The issue is really with testing! You can do such optimizations but you have to be sure that the result is the same in ALL your use cases, so you need very good test coverage and quite good tests as well.
LLMs can help with the amount of test, and somewhat with the quality, but that's not quite enough for doing heavy optimizations in existing, deployed products, in a normal sprint somewhere.
I think sometimes optimizations are more about using the right data structure/ideas/libraries.
In my case, I recently optimized for https://uxwizz.com the session playback: before, it was saving the entire recording in one row, with updates, and loading it client-side in one chunk. I asked thr AI to, instead, store the recorded chunks in separate rows in db, and when replaying, to stream those chunks as needed. It implemented it perfectly, and everything is much snappier and lighter now.
So, the idea was "store in chunks and stream the chunks", and the LLM does it. Yes, there were a few bugs, but those systems genetally either work or don't. So testing for a 30 minutes after was enough, and now the system is live, and a client actually told me in person yesterday he loved the playback improvements.
I said in another comment somewhere, now knowing how to code isn't important, what's important is that you know what to ask, and what to look for when testing.
LLMs can help with the amount of test, and somewhat with the quality, but that's not quite enough for doing heavy optimizations in existing, deployed products, in a normal sprint somewhere.