AI written code carries no visible sign of doubt, so the errors that matter most look exactly like the code that works.
The Details:
Human bugs usually announce themselves. A junior developer unsure of a decision tends to leave traces: awkward names, a comment that trails off, logic crammed into one function instead of split cleanly. AI generated code skips that tell entirely, because the model has absorbed the surface habits of correct code from millions of examples, so a broken concurrency assumption gets the same clean formatting as a trivial getter.
A specific trap is the plausible-adjacent error: the model invents an API call or a config flag that sounds exactly right for the library in question but does not actually exist that way. A human guessing wrong usually fails loudly and fast. The model's version fails quietly, often surfacing only when a rare input path finally exercises the invented behavior months later.
Testing catches functional slips but has almost no reach into structural ones. A model sees the file in front of it clearly and the surrounding system barely at all, so it can hand back a well built solution to a poorly chosen decomposition of the problem, with nothing in the output flagging that the boundary itself is wrong. Catching this requires someone holding the system's shape in their head and treating the output as a proposal, not a finished piece.
Writing tests before reading the generated implementation changes what gets checked. Tests written after tend to confirm whatever logic already exists; tests written first pin down the actual requirement and expose gaps the code quietly skipped, including the empty list, the timezone edge, the concurrent write.
Bottom Line: Speed from AI generated code is only real if verification habits are built in before the debt comes due, not after.
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