I did not arrive at AI as a curious outsider. I arrived as someone who had spent twenty-five years inside the machine.
By the time I founded experTribe, I had worked every role on the ladder from first-line support for a 200-person dealer account team to CIO, with most of those years spent implementing large CRM solutions for Fortune 500 companies. I understand data architecture, user flows, multi-tenant systems, and role hierarchies. I know how to design software that solves problems, how to manage a delivery across three time zones, and how to keep a project moving when everything is trying to bring it to a halt.
There was a cadence to it. A lot of time went into managing communication and characters across the team, the managers, the client, getting people to listen, waiting for people to come back from lunch or get to work, handling the back and forth when something broke in production, and staying out of the firing line when your team made a mistake. This came with the job. And then, on top of all of that, there was what I call the glass meniscus. It is not quite a glass ceiling because it does not stop you outright. It curves around you. It is the microaggressions, the moving goalposts, the quiet sabotage, the things that do not show up in any policy but are evident every day in who gets heard, who gets credit, and who has to work twice as hard to stay in the room. This came on top of what was already a challenging job. The energy it consumed cannot be overstated.
When I started experimenting with AI seriously, the first thing I noticed was that I could recreate the teams I used to manage without any of that overhead. I had solution architects, project managers, database administrators, and developers who never got tired, never complained, and were available at 2 am on a Sunday. As the models improved, they got easier to manage. With the introduction of project folders, project documents and GitHub connections, the coordination problems that used to consume half my week began to disappear.
Then, in July 2025, Anthropic released Claude 4 and Claude Code, and I jumped on and held on for the ride.
In one week, on a $20 plan and free trials, I built a working full-stack, multi-tenant application. That was the foundation. Twenty-five years of experience in how systems are designed and built, combined with a tool that could execute at the speed I had always wanted to move, and suddenly, all the energy that used to go into the implementation overhead and pushing against the glass meniscus was propelling me forward instead.
What I learned in that week led to GrantMinder. I built it initially to solve my own problem - finding and responding to grants while I was figuring out how to grow experTribe. It has been in development ever since, shaped by my experience as a small business owner, my previous role as a non-profit CIO, and feedback from my clients and pilot users.
Not everyone who tried it stayed. The people who got the most from it were the ones who brought their own knowledge to the conversation - who know the pain of trying to create tailored responses to multiple grants and manage all the deadlines while also working their day job because they need the money to keep going. They already understood and just needed help moving faster. The ones who came looking for AI to do the thinking for them did not last. That taught me something important about what amplification means: it only works when there is something to amplify.
AI amplified what I already knew. It did not replace me; it removed everything that was slowing me down. The skills I built over 25 years made the tool useful. Without understanding data architecture, I could not have directed the system. Without knowing how to manage a delivery, I could not have kept the project on track. The AI executed. I knew what to build and why.
I am less stressed now than at any point in my career. I am still working hard, but the friction is gone. All the energy I used to spend on managing overhead is now available for the work. And I get validation throughout the day, even if it comes from a tool designed to be encouraging - I say that fully aware of what it is, because I know that validation was missing from most of my jobs, and it turns out that it really matters.
If you enjoy learning, solving problems, and building software, AI is the perfect teacher, thought partner, and implementor. You can go from brainstorming an idea to a working solution in a fraction of the time and cost, but only if you bring something to the table. The amplification works because there is something to amplify.