I'm not a mathematician, but this seems like a weak and slightly bizarre argument.
Sorry, was there a typo here? Both sides of the comparison are AI, and in the affirmative?
The right side (how to use the maths, what maths to do, etc.) should have said it should be done by people.
On Proof and Progress in Mathematics talks about how much gets lost of the geometric understanding when translated to a paper. The kinds of things many mathematicians visualize in their head will be much easier to transmit.
I don't know if that is enough to offset the other affects, but learning and transmitting the understanding should be able to get much easier for a lot of people in principle.
And if human mathematicians are drummed out of producing future training data, then can AI end up proving itself so much "eating the seed corn", only at scale?
Seems to me not impossible that given current knowledge, AI generate one nugget more of knowledge (eg a proof of Navier Stokes), and given current knowledge + the nugget, generate yet some more new knowledge.
Not a given, but not obviously impossible either.
Well, through the metaphysical lens that has both powered innovation and stumped the Really Smart Types since antiquity.
The most common answer I’ve heard so far is “well, AI will train on its own output… maybe”.
I don’t think that’s even possible.
Because it being validated as correct resolves the main issue with incestuous training, which is compounding error.
AI creates novel discovery> incorporates this information > makes new discovery
This is how it works for humans too.
"AI slop", for example, appears a regression toward some "mean".
The same is true for science and Engineering.
What matters is if you have a method of sorting through the repetitive trash
Rather, that AI is unlikely to produce a fresh Marcin Patrzalek[1].