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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 Gahas, and this is 90 Seconds on the Verge for Tuesday, October 20th, 2026. Spotify announced today that it has removed over 50 million AI slab 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 DZ are ticket-tougher stands. 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 and 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 me and 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. Yarnu, Grand Theft Auto 6. You can read more at the Verge.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 they're 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 that everyone involved, I imagine, but exciting.
Has it actually been? For me, some of the fun things about being a journalist is randomly getting to suddenly get at least a little bit of knowledge on something? There's no shot of me being an expert on this thing, but I now have an excuse to get weirdly into this esoteric thing for two weeks. Has this been the last 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 it particularly well, far from doing a maths degree as well. But yeah, I've covered a lot of science in my past, but mathematics has always been a tough one to get into. So it's exciting and it's a people's story as well. I agree.
So, and this is actually why I think this is so interesting because I, 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 effective by this thing. And I want to get into it because I think that group of people is interesting and important. 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 way to start is probably with the labs themselves and I guess specifically OpenAI, which at some point in the relatively recent past seem 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 things are the labs have been focused on this for years. It's like mathematics has been a, I think they've focused on them for a very, very, very long time. And it's kind of a lot of mathematician I suppose to say that's like, oh, it's just this kind of rightly or wrongly, whatever is a kind of pinnacle of knowledge.
And it's, I think, easy for them to show off in that area. And yeah, and it's always been quite terrible. And in the last few months, very good, it seems, 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 hard at the time. You can even tell it works. They sort of rely on the mathematicians. But yeah, basically, it's like there's always problems that kind of labeled as open problems in the field and they just kind of in mowing through them.
And it's gone from kind of problems no one really cared about or noticed or maybe heard of that here or there to ones that are 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 we're where the people steeped in this on the mathematics side surprised by this. Like, obviously, this has been a surprise to those of us who were not paying close attention.
But there's a 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 too. It works. Yeah. I mean, I think a lot of that perhaps depends on where in mathematics, you look for it.
It's kind of a very, a field that varies between you've got some who are very much like, I use my chalkboard. 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 said, kind of really early adopters and 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 of weeks, like, Oh, OK, I'm really adjusting that timeline and figuring out what that means now.
Interesting. Well, 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 star on firepower on me specifically and is going to run me out of a job all at once.
What is this felt like for the people at the sort of tip of the spear in the mouth world who are suddenly finding themselves competing with open AI to solve these problems? Yeah, I mean, the ice tower on Funnily enough is something that came up really probably around three times with different mathematicians I spoke to because it's it's not even spread across this field either. So someone like, Oh, it's coming my way. It's coming my way. Maybe who knows.
But and that adaptation, I think, so how people react to it's happening so quickly that people almost don't know how to feel. And I mean, I first started carving 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, I'm saying, I'm really worried that the number of the necessary things I'm struggling to sleep. Or I don't know whether there's any point in continuing because I mean, opening I were quite clear after one of their big problems, they solved it. Like we've got more than a hundred waiting.
That was it. So it was 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 to race to put out as much material as I can possibly put out that I'm working on before I get scooted by open AI 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 open AI 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 able to dream of before. And that could be very exciting. Still the way open AI is doing this feels bad. Can you tease that apart for me?
Like, what is it about the way open AI 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, I mean, scooping happens. Like that's one thing I think a lot of folks has been on whether or not they've scooted researchers because they're a millennium prize problem that they're kind of supposed to crown achievement in this. The big allocation they had, it was they, and this is by their own admission as well, which is almost quite stunning. They'd heard a rumor that some researchers are getting close.
And so they then decided to basically throw everything to see if they could. And 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 80, 80 hours odd, they don't know. Well, and it was a big achievement, but it just feels a bit new.
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 it happens in academia. Right. I think what is new is it's quite rare that they can turn their eye and academic return right 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 I think has left a lot of people thinking. It's against an all. Yeah, I'm sure your average math professor is sitting here being like, gosh, if you give 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? It feels like a not reasonable place to land for these folks.
Well, I mean, the fanist that comes to it as well, I mean, opening our eyes throughout have said that we want to know tools that 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 that there's another element as to what they do as well as 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 they're more than 100 results. I mean, turns out the more than was doing quite a lot of work. 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 this is like math problems that have been out there unsolved with people working on them for a long time and opening hours just like, we fixed them all. Yeah. Except it was a big question mark there because not all of these have been said, 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 have 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, it would have 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 like a lot of it, the norm there is that they're not only sort of saying, oh, we've solved all these problems, aren't you grateful? It's dumping a load of work on the field as well. That fundamentally misunderstands field. I mean, I think a lot of people, 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, not sure that which is part of it.
Sure. But like, there's a lot of value in the kind of understanding whether it's fit, forging new methods or kind of understanding that. And that will come as when human 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. And all of that other bit is kind of missed as well.
And that's I think where it's kind of annoying because they've said this most recent focus of $400 results, I'm talking to years, at least just all through. Wow, just on a basic level. Support for the show comes from Shopify. Starting a business requires you to be resourceful. And the more you flex that muscle, the more you start to see opportunities for growth.
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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 solve this, even if the thing arrives at the right answer, most of the value of doing this research is in figuring out how to arrive at the right answer. We learn 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 was 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 open AI 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 and how we got here. And even the people at the labs don't know.
They're just like, we put a bunch of compute at this and it arrives at an answer that we think is right. And I just I don't 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 the part of the problem here is that you have one side to like, haven't we done a great thing? And you just great. We answered your question.
Yeah. But it's almost like that wasn't what we were trying to do. That was all of what we were trying to do. And you've kind of like made the rest of it worse, maybe on the way. I'm not asking you can't even tell because it's so like some of the things, I mean, three of those papers that they put out of, they already retracted due to what they say was a sign error, which I mean, I'm sure it's a bit more complicated than it sounds, but I mean, that's the kind of thing you make in school, isn't it?
And you're like, Oh, silly me, right, which feels like it could have been, you know, checked before they put it out with hundreds of others. And it's just almost like a lack of care. I think it's what a lot of mathematicians spoke to. It feels like they don't really care about the field. They just care about it. I mean, the headline for the story that was like,
opening, I 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 opening, but I 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 there are 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 have a hard time figuring out how those two things come together. But 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 opening eye learning how to be a better citizen of the math community? Well, it's it's complicated, I think, on that.
I mean, it's trying or at least it's saying it's trying. It's not doing much like that. It's not doing nothing. It has perhaps sometimes shot itself in the foot in trying to do so. So we're going to be able to do that in other places.
Never, never. I'm not so impressed. But so one big thing that they were sort of saying, well, when they sort of result this but I mean, prize problem, we've got a hundred more. We want to be quite responsible with those. Right.
So we're going to consult some. Which by the way, it seems had the opposite effect of essentially dangling a hundred more envels over the heads of mathematicians for some unknown amount of time. Oh, yeah, absolutely. That was when I started getting texts and people said, I can't say this is absurd. Or I published three papers.
Well, we've dropped this one bomb on you. We have a hundred more over here, but don't worry about those yet. I just had that it was so much bigger than it appeared either. Which was the reason the question is there something that prompted them to release that many at once? Right.
But yeah, but so that's the sense of the way they've been trying. And one of that was to consult a group of acquisitions who open AI had initially approached. And this was an elite group. I think it's about maybe nine people among them sort of that they're all kind of top of the field, basically. It's not representative of the field as a whole, but it's kind of a crime.
And open AI had initially approached some of these people to form this board who had responded with saying, well, we'd rather not be an open AI thing. We're going to form a room thing, they'd approached a few more form the group, except then open AI somewhat sort of quickly hijacked their announcement. I mean, they've got a bigger footprint and it kind of made it sound like they were opening eyes thing and so a lot of mathematicians I was seeking to around that were like, Oh, yeah, it's opening eyes group who knows if they're being paid by them. And I chatted to a few people in this group and they're like categorically nothing. The only thing we have is like maybe a bit of advanced access to help them communicate.
And that is it. And they said they've been talking to other AI companies as well about communicating their results, but open AI sort of hijacked that and made them look less than independent, which immediately dent in their credibility, the effort. And then open AI also made quite clear that we're not bound by what they say either. So this is like the Facebook oversight board, which is it's it's it's nice in theory and everyone will, you know, feel bad when they do bad things, but there's nothing actually real or binding here if they don't want it to be. Yeah, pretty much.
And they've taken a few bits on board. So they obviously dropped that sort of bombshell of 400 results. The quality is better. I mean, it's poor and variable, but it is a lot better from what I've been told. They've also one of their other things was to commit to sort of helping foster that understanding element we spoke about.
So whether that's your conferences or talks or workshops or that and I believe yesterday, this is both open and on topic said that they put five million each towards the Simon Foundation initiative to do just that. I have seen skepticism over the amount of money considering what they send on the field and the disruption. But again, it is something but it's again like as to they've also very clearly said that we are not going to be doing what was, I think, arguably the biggest recommendation or urging from this group was please stop testing internal frontier models on everything you can find in our field, please. And I quite clearly said that in when they denounce these results, we are trying to develop tools scientists and as a result, we will be testing everything on these models that we can.
I have to say, my immediate reaction is that A, of course, opening eyes is going to say no to that and B, it's kind of an unreasonable request to make. I don't know, I need to think more about this, but I'm curious, having talked a bunch of other sides about this, what do you make of that request from the math community of essentially saying, if not stop, at least slow way down? I think even it's that level playing field we were talking about, I think, that is, I agree, I think to say, please don't test this thing on that is perhaps a big stretch. I also think it's perhaps not a stretch to say, OK, well, there is a history here, there is a tradition here, there is there are contributions here that you are not acknowledging, like in the early hours of that kind of the big drop.
Some of the texts are getting something to look like they've just got a paper that someone else did like a month ago, like I don't know whether or not that hand out, but like you don't know because of the way they credit these things, like it's well known that they have been quite spotty that and the past including in the mathematics roles, like they've acknowledged it themselves and I've spoken to them. So I don't think that's necessarily like to say, hey, could you level the playing field a little bit or at least give us a chance, especially when they're using a tier that I mean, it's the same argument that you have in like a lot of these fields with with copyrights, so media or writing or anything. It's you've used public works for a private model. And I still see equivalent here, if you're thinking of images, maybe we'll now know if you were allowed to paint because we've done all the painting, painting solve.
Like we made the best painting and it's galling enough when it comes down to creative endeavors and whilst it may look at that profession, it does not stop people from actually doing that. You could argue that yes, that elements of mathematics and it doesn't stop either, but it feels a slightly different type of disruption than it would for those other areas. I also think given that they then like one mathematician said, they need us to celebrate their work or to validate it, to prove it, to know that it's good and to use it for whatever hypothesis they want, they need the mathematicians. And I think that I don't think they're even asking by dropping on and they're demanding or presuming even worse. I don't think it's that unreasonable to say, could you at least even if it's not playing fair, not playing what feels underhanded.
I buy that. It seems to me that the good news of that line of thinking is that that is a relatively straightforward thing to accomplish, right? That kind of interaction and symbiosis and communication would not be that hard to do. The problem is OpenAI has essentially no incentive to do it. And this is what we've seen over and over, right?
And it's not just OpenAI, these companies have discovered that they can essentially get their way by just running at such speed with so many resources that you like, you know, the one you just steamrolled is going to complain about it. What are they going to do? They're flat on the ground. Do the folks that you talk to on the math side have any optimism that this can happen? Because it does seem to me like if I'm going to be a little Pollyanna, I should about this for a second, there is a world in which something incredible is happening here, right? That what we've given is these people who have been struggling with
some of these problems, a new set of tools to help them not only solve those problems, but invent and solve the next ones and that we can speed this stuff up. And that like the way that we talk about, you know, drug discovery and the way that stuff can work can be applied to all of these other industries. There's something here that feels like it could work. And I spend a lot of time vacillating between optimistic that we will get there and completely nihilist about it. What do you hear from the mathematicians right now?
Yeah, I mean, again, it's I think it's a very mixed bag. I think the seed makes it very hard. I think people should imagine where to go. So, like, I mean, the disruption of this many results in one go, like, the amount of people I didn't really speak to anyone who directly had this. But they said they had colleagues who's research programs have blown up over now.
What do you do with that? I mean, and I remember in seeing some of the comments from from the story of Hanzo, well, there's still clearly a job for mathematicians, which correct there is a lot to do in understanding, contextualizing or even using these tools, which I'll return to. But the point is, like, does the professions survive that long enough, given the speed of this to do that? Or does it change its value system in crediting more than just the end result in time to recognize the value of people doing all that other stuff? And that's, I think the question here is that because it's happening so quickly and it's being done with seemingly such little care that those systems in place, like, well, what do you do if you then obliterate an entire generation of postdocs and PhD students because you solve their problems? Yeah.
What happens after that? Like, there's then a both a need for them and an absence of trained mathematicians. But some people are also very excited about what all the shows and in saying that, well, one is that a lot of what's happened isn't necessarily that creative, opening up new area kind of stuff. So there's either excitement that maybe AI could do that or that still leaves lots of avenues for humans. The other is that people, I know how people sort of text me, like preprints or papers they're working on, which I understand all of.
I love the idea of you at like two o'clock in the morning, just getting a text that's like 8,000 words of equation that is just completely unreadable. And they're like, look what I've discovered. Like, this means nothing. I mean, with the time difference as well, that is about the time it would come through. And I also didn't know some notation could go by text, but it can.
And it's fascinating, though. And it's a hugely accelerating research people already. I was texting a few people today, recording this, saying how's it going and they're kind of like working on that long process of digesting what's there, but they're already building on it on some of the results. And that's really exciting and using these tools as well. And so, I mean, there's a fear as to whether it will sort of kind of pay for play type thing as well, because mathematics isn't notoriously a very like flush with cash academic discipline and so even a relatively modest kind of cost for AI could be too much. But it's yeah, I mean, but a lot of people are excited.
Some people are very scared of using these tools for reasons of, I mean, some of the allegations that have gone around with a lot of these results, particularly in the early days of the millennium prize problem they solve, was that are these lands using material from the conversations that research are having with their tools in their models, which later go through. Now, it's a very difficult one to answer it in either direction, in either direction, in order to prove it to disprove. Yeah. And so, opening eyes initial comments with the millennium prize was basically, we can't say no, which obviously does not sound great. They have since come ahead and come out and said, like, we did not, I will say and I'm not dug into this, but it is also very precisely worded language that is used because we know like there are a lot of things that these models come, you can't tell whether they've either of it up or not, or in what form or whether it's a synthetic database on conversations, which is not the same as the conversations. And so a lot of people are now quite concerned about using these
tools as powerful as they are for their research out of possible fear that they would be giving their own work away to some competitor. And it's a difficult one to do. And again, it's so similar with any other discipline that we've seen. Well, that's kind of the last thing I'm wondering is like, how long until you start getting texts from every other professor at every one of these universities? Like, I've got to imagine that everyone in academia is watching this particular thing happen. And again, looking and saying, okay, at what point is it coming for me?
And there's something specific about math that it is pointed at these specific kinds of problems. Like there's not there is no equivalent of that that I'm aware of in like the English department at these universities. But this question of can it, can these things come and essentially attempt to obviate me by quote unquote answering all of these questions? It does if I'm open AI, like everything about what we know that everybody suggested again, they are giant hammers looking for nails and they will find them everywhere they can. And I have to assume you're going to start getting the numbers of every professor at every college around the world.
Yeah, I mean, the answer to that question, I think, requires me going back in time. I've already started getting those texts. Any listening also, please get in touch. I mean, I've been following it closely for a long time, but a mathematics is an interesting one, I suppose, because it doesn't have a practical element most of the time in that you don't need to go and verify in the lab. Antropic have quite famously given Claude a lab somewhere in the Bay Area, I believe.
I didn't know that. I'm entirely sure on the specifics we're working for it, but it's kind of the hallmarks there have been questions over whether they announced their first big finding a month or so ago. I'd say the press release left a lot to be desired. They likened it to CRISPR a lot at the same time, which obviously is a huge revolutionary discovery was in the same sentence saying we don't quite know what this does. So great.
And they'd followed it, but they said, well, we're still working on all of that, but we wanted to let people know what Claude could do, which if ever there was an IPO coming, right? There you go. Well, we're not beat these. This is all just a hyper product allegations. But yeah, so it's already starting to happen in other there was also similar allegations to whether or not I've not had a chance of getting myself on topic tonight, but it was to whether or not they used research or work from other researchers in similar areas.
And like, but it's the same type of thing you can see happening. It's let's find things that we can throw computer dot dot dot. So I've already been speaking briefly, mind, but kind of people in areas, if you think sort of somewhat similar to mathematics and this sort of areas of economics, for example, or computer science or like engineering or kind of elements of those, like they're all, I think, worried, undershows the sense of almost impending doom. I mean, a lot of very excited about these tools, but like what you do when a giant turns its eye on you and decides to just walk in your direction, you just kind of vote for the best at that point, but and figure out what happened after there's a lot of these efforts now to kind of get more data in same material science or biology or even automating the robotics sort of parts with labs so they can generate that data and then do some more and do some more.
And I mean, I've been around labs that are sort of starting to do this and it's fascinating, but it's all sort of heading in this direction and at least for some their practical applications, like mathematics is kind of a pure research basis, but it will be interesting to see where where this lands when they do start finding something that they could immediately be useful. I found it fascinating that anthropic were very clear going, Claude found this enzyme system, which if you were to try and patent it would immediately make it impossible to do so I believe there's probably a new ones there, like as far as I'm aware, the US and AI cannot hold a patent at least for now, at least for now. That's just when they turn Claude to law. Yeah, exactly.
Yeah, well, it does seem like I think we are going to play out some version of this in discipline after discipline and profession after profession. And the only hope is that each one of them we get a little more sophisticated in how to actually make this work for everybody. But the way it's gone with math so far, at least I can't say fills me with hope about where it's going to go. No, that's not in spy confidence. No.
All right, well, everybody, if you are watching or listening to this, get in touch with Robert. He doesn't get enough to AM text messages. He's just telling me he wants more people to blow up his inbox to AM. Get in touch. We love hearing from you.
Robert, great to see you. Thanks for being here. Thanks for being here. Thanks for being here. Thank you.
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We are the Internet's Wall Street and Silicon Valley big sisters. Tune in every Monday for late night's sister talk meets boardroom strategy. Check out Tiger Sisters on YouTube, Spotify or wherever you get your podcast. Welcome to the Tiger fam. All right, before we go, I have one more thing for you.
I have a movie recommendation. So obviously there's a bunch of tech movies out right now. You should absolutely go see, you should see everything, the three hour documentary about Elizabeth Holmes, everybody says, that's incredible. You should go see Musk, the I think four hour documentary about Elon Musk. Everybody says, that's incredible.
You should go see the social reckoning, which everybody says is kind of near, but it's probably worth seeing. I feel like there's a really fun like 11 hour day where you just go watch all three of those things. But in addition to that, I just want to recommend a movie that I watched recently that is about technology and kind of went under the radar. It's called Good Luck Have Fun Don't Die. It's our Sam Rockwell who comes from the future to recruit a bunch of people in a diner to try to save the world from AI.
It's a super weird movie. I don't pretend that it perfectly holds together as a movie every single second. It tells the story of how all these people came together and how technology invades their lives and changes the way they work. But in it, there's actually a bunch of really interesting ideas about how we think about AI safety and how we regulate AI. Part of the premise of this movie is that we invented AI and then didn't invent ways to control it for decades and in the interim phase, the world kind of fell apart.
And boy, does that feel relevant to every single thing that is happening right now. So it's full of interesting ideas about how we think about technology and how all these things relate to each other. And what it means if we let technology run away from our ability to control it, it's also just a really fun movie. Sam Rockwell is fantastic in it. It's bizarre and funny and just a good time.
I really highly recommend this movie. I think it went under the radar in part because it's a relatively small movie, but in part because Good Luck, Have Fun, Don't Die is a bad name for a movie. And one of my closest held theories is that nothing can survive a bad title. It's like that movie Mike and Nick and Nick and Alice. Also a good movie, Stupid Name, Nobody Watch It.
So anyway, good luck Have Fun, Don't Die. Fun Tech Movie, I recommend it. Go check it out. Let me know what you think. That's it for the show.
Thank you to Robert for being here and thank you as always for watching and listening. If you have thoughts, questions, feelings, again, reach out to Robert. He wants to hear from everybody in all disciplines about this. This is an important story for him, for the Verge, for all of us right now, figuring out what is happening when AI points itself at you and your job and your life is the question of our time in so many ways. And we want to hear all about it.
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Right. For 50 years, Canadians have wondered what the names of IKEA furniture mean. Like, Natopa, the guardrail for children's beds. Natopa means giving your favorite person room to grow. It means their safety and your piece of...
No. In Swedish, not all bummies, night monkey. Mom, he's back. IKEA, 50 years of Swedishness at home. Look, he's wearing middle pajamas.
So cute.