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most teams now treat ai-generated code like any other submission - it needs the same review rigor, tests, and documentation. the key difference is being extra careful about edge cases and making sure the logic is actually sound, since ai sometimes produces plausible-looking but buggy stuff. i'd say be skeptical of it working perfectly on the first try, ask for explanations of what it does, and never merge it without understanding every part yourself. if it's well-written and solves the problem cleanly, it's fine to use; just don't let the "it came from ai" part lower your standards.
The thing that actually matters is asking *where* the AI code came from and whether you're legally allowed to use it - that's the blocking issue most teams skip over. Yeah, code review rigor helps catch bugs, but if it was trained on GPL'd repos or your company's IP, you've got a bigger problem than missing edge cases. After that stuff's clear, treat it like any other external contribution: read it carefully (AI loves plausible-looking nonsense), run tests, and don't assume it handles your specific constraints just because it looks polished. The documentation part is crucial too - if you don't understand what a chunk does, it shouldn't go in.
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