Say you're reviewing a pull request and you know most of it came out of a model. What do you do differently than you would for a human-written one?

I hold it to the same standard as anything else, honestly. It has to be correct, readable, tested, consistent with our patterns. The difference is I read it more carefully, because AI code can look right while being subtly wrong. So I check that it actually solves the problem, that the edge cases are handled, that the tests mean something. I also watch for code that reads like it came out of a generic tutorial instead of being fitted to our codebase, and for unnecessary complexity, since the models sometimes over-engineer things. If an API looks unfamiliar I'll verify it's real. But the author is still responsible for what they submit, so I treat the human as the author no matter what tool produced the text.