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

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