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Thanks for the tag. I'll try to (speed)watch it at some point today.
First, @gmd, strongly recommend reading this related article by Tao: #1551655. It mentions several of the fears that Tsimerman is alluding to. But Tao is less pessimistic; he's been embracing AI for a long time in his research, and pushes one to think about why one does math, and that by properly (re)defining the goals for said endeavour, math may come out of it better and stronger.
I believe that we are now entering a era of comparable turbulence in mathematics. This time, though, what is being stress-tested is not our foundational framework for mathematical truth, but rather the largely implicit framework of mathematical values and practices: what we consider a contribution to be, what we reward, what we regard as understood, and who — or what — we regard as having done the work. I argue that it will become necessary to make these unwritten goals of mathematics much more explicit; but once we have thoroughly examined and codified them, our community will emerge stronger and more resilient than before.
And he also clearly agrees with @SimpleStacker that without properly explaining them, a solved problem is worthless. This is best illustrated with this screenshot from his article.
I believe we'll get to a point where each problem will implicitly be assumed to have been solved (by AI), but unless it has been digested by humans, incorporated into the curriculum, it may very well be considered unsolved. Until it makes it into a technical audience presentation by a human, who is able to explain it to its peers, its status will remain that of an incomplete problem without a solution. Now more than ever, humans will have a role of communicating the science. There will just be an abundance of AI-solved problems to chose from on which you can spend the necessary time to further the steps from Fig. 4 above. Not saying this will attract the same kind of people that got attracted to it in the first place, but I believe that a new market of mathematicians will emerge that will see value in doing so for the right problems.
In my last paper submission, I provided theory support to an experimental group. It is my first paper where I would not have been able to provide the level of support I did if I had not had access to AI. They were deeply satisfied, unlike several previous interactions with other groups where the tacit understanding is that we provide _minimal_ theory support, just enough to get them to publish their experimental findings in Nature or Science journals. The actual theory is expected to come later in separate theory-only papers where one has time to do everything properly. Now, in this latest paper, the quality of theory was at the same level as the quality of experiments. I'm still trying to come to terms with this new role I am taking on, using AI as a partner, but in the end, it's all beneficial to the reader of the paper, who will have been given a fuller understanding of the experimental findings.
But are the topics by nature more comprehensible, or are the comprehensible because he does the work to make them comprehensible?
Fatalistic thinking probably but having spent some time trying to tutor very basic math and physics in the past I'm afraid I believe many things are simply incomprehensible to people below certain abilities and IQ. And if you don't interact with the "average" human regularly you don't realize how functionally illiterate many of the population is (I'm amazed more people don't off themselves with the insulin I prescribe).
In the interview he talks about many topics outside of his expertise he says it would take him weeks or months to understand, as someone from an elite competitive math background. Versus I could spend my entire life and not get closer to answering IMO questions.
It's hard to tell if they're really just that unable to do math or the interest is just literally zero to invest any effort in it. Like, I'm often surprised at how sophisticated a person can be in discussing football (and able to do math in that context) while being completely blanked out about it in other contexts.
I do think the number of people who actually find pleasure in thinking about something and understanding something is pretty rare.
Really fascinating.
But perhaps it says more about high level esoteric math than it does about AI vs human. It sounds like the math has become overly focused on proving esoteric problems and less focused on communication, intuition, and understanding.
Look at 3blue1brown. This may be blasphemous, but he's probably a more influential mathematician for most people than Terrence Tao. But not because he's more brilliant, or solved more problems, but because he's able to communicate and teach so beautifully.
Expanding peoples' minds is more important than formally proving something new without any understanding.
Also tagging @south_korea_ln since he might be interested in this topic