AI-generated code does not fail where human code fails. Human bugs cluster where attention gets tired: nested conditionals, timezone math, concurrency. AI-authored code fails where the spec went quiet, and the model filled that gap with something plausible instead of flagging it.
The Details:
- Plausibility is the trap. A human mistake usually looks off on inspection: an odd variable name, a missing branch. AI-written functions read clean, pass their tests, and still rest on an assumption made several decisions earlier that nothing in the code discloses.
- The interface is not the contract. Treating an AI component as a black box only gets you the stated interface. The real contract is whatever the model assumed but never wrote down, so isolation has to do double duty: contain damage and surface hidden assumptions before production does.
- Narrow the seams on purpose. Small, opinionated interfaces, explicit types at every boundary, and assertions at entry points force invariants into view instead of leaving them buried. Logging what actually crosses a boundary lets you compare reality against what the code silently expected.
- Speed does not delete the work, it relocates it. Generating a component in an afternoon just moves the cost downstream into incidents that take longer to diagnose, since the failure sits behind an assumption nobody documented. Splitting "code complete" from "contract understood" into separate milestones is what keeps that cost visible instead of deferred.
One concrete habit closes most of the gap: before merging, write down what you believe the code assumes about its inputs, then go check. That single exercise usually reveals that the assumption was never verified in the first place, which is the entire failure mode in miniature.
Bottom Line: AI code does not need more scrutiny everywhere. It needs scrutiny at the boundaries, where confidence hides the assumptions nobody wrote down.
