AI-generated code rarely breaks in obvious ways. It passes review, ships to production, and behaves correctly in controlled scenarios. The problem is what happens after: failures appear only under timing, load, retries, or inconsistent state transitions. The core issue is not obvious bugs. It is code that looks structurally correct while silently ignoring real-world failure modes. Why AI code feels correct AI tends to generate implementations with strong surface-level signals: consistent TypeScript types standard architectural patterns clean async/await flows readable naming conventions familiar framework usage This produces a strong cognitive bias during review. The code does not look "risky", so it is assumed to be correct. The gap appears because readability is not equivalent to correctness under production conditions. Where AI-generated code typically fails 1. Concurrency and race conditions async function updateProfile ( data : Profile ) { setLoading ( true ); const response = await api .…