Big difference between gaining knowledge and building/maintaining cognitive skills.

Sounds like you're talking about research AI and not generative AI. You can't learn artistic/creative techniques when you're not practicing those techniques. You can have a vision, but the AI will execute that vision, and you only get the end result without learning the techniques used to execute it.

> I have more time for it because I am not spending nearly as much time tracking down stupid issues.

It is a truism that the majority of effort and time a software dev spends is allocated toward boilerplate, plumbing, and other tedious and intellectually uninteresting drudgery. LLMs can alleviate much of that, and if used wisely, function as a tool for aiding the understanding of principles, which is ultimately what knowledge concerns, and not absorbing the mind in ephemeral and essentially arbitrary fluff. In fact, the occupation hazard is that you'll become so absorbed in some bit of minutia, you'll forget the context you were operating in. You'll forget what the point of it all was.

Life is short. While knowing how to calculate mentally and/or with pen and paper is good for mastering principles and basic facility (the same is true of programming, btw), no one is clamoring to go back to the days before the calculator. There's a reason physicists would outsource the numerical bullshit to teams of human computers.

Are you sure that is not the illusion of learning? If you don't know the domains, how can you know how much you now know? Especially consider that these models are all Dunning Kruger-inducing machines.

Agree on that too. And I use these as tools. I don't think I'm missing out on anything if I use this drill press to put a hole through an inch of steel instead of trying to spend a day doing it wobbly with a hand-drill.