I have found in the last year I "get more done" but I learn less. Whats even worse is if I burn though my token quota I am less likely(and less able) to "switch to manual". LLMs make an ocean of decisions and assumptions very quickly and if you don't slow it down very intentionally (and lose the promise of the AI providers are pitching) you will quickly not have a good idea of how systems work.
My actual coding ability is probably lower, but I’ve learned so much about music theory and production, CAD, and statistics. All because my hobby projects can go so much further than before.
OTOH, the output might stick?
Then the question becomes do you value knowledge for its own sake or for what it enables you to do? I don't have an answer.
I don’t expect it to be any different with AI, but YMMV.
How I experience it at least, there is no free lunch. Something that gives you knowledge and experience will almost by definition be painful and uncomfortable.
We use AI because it is faster, but that speed has a consequence.
I’d say both are valid. I’m definitely an outcome person. I like making things. I’m taking a multi-year break from a decades-long CNC ceramics project, and I have no doubt my knowledge and skills will atrophy. That’s fine, I’m confident I can pick them up again if/when I return.
If I fail to work hard enough on understanding the physics of alternate tunings and rely “too much” on AI to produce a piece, I’m ok with that. If and when I hit a wall and AI isn’t giving the results I want, I’ll dive deeper.