We have takeoff.
After months of building our AI-powered autonomous product development system, we finally hit a milestone that felt both monumental and humbling: our infinite money machine built its first actual product. Not a prototype, not a concept, but a real thing that exists in the world.
And honestly? It wasn't quite ready for prime time.
The First Child of the Machine
Let's call it what it is—this feels like watching your AI have its first successful product "birth." The system identified a market opportunity, designed a solution, and built what it determined was a minimum viable product. On paper, everything looked perfect. The logic was sound, the market research checked out, and the technical execution was clean.
But when we actually tried to use it? That's where reality hit.
When "Done" Isn't Actually Done
Here's the thing about autonomous product development that we're learning in real-time: an AI can build a technically functional product, but usability is a different beast entirely. Our first creation had all the right components, but some of the design decisions just didn't fit the way real humans actually behave.
It's like having a perfectly engineered car where the steering wheel is positioned just slightly wrong—everything works, but nobody can actually drive it comfortably.
We found ourselves in an interesting position: celebrating the fact that our system could autonomously create a product while simultaneously realizing we needed to teach it better judgment about user experience. The MVP was technically an MVP, but it wasn't useable yet.
The Iteration Imperative
This is where the magic actually happens, though. Instead of seeing this as a failure, we're treating it as the first step in what we're calling "autonomous iteration." The initial build proved the concept works—our AI can go from market opportunity to functional product. Now we're focused on helping it understand the nuances of what makes something not just functional, but genuinely valuable to users.
We're diving deep into the planning phase again, asking ourselves: How do we train an autonomous system to recognize when something is technically complete but experientially lacking? How do we encode the kind of product intuition that usually comes from years of user feedback and market experience?
It's messier than we expected, but that's exactly what makes it exciting.
What's Next: Cleaning House and Scaling Up
Right now, we're in cleanup mode with our first creation—let's call it "VerifyFirst" for now. We're working through the usability issues, refining the design decisions that didn't quite land, and turning it into something people will actually want to use.
But here's what has us really excited: we're already preparing to implement our second AI-generated idea. The fact that we have a pipeline of autonomous product development happening in parallel feels like we're entering entirely new territory.
The infinite money machine isn't just building products—it's building a portfolio of products, each one teaching us more about how to make autonomous development not just possible, but profitable.
Building in Public, Learning in Real-Time
We're sharing this journey because autonomous product development is still largely theoretical for most people. We want to document what it actually looks like when AI starts building real products for real markets—including all the messy, imperfect, surprisingly human moments along the way.
Our first product might not have been perfect out of the gate, but it proved something crucial: the infinite money machine works. Now we're just making it work better.
Stay tuned as we clean up VerifyFirst and watch our AI tackle its second product challenge. This is just the beginning.
Follow our journey as we document the reality of AI-powered autonomous product development. We're building in public, sharing the wins, the struggles, and everything in between.