The last point I think is most important: "very subtle and silently introduced mistakes" -- LLMs may be able to complete many tasks as well (or better) than humans, but that doesn't mean they complete them the same way, and that's critically important when considering failure modes.

In particular, code review is one layer of the conventional swiss cheese model of preventing bugs, but code review becomes much less effective when suddenly the categories of errors to look out for change.

When I review a PR with large code moves, it was historically relatively safe to assume that a block of code was moved as-is (sadly only an assumption because GitHub still doesn't have indicators of duplicated/moved code like Phabricator had 10 years ago...), so I can focus my attention on higher level concerns, like does the new API design make sense? But if an LLM did the refactor, I need to scrutinize every character that was touched in the block of code that was "moved" because, as the parent commenter points out, that "moved" code may have actually been ingested, summarized, then rewritten from scratch based on that summary.

For this reason, I'm a big advocate of an "AI use" section in PR description templates; not because I care whether you used AI or not, but because some hints about where or how you used it will help me focus my efforts when reviewing your change, and tune the categories of errors I look out for.