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One of the earliest uses we had for LLMs was literally just asking it to optimize several large codebases. Lots of pointless changes suggested; several huge performance wins we had overlooked.
And all done -- implemented, tested, and human-reviewed -- in about a person-week, compared to at least half a dozen person-months to go through all that by hand.
I mean, sometimes the LLMs generate slow algos. But less often than human coders.
You're not gonna believe this, but another of the first things we did was ask the LLMs to review the codebase for security issues (and review any new PRs)
OFC the code also gets reviewed for security vulns like it always has, by old-school automation (eg valgrind, fortify, yadda), human review, and red-teaming exercises. I don't think I've seen enough data yet to say whether it's got more/worse security issues than human-generated code (which, need I remind you, is often highly insecure)