The AI's Code Looks Right. That's the Problem.
Justin's IT Blog, Thursday, July 30th, 2026
AI-generated code looks polished regardless of correctness, requiring reviewers to verify logic rather than trust visual cleanliness.
Human code reviewers have traditionally relied on visual cleanliness as a signal of correctness, but AI produces uniformly fluent output that masks bugs.
The author identifies four characteristic traps in AI-generated diffs: invented APIs that don't exist, scope creep introducing unrequested changes, deleted edge-case handling that silences errors, and convincing-but-wrong logic that appears correct.
Effective review requires treating every diff as potentially flawed, carefully checking deletions, verifying unfamiliar APIs, and tracing real failure cases rather than happy paths.