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This does not quite fall under culture wars, but I thought it's still relevant given that it's a big story.
On October 6, OpenAI released a large batch of mathematical results, with more than 700 manuscripts covering hundreds of problems: Sharing AI progress in mathematics.
This has led to another round of controversy and discussion about AI making mathematicians obsolete or disrupting research programs that people have spent years working on. The Verge covers some of these reactions in “Mathematicians will need years to make sense of OpenAI’s latest drop”.
Many of the results are still being checked, but the release has called into question if mathematics can survive as a profession, or what purpose it serves if machines can outpace humans. Research programs have supposedly been gutted by these results. "Careers ruined," as the narrative goes. I think that is an exaggeration or overreaction. There are thousands of mathematicians and their interests are very wide ranging. The odds of someone's research program or problem being "solved" by AI are very small.
I don't see anything changing much. The status quo of journals and academia will remain intact even with rapid progress in AI, save for the occasional breakthrough in an unsolved problem. What is overlooked or ignored is the massive gap between cotemporary research-level math vs. LLM-produced math. A math PhD program, especially from a good school, teaches or guides future researchers towards productive areas of research that are of interest to the "math community," rather than results that are merely "personally interesting". AI cannot recreate such communities or networks.
AI is great for generating computation-laden results on-the-fly, but research-level math is more concerned with abstractions and relations between them, than crunching. I recall one example where ChatGPT’s Astra program tried to pass off a finite computational cutoff as a “theorem.” Um, you can't do that unless you prove that the bound is sufficient.
To test this, I produced some AI papers which I thought were great and novel, and the AI said so, and journals rejected them (although others are still pending at various stages of peer review). I had some knowledge in said area and could assess novelty, but still not good enough. I don't see laypeople doing better in this regard, although could be wrong. I ran my ideas through the Ai and got 12 papers out of it, which saved considerable time had I written them myself. It also found non-obvious connections and results. I am really satisfied with the results, but does this meet the bar for publication? Time will tell. I bet not.
Also, success is heavily dependent on the prompt. It's not as easy as blindly prompting something. I made a post about that https://greyenlightenment.com/2026/10/11/the-nuance-the-math-debate-is-missing/ I suspect many of these successful proofs were prompted by experts who knew what to look for, rather than just naively trying. What is touted by the media as an AI breakthrough, may involve surgically precise prompting in the background by an expert in said field.
Alluding to the "airplane meme," there is also survivorship bias: you don't see all the failed attempts with AI . Sometimes it works great, but other times hit or miss. Presumably , the "top unsolved problems" have been queried by frontier models and using large token budget, with few proofs to show for it (save for the Open AI "dump").
It would be cool to see AI truly shake -up math and breathe new life into ignored areas, but that is not going to happen, imho. But I do see AI leading to a 2-track system of decentralized preprints and GitHub repositories of AI-produced results, alongside established academia and journals. They will coexist, rather than the later being obsolete. Overall, mathematicians don't have to fear losing their jobs anytime soon (unless of course you happen to have the precise problem you are working on scooped by AI, and even then, your skills would presumably transfer to new problems.
I was hoping someone would post about this. I have a few reactions.
Solving math problems is a fairly low risk way to demonstrate Ai models ability to solve these sorts of problems. I can’t be the only one to wonder: if it can solve obscure math problems, I wonder what it could do with physics? Apparently nothing really changes when you solve these 700 math problems. But if you solve 100 physics problems, you may wind up with some commercially valuable or dangerous tech. Seems unlikely they would dish it out so readily. People seem to have made this assumptions that we’ve hit the limits of new physics. Anyone claiming interesting effects or results is a crank and it’s all fantasy. Perhaps that assumption will hold up with AI, but perhaps not. It seems to me there’s a chance that the breakthroughs have already happened this year, but they haven’t been released yet.
The reactions from many mathematicians have been a sight to behold. These sorts of intelligent academics truly behave like the world owes them something. By and large they are an unsympathetic bunch that I personally will enjoy watching them be made obsolete. There was one guy on Reddit, who turned out to be a young postdoc, giving the same tired routine that I see from so many of them. Bemoaning way that OpenAi released the work. Complaining that it’s a pile of papers that he will never read because it would take too long to digest and he’s got more important things to do. That perhaps if open Ai went through the proper channels and released peer reviewed articles in a real academic journal then, and only then, would the academic community accept the ideas. Maybe I’m generalizing too much from one guy, but I think this is a common refrain from these academic types.
I can’t wait to see how physicists react when the time comes.
Yeah, apparently the Teller-Ulam design for a Hydrogen bomb relies on some clever insights and is not fully public. So it seems plausible that there are some really dangerous ideas out there, ready for an AI to discover.
Agreed.
Well I think they are used to owning mathematics like a medieval guild. Which makes sense (or at least used to make sense) given that you need gatekeepers to keep out idiots and cranks.
FWIW I have a professional physicist in my family and he is pretty much resigned to the idea of studying physics out of love for learning more about the way the universe works. Which I guess is a good attitude, but if it were me I would be kind of disappointed if I suddenly had effectively no chance of discovering and publicizing something significant. Because the AI is going to beat me to the punch.
I suppose one option available to physicists (but not available to mathematicians) is to keep their experimental data to themselves for a year or two so that they have a chance to analyze it with their own AI in a proprietary way.
I'm not sure if that would be enough. Einstein famously didn't need experimental data to come up with relativity, he just thought about it really hard.
FWIW that's not what I was taught in my history of science class in college. What I was taught was that in the late 19th century experimental physicists did what was known as an "ether drift experiment" and famously found that light seems to travel at the same speed regardless of the motion of the reference frame. And that subsequently Einstein started with this assumption and worked out the seemingly bizarre theoretical implications. Is this an accurate account? I am always skeptical of what I am taught in school, but it seems plausible to me that you can't do physics in a vacuum, so to speak. Even Isaac Newton had to start by looking at the motion of the planets and earth-bound objects, kind of a rudimentary form of experimental data.
Arguably the experimental data of today is more easily gate-kept. It's the observations of some orbiting telescope or readouts on some huge particle accelerator. Even so, if most of what the physicist is doing consists of feeding the experimental data into the AI and asking "Is there anything interesting in here? If so, write it up in a paper," it's, well, not quite the same as starting with the ether drift result and putting together a theory of relativity.
I also recall being taught in school that Einstein's development of the postulates of special relativity (particularly that the speed of light is equivalent in all inertial reference frames) was influenced by the results of the Michelson-Morley Experiment, which failed to identify expected changes to the measured speed of light when the Earth's motion changed relative to the position of the sun.
However, this discussion suggests that Einstein's development of special relativity was probably not significantly influenced by the MM experiment, and was mainly influenced by assymetries in classical electromagnetism.
Thanks for posting this. It's perspectives like this that make me skeptical of the pat facts I was sometimes taught in school. To be sure, it's possible that Einstein was mistaken or even lying about the influence of ether drift experiments on his thinking.
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