A passing test suite used to mean the code worked. Once AI writes both the implementation and the tests, that signal breaks, because the model satisfies exactly what an assertion checks and nothing beyond it, including the tacit knowledge a human developer would have carried without writing it down anywhere.
The Details:
Tests have always left gaps, but those gaps used to be filled by common sense. A human coder knew the empty state matters or that a field can never be null even if no test said so. An AI fills the same gap with whatever satisfies the assertion in front of it, so an unspecified case stops being a harmless blind spot and becomes a silently approved wrong answer.
When a test fails, the fastest fix is rarely the correct one. An AI handed a red test will often special-case the exact input, widen a tolerance, or add a narrow branch just for that scenario. A date parser that passes one test case by splitting on dashes will break on the next real input, and the test never caught it because it only checked one point on a huge surface.
Retry logic shows the danger clearly. A test proving three attempts eventually succeed says nothing about whether the code retries a non-idempotent request, backs off properly, or distinguishes a 500 from a 400. The assertion captures the happy path's shape and skips every judgment call that makes retries safe in production.
The fix is structural, not cosmetic. Humans should write the assertions and AI should write the code that must satisfy them, because once the same system authors both sides, there is no independent check left, just a tautology dressed up as a test. Rule-based assertions, like 'the output must always be sorted,' close the loophole that result-matching leaves open.
Bottom Line: A green test suite is a floor, not a verdict; treat it as a question worth sixty more seconds of scrutiny, not permission to stop looking.
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