Their default solution is to keep digging. It has a compounding effect of generating more and more code. If they implement something with a not-so-great approach, they'll keep adding workarounds or redundant code every time they run into limitations later. If you tell them the code is slow, they'll try to add optimized fast paths (more code), specialized routines (more code), custom data structures (even more code).…
My sense is that the code generation is fast, but then you always need to spend several hours making sure the implementation is appropriate, correct, well tested, based on correct assumptions, and doesn't introduce technical debt. You need to do this when coding manually as well, but the speed at which AI tools can output bad code means it's so much more important.
If a human dropped a PR on me that took "several hours" to go through (10k+ lines or non-trivial changes), I'd jump in my car and drive to the office just to specifically slap them on the back of the head ffs.