Reviewing AI-generated code needs its own checklist

AI-generated code tends to fail in a specific way: it's syntactically confident and locally correct, but it can quietly misunderstand the surrounding system — calling a deprecated internal API correctly, or handling an edge case that doesn't actually apply to your data model.

The checklist I use is different from a normal PR review. I look harder at boundary conditions the model couldn't have known about (what does null actually mean in this specific table), at whether error handling was generated generically versus matching how the rest of the codebase actually surfaces failures, and at whether test coverage was generated to match the implementation rather than the intended behavior — which happens more than people expect, and passes CI every time.

None of this is a reason to avoid AI-assisted engineering. It's a reason to stop reviewing it the same way you'd review a junior engineer's PR, because the failure modes aren't the same.