There’s a comfortable lie going around. AI coding tools let you move faster, ship more, worry less. Just describe what you want, review what comes out, and get on with your life.
That’s not wrong. But it’s incomplete in a way that’s going to bite a lot of people.
Abstractions Always Leak
Every abstraction, no matter how elegant, eventually exposes the messy reality it was trying to hide. AI-assisted coding is an abstraction. A powerful one. But it leaks.
At some point and this comes faster than people expect, you’ll hit a bug the AI can’t explain, a performance problem it can’t diagnose, or a design decision that’s quietly poisoning your system. When that moment arrives, you can’t prompt your way out. You have to think. Not the fast kind. The slow, meticulous, System-2(i.e., the slow brain) kind.
That bottleneck doesn’t disappear because the AI wrote the code. It just gets deferred.
You Can’t Build a Mental Model You Never Had to Build
When you write code from scratch, you’re making decisions, hitting friction, asking why. That friction is annoying. It’s also how understanding gets encoded.
With AI-generated code, you get output that works, enough to pass tests, enough to ship. And because it works, there’s no obvious reason to dig deeper. You never built the model. You never had to hold the abstraction in your head long enough for it to become intuition.
This shows up later when you can’t reason about the system under load, don’t know which parts are fragile, and can’t make confident decisions about what to change. You’re maintaining code you don’t really understand, except this time, you wrote it.
So What Do You Do?
Read the code the AI generates, actually trace it, not just skim it. When something isn’t clear, that’s exactly the moment to slow down, not move on.
The best developers using these tools aren’t the ones who trust them most. They’re the ones who know exactly when to stop trusting them. You have to question the output generated by these tools.
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