Ok, I'm not suggesting replacing humans with AI and I despise companies trying to do this unsustainable practice.
With that out of the way, I'll restate that LLMs follow some rules more reliably than humans today. It's also easier to give feedback when you don't have to worry about coming across as a pedantic prick for pointing out the smaller things.
On your point that LLMs are not improving; well, agents and tooling are definitely improving. 6 months ago I would need to babysit an agent to implement a moderately complex feature that touches a handful of files. Nowadays, not as much. It might get some things wrong, but usually because it lacks context rather than ability. They can write tests, run them, and iterate until it passes, then I can just look at the diff to make sure the tests and solution makes sense. Again, something that would fail to yield decent results just in the last year.
It's not, I read the code. It's not merely asking the LLM for recommendations, it's using embeddings to compute scores based on similarities.
It's a lot closer to a more traditional natural language processing than to how my dad would use GPT to discuss philosophy.