Code that passes every test can still collapse the moment real traffic arrives, because the tests were written by the same model that wrote the code, so they confirm its assumptions instead of challenging them.
The Details:
AI treats a finished feature as code that matches your description and passes its own checks. That is a narrow claim. It says nothing about ten concurrent requests, a dropped connection mid-write, or a duplicate filename arriving in the same second, because nobody asked it to imagine those things.
Concurrency breaks first, and it breaks quietly during development. A test environment runs one request against a clean database, so a check-then-insert pattern looks fine until two real users hit it at once and produce duplicates, a stampeded cache, or an emptied connection pool. The function was never wrong; it was correct for a system with exactly one user, a system that never exists in production.
The fix is a small, deliberate dose of chaos before launch, not after. Fire a hundred concurrent requests at the endpoint, kill the database mid-transaction, send the payload you assumed nobody would send. This is the same instinct behind chaos engineering, just scaled down to something one developer can run in an afternoon.
The real leverage comes from feeding the AI your team's actual incident history instead of a clean spec. A prompt that says storage times out ten percent of the time, or that two uploads can collide in the same second, produces code built for the world you actually operate in. Past postmortems are the cheapest, most specific failure data you own; turning them into a test, a lint rule, or a line in your project's context file means that knowledge gets used every time, not just remembered by whoever was on call.
Bottom Line: Stop asking whether the code works and start asking what it has never been tested against, because the AI will faithfully finish whatever you point it at, and it cannot point itself at the failure you forgot to describe.
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