AI code generation is making developers worse

I watched a junior developer spend three hours debugging a React component last week. The bug? A missing dependency in useEffect. The kind of thing that would have taken 30 seconds if they understood what useEffect actually does. But they didn't—Copilot had been writing their hooks for months.

This is happening everywhere, and nobody wants to talk about it.

The abstraction paradox

AI code generation is the ultimate abstraction. You describe what you want in English, and code appears. It feels like the future. It feels like democratization. It feels like everything we were promised.

It's also creating developers who can't function without it.

I'm not being hyperbolic. I've interviewed candidates who literally couldn't write a for loop without autocomplete. Senior developers who can't explain why their code works, just that Copilot suggested it and the tests pass. Entire codebases that are quilts of AI suggestions stitched together with hopes and prayers.

The tools aren't the problem. The tools are incredible. The problem is that we've confused code generation with software engineering.

What we're actually losing

Mental models are built through struggle. You don't really understand promises until you've debugged callback hell. You don't grok memory management until you've found a memory leak. You don't appreciate type systems until you've spent a day tracking down a undefined is not a function error.

AI code generation skips the struggle. It's like learning to drive by only ever being a passenger. You might know where the brake pedal is, but you've never felt the car start to skid.

Debugging is becoming archaeological. When you write code, even bad code, you understand its intent. When AI writes code, you're debugging a stranger's logic. A stranger who might have been trained on Stack Overflow answers from 2012.

I've seen developers spend hours debugging AI-generated code that would have taken minutes to write from scratch. But writing from scratch requires understanding, and understanding requires experience, and experience requires practice—none of which you get when AI does the work.

Code review is becoming theatrical. How do you review code when neither the author nor the reviewer understands it? I've sat in reviews where everyone's nodding along to AI-generated implementations, afraid to admit they don't follow the logic. The tests pass. Ship it.

The senior developer trap

It's not just juniors. Senior developers are falling into a different trap: becoming prompt engineers instead of software engineers.

They're optimizing for better Copilot suggestions instead of better architecture. They're structuring code to be AI-friendly instead of human-friendly. They're choosing patterns based on what generates well, not what maintains well.

I know a team that rewrote their entire component library because the new structure "worked better with Cursor." The components are now 3x larger, harder to test, and impossible to understand without reading the AI prompts that generated them. But hey, they ship features faster.

The inconvenient truth

AI code generation is a tool, and like any tool, it amplifies what's already there. If you're a strong developer, it makes you faster. If you're a weak developer, it makes you faster at writing bad code.

The problem is that we're using it as a crutch instead of a lever. We're using it to avoid learning instead of accelerate learning. We're using it to skip understanding instead of deepen understanding.

What actually works

The developers who are thriving with AI tools all do the same things:

They write first, generate second. They understand the problem, sketch the solution, then use AI to handle the boilerplate. They're not asking AI "how do I do X?" They're saying "implement X using pattern Y with constraints Z."

They read the generated code. Every line. Every time. They refactor it, optimize it, understand it. They treat AI suggestions like they'd treat code from a questionable Stack Overflow answer—potentially useful, definitely suspect.

They turn it off regularly. They do code katas without AI. They implement algorithms from scratch. They build toy projects with autocomplete disabled. They maintain their edge by occasionally training without the weights.

The path forward

AI code generation isn't going away. It shouldn't. It's too useful, too powerful, too much of a productivity multiplier. But we need to be honest about what we're trading for that productivity.

Every line of code you don't write is a lesson you don't learn. Every problem AI solves for you is an opportunity to atrophy. Every abstraction that hides complexity is complexity you can't handle when the abstraction leaks.

Use AI. Use it aggressively. But use it like you'd use a calculator—to check your work, not to avoid learning math.

Because when the AI suggests something subtly wrong, or when you're debugging production at 3 AM and Copilot can't help, or when you need to optimize code that no AI has seen before—you'll need to actually be a developer.

And by then, it might be too late to learn.