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Notes -
Now that Stable Diffusion has been public for a week - what will be the next field to be revolutionized by AI?
(And if your answer is "writing" or "music", I'd like to hear what field you think will be next after those. Those are obvious candidates because AI systems are already in use in those fields and/or will be shortly, but due to structural differences between those fields and the visual arts, I'm skeptical that AI will have the same seismic impact there that it's currently having in art.)
I expect less new fields getting revolutionized and more currently AI friendly fields getting huge upgrades.
Imagine call center AI that actually sounds fully human or AI assistants that sound fully human.
However, sticking to the spirit of your question, my answer would be mathematics. With Mathematicians only existing to reevaluate the solutions given by AI to confirm they would work. Mathematics would become far more an engineering field than a person coming up with a solution on their own field.
I think this is misguided because it's the opposite of what currently happen, with theorem provers doing so much of the evaluation work relative to the 'creative' work. I can definitely see AI expanding the search space, though, with mathematicians working with the machine to find more novel or interesting results as a consequence. Much like art, I think AI are at the present time both a job-destroyer for the bottom end of the market (want your fursona fucking a famous politician? No longer do you have to pay $50, you can just get the machine to do it!) such as commissions but will ultimately enable people who understand art (colour, composition, etc, and consequently how to more reliably get the machine to do the work you want) to create more interesting and varied things at the top end.
Academic mathematicians are towards the top end of what you'd consider 'stem jobs' IQ-wise, so I'd anticipate a similar effect there.
Theorem provers do all of the evaluation work ... for those specific results which have been painstakingly translated into the theorem prover's language, which to a first order approximation is zero percent of the new and interesting results.
Training a transformer AI on MetaMath (or on Coq results, whatever large database has both complete proofs and adequately verbose comments), combined with the verifier itself, might be enough at this point to create a "math-paper to formal-proof" translator. Skimming through comments I see a lot of links back to the papers which originally published each theorem, which certainly ought to qualify as "adequately verbose" even if the database comments themselves are fairly terse.
Doing creative work would of course be more interesting ... if we could only define what's "interesting". 378+135=513 is a theorem among infinite others, but nobody cares about it. We tend to like math if it eventually has endpoints with real-world applications, and it's a bit hard to put that into an evaluable loss function. We also tend to like theorems if they're more general, and if they're short to state but long to prove, and if they're on the shortest path to proving other theorems, and maybe there's something to those criteria that could be quantified well enough to point a Neural Net Monte Carlo Tree Search in the right direction?
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