Developers are shipping code faster than ever. Some of them are also reviewing it less carefully than ever.
That tension is at the center of a real problem in AI-assisted development. Tools like Cursor, GitHub Copilot, and Claude Code can generate working functions, fix bugs, and write entire files in seconds. But speed creates its own trap: when code arrives pre-written and plausible-looking, it gets reviewed faster and with less scrutiny than code you'd have written yourself.
Why AI Code Is Harder to Review Than It Looks
When you write code, you understand every decision in it - even if you can't explain why you made them in the moment. You've thought through the edge cases, even implicitly. When AI generates code, you're reviewing someone else's work under time pressure, with a built-in bias toward accepting it because it saved you the effort to generate.
AI coding tools are confidently wrong in ways that are difficult to catch on a quick read. They generate code that compiles, passes basic tests, and handles the expected case correctly - then fails silently on edge cases, or introduces a subtle security problem that looks like standard practice. The output looks authoritative because the model has processed millions of examples of similar code and writes with the same tone regardless of whether the specific implementation is correct.
The failure mode isn't random errors the model doesn't know it's making. It's structurally plausible code with wrong assumptions baked in.
Senior Engineers Aren't Automatically Safe
There's a counterintuitive risk for experienced developers: they're fast readers of code, which sounds like an advantage but works against you here. Pattern recognition helps you scan quickly, but AI-generated code is optimized to match familiar patterns. A senior engineer who trusts their ability to spot problems fast may miss the same issue that would make a junior developer slow down and ask questions.
Confidence in your own review speed is exactly what AI code exploits.
What Changes When You Take It Seriously
The framing that works is treating AI-generated code the way you'd treat a pull request from a capable but junior developer - someone who can do the work but needs their assumptions checked. In practice, that means:
- Reading for logic, not just syntax. Does this code actually do what you asked, or does it do something adjacent that passes your tests?
- Testing edge cases, not just the happy path. AI output tends to optimise for the expected case.
- Asking why for non-obvious choices. If you don't understand why the AI made a specific decision, that's a signal to dig in, not trust it.
- Not treating local test success as production proof. Tests don't cover what they weren't written to cover.
The productivity gains from AI coding tools are real and measurable. This isn't an argument for slowing down or abandoning them. It's an argument for not letting speed-to-merge become the metric that drowns out correctness.
Debugging AI-generated bugs in production two weeks after shipping is slower, not faster, than reviewing the code properly before it shipped.