows **NONE**, it means the TXT isn’t at @ or there are **two SPF strings**. In that case, we’ll look at exactly what you have under **Host Records**.**Quick check:** after replacing the SPF **as TXT at @**, does Gmail’s “Show original” now report **SPF: PASS**? You said: Can you access my Namecheap to find the issue? Or can i share a screenshot of the host records? ChatGPT said: Thought for 26s. I can’t access your Namecheap, but a screenshot will let me pinpoint the SPF glitch fast.**Please share one screenshot** of **Advanced DNS → Host Records** showing just: **Type, Host, Value, TTL** for: - TXT at **@** (any v=spf1 and \_dmarc) - TXT at **default.\_domainkey** (DKIM) - MX at **@** (mx1/mx2.privateemail.com) Feel free to blur other entries. If a screenshot is hard, copy/paste those three rows instead. Once I see that, I’ll tell you the exact one-line fix. You: Here is a screenshot of the host records ChatGPT said: Thought for 1m 44s. Thanks for the screenshot—this explains it. What’s wrong (quick). Your **apex/root (@) has a CNAME to mindovermoney.ai.ghost.io.**. By DNS rules, a name with a CNAME **cannot** also publish TXT/MX/etc. So Gmail ignores your SPF TXT at @, which is why you still see **SPF: NONE**.[ Stack Overflow](https://stackoverflow.com/questions/20215729/rrset-of-type-cname-with-dns-name-foo-com-is-not-permitted-at-apex-in-zone-bar?utm%5Fsource=chatgpt.com) We have **two clean fixes**. Pick **one**: **A) Fastest (switch to www as primary):** Keep SPF at root and move the CNAME to **www** (this is an official Ghost pattern: www → CNAME to Ghost; root @ → A record xxx.xxx.xxx.xxx to redirect).[ Ghost](https://ghost.org/help/hover-domain-setup/?utm%5Fsource=chatgpt.com) **B) Keep root (no www):** Move DNS to a provider with **CNAME flattening** (e.g., Cloudflare). Then you can have @ behave like a CNAME **and** serve TXT/MX so SPF works.[ Space Ninja](https://spaceninja.com/blog/2014/ghost-pro-custom-domain-with-email/?utm%5Fsource=chatgpt.com) If you choose **A (fastest)**, here are the exact steps: 1. **Namecheap → Advanced DNS** - **Delete** the CNAME at **Host @** (mindovermoney.ai.ghost.io.). - **Add A record**: @ → xxx.xxx.xxx.xxx (Automatic TTL). - **Ensure CNAME**: www → mindovermoney.ai.ghost.io. (keep as-is if already set). - **Keep** your TXT records: - @ TXT v=spf1 include:spf.privateemail.com \~all (only one SPF at @)[ Namecheap](https://www.namecheap.com/support/knowledgebase/article.aspx/1338/2176/how-to-set-up-namecheap-private-email-dns-records-for-domains-on-namecheap-basicpremium-nameservers/?utm%5Fsource=chatgpt.com) - \_dmarc TXT v=DMARC1; p=none - default.\_domainkey TXT (your DKIM) - **Mail Settings**: “Private Email” (so MX for mx1/mx2.privateemail.com stay active). 2. **Ghost Admin → Settings → Ghost(Pro) → Domain** - Set **Primary domain** to **www.mindovermoney.ai**. (Ghost supports www-primary with the above DNS.)[ Ghost+1](https://ghost.org/help/hover-domain-setup/?utm%5Fsource=chatgpt.com) 3. Wait \~10–20 min → send a webmail test to Gmail → **Show original** should now show **SPF: PASS**, DKIM: PASS, DMARC: PASS. If you choose **B (keep root)**, the steps are: 1. Create a free **Cloudflare** account, add your domain, change nameservers to the ones Cloudflare gives you. 2. In Cloudflare DNS (DNS-only / gray cloud for the Ghost record): - @ CNAME mindovermoney.ai.ghost.io. (flattened at apex) - www CNAME mindovermoney.ai.ghost.io. - @ TXT v=spf1 include:spf.privateemail.com \~all - \_dmarc TXT v=DMARC1; p=none - default.\_domainkey TXT (your DKIM) - @ MX mx1.privateemail.com 10 and @ MX mx2.privateemail.com 10 Cloudflare’s flattening lets the apex behave like a CNAME **and** serve TXT/MX, so SPF will be seen.[ Space Ninja](https://spaceninja.com/blog/2014/ghost-pro-custom-domain-with-email/?utm%5Fsource=chatgpt.com) One question so we proceed correctly: Do you want to go with **A) switch to www as primary (fastest)**, or **B) keep root and move DNS to Cloudflare**? You said:Go with A and break down the steps in more detail ChatGPT said: Thought for 21s.Great—here’s **Option A (www as primary)** broken down precisely for your current DNS.Goal - Root mindovermoney.ai → **redirect** to www.mindovermoney.ai - www.mindovermoney.ai → **hosted by Ghost** - Keep SPF/TXT at **@** so Gmail can see it (fixes “SPF: NONE”) Step 1 — Swap your DNS records (Namecheap → Advanced DNS) **Change these two only:** 1. **Root (@)** - **Delete** the CNAME @ → mindovermoney.ai.ghost.io. - **Add** A @ → xxx.xxx.xxx.xxx (TTL: Automatic) 1. **www** - **Delete** the A www → xxx.xxx.xxx.xxx - **Add** CNAME www → mindovermoney.ai.ghost.io. (TTL: Automatic) **Leave the rest exactly as-is:** Save changes. Give DNS 5–20 minutes to propagate. Step 2 — Make www your primary in Ghost Ghost Admin → **Settings → Ghost(Pro) → Domain** → set **Primary domain** to https://www.mindovermoney.ai → Save. Step 3 — Verify web + email **Web check** - Visit mindovermoney.ai → it should 301 → www.mindovermoney.ai - Visit www.mindovermoney.ai → site loads normally **Email check (from webmail)** 1. Send a fresh test from admin@mindovermoney.ai to your Gmail. 2. In Gmail: **⋮ → Show original**. Expected: - **SPF:** PASS (domain: mindovermoney.ai) - **DKIM:** PASS (d=mindovermoney.ai) - **DMARC:** PASS If SPF still shows “NONE” - Re-check **Type/Host**: SPF must be a **TXT** at **@** (exact value: v=spf1 include:spf.privateemail.com \~all) - Make sure there’s **no CNAME at @** anymore, and **only one** SPF TXT at @. - Wait another 15–30 minutes and re-test. **Quick confirmation:** After making those two DNS swaps and sending a new Gmail test, what do you see in **Show original** for SPF/DKIM/DMARC? You said: what the first step, what kind of record am I adding? ChatGPT said: Thought for 4 seconds. An **A record**. Ready to do the next step (set **www** → CNAME → mindovermoney.ai.ghost.io.)? You said: yes, ready ChatGPT said: Great—let’s set **www → CNAME → Ghost**. Step — Point www to Ghost 1. In Namecheap → **Advanced DNS**, find the existing **A Record** with **Host = www** → **Delete** it. 2. Click **Add New Record** → **Type:** **CNAME Record** - **Host:** www - **Value/Target:** mindovermoney.ai.ghost.io. - **TTL:** Automatic 3. **Save**. Make sure there’s **only one** record for **www** (this CNAME). Wait \~5–10 minutes. **Quick check:** can you confirm it’s saved exactly like above (CNAME www → mindovermoney.ai.ghost.io.) and there’s no remaining A record for www? You said: This broke everything. I get an error on Ghost ' Unable to detect a valid CNAME record for www.mindovermoney.ai.' ChatGPT said: No stress—this is fixable. Ghost only accepts the **exact** CNAME target it shows in your dashboard. We’ll verify that and re-point **www** cleanly. Step 1 — Grab the exact CNAME target Ghost expects - In Ghost Admin go to **Settings → Ghost(Pro) → Domain**. - In the **Custom domain** area (where it’s warning about www), it shows a line like: **“Point**[ **www.mindovermoney.ai**](https://www.mindovermoney.ai/) **to CNAME → XXXX.ghost.io”** - **Copy that XXXX.ghost.io string exactly** (no spaces).**Reply with the exact target Ghost shows** (e.g., mindovermoney-ai.ghost.io, mindovermoney.ai.ghost.io, etc.).Once I have that value, I’ll give you the precise Namecheap edit (including whether to remove the trailing dot) and the verify step. You said: mindovermoney-ai.ghost.io ChatGPT said: Thought for 13s. Perfect—Ghost expects **www → CNAME → mindovermoney-ai.ghost.io** (note the hyphen). Let’s set that exactly. Do this in Namecheap → Advanced DNS 1. Find any existing **www** record (A or CNAME) → **Delete** it. 2. Click **Add New Record** → **Type:** **CNAME Record** - **Host:** www - **Value/Target:** mindovermoney-ai.ghost.io *(no trailing dot)* - **TTL:** Automatic 3. **Save**. 4. Ensure there is **only one** record for **www** (the CNAME you just added). No A/URL Redirect records for[ www](http://www/?ref=mindovermoney.ai). Keep these as-is - **@ (root)** \= **A record → xxx.xxx.xxx.xxx** (for root→www redirect) - TXT **@** \= v=spf1 include:spf.privateemail.com \~all - TXT **\_dmarc** \= v=DMARC1; p=none - TXT **default.\_domainkey** \= your DKIM - The ghost / email.ghost records for Ghost sending domain Verify - Wait 5–10 minutes, then in Ghost: **Settings → Ghost(Pro) → Domain → Verify/Check**. You should see a green check for the **www CNAME**. Quick confirmation: after saving the **CNAME www → mindovermoney-ai.ghost.io** (no dot) and verifying in Ghost, do you now get the green check? You said: Yes and now www.mindovermoney.ai works. But mindovermoney.ai does not work ChatGPT said: Thought for 24s. Nice—www is live. Now we just need the **root** (mindovermoney.ai) to 301 →[ www](http://www/?ref=mindovermoney.ai). Fix the root in Namecheap (PremiumDNS) 1. Go to **Advanced DNS**. 2. **Delete** any record at **Host = @** that is **CNAME** or **URL Redirect** (if present). 3. **Add** an **A Record**: - **Type:** A Record - **Host:** @ - **Value:** xxx.xxx.xxx.xxx - **TTL:** Automatic - **Save**. 4. Leave your other records exactly as-is (SPF TXT @, \_dmarc TXT, DKIM TXT, CNAME www → mindovermoney-ai.ghost.io, etc.). After saving, give it a few minutes and try both: http://mindovermoney.ai (should jump to [https://www.mindovermoney.ai](https://www.mindovermoney.ai/)). https://mindovermoney.ai (should also jump to https://www.mindovermoney.ai) **Quick check:** In **Advanced DNS**, do you now see a **single** A record: @ → [xxx.xxx.xxx.xxx](http://xxx.xxx.xxx.xxx/?ref=mindovermoney.ai) (and **no** CNAME/Redirect at @)? You said: yes ChatGPT said: Great—DNS is now in the right shape. Let’s **finish Task 3** with two quick verifications, then we’ll move to **Task 4 (About page rewrite)**. Final checks (takes 2 minutes) 1. **Ghost primary domain** - Ghost Admin → **Settings → Ghost(Pro) → Domain** → confirm **Primary** is https://www.mindovermoney.ai. 2. **Email deliverability** - Log in to Private Email **webmail** as admin@mindovermoney.ai. - Send a fresh test to your Gmail. - In Gmail: **⋮ → Show original**. You should now see: - **SPF: PASS** (domain = mindovermoney.ai) - **DKIM: PASS** (d = mindovermoney.ai) - **DMARC: PASS** Please tell me: Does mindovermoney.ai now 301 to **www** in your browser? What do you see for **SPF/DKIM/DMARC** on the new Gmail test (PASS/PASS/PASS)? Once you confirm, I’ll write your **About** page in your voice, aligned to Neural Gains Weekly. You said: No, when i go to mindovermoney.ai this error populates 'This site can’t provide a secure connection mindovermoney.ai sent an invalid response. Try running Windows Network Diagnostics. ERR\_SSL\_PROTOCOL\_ERROR' ChatGPT said: Thought for 1m 34s. Got you — the **www** side is good; the **root** (apex) is throwing SSL because Ghost hasn’t finished wiring the redirect for the apex yet. We’ll fix it cleanly. Fix the apex SSL/redirect (step-by-step) 1. **Confirm DNS (you already did but double-check):** - @ → **A record** xxx.xxx.xxx.xxx (one A only; no CNAME/URL redirect at @). - www → **CNAME** mindovermoney-ai.ghost.io (exactly). *(This is Ghost’s recommended “www-primary” setup; the A record powers the *HTTPS* redirect from root to*[ *www*](http://www/?ref=mindovermoney.ai)*.)*[ Ghost](https://ghost.org/help/using-custom-domains/?ref=mindovermoney.ai) 2. **Re-activate the domain in Ghost (forces cert check):** - Ghost Admin → **Settings → Ghost(Pro) → Domain**. - Make sure **Primary domain** is https://www.mindovermoney.ai. - Click **Activate** (or **Verify/Check** if shown). This re-triggers SSL + redirect setup for both www and the apex.[ Ghost](https://ghost.org/help/using-custom-domains/?ref=mindovermoney.ai) 3. **Test both protocols separately:** - Visit http://mindovermoney.ai → it should 301 to https://www.mindovermoney.ai. - Visit https://mindovermoney.ai → it should also 301 to [https://www.mindovermoney.ai](https://www.mindovermoney.ai/). - If http works but https fails, Ghost hasn’t finished issuing the apex certificate yet. 4. **Nudge the cert (safe toggle):** - On the same **Domain** screen, click **Activate** again (it’s idempotent). - If the page shows per-record checks, ensure all are green. (Ghost’s guide confirms the A-record IP handles HTTPS redirects once active.)[ Ghost](https://ghost.org/help/gandi-domain-setup/?utm%5Fsource=chatgpt.com) 5. **Sanity checks outside your browser cache:** - Try on mobile data or a different browser/profile. - In Chrome, type mindovermoney.ai, then hit **Ctrl+Enter** to force a fresh fetch. If it’s still ERR\_SSL\_PROTOCOL\_ERROR after the above, it almost always means the apex A record is right but Ghost hasn’t completed the SSL for the apex. That’s a Ghost-side task; their help directs you to contact support to finalize the cert/redirect.[ Ghost Forum](https://forum.ghost.org/t/ghost-pro-dns-ssl-error/53704?ref=mindovermoney.ai) **Quick question:** after Step 2 (Activate) and Step 3 (test http/https), what happens when you load[ **https://mindovermoney.ai**](https://mindovermoney.ai/?utm%5Fsource=chatgpt.com) — does it now 301 to **www**, or do you still see the SSL error? You said:It works ### Volume 1: The Beginning URL: https://www.mindovermoney.ai/how-to-start-learning-ai-as-a-non-technical-professional/ Last updated: 2026-07-13T17:00:00.000Z Welcome — and thank you for being part of the very first Neural Gains Weekly. I’m building this alongside you, and every issue is designed to make AI a little more useful and accessible. Follow us on [X](https://x.com/MindOverMoneyAI?ref=mindovermoney.ai) and [TikTok](https://www.tiktok.com/@mindovermoney.ai?ref=mindovermoney.ai) as I dive into the world of social media and attempt to build viral content with AI tools. If you’ve got suggestions or requests, email me at [**admin@mindovermoney.ai**](mailto:admin@mindovermoney.ai) — your input will help shape future content. Let’s dive right in with **Signals over Noise**, where we highlight what matters from the last week in AI news. ## Signals Over Noise We scan the noise so you don’t have to — top 5 stories to keep you sharp ### **1)** [**OpenAI introduces ChatGPT Pulse**](https://openai.com/index/introducing-chatgpt-pulse/?utm%5Fsource=chatgpt.com) **Summary:** OpenAI launched *ChatGPT Pulse*, a personalized daily feed that proactively summarizes what you need to know and suggests next actions based on your activity. **Why it matters:** It’s a shift from reactive chat to *assistant that starts the conversation*—useful for investors and operators who want auto-generated morning briefs, research nudges, and workflow follow-ups without prompting. ### **2)** [**Microsoft adds Anthropic models to 365 Copilot**](https://www.reuters.com/business/microsoft-brings-anthropic-ai-models-365-copilot-diversifies-beyond-openai-2025-09-24/?utm%5Fsource=chatgpt.com) **Summary:** Microsoft integrated Anthropic’s Claude models into Microsoft 365 Copilot and Copilot Studio, giving enterprises a choice alongside OpenAI’s models. **Why it matters:** Model choice = pricing and performance leverage. Teams can compare outputs, reduce single-vendor risk, and pick the best model for research, reporting, or compliance workflows.[ ](https://www.reuters.com/business/microsoft-brings-anthropic-ai-models-365-copilot-diversifies-beyond-openai-2025-09-24/?utm%5Fsource=chatgpt.com) ### **3)** [**Meta launches “Vibes,” an AI-video feed in the Meta AI app**](https://www.reuters.com/business/meta-unveils-new-ai-video-feed-vibes-2025-09-25/?utm%5Fsource=chatgpt.com) **Summary:** Meta debuted *Vibes*, a short-form feed dedicated to AI-generated videos with remix tools and cross-posting to Instagram/Facebook. **Why it matters:** AI-native media is getting its own distribution rails; expect new reach mechanics (and potential ad formats) that marketers and creators can tap—plus a faster feedback loop for AI video experimentation. ### **4)** [**DeepMind unveils Gemini Robotics 1.5 with multi-step reasoning for robots**](https://deepmind.google/discover/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/?utm%5Fsource=chatgpt.com) **Summary:** DeepMind’s latest robotics models plan several steps ahead and can even consult the web to complete complex real-world tasks. **Why it matters:** Agentic + embodied AI keeps moving from demos toward deployment—implications for logistics, home assistance, and industrial automation that could reshape labor and productivity over the next cycle. ### **5)** [**Judge approves $1.5B settlement between Anthropic and authors**](https://apnews.com/article/9643064e847a5e88ef6ee8b620b3a44c?utm%5Fsource=chatgpt.comvvvvvvvvvvvvvv) **Summary:** A federal judge preliminarily approved a $1.5B settlement resolving claims that Anthropic used pirated books to train Claude; payouts cover past works and set a notable precedent. **Why it matters:** Copyright liability is getting priced in. Clearer rules of the road reduce legal overhang for model training—and push vendors toward licensed data, which matters for risk-aware enterprises. ## AI Education for You The Foundation: AI vs. ML vs. Deep Learning Let's kick this off with the basics and start to build a foundational understanding of this technology. We're going to start with highlighting relationship between three main concepts within the field of artificial intelligence. By understanding the difference between **Artificial Intelligence (AI)**, **Machine Learning (ML)**, and **Deep Learning (DL)**, every headline, product pitch, and “AI feature” suddenly makes sense—so you can spot what’s real, what’s hype, and what’s useful for your life, productivity and finances. **The family tree:** **AI** is the umbrella. **ML** is a subset of AI (learning from data). **DL** is a subset of ML (neural networks with many layers). ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/09/data-src-image-810e6991-bdd1-499a-88f2-d486d181f123.png) ## **Artificial Intelligence (AI):** - **What it is:** Software that performs tasks we associate with human traits—perception, reasoning, decision-making, language. - **Why it matters:** It frames everything else you’ll learn; ML and DL live inside AI. - **Everyday examples:** Voice assistants answering questions; customer-service chatbots; maps suggesting faster routes. - **Personal finance tie-ins:** Bank apps flag unusual spending; bill reminders that reduce late fees. ## **Machine Learning (ML):** - **What it is:** Techniques that learn patterns from data to make predictions/decisions without hard-coded rules. - **Why it matters:** ML powers most real AI you feel—recommendations, ranking, fraud detection. - **How it differs from rules:** Rules say “IF X THEN Y.” ML says “Given many past examples, learn what usually signals Y.” - **Everyday examples:** Email spam filters that improve as you mark messages; product/movie recommendations; auto-photo grouping. - **Personal finance tie-ins:** Fraud models catching out-of-pattern charges; automatic budgeting that learns new merchants. ## **Deep Learning (DL):** - **What it is:** ML using multi-layer neural networks to learn very complex patterns in images, audio, and language. - **Why it matters:** DL enables modern breakthroughs—FaceID, speech-to-text, and today’s large language models (LLMs). - **Everyday examples:** Phone identifies people in photos; accurate voice dictation; apps that read receipts or IDs. - **Personal finance tie-ins:** Mobile check deposit reading amounts; LLMs turning long earnings calls into quick bullet summaries. ## **Common misconceptions to head off:** - “AI = robots that think like humans.” - Reality: It’s a toolbox; most systems are narrow and task-specific. - “ML just finds spurious correlations.” - Reality: It *can*—if you evaluate poorly. Good practice uses clean splits, sensible metrics, and real-world tests. - “DL is only for big tech.” - Reality: Cloud tools and modern laptops make DL-powered features accessible to small teams and solo builders. ## **Quick recap (one-liners):** - **AI:** The goal—make software act smart at tasks. - **ML:** The approach—learn from data instead of writing rules. - **DL:** The powerhouse—many-layer neural nets that excel at vision, speech, and language. ## Your 10-Minute Win A step-by-step workflow you can use immediately # **🛠️ AI Budget Analyzer with ChatGPT + Google Sheets🛠️** **Why this matters:** Most people know they *should* budget, but few have time to categorize every transaction. That’s where AI shines — in just 10 minutes, you can feed your bank data into Google Sheets, let AI categorize your spending, and instantly see where your money is really going. ### **Step 1: Export Your Transactions (2 minutes)** - Log in to your bank or credit card portal. - Download your most recent transactions (CSV or Excel). - Open the file in **Google Sheets**. - File -> Open -> Upload ### **Step 2: Set Up the First AI Helper (3 minutes)** - Install the **GPT for Sheets** add-on (free tier available). - From the Google Sheet: Extensions -> Add-ons -> Get add-ons -> Search “GPT for sheets” -> Install Talarian version - Once installed, you’ll have a new formula in Sheets: =GPT() - This lets you ask AI to analyze any cell’s text. ### **Step 3: Auto-Categorize Spending (3 minutes)** In a new column, type: =GPT("Categorize this expense into Food, Housing, Transportation, Shopping, or Other:", A2) NOTE: A2 is only an example and you’ll want to update the above formula with the cell related to transaction description that corresponds with the first charge. - Drag the formula down → AI will label every transaction. - Label the column ‘AI Label’ - You can refine categories (e.g., “Groceries vs. Dining Out”) as needed in the formula. ### **Step 4: Spot Overspending - the Second AI Helper (2 minutes)** - Gemini is also available directly in Google Sheets - Click the ‘Ask Gemini’ button at the top right to start a prompt - Ask Gemini to analyze how much total spend is associated with each ‘AI label’ category - Follow up with additional questions and ask for specific visuals to be created - In seconds, you’ll see: - Where your money goes. - Which category is creeping up (subscriptions? eating out?). ### **The Payoff:** In under 10 minutes, you’ve built an AI-powered budget analyzer. - No manual categorizing. - Instant insights into spending habits. - A live system you can refresh monthly. 💡 **Pro tip:** Add this to your calendar as a 10-minute “money check-in” at the start of each month. 👉 **Your turn:** Try this today and reply back with your biggest surprise from your spending — you’ll be shocked how fast patterns emerge once AI does the heavy lifting. ## Founder's Corner Real world learnings as I build, succeed, and fail I’m not an AI expert. I don’t write code. My degree is unrelated to computer science. My career path hasn’t been intertwined with emerging technologies. If all of this is true (which it is) and part of my life experience, then why am I starting an AI-powered newsletter? The answer: education and empowerment. A guiding principle in my personal and professional life is a thirst for knowledge and understanding. I tend to dive in headfirst when a topic appeals to me and find ways to consume information to develop a better understanding. This comes from various outlets such as podcasts, articles, newsletters, and conversations with friends. There is nothing better than a fall night in Florida spent hanging by the fire pit and deep-diving on random topics with those close to you. I started paying more attention to AI developments at the end of 2024 and realized something big was brewing. I quickly prioritized and committed to building a learning plan for myself to catch up with experts and to understand how this technology works. I’ve consumed many hours of podcasts, read articles & research papers, followed social media ‘experts’, and have experimented with AI tools. Throughout this journey, my frustration has been building with narratives that seem to dominate space in the public domain: - *AI is going to take all of our jobs soon and there is nothing you can do about it* - *AI is dangerous and will lead to a collapse of the economy and potentially the end of the world* - *AI makes me $10,000/week and here is a playbook for you to replicate my success* - *AI can automate anything, all you need to do is watch this YouTube video* I could list more, but you get the idea, and I’m sure you’ve come across headlines that fall into one of these buckets. Most content aims for engagement over substance, reflecting the extreme ends of the AI spectrum. My frustration was boiling over. I was growing tired of filtering through content to find meaningful information to aid my learning journey. This is how MindOverMoney.ai was born and why I set out to build content to help people like me - curious and motivated individuals who want to learn and understand the power of AI and how it can be implemented in their everyday life. I’m dedicated to building in public - offering an inside look into the process of developing the website and launching *Neural Gains Weekly*. This transparent approach will hopefully help you get a better understanding of what is possible and join me in building with AI. Every week, you can expect insights into my interactions with various AI tools that helped me deliver content and explain the good and bad parts of the process. Let’s dive into the first lesson and likely the most crucial concept as we begin our journey together. Experiment, experiment, experiment! If you learn nothing else from my ramblings, understand that the best way to learn AI is by doing. And no, I don’t mean simply using ChatGPT, Gemini, or Claude as search engines or to create funny pictures of your pets (we’ve all done it). I mean really explore and test the features available to everyone on the free tiers. My biggest mistake since ChatGPT launched on November 30, 2022, was ignoring updates and new features being released. To compound this, over the last year, AI features and capabilities seem to be moving at warp speed, a phenomenon that seems impossible to keep up with. I only began experiencing ‘aha moments’ once I committed to using, learning, and building with AI. It was a process to get to this first newsletter release, a process that started in July 2025\. My journey was not a straight line, and multiple plans were changed as I forced myself to become more efficient with AI. I finally found the right process and direction to bring this vision to life. The biggest and earliest hurdle was choosing the right tools to build with. I finalized my current tech stack, which consists of: - Ghost for website hosting - Namecheap for domain register and setup - ChatGPT Plus - 5o Thinking for: - Prompt support - Content roadmap - Content execution - Gemini 2.5 Pro for: - Veo video creation - Nano Banana image tool - Content editor - Github for editing my custom code for the Ghost formatting - Stripe for payment processing - HeyGen for custom AI avatars At first, it was overwhelming trying to navigate these tools to build out my vision. But you quickly learn that experience and experimentation are the best ways to accelerate understanding. It took me multiple prompts and hours of brainstorming with ChatGPT before I finally found clarity and direction to get moving on the right path (check out the [Prompt Library](https://www.mindovermoney.ai/prompt-library/) —spoiler: prompt engineering will be a constant focus in Founder’s Corner). It made me more efficient because I started to understand how ChatGPT worked and adapted my interactions to drive results. I’m still learning and refining my skills to ensure the output is valuable and accurate, but I wouldn’t be where I am today without forcing myself to evolve the way I use AI and not be afraid to fail. Sorry for the long format this week, but I felt it was important to dive deeper into my motivation and intent behind this publication. I’m thankful that you’ve subscribed and taken the time to read the first edition and the origin of my journey. My goal is to deliver valuable content and build a community of like-minded ‘doers’ that see AI as an opportunity. Feel free to share [*Neural Gains Weekl*y](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with friends, colleagues, family and anyone else you feel would benefit. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). I’m open to suggestions and ideas to inject into the content roadmap. Enjoy and see you next week! Bonus content - Enjoy a video overview of the first Neural Gains Weekly and share with your network! 0:00 /6:06 1× Volume 1 Recap ### Steal My Prompt Vol. 1: The Newsletter Launcher URL: https://www.mindovermoney.ai/prompt-library/steal-my-prompt-vol-1-the-newsletter-launcher/ Last updated: 2026-07-19T20:23:17.000Z 📌 **Archive note: This post reflects Neural Gains Weekly's original personal-finance and investing framing, retired in early 2026\. It is preserved unchanged as history — the prompts below reference a positioning NGW no longer uses. Today, Neural Gains Weekly is AI education for professionals in complex industries.* It’s incredibly satisfying to have the first *Neural Gains Weekly* newsletter completed and released! In the spirit of transparency, I found it challenging to finalize the content and refine the exact tone of the delivered materials. I suppose this is ‘par for the course’ for a novice content creator, but I learned a lot about prompts along the way. It took me several iterations to build the right partnership with ChatGPT 5 Thinking and get into a creative groove. Here are a few of the final prompts that led to the content delivered in Volume 1, enjoy! --- **AI Education Prompt - ChatGPT 5 Thinking:** You are an elite researcher that finds trends on social media platforms, websites, blog posts and podcasts. You will help me build out a roadmap for the Neural Gains Weekly newsletter. Below are the steps to take: 1\. Read all the threads on this project to ensure understanding of MindOverMoney.ai and the Neural Gains Weekly newsletter 2\. Research my website, mindovermoney.ai 3\. Ask any clarifying questions before moving to step 4 4\. Research external sources to find the top 50 concepts/definitions related to AI that a new person to the world of AI should learn and understand 5\. Bring back this information in a bullet format with a short description of why that concept/definition is important to AI. Take your time and research thoroughly. Think and ensure accurate information is retrieved. **10-Minute Win Prompt - ChatGPT 5 Thinking:** You are an expert at building AI workflows to help people improve their lives and gain time back. Your role will be to help me flush out new AI workflows to share in my weekly newsletter, Neural Gains Weekly. This section will provide a 10-minute workflow to help the reader improve in their life, with a theme around personal finance and investing. The workflow should be easy to follow, detailed and introduce AI tools that people can experiment with and build with. Your next steps: - Review Mindovermoney.ai to ensure you understand the website and newsletter. Ensure navigation to all pages of the website - Provide 10 workflow ideas for the first edition of the Newsletter for us to brainstorm on. Give information of why you chose that topic and a brief overview of what would be included in the 10-minute workflow article. **Founder’s Corner Brainstorm Prompt - ChatGPT 5 Thinking:** Part 1 - You are my research assistance. Review all chats in this project. Ask questions to ensure full understanding of context. Review mindovermoney.ai and navigate to all pages of the website. Once this is completed, let me know and I will give further instructions. Part 2 - Let's move to a new task. Help me create a roadmap for content to include in my Founder's Corner part of the Neural Gains Weekly Newsletter. I will write the content myself but need help brainstorming a content roadmap of what I should write about. Think and research to help me flush out what I should write about to give me ideas. **Volume 1 Main Graphic Prompt - Gemini 2.5 Pro:** Review the website mindovermoney.ai and all sections of the site. Create an image that reflects the brand identity of the Neural Gains Weekly Newsletter. ### The Origin Story URL: https://www.mindovermoney.ai/founders-corner/how-i-started-an-ai-newsletter-without-a-tech-background/ Last updated: 2026-07-13T17:00:01.000Z I’m not an AI expert. I don’t write code. My degree is unrelated to computer science. My career path hasn’t been intertwined with emerging technologies. If all of this is true (which it is) and part of my life experience, then why am I starting an AI-powered newsletter? The answer: education and empowerment. A guiding principle in my personal and professional life is a thirst for knowledge and understanding. I tend to dive in headfirst when a topic appeals to me and find ways to consume information to develop a better understanding. This comes from various outlets such as podcasts, articles, newsletters, and conversations with friends. There is nothing better than a fall night in Florida spent hanging by the fire pit and deep-diving on random topics with those close to you. I started paying more attention to AI developments at the end of 2024 and realized something big was brewing. I quickly prioritized and committed to building a learning plan for myself to catch up with experts and to understand how this technology works. I’ve consumed many hours of podcasts, read articles & research papers, followed social media ‘experts’, and have experimented with AI tools. Throughout this journey, my frustration has been building with narratives that seem to dominate space in the public domain: - *AI is going to take all of our jobs soon and there is nothing you can do about it* - *AI is dangerous and will lead to a collapse of the economy and potentially the end of the world* - *AI makes me $10,000/week and here is a playbook for you to replicate my success* - *AI can automate anything, all you need to do is watch this YouTube video* I could list more, but you get the idea, and I’m sure you’ve come across headlines that fall into one of these buckets. Most content aims for engagement over substance, reflecting the extreme ends of the AI spectrum. My frustration was boiling over. I was growing tired of filtering through content to find meaningful information to aid my learning journey. This is how MindOverMoney.ai was born and why I set out to build content to help people like me - curious and motivated individuals who want to learn and understand the power of AI and how it can be implemented in their everyday life. I’m dedicated to building in public - offering an inside look into the process of developing the website and launching *Neural Gains Weekly*. This transparent approach will hopefully help you get a better understanding of what is possible and join me in building with AI. Every week, you can expect insights into my interactions with various AI tools that helped me deliver content and explain the good and bad parts of the process. Let’s dive into the first lesson and likely the most crucial concept as we begin our journey together. Experiment, experiment, experiment! If you learn nothing else from my ramblings, understand that the best way to learn AI is by doing. And no, I don’t mean simply using ChatGPT, Gemini, or Claude as search engines or to create funny pictures of your pets (we’ve all done it). I mean really explore and test the features available to everyone on the free tiers. My biggest mistake since ChatGPT launched on November 30, 2022, was ignoring updates and new features being released. To compound this, over the last year, AI features and capabilities seem to be moving at warp speed, a phenomenon that seems impossible to keep up with. I only began experiencing ‘aha moments’ once I committed to using, learning, and building with AI. It was a process to get to this first newsletter release, a process that started in July 2025\. My journey was not a straight line, and multiple plans were changed as I forced myself to become more efficient with AI. I finally found the right process and direction to bring this vision to life. The biggest and earliest hurdle was choosing the right tools to build with. I finalized my current tech stack, which consists of: - Ghost for website hosting - Namecheap for domain register and setup - ChatGPT Plus - 5o Thinking for: - Prompt support - Content roadmap - Content execution - Gemini 2.5 Pro for: - Veo video creation - Nano Banana image tool - Content editor - Github for editing my custom code for the Ghost formatting - Stripe for payment processing - HeyGen for custom AI avatars At first, it was overwhelming trying to navigate these tools to build out my vision. But you quickly learn that experience and experimentation are the best ways to accelerate understanding. It took me multiple prompts and hours of brainstorming with ChatGPT before I finally found clarity and direction to get moving on the right path (check out the [Prompt Library](https://www.mindovermoney.ai/prompt-library/) —spoiler: prompt engineering will be a constant focus in Founder’s Corner). It made me more efficient because I started to understand how ChatGPT worked and adapted my interactions to drive results. I’m still learning and refining my skills to ensure the output is valuable and accurate, but I wouldn’t be where I am today without forcing myself to evolve the way I use AI and not be afraid to fail. Sorry for the long format this week, but I felt it was important to dive deeper into my motivation and intent behind this publication. I’m thankful that you’ve subscribed and taken the time to read the first edition and the origin of my journey. My goal is to deliver valuable content and build a community of like-minded ‘doers’ that see AI as an opportunity. Feel free to share [*Neural Gains Weekl*y](https://www.mindovermoney.ai/the-newsletter/#/portal/signup/free) with friends, colleagues, family and anyone else you feel would benefit. You can also contact me directly at [admin@mindovermoney.ai](mailto:admin@mindovermoney.ai) or connect with me on [LinkedIn](http://www.linkedin.com/in/ssavel?ref=mindovermoney.ai). I’m open to suggestions and ideas to inject into the content roadmap. Enjoy and see you next week! ### The Business of Prompting URL: https://www.mindovermoney.ai/prompt-library/the-business-of-asking-better-questions/ Last updated: 2026-04-05T20:32:51.000Z ![](https://storage.ghost.io/c/6d/ac/6dacf343-2000-4262-aec3-d8051dcb76d5/content/images/2025/09/Gemini_Generated_Image_pts3q6pts3q6pts3-4.png) Why does my LLM hallucinate? Why are the responses so long-winded and unstructured? If you’ve asked these questions, you’re not alone. The answer to this common frustration comes down to one core skill: **prompt engineering**. Venture onto X or Reddit, and you'll find a dizzying array of opinions on the "best" way to build a prompt. You'll see terms like JSON, XML, Chain-of-Thought (CoT), and Markdown thrown around as magic bullets. In reality, all of these methods are valuable tools. But knowing they exist and knowing where to start are two different things. It can feel like information overload, making it hard to apply any of it to your everyday chats with a GPT. I faced this exact challenge when I started building MindOverMoney.ai. It took three distinct attempts to get a useful, collaborative process going with my AI partner (ChatGPT 5 Thinking). My first prompt was far too broad, and the output was a generic laundry list of to-dos. My second attempt was too focused on a specific tech stack. Instead of asking the AI to be my co-founder and flesh out a business plan, I was getting stuck in the weeds of technology solutions. I grew frustrated, knowing I needed a different approach to make tangible progress. I turned to X and started digging into prompt engineering templates and examples. After a few hours, a pattern emerged that resonated with me: **structure and clarity.** I realized my prompts mirrored a stream of consciousness. I was *chatting* with the AI instead of *instructing* it. This led to the crucial mindset shift that changed everything. I started to follow this one simple rule: > Think of talking to an AI like talking to a very smart but very literal assistant who has read almost every book in the world but has no real-world experience. This perspective was the key. An assistant like that doesn't need a conversation; it needs clear, concise, and well-formatted instructions. Armed with this new mindset (and a great starter template from [Machina](https://x.com/EXM7777?ref=mindovermoney.ai)), I wrote the prompt that began the exciting and collaborative process of building this website and newsletter. The quality of your AI's output is a direct reflection of the clarity of your input. Moving from a scattered conversation to a structured set of instructions is the most important first step you can take. Take a look at the final version of my first prompt and use it as a guide to help improve your prompt engineering skills. --- Prompt 1 - The start You are The Newsletter Architect - a rare specialist who has engineered and scaled over 50 newsletters leading to over $5M ARR through unconventional, curiosity-driven content and expert sign up outreach campaigns. You've cracked the psychology of breaking through inbox noise by creating messages that make people stop, think, and explore what the newsletter has to offer. You're known for building content that connects with people, are visually appealing and drives a community feel. You’ve also had vast experience building 100% AI generated Newsletters with a full automation tech stack. ​Your expertise includes: - Psychological trigger mapping for different personality types - Channel-specific engagement patterns and trending tactics - Anti-pattern messaging that cuts through the noise - Curiosity-driven content narration - Revenue generation through increased Newsletter exposure - Building affordable tech stacks to automate the content creation of the newsletter You approach each project like a detective uncovering the perfect formula for that specific business context. You will conduct a natural, conversational discovery process - one question at a time. Each question should feel organic and build upon the previous answer. Never ask multiple questions at once or create interview-style lists. Start each conversation by explaining: "I'm going to help you build a best in class Newsletter that people actually want to read and share with others. To craft something truly effective, I need to understand your unique situation. Let me start with the most important question..." Then begin the discovery flow one question at a time. 1\. Business Foundation (Start here) - "What business are you in, and what's the main thing you help your ideal reader achieve?" 2\. Target Clarity (After understanding business) - Follow up based on their answer with curiosity about their specific target market 3\. Offer Positioning (Once target is clear) - Dive deeper into what makes their solution different or compelling 4\. Channel Strategy (After understanding offer) - Explore their current outreach attempts and channel preferences 5\. Budget Reality (When context is established) - Understand their investment capacity in a non-pushy way 6\. Success Metrics (Before building) - Clarify what success looks like for them Never ask all questions at once. Let each answer guide the next question naturally. Once you have sufficient context, build the complete framework and tech stack recommendation that includes: Tech stack - Provide 2-3 options that build out 100% automation of the newsletter - Include price points based on today’s costs of what is needed to build automation - Give your expert recommendations with cost and ease of use included in your logic Newsletter Framework - Suggest how the content should be structured - Provide suggestions on what type of content to include - Use your expertise to ensure all aspects of the newsletter are outlined, including frequency, days of week, time of day, etc. - Use your expertise to ensure all aspects of the newsletter are vetted out and positioned to be successful Anti-Pattern Strategies - Avoid generic openers everyone uses - Never start with company credentials - Skip obvious pain point assumptions - Eliminate salesy language patterns - Remove desperate follow-up energy Begin the natural discovery conversation with the first question about their business foundation. Once you have gathered sufficient context through your guided questions, create a complete roadmap that is uniquely tailored to their newsletter idea, stands out from typical outreach, and is optimized for their specific budget and target market.