AI Coding Tools Are Winning By Making Us Worse Programmers (And That's Fine)

The Uncomfortable Truth About Cursor and Copilot

Cursor just crossed 100,000 paying subscribers. GitHub Copilot has millions of users. Every major IDE now ships with AI completion built in. The adoption curve is vertical, and the discourse around these tools remains stuck in 2022.

We're still having the "does it make you lazy" debate while the actual story is playing out in production codebases everywhere. AI coding tools aren't making us lazy—they're fundamentally changing what programming skill means, and we're too busy arguing about autocomplete to notice.

What Actually Changed in the Last Six Months

The gap between "AI suggests a function" and "AI writes the entire feature" collapsed faster than anyone predicted. Cursor's agent mode doesn't just complete your code—it reads your codebase, understands your patterns, and implements features across multiple files. The difference between this and GitHub Copilot's early days isn't incremental. It's categorical.

Here's what I'm seeing in real projects: junior developers shipping features that would have taken senior developers hours. Not because the juniors got better, but because the tool absorbed the architectural knowledge that used to live in someone's head. The code quality isn't always great, but it's consistent, and consistency beats brilliance when you're trying to ship.

The uncomfortable part? The senior developers using these tools are also getting worse at certain things. Pattern matching, API memorization, syntax recall—all degrading in real time. And before you say "those things don't matter," remember that we've spent decades insisting they absolutely do matter.

The Skills That Stopped Mattering

Memorizing standard library functions used to be a proxy for experience. Now it's just trivia. Knowing the idiomatic way to structure a React component used to signal competence. Now the AI knows the idioms better than most humans, because it's seen every React codebase on GitHub.

This isn't hypothetical. I've watched developers with five years of experience struggle to write basic array operations without AI assistance. Not because they're incompetent, but because they've offloaded that cognitive load and their brain stopped maintaining those neural pathways. Use it or lose it, and we're collectively choosing to lose it.

The question isn't whether this is happening—it obviously is. The question is whether the trade-off is worth it. And here's where I'm going to lose half the audience: it probably is.

What We're Trading Up To

The skills degrading are the ones that computers are genuinely better at. Syntax, boilerplate, pattern application—these are lookup problems, and LLMs are very good lookup engines. What they're not good at (yet) is understanding what you're actually trying to build and why.

The developers thriving with AI tools aren't the ones who memorized the most documentation. They're the ones who can clearly articulate intent, spot architectural problems, and understand system-level trade-offs. These skills were always more valuable, but now they're the only skills that matter.

Here's the part that makes people uncomfortable: this probably means most "senior" developers weren't actually that senior. If your entire value proposition was knowing the framework better than the documentation, you were always one good search away from obsolescence. AI tools just accelerated the timeline.

The New Skill Hierarchy

What matters now:

• Understanding what to build (product sense, user empathy, business context)
• Architecting systems that won't collapse under their own complexity
• Reading AI-generated code fast enough to catch the subtle bugs
• Knowing when the AI is confidently wrong (which is often)
• Communicating intent clearly enough that the tool builds the right thing

What doesn't matter anymore:

• Memorizing API signatures
• Writing boilerplate quickly
• Knowing every edge case of the language spec
• Typing speed (surprisingly, this one died fast)

The shift feels sudden, but it's been coming for years. We just called it "Googling" instead of "AI assistance" and pretended it was different.

The Codebases We're Creating

Here's what nobody wants to talk about: AI-assisted code has a smell. It's correct, it's functional, and it's subtly wrong in ways that are hard to articulate. The abstractions are slightly off. The naming is plausible but not quite right. The structure works but doesn't feel intentional.

We're creating a generation of codebases that work perfectly well but that nobody fully understands. Not because they're complex, but because they were never really designed—they were negotiated between a human's vague intent and an AI's pattern matching.

This might be fine. Most code is throwaway anyway. But the codebases that stick around for a decade are going to be interesting archaeological sites. Future developers will look at our AI-assisted code the way we look at PHP codebases from 2008—functional, ubiquitous, and vaguely embarrassing.

Where This Goes Next

The current generation of tools is already obsolete. Cursor and Copilot are training wheels for whatever comes after—probably agents that don't just write code but deploy it, monitor it, and fix it when it breaks. The human role shifts from "programmer" to "product manager who can read code."

This terrifies people who built their identity around being good at programming. It should. The skill you spent ten years developing is being commoditized in real time, and no amount of "but AI can't really understand context" is going to stop it.

The developers who adapt aren't the ones fighting to preserve the old skill hierarchy. They're the ones figuring out what remains valuable when the code-writing part becomes trivial. Turns out, that's most of the actually hard stuff we were avoiding by focusing on syntax.

The Part Where I Hedge

Are AI coding tools making us worse programmers? Yes, by the old definition of what a programmer is. Are they making us worse at building software? Probably not—the teams shipping fastest are the ones leaning into these tools hardest.

The real question is whether we're okay with a future where fewer people understand how the systems actually work. We're already there with most technology—nobody writing React apps understands how the JavaScript engine works, and that's fine. This is just the next layer of abstraction.

We'll adapt. We always do. But the people insisting nothing fundamental has changed are the ones who'll get left behind, still arguing about whether AI-generated code counts as "real" programming while everyone else ships features and moves on.