AI models write code against a memory of how a library behaved, not against the version sitting in your lockfile. That gap stays hidden until a dependency update quietly changes a return type or an error path, and the code that looked perfect in review starts failing in production without ever throwing an exception.
The Details:
A model trained on documentation and old GitHub repos builds an average picture of a library's behavior, weighted toward whatever version was most discussed at training time. It has no idea what you have installed, so the code it writes is version-agnostic in appearance but version-specific in practice. When a library changes how it serializes large numbers or handles a parameter, nothing in the function signature warns you.
The real danger sits in unwritten assumptions: return shapes, error semantics, whether a call is sync or async. These were true when the code was generated and may no longer be true after an update, and nobody recorded them because nobody thought to. Since the model can't be asked later what it assumed, those assumptions vanish the moment the conversation ends.
An explicit crash is cheap to fix because it points straight at the failure. A silent behavioral shift is expensive because it runs, produces a wrong result, and surfaces the damage several layers away from the actual cause. This asymmetry is why quiet dependency changes cause more damage than loud ones.
The fix is procedural, not technical: tell the model your exact library version, ask it to state its assumptions before writing anything, then lock those assumptions into characterization tests and schema validation at the boundary. A test that checks for an integer in a range, not just a truthy value, turns a future silent break into a loud one.
Bottom Line: Dependencies do not break AI code; unspoken assumptions do, and writing them down is the only defense that scales.
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