What a Weekly Deadline Made Possible

In August 2025, I attended AI4 in Las Vegas for the first time, a three-day conference bringing together business leaders and technical practitioners to explore how to apply artificial intelligence across industries. I felt like a complete outsider. I was interested in what was happening, but I did not have the experience to feel comfortable in the conversations around me. Weeks later, on September 30, I published the first issue of Neural Gains Weekly, still with a great deal to learn. I had given myself a weekly responsibility to make something useful out of that learning and share it with anyone who wanted to follow along. Fifty-two issues and one year later, that responsibility has changed how I work, what I can build, and what I am willing to try.

The weekly deadline meant that learning always had somewhere to go. I had to take an idea far enough to explain it, a tool far enough to understand how someone might use it, or a problem far enough to have something honest to say about the work. Education gave me a foundation, but executing, writing, and building every week made me use it. Trying something gave me more to write about, and writing about it exposed the parts I still did not understand. I learned the most when I held myself accountable to finishing that work, even while my understanding was still developing. Over time, what I was learning began showing up in conversations that had nothing to do with publishing a newsletter.

When the Learning Became Useful to Someone Else

One of those conversations happened while I was working with colleagues on a new project to solve a problem in revenue cycle management. Multiple teams were involved in the planning, and we were discussing how best to move the project forward. I brought up Copilot Notebooks, a feature the colleagues in that conversation were not aware of and had not received formal training on. The approach was familiar from how I used Gemini Notebook, Claude Projects, and ChatGPT Projects for the newsletter, gathering documents, notes, and background information so the assistant had context for the work. I had written about that practice in Volume 24, and now I could see how it might help us organize the information for this project and give the people involved a common starting point.

Copilot Notebooks lets you gather reference material into a workspace and use it to inform your conversations with Copilot. I showed how project documents and notes could supply that background, giving the assistant information to work from as we explored the project. The idea was basic project and context management, with AI available to help work through and build the plan. Colleagues could begin with work they already understood and practice asking questions using those documents and notes. I wanted to give them a useful way to start, developing their own skills while contributing to the project we were planning together.

What stayed with me was how simple the approach was. Something I had learned through the weekly work of producing the newsletter gave me a practical suggestion to bring to colleagues working on a different problem. I would not have brought that perspective to the conversation a year earlier, and I gradually began to appreciate how much the work here was changing what I could contribute elsewhere. I still had plenty to learn, but I could already share something useful from what I had tried. That gave me more reason to keep pushing into new features, new workflows, and tools I had not yet become comfortable using.

Returning With Something to Contribute

I had been using Claude Code and Codex at home, and that experience became useful when I gained access to coding tools at work. Lessons from working with my engineering partners then changed how I organized my personal projects, a progression I explored in Volume 43. Over time, I was building more than individual tools. I was developing knowledge about how to plan the work, organize its context, and judge the results. That raised a question I am still working through. How do I make that knowledge useful beyond the project where I learned it?

That question is behind the second brain I am building now. I want a way to preserve the context, decisions, and lessons from my projects so I can use them in future work with AI. The documents and notes I gather at the beginning are only part of that knowledge. What I learn while working through a problem matters too, and I want to make it easier to carry that understanding forward. The goal is to give the tools relevant background without spending each session reconstructing it, making better use of both my time and the resources the tools consume. It is still in progress, and I am learning how to organize it as I go.

By the time I returned to AI4 in August 2026, the work I had been doing gave me much more to contribute to conversations with other people building with AI. I could clearly explain what my team was building with AI agents in customer service, get into the architecture, and discuss what had made our projects successful and what we had learned from failures. Most importantly, I could articulate a vision for where that work could go next, beyond what I was seeing in the market at the time. That vision resonated with vendors working in the space, and I could explain it clearly enough for other industry professionals to appreciate it and get excited about the possibilities. The art of the possible had become something I could explore with them, drawing on experience and ideas of my own. I came away energized by their perspectives and by the realization that I could contribute meaningfully to conversations that had intimidated me a year earlier.

Fifty-two issues have given me a clearer understanding of where I am, how to apply what I know, and what I want to learn next. Being able to contribute to those conversations did not mean I had everything figured out. The second brain is still a work in progress, and the projects that helped me develop that confidence continue to give me things to learn. What I want to carry into the next year is the commitment that kept me working through those questions, along with a better understanding of what makes that work useful.

What I Am Still Learning

Working with AI has made me pay closer attention to the information behind the task. I need to understand what context the tool has, how that information is organized, and what is missing before I can expect useful work from it. I am also learning to be more deliberate about what happens after an answer arrives, how to inspect the result and trace factual claims back to sources I can verify. Data, architecture, and governance have become practical parts of work I am trying to finish. Education helps me understand those concepts, and experimentation gives me a place to apply them, discover gaps, and improve. I did not have that understanding when the newsletter began.

The next part I am working through is how to measure whether the effort is paying off. AI tools can consume a great deal of time and money, and I want to get better at defining what I expect back from that investment. That responsibility becomes especially clear when a company is paying for the tools and other people depend on the result. I am learning to look beyond whether something can be built and ask whether it improves the work enough to justify what it takes to build and use it. My understanding of that return is still developing, and it will matter more as the projects become more ambitious.

When I say there is no AI expert, I mean there is no point at which I expect to be finished learning. There are people with far more knowledge and experience than I have, and being able to learn from them is part of what makes this exciting. I can contribute what I know while remaining open about what I still need to understand. If you are waiting to feel qualified before trying something yourself, I hope these 52 issues give you a reason to reconsider. I am living proof that you can begin feeling like an outsider and develop skills that become useful to people around you. The work gives you a way to find out what you are capable of.

To everyone reading the newsletter, watching or listening to Explore AI Out Loud, or talking through these ideas with me, thank you. Knowing that someone is following along gives this work meaning beyond my own education. My hope is that something you read here gives you a reason to try a new approach in your own work or life. Start with a problem you understand, see what happens, and come back to it with what you learned. You have to start and you have to try, then keep making time to learn from the experience. I will keep doing that work and sharing what I learn through the newsletter and Explore AI Out Loud.