Every developer I know has a story about AI-generated code that looked completely right and was completely wrong. Not "wrong" in an obvious way — wrong in the way that costs you a Tuesday. After shipping production systems where AI wrote a meaningful portion of the codebase, here are the failure modes I've stopped being surprised by. 1. Hallucinated imports that pass linting The model confidently reaches for pandas.DataFrame.to_parquet(engine='pyarrow', schema_evolution=True) . That parameter does not exist. The code passes a static linter because the import resolves. It fails at runtime, in production, on the one path you didn't test. Why it happens: the model has seen thousands of DataFrame snippets and infers plausible-sounding parameters from patterns across libraries.…