A method that reads beautifully in review can still fire a database query for every item in a list once it hits production traffic. That gap between tidy naming and actual runtime behavior is the real risk in AI-generated code, and it rarely shows up until load doubles.
The Details:
- Good abstraction is supposed to be a claim: someone reasoned about what's expensive, what's allowed to fail, and what stays consistent across a boundary. A model completing a pattern can produce the same clean shape, a function name, tidy parameters, without any of that reasoning underneath, so the interface looks trustworthy but encodes nothing.
- The repository pattern shows this clearly. A method like get_user_with_orders reads as a simple accessor, but it can quietly issue one query per order or load an entire history the caller never needed. The problem stays invisible because the seam hides the query, and it only surfaces when connection pools start draining under real traffic.
- Catching this requires treating boundaries as testable claims rather than trusted labels. Turn on query logging behind a suspect call and set a rough budget, one database touch, or fan-out capped at a fixed number. A boundary that cannot survive that budget was never actually a boundary, it was decoration.
- Review has to ask a different question than usual. Instead of checking whether code reads well, reviewers need to ask what would have to be true underneath for it to be safe, then verify those conditions actually exist in the implementation rather than just in the method's name.
Bottom Line: AI can render a boundary fluently, but only a human deciding what belongs inside it keeps complexity from shipping unowned.
