Why AI-Generated Code Needs Its Own Review Checklist, Not a Generic One

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A software developer discovered a critical flaw in AI-generated code that appeared clean, well-typed, and correctly named, yet silently assumed every Stripe customer would have exactly one tax ID — an assumption that would fail in real-world use. Unlike human-written code, which tends to look uncertain where the author struggled, AI-generated code is uniformly fluent, making surface readability a useless signal for correctness. The author describes this failure mode as 'confidently wrong' — code that is idiomatic and articulate while hiding subtle logical errors. Because standard code review relies on slowing down at messy or hesitant-looking sections, that instinct actively misfires when applied to AI output. The developer argues that reviewing AI-generated code is a discipline of its own, requiring a separate checklist ordered by the cost of potential errors rather than their frequency.

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