An AI-generated async function can pass every test and still corrupt data the moment two requests hit it at once, because the tool never modeled what happens when timelines overlap.
Why it matters: teams that trust clean-looking async code without stress testing it are shipping race conditions that only surface under real production load.
The Details:
AI coding tools read functions as a single-threaded story, top to bottom, so the code they produce is optimized to look correct in isolation. That isolation vanishes the instant concurrent requests actually overlap, and nothing in a linter or a compiler flags the gap. Unit tests exercise one request path at a time, so they cannot generate the interleaving that breaks things at scale.
The output carries no hedge. A junior developer writing shared-state logic will flag uncertainty and ask for a second look; AI tools deliver a locking scheme and a string formatter in the same confident tone. That missing signal is often the only thing standing between a subtle bug and a midnight page.
Fixing this starts before generation, not after. Stating the concurrency contract up front, how many callers hit this handler, what shared state it touches, changes what the model reaches for: locks, single-flight guards, questions about transaction boundaries. Following up with an adversarial prompt, asking the tool to walk through two simultaneous calls await point by await point, surfaces bugs a first pass buried.
Connection pool sizes get treated as tuning knobs when they are actually correctness boundaries. A pool sized for one worker and deployed across eight will starve silently under burst traffic, and no amount of clean code review catches that because it looks like configuration, not logic.
Bottom Line: Confident-looking async code is not the same as concurrency-safe code, and only load tests written before the code exist can tell the difference.
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