“I knew what I wanted. I just couldn’t make it real.”

You had a clear vision: how a system should work, what the flow should feel like. But unless you could code, or convince someone who could, your ideas stayed stuck in mockups or Notion docs. Translators (developers, PMs, designers) only ever got it about 50% right. You were trapped by what I call the “implementation gap.” That gap is closing fast.

AI tools let you go from high-level intention to working code. If you understand the domain deeply, you can now build directly. Not by learning every syntax quirk, but by expressing your model clearly. The bottleneck has flipped. It’s no longer “can you code?” It’s “do you know what should be built?” This shift is bigger than most people realize.

Low-level code is becoming a commodity. Understanding what code to write isn’t. What’s scarce now is the ability to define entities, relationships, constraints, flows. The stuff that actually matters. AI will happily scaffold garbage for you. Without a model, it’s confident nonsense. But with a crisp mental model, AI becomes a multiplier. You can generate working prototypes fast. You can iterate without waiting on engineers. You can test real logic, not just sketch ideas on a napkin.

Consider something as simple as seat reservations. Let users pick a seat. Sounds straightforward, right? But if you’ve actually built this, you know the edge cases. Temporary holds vs confirmed bookings. Time-based release and locking. VIP access windows, group holds, overbooking policies. You can prompt an LLM all day, but unless you understand the rules of the domain, you’ll end up with brittle code or misaligned logic. The AI doesn’t know that a “hold” expires after 10 minutes, or that VIP members get early access, or that you need to handle race conditions when two people try to book the same seat.

You don’t need to be an “AI founder” to benefit from AI. You just need clarity. On what matters, how it works, and where the edge cases lie. That’s why domain modelers will win this era. It’ll be doctors modeling better workflows. Teachers building custom tools for students. Logistics experts automating scheduling logic. AI puts domain experts in the driver’s seat. If you’ve done the thinking, you can now do the building.

This is actually a return to something we lost. In the early days of computing, the people who understood the problem were often the ones who built the solution. Then we created layers of abstraction and specialization. Now AI is collapsing those layers again. Prompting is easy. Modeling is hard. But modeling is what makes the difference between a demo and a product. Between something that works in the happy path and something that handles reality. Don’t just chase what’s possible. Define what’s true.

The age of “I have an idea, I just need a dev” is fading. If you know your domain, AI lets you go from zero to real. I used to know what I wanted but couldn’t make it real. Now I can. And so can you, if you do the hard part first.