Volume 2: Building a Partnership

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 at 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. And don’t forget to follow us on X and TikTok 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.

1) OpenAI inks a multibillion-dollar AI-chip pact with AMD

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

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

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

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

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.

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:

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.

🧠 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 (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:

• 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)
  • 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 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.

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.ai. 

A Major ‘Aha’ Moment: Domain & Email Routing:

In The Business of Prompting, 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. 

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.

Follow us on social media and share Neural Gains Weekly with your network to help grow our community of ‘AI doers’. You can also contact me directly at admin@mindovermoney.ai or connect with me on LinkedIn. Enjoy a video overview of Volume 2, powered by NotebookLM, and see you next week!