Hello and welcome to The Vergecast, the flagship podcast of the Navier-Stokes problem. I'm your friend David Pierce, and today on the show: why is OpenAI suddenly solving all of these big math problems, and what does it mean for the future of being a mathematician? But first, here's everything else happening on The Verge today.
I'm Josh Kahas, and this is 90 Seconds on The Verge for Tuesday, October 20th, 2026. Spotify announced today that it has removed over 50 million AI slop and spam tracks from user recommendations over the past year, while also changing its system to highlight more human-made music. Other streaming platforms like YouTube Music and Deezer take a tougher stance: they put labels on AI songs and filter out AI slop. Spotify, on the other hand, doesn't mandate AI labels on music. It even partnered with Universal Music Group to launch a tool for creating AI music remixes. So, hooray for humans, I guess.
Meanwhile, Adobe announced yesterday that the mobile version of its photo editing app, Lightroom, can now open closed eyes in photos. The app uses generative AI to match the lighting and pose when generating these replacement eyes, as well as reference other images of the same person in your Lightroom library to create an accurate set of peepers. This immediately reminds me of the Wesley Snipes Blade meme where the studio added CGI eyes to his face because he refused to open them.
And finally, Switch 2 owners rejoice. Nintendo Switch Sports Resort launches today with 12 sports, including two fan favorites from the original Wii Sports: tennis and bowling. The game takes advantage of the Switch 2's motion controls to allow you to jet ski by using the Joy-Cons like handlebars and use its mouse controls to skateboard. Sports Resort even takes place in an open world complete with a sightseeing mode where you can explore the island via land, water, and air. Your move, Grand Theft Auto 6. You can read more at theverge.com. That's 90 Seconds on The Verge for Tuesday, October 20th, 2026.
Over the last few months, OpenAI in particular has started to release what it calls answers to some of the biggest problems in math. Problems that professional mathematicians have been working on in some cases for decades, even a century. Some of these are big problems that everyone has been working on that there are big prizes for solving. Some of them are just things that researchers have been noodling on forever. This has given the math community kind of a shock. People are worried about what OpenAI is doing. People are worried about whether OpenAI is taking credit from them. People are worried about what this means for the existence of the life of a mathematician going forward.
The Verge's Robert Hart has been covering all of this. He's been talking to professors around the world. He's been dealing with the problems. He's been learning what the Navier-Stokes problem actually is. He's going to guide us through all of what's happening and what this means for everyone else who does other things that might someday have AI pointed at them. Let's get into it. Robert Hart, welcome back to the show.
Yeah. Hi. Thanks for having me.
You are now semi-officially The Verge's expert in frontier mathematics. How does this feel?
A worrying prospect for everyone involved, I imagine, but exciting. It has actually been, like, for me, one of the fun things about being a journalist is, like, randomly getting to suddenly get at least a little bit of knowledge on something where it's like, there's no shot of me being an expert on this thing, but I now have an excuse to get, like, weirdly into this esoteric thing for two weeks.
Has this been the last, like, three or four months of your life, just learning math terms?
Yeah, I mean, I've tried learning them before. I've tried at university. I can't say I did particularly well. Far from doing a maths degree as well. But, yeah, I mean, I've covered a lot of science in my past, but mathematics has always kind of been a tough one to get into. So it's, I mean, it's exciting and it's, I think, at heart a people story as well.
So, and this is actually why I think this is so interesting, because as I first started learning about what's been going on between the AI industry and the mathematics world, it seemed sort of esoteric and niche, right? Like this is this one group of people who are very affected by this thing. And I want to get into it because I think that group of people is interesting and important. But I actually think there is a much bigger, more broadly applicable thing happening here.
But let's start a little bit at the beginning. And I think the easiest place to start is probably with the labs themselves, and I guess specifically OpenAI, which at some point in the relatively recent past seemed to have turned their attention to solving big math problems. Can you just kind of catch me up on the last few months of activity in this space from these labs?
Yeah, I mean, probably best saying as well, the labs have been focused on this for years. Like, mathematics has been, I think, a big focus of them for a very, very, very long time. And it's kind of a lot of mathematicians I spoke to say that it's like, oh, it's viewed as this kind of, rightly or wrongly, whatever, as a kind of pinnacle of knowledge, and it's, I think, easy for them to try and sort of show off in that area. And yeah, and it's always been quite terrible, and then in the last few months, very good, it seems. And like, and by very good, it's kind of on par with the best human mathematicians.
So a little bit like the coding phenomenon kind of nine months ago, that all of a sudden it went from a thing computers can do that it seems like AI might someday be able to do but isn't quite there yet, to all of a sudden overnight, oh my god, this actually works.
Yeah. And I mean, well, not that half the time you can even tell it works. They sort of rely on the mathematicians for that. But, but yeah, basically it's like there's all these problems that are kind of labeled as open problems in the field, and they've just kind of been mowing through them. And it's gone from kind of problems no one really cared about or noticed, or, oh, I've maybe heard of that here or there, to ones that, oh, some very accomplished mathematicians have spent their entire careers trying and failing to do this, and they did it in like three days. So yeah, it's a very quick, quick change.
Yeah. And were the people steeped in this on the mathematics side surprised by this? Like, obviously this has been a surprise to those of us who are not paying close attention, but there's an industry of people who, you know, spend their days and nights steeped in this stuff at universities all over the world and stuff like that. Were they caught off guard by this sort of turn from it doesn't work to it works?
Yeah. I mean, I think a lot of that perhaps depends on where in mathematics you look. It's kind of a field that varies between you've got some who are very much like, I use my chalkboard and my talk and that is how I work, through to they're using these types of tools and so they're quite familiar with it. But even among, I'd say, the kind of really early adopters and the enthusiastic users, a lot of them that I spoke to were still quite surprised that we'd got here now. They thought that some of the problems we're seeing going down, maybe give it a few years possibly. And then within the space of like a couple weeks, they're like, 'Oh, okay.' I'm readjusting that timeline and figuring out what that means now.
Interesting. Well, and yeah, walk me through the figuring out what it means, because I think when you say this is a human story, I think this is a lot of what we're talking about, right? There is this thing that happens, and I think a lot of people are either dealing with the reality of this already or the theoretical possibility of this coming soon, where a thing that I have spent my life learning how to do and doing, all of a sudden there is this massively resourced AI lab over here that has turned its, like, eye of Sauron firepower on me specifically and is going to run me out of a job all at once.
What has this felt like for the people at the sort of tip of the spear in the math world who are suddenly finding themselves competing with OpenAI to solve these problems?
Yeah, I mean, the eye of Sauron, funnily enough, is something that came up probably around three times with different mathematicians I spoke to, because it's not evenly spread across this field either. So some are like, is it coming my way? Is it coming my way? Maybe. Who knows? But, and that adaptation, I think, I suppose how people are reacting is happening so quickly that people almost don't know how to feel.
And I mean, I first started covering this more diligently maybe two months ago, maybe three at best, when there was some sort of progress with an internal model that looked impressive, and then it escalated massively from there. And ever since, I've had a lot of mathematicians sort of texting me or something saying, I'm really worried. The number of them that messaged me saying, I'm struggling to sleep, or I don't know whether there's any point in continuing, because, I mean, OpenAI were quite clear after one of their big problems they solved, they're like, we've got more than 100 waiting. That was it. So it was that sort of like, is the eye of Sauron on my discipline already? Should I bother? And the reactions really were from kind of despair to let's just carry on to I'm going a race to put out as much material as I can possibly put out that I'm working on before I get scooped by OpenAI or someone else.
Yeah. And it seems like obviously there are a lot of people feeling a lot of different things, but it does seem like one of the overwhelming reactions to this has been frustration with OpenAI in particular, not necessarily just for advancing the technology. Like one obvious thing to feel here is a sort of professional jealousy and fear, right? That like, oh, if the computer can do my job now, what am I here for? Right? And I think again, that's a thing a lot of people are experiencing right now. And I think that's very real.
But it seems like a lot of people you've talked to are able to set that aside and say, even if it's cool that we now have these tools that are able to crack these problems, even if I can partner with this thing to do work I've never even been able to dream of before, and that could be very exciting, still the way OpenAI is doing this feels bad. Can you tease that apart for me? Like, what is it about the way OpenAI in particular is handling this that is rubbing all these mathematicians so much the wrong way?
Yeah, I mean, it's almost kind of like Silicon Valley comes to insert area here. Like, it's that very sort of move fast, break things, and move on before you've even kind of, not even picked up the pieces, but even seen the pieces fall, right? And it very much feels like that, I think, with a lot in the area. And like, the conduct is, I mean, it's quite a world away from what happens in a lot of academia anyway.
I mean, so scooping happens, like, that's one thing. I think a lot of the focus has been on whether or not they've scooped researchers, because their Millennium Prize problem, their kind of, I suppose, crowning achievement in this. The big allegation they had here was that they, and this is by their own admission as well, which is almost quite stunning, they'd heard a rumor that some researchers were getting close, and so they then decided to basically throw everything to see if they could. They spent, I don't know quite how much. Rumor has it it's in the tens of millions of dollars to spin up about 10,000 agents, and in, I think, 88 hours odd, they'd done it.
And it was a big achievement, but it just feels a bit, it's an unpleasant taste it leaves in the mouth when you sort of, the only reason they did it was someone else was, which again, it happens in academia, right? I think what is new is it's quite rare that they can turn their eye, an academic could turn their eye to anything and do it capably, and the resources as well, like, it's without precedent, really, that level of scale. And I think that's what's, that's one element that I think has left a lot of people thinking, like, it's against a norm.
Yeah, I'm sure your average maths professor is sitting here being like, gosh, if you gave me tens of millions of dollars and unlimited compute, I could also do some pretty interesting stuff. Like, wouldn't it be great if we were fighting a fair fight here? Feels like a not unreasonable place to land for these folks.
Well, true. I mean, the fairness that comes to it as well. I mean, OpenAI throughout have said that we want to build tools that let scientists do great things, which is a lovely aim. Why aren't you giving the scientists or mathematicians in this case, like, the tools? Like, this is an internal model that is not publicly available. No one can even verify.
But there's another element as to what they do as well, is that obviously preprints have been a big thing within the scientific community for a while now, but they're not peer-reviewed. They're not necessarily checked. And in a lot of cases that's happening here, they're written by AI, which we all know is really high quality and really easy to understand, especially when dealing with really complicated material.
And so what they did most recently is, there are more than a hundred results. It turns out the 'more than' was doing quite a lot of work there. It was nearly 400. Which they spread out on, I think, more than 700 preprints that they just dropped one evening.
Wow. And this is like math problems that have been out there unsolved with people working on them for a long time, and OpenAI was just like, we fixed them all.
Yeah. Except... Except it was a big question mark there, because not all of these have been... So there's a thing in mathematics, you can formalize something so you can kind of prove it computationally even if you don't understand it. I think, like, not all of these had that with it. So you can't easily check that it's necessarily right.
Obviously it's specialized material. It's difficult to go through these papers. One mathematician I spoke to was like, if this was written by a human, I'd be surprised they were able to write it, kind of thing. It was that badly written. Attribution is all over the place, which is a big thing with AI, as we know. And so it normally also misunderstands the field. I mean, I think a lot of people, and I'm myself perhaps guilty of this in the past, are thinking that mathematics is sort of a list of let's solve this, this, this, and that, like, which is part of it, sure, but like, there's a lot of value in the kind of understanding where those fit, forging new methods, or kind of understanding that, and that all comes when humans solve problems, is a natural part of that process. We've now just got that end point, maybe if it turns out to be true, right, and all of that other bit is kind of missed as well.
And that's, I think, where it's kind of annoying, because, I mean, they've said this most recent drop of sort of 400 odd results, I've been told, take years at least to sort through, just on a basic level.
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This is the part of all of this to me that is, I think, the most sort of generalizable in really fascinating ways, and is a thing everyone on both sides of this question ought to be reckoning with in really real ways, which is there's a thread in a lot of your stories that is people being like, again, look, even if they solved this, even if the thing arrived at the right answer, most of the value of doing this research is in figuring out how to arrive at the right answer, right? We learn new tools. We learn new systems. We learn new methods. And it is the work that then gets us to the next thing.
And this assumes that just knowing the answer to the test is enough. And if you give us the answer to the test, you have given us everything that is required. And they keep just being like, no, that's actually not what this is. Because now what's going to happen is there is no next problem for this AI system to be pointed at, because we don't know how we got to the answer to this one. And like, are we now betting on the next OpenAI model to be able to creatively imagine new kinds of problems, which I think is sort of a leap beyond anything that has been able to happen before.
But there's this real sense of people being like, we need to understand what works and how we got here. And even the people at the labs don't know. Like, they're just like, we put a bunch of compute at this and it arrived at an answer that we think is right. And I just, I almost don't know how to bring those two sides closer to one another given the way this technology actually works.
Yeah. I mean, I think that's almost at the heart of the problem here, is that you have one side who are like, haven't we done a great thing? And it is great. We answered your question. But it's almost like, but that wasn't what we were trying to do. Well, like, that wasn't all of what we were trying to do, and you've kind of like made the rest of it worse maybe on the way.
Not that thing you can even tell, because it's so... Like, some of the papers, I mean, three of those papers that they put out have they already retracted due to what they said was a sign error, which, I mean, I'm sure it's a bit more complicated than it sounds, but like, I mean, that's the kind of thing you make in school, isn't it? And you're like, oh, silly me, which feels like it could have been, you know, checked before they put it out with hundreds of others.
And it just feels almost like a lack of care, I think, is what a lot of mathematicians spoke to. And it feels like they don't really care about the field, they just care about... And I mean, this, the headline for one of the stories I was like, OpenAI just wants to win, and I feel that is a reasonable kind of attitude for a lot of how these big AI labs work. They want to get something big and they want to get there first.
Yeah. I was thinking a lot about the race to AGI in the context of the OpenAI just wants to win thing, that what happens in so many of these cases is these companies are given some kind of brass ring, right? Like, we're going to achieve AGI, we're going to solve a Millennium Prize math problem, we're going to do XYZ thing successfully, and all that matters, all that matters, all that matters for your promotion, all that matters for your company valuation, all that matters for your reputation in the tech industry is whether you did it and whether you did it first. The end.
And that is just the system we have built, and especially with AI, which is just this giant hammer looking for nails at all times, that it feels like that has become so exacerbated. And the idea of having a thing that builds on itself, and that there's a community of people learning together, and that all of that is actually designed successfully to make progress happen, is just falling apart in this race to just hit milestones because they're things we can measure that look like success.
And again, this is maybe just toothpaste that has been out of the tube for some time. But we're in a moment where anything that is quantifiable, everyone in tech is just racing towards as fast as they can and burning the boats behind them. And it's just the culture of that next to, in particular, this academic culture that has been so different and is so deliberative in its way. Again, I just, I have a hard time figuring out how those two things come together.
But I will say to... I was going to say to their credit, but even that might be too generous. It does seem like there is some effort on both sides to figure out how this can be slightly more harmonious. What are you hearing? Is any of this working? Is OpenAI learning how to be a better citizen of the math community?
