I'm actually curious about the "lose their skills" angle though. In the open source community it's well understood that if anything reviewing a lot of code tends to sharpen your skills.
What happens if the reader no longer has enough of that authorial instinct, their own (opinionated) independent understanding?
I think the average experience would drift away from "I thought X was the obvious way but now I see by doing Y you were avoid that other problem, cool" and towards "I don't see the LLM doing anything too unusual compared to when I ask it for things, LGTM."
Let's say you're right though, and you lose that authorial instinct. If you've got five different proposals/PRs from five different models, each one critiqued by the other four, the needs for authorial instinct diminish significantly.
There's an old expression: "code as if your work will be read by a psychopath who knows where you live" followed by the joke "they know where you live because it is future you".
Generative AI coding just forces the mindset you should have had all along: start with acceptance criteria, figure out how you're going to rigorously validate correctness (ideally through regression tests more than code reviews), and use the review process to come up with consistent practices (which you then document so that the LLM can refer to it).
It's definitely not always faster, but waking up in the morning to a well documented PR, that's already been reviewed by multiple LLMs, with successfully passing test runs attached to it sure seems like I'm spending more of my time focused on what I should have been focused on all along.