Code Review Checklists Are Failing in the Age of AI-Generated Diffs

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A developer essay argues that traditional code review checklists were built on an unspoken assumption: the author understood every line they submitted. With AI-generated code now forming a significant share of pull requests, that assumption no longer holds, as models can produce stylistically correct, confident-looking code without any underlying reasoning. Signals reviewers have long relied on — such as style consistency, hedging comments, and visible uncertainty — are no longer reliable indicators of comprehension or correctness. The piece warns that plausible-looking errors, such as non-idempotent operations wrapped in retry logic or referencing non-existent config flags, now routinely pass surface-level checks. The author proposes a revised set of 12 review questions, emphasising that spot checks must become thorough examinations when the authorship of code cannot be assumed.

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