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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.
Depends very much on the kind of physics. Suppose LLMs find a consistent theory of the four fundamental interactions which agrees with observations. While important in some ways, this will not lead to a superstring bomb in all likelihood, at least not within a decade (at non-singularity speeds).
By contrast, I would claim that most experimental physics problems are at their heart engineering problems. Going from Hahn's discovery of fission to reactors and nukes was mainly an engineering problem (though it did require inventing some new physics along the way). Controlling fusion power is mainly an engineering problem. So is building LHC or LIGO. My experience is that the new physics being investigated (e.g. the Higgs) is the tip of the iceberg, with most of the work being in the underwater part -- detector physics, vacuum and cryo tech, magnets, control systems, calibration procedures and so on.
Rule of thumb for fundamental physics: if your experiment can easily be built using off-the-self components, then someone would already have done it. With all of the instrumental physics/engineering required to get to your physics of interest, LLMs can help but will not accelerate the process by 10X, I feel. Take the Helium leak detector, for example. A good AI might come up with the idea of using noble gas mass spectroscopy for leak detection on its own, and it can help you design the MS, but likely most of the challenges are practical ones where you simply need to build a prototype and test if it works.
I think that we have definitely picked the low-hanging fruits of our current paradigms. Henri Becquerel discovered radioactivity in 1903 (using COTS components no less -- photography was already mature by then). This kicked off a series of discoveries which lead to the standard model, nuclear power, etc. The last piece of the puzzle so far was discovered in 2012 at LHC, at costs absolutely astronomical compared to what earned Becquerel his Nobel.
I think the physics community has roughly the right priors on someone making a revolutionary experiment in their garage. And even if individual members are not always amendable to evidence, eventually the physicists would come around. If your cold fusion is providing energy to cheap to meter, if your conservation-of-momentum-violating space drive is getting you to Mars, the physicists will be very interested in figuring these out. The problem is that so far, it seems like the alternative proposals are exclusively by cranks and snakeoil salesmen.
I appreciate the perspective and it’s a good explanation for why we don’t expect paradigm changing physics to come from outside the official research/experimental channels.
I don’t think these prior should be so strong for advanced AI models. I’m sure mathematicians did not expect the avalanche of proofs that came out. And that they could have given very detailed explanations of why it wouldn’t happen.
I don’t know if GPT 6 or 7 will crack some novel physics. But i suppose I think it’s approaching more likely than not.
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