Limitless: An AI Podcast - OpenAI’s Unreleased Model Solved Math: Is Biology Next?

Episode Date: October 12, 2026

An internal model from OpenAI reportedly solved around 90 of the hardest open mathematics problems and what that could mean for fields like physics, chemistry, and biology. We cover expert reactions, verification challenges, and the potential for AI to accelerate work in science and pharmaceuticals.==Timestamps0:00 OpenAI’s Math Breakthrough1:53 From Impossible to 90 Problems4:39 Why This Changes Everything8:06 Mathematicians Feel the Shock10:30 Reasoning Spills Into Other Fields11:37 The Cryptography Alarm14:19 Humans Become the Bottleneck15:56 Biology Is Next17:26 Beyond Math, Toward AI Supremacy#openai #maths #physics #chemistry #ai #science ==AlphaSpace by Yahoo Finance: A Professional-Grade Investment Platform Built for Everyday Investors.👉 https://finance.yahoo.com/about/promos/gold/alphaspace/?ncid=100003571

What's up, guys? I just got to Japan. I am the most jet lagged I have ever felt in my life. And I'm sat on my hotel room floor because I need to record this video. Open AI just dropped one of their biggest releases and it's not an AI model. It's the announcement that they solved around 90 of the toughest problems in mathematics ever. And I know what you're thinking. You're wondering, why on earth should I or you care about math? That's something I haven't touched since high school or college. And the point of this video isn't that they solve these math problems. This will slowly and very quickly roll over into every particular field that you could think of

that you probably studied to do your job today. It'll roll up into chemistry, into engineering, into physics, into whatever requires reasoning. And it's all happening much quicker than anyone can even imagine. So in this episode, I'm going to get into what exactly it's solved. but also more importantly, what happens next and what this means for you. All of that and more on today's episode of In the Loop, brought to you by Corecom. Let me just set some quick context. Three years ago, AI could not do math at all.

Two plus two didn't equal four. It equaled 11. Strawberry had five R's in it. LLMs just weren't smart enough. And I remember commentary from Terence Tao, who is one of the leading mathematicians, in the entire world. And he said, this is a reasonable attempt by AI, but I do not see how reasoning can eventually lead to solving maths. Reasoning is basically how you or I or any other human

reasons about a problem. We don't just take it for face value. We apply different context to it. Back then, AIs could not do that, but they were at the start of their journey of figuring out this whole thing. Now, fast forward to six weeks ago and open AI who are back, then and still are the experts in reasoning for training AI models took that to its fullest extent when they announced that they had an internal model that was only 11 days old had solved one of the Millennium Prize problems. Now Millennium Prize problems, they're seven of them, and they're all in the field of mathematics. If anyone can go out there and solve any of these problems, you get $1 million. Now, for 200 years, these problems have existed. People have made progress, but the

truth is, they're just too few mathematicians and experts in the world to try and figure this out. We are limited by human cognition and capacity. Open AI came along with zero experience in maths, with zero help from math researchers, and they just decided, hmm, I wonder if I just use this powerful model that's more powerful than GPT6 Astra, and just let it go until it was able to solve the problems. There was infinite compute, infinite money, just get it done. And it solved it. In 88 hours, 10,000 agents, it was one of the most costly experiments that they've done, probably that anyone's ever done, for a research problem. It cost over $10 million. And there was a few interesting things about that. One, this was the first instance that an AI

model had demonstrated that it wasn't just mimicking human intelligence. Up until then, people had been thinking, okay, AI models are smart, but they're just mimicking what it's learned on its data set. It's a next token predictor. What it means is it's trained on all human knowledge that we put out there. It's limited by the cap or the ceiling of how intelligent we already are. It solving Navier Stokes was the first demonstration that it could go above that level, that it knew things that we as humans couldn't understand. And when Navia Stokes got resolved, people weren't as excited as you might expect them to be, particularly mathematicians. They looked at this result and they weren't happy. They were perplexed. They were perplexed because they couldn't

make sense of the solution. Now fast forward to today and we have news that their new internal model has solved not just one, not two, but 90 out of 500 of the world's most hardest open math problems. That's 20% of all the hardest math problems that have existed for decades at this point. But there's a few differences between this and Nabil Stokes. It solved it in a much quicker way. On average, each problem was solved in three hours. it also got it done at a much cheaper cost, costing only tens of thousands of dollars. So you can imagine or extrapolate in the future that these models will get faster or quicker and even cheaper to the point where the cost of solving problems that humans have spent decades

trying to figure out becomes undeniable. You should just use AI and you don't need humans anymore. But I was critical. I saw this announcement from Open AI here and I saw that they had dumped a massive GitHub, which was interesting. They open source the entire thing. So it was all the formal verification and proofs were laid out for anyone and everyone to critique. And it got me thinking, okay, it's solved a bunch of problems, but how is this useful for you or I? Like, how should I think about this being applied to the real world? And our friend, Oleg, actually had a great breakdown in his tweet. He ran through a few of them, but being very high level here, because I'm not a mathematician.

One of these problems are called Laslov Maxwell. And if it's truly solved, this research can then result in a way to create virtual. limitless clean energy to power civilization. Another example is the inverse elasticity problem that if it's solved can scan and map tissue stiffness, basically allowing you to evaluate and medicate and help individuals in the medical field. There's also another one called matrix multiplication. If you've heard that term before, it's because we're probably talking about it on this show. It's the foundation of how AI models run and how they run and compute all the important data to give you an amazing result. It's also how GPUs and any other modern graphics card

processor works for videos, gaming, or whatever that might be. It's a very important fundamental mechanism of computing. It solved one of the most incredibly hard problems that that industry was facing. And if it's able to figure out, guess what, you get cheaper AI and bigger scientific simulations that you could use to map or design proteins that could result or end in the cure for cancer, for example. So the idea here is this isn't just a list. of random niche arbitrary mathematical problems. These are things that have stumped scientists, builders, engineers and creators in the real world from innovating.

It is prevented humans from innovating and building something that is marvelous and novel that would help world and humanity as it is today. So when you frame things this way, you start to realize that humanity's progress has always been dependent on the geniuses that they have in its civilization, in its society at the time, who is smart enough to fix or figure out some of the heart. hardest problems in the world so that we can progress as a human race. I loved this tweet from Will DePue, where he basically asked Fable 5.1 and GPD6 Pro to rank all the discoveries that Open AI's internal model had made over the last three years, just to see how important these

discoveries actually were relative to that perspective that I just laid out, which is, you know, solving, you know, human innovation. And 81% of them were released on the day that Open AI had solved these problems, meaning that a lot of these mathematical problems were fundamentally crucial. Just think about that for a second. The next Albert Einstein, the next Galileo isn't going to be a human. It's going to be a computer. It's going to be an AI because it's cheaper. It can work 24-7. Most importantly, it's now demonstrated that it's smarter than us. Framing it the way that Alex has here makes a lot of sense. He goes, quasi-R-H. He's referencing one of the problems that it's solved. Are you kidding me? If a human did this, it would be an instant Fields Medal. No questions ask. A

Fields Medal is one of the highest honors you can earn in the mathematics field. PhDs, mathematicians make it one of their life goals to try and earn a Fields Medal. It is incredibly hard and a rare thing to do. It's very prestigious. And now the cost and effectiveness of that has been completely taken from mathematicians. And understandably, their reaction to this has mostly been mathematicians being very uncomfortable with the way Open Air has approached this. Actually, it started off when they solved Nabia Stoke six weeks ago. You might have thought mathematicians would have reacted positively to the news, but they really didn't.

They were actually offended. They thought that the act of Open Air using these LLMs to solve mathematical research, the act or thought of a computer doing their job that they had spent three to four years studying at a master's and bachelor's degree level and then doing a PhD on top of this to, you know, their sole goal as a PhD student is to create net new information can now be automated, that their prestige and their status that they've dedicated their life to has suddenly been taken away. And these are people that are performing at the top level of their field. Suddenly automated overnight was a very uncomfortable feeling for them and I totally get it. But at the same time,

I'm grappling with this thinking, okay, well, the whole point about mathematicians doing their thing, chemists doing their things, noble laureates doing their thing, is so that we can push humanity forward, that we can have a major invention or discovery that can make a lot of money, generate a lot of benefit for humans, and really help push us along. So why shouldn't we be happy about this AI model doing this for us? And I kind of tried to lay out my point in this tweet where math PhDs basically dedicate their lives to trying to contribute in as small or as large a way as they can. But often, because we're humans, because we're limited in our ingenuity, because it's so hard of a feel to figure out all these different problems

because our mental cognition and our context windows compared to AI LLMs are tiny, small, we need sleep, we're moody, we're irrational, we're emotional beings. Now we have a computer that can just work in the perfectly optimized way that we need to. And the thing that was holding back AI before, prior to this announcement, was that it was just trained on existing human data.

It was dumbed down and held back by the limits of human capacity, by even the smartest people. And now we see AI surpass that ingenuity, and that is a genuinely uncomfortable feeling, understandably. But you have to understand, this story isn't actually about the mathematicians. The way that they're feeling today is the way that experts across any single field

are going to feel in the very near future. So I want to get into what that's going to look like and the major implications of that. On today's show, brought to you by Qualcomm. So the first thing we have to understand about this, aside from maths, is if reason, is solving mathematics, which is one of the hardest fields, most complex to understand, learn, higher barrier of adoption to even enter the field, then it seems logical that this can apply to

any other field that requires intense reasoning. The first thing that pops into mind that is closest to maths, it's kind of like the sister of maths, is physics. And then you kind of think of chemistry or the numbers, equations that are required to create chemical compounds that could end up being a pharmaceutical product that cures a major ailment or illness. That is a big part of chemistry. And then it kind of rolls into systems biology and different aspects, but it's still very mathematical. And so those folks will probably push back and they'll say, but you do realize you need human intuition. You need judgment. And any field that requires judgment is something that AILLMs can't solve with just reasoning. And then you realize, well, actually, that's not strictly

true because you can technically take that logic and tell an LLM, hey, like, can you also try and reason and figure out human intuition and apply that? And it could end up doing that. But it's also about what Open AI is not saying. And this is arguably the biggest point of the entire episode. This is posed by Justin Drake. He is very renowned in cryptography. And he says, today I call upon the blockchain industry to calmly begin planning for bunker mode. What he goes on to explain is bunker mode is we need to shift off of blockchain addresses because I think cryptography has been broken, which is weird because there's been no proof that an AI has done this. But he referred to Open Air's announcement of solving all these mathematical problems,

and he said the biggest problem about this is what they haven't said. He said, what is the coincidence of Open Air's internal model being so smart to solve these niche problems that have existed for hundreds of years and somehow not been able to break the fundamentals of cryptography that has secured in the industry worth trillions and trillions of dollars. And so the point he makes is, if an internal lab such as open air has been able to do that, they wouldn't tell the public.

And then I started to realize, oh wait, this model that open air just used to do all these cool stuff isn't even accessible to you or I. It's accessible to open air, and they've had it for a while now, for months, for weeks. And look how much damage that they've done in that very little time. And then you start to think, Well, if I were open AI and I had this model that could solve all these hard problems,

I wonder what would happen if I pointed it at a problem that would give me a huge commercial advantage against my competitor Anthropic or Meta or Google. That's something that I would keep internally. And so we're starting to realize this gap being created, where the model labs are forming these models that are very capable of self-improving, doing it in a very cost-effective manner, so the cost of human intelligence is rapidly declining, but it's not at the benefit of humans, it's at the benefit of these models,

which are owned by these private companies that have the incentive to not publicly release this for their own commercial or specific advantage. But I guess to challenge myself, on the flip side, if these models were to become publicly accessible, it creates another interesting challenge, which is anyone and everyone with access to this model who can think of a smart enough prompt

can end up being as smart as Einstein or at least achieving some of the greatest things that these former researchers have built over history in humanity. The future of human innovation is not really human at all. It's human and AI-assisted. There is no future where we do not use this tool

because this has now become cheap enough and more capable than us to be able to do this. So we need to work in confluence with this. But there is one major issue. which is humans, particularly the experts, have flipped from being the experts to becoming a complete bottleneck. Let me explain.

With the math problems that have just been solved, there are 90 of them that have what's known as a lean resolution. So this is a formula that's been tested in a computer and it's come out with a big green check, 100 out of 100. But according to research practices today, you need a human to actually verify that it's true. We can't trust the computer. We need to trust the human. Problem is, the humans, the smartest mathematicians, don't understand the formulas,

don't understand the solutions that it's in front of them. So it's going to take a lot of time. That time can cost us major innovation and leaps in the future. So I'm sitting here, and I'm thinking about this and I'm thinking, hmm, what could help humans do this faster? AI. We should just use AI to check the AI's solution. And then it becomes this weird thing where it's like, okay, well, why do we need the human in the loop anyway? Why do we need humans at all? Now, that is a very doomer and bearish take. And I just want to be very upfront and clear. That's not actually where I think things will settle. I think to my earlier point, human intuition is a very important thing that is required to make the right judgment calls.

I think there are plenty of fields out that require this. And it's not something that I think an air model can intuitively figure out anytime soon. However, it's something that we should be wary of. And most importantly, that researchers of any other field should be wary of because it's going to slowly roll over. This is like a tidal tsunami that's going to sweep in very unexpectedly. And if you're wondering where I'm betting the next field is going to be, I think it's going to be biology, particularly focused on medical services or medical cures for the pharmaceutical industry. And there's good reasoning behind this. For the last six to 12 months, major AI labs have been buying up as much scientific and biological data as they can.

This data includes things like lab experiments and how to run these lab experiments and details on DNA and sequences that they can run through AI models. Anthropic has released a bunch of stuff on this. In fact, Anthropic two weeks ago announced that they have a automated wetware lab that they're building that you plug clawed into and it can operate robotic arms to try and run experiments autonomously. Now, the thing that a lot of critics very validly say is AI models don't work in the physical world. You still need humans and intuition to go do the thing.

But what happens when robotics accelerate and become good enough over the next three to five years? And these models are already vastly more capable than human intelligence. And you can just kind of plug them in and, you know, they create a cure for something the next day. That is a very weird world to kind of think of. And I fully think that humans are going to be heavily integrated into this. I guess I just don't know how. It's honestly hard for me to wrap my head around if I'm being very honest with you. But I think it's an important one to talk about. Obviously, as I'm sitting on this hotel floor and my fiance is trying to sleep in the bed next to me. So I hope you found this interesting. I felt very compelled to talk to you guys about this because this isn't a story about maths at all. It's a story about AI finally being able to surpass human cognition, but doing it much quicker and cheaper than we could. have anticipated. And so I'm scared or slightly nervous about how quickly this rolls into any and every other industry. And the ramifications that have on degree holders, workers, job roles that have

existed for centuries that have been highly revered, suddenly gets turned off overnight. Let me know what you guys think. Do you agree with my takes? Are there anything that I potentially missed? But thank you so much for listening if you've listened this far. If you enjoyed this episode, you should definitely go check out some of our other episodes. We are back to posting day in and day out five times a week. If you have a friend that you think might find this episode or other episodes interesting, please share it with them. It helps us out massively. I'm anywhere and everywhere, by the way, on X in the description below,

or you can just leave a comment here. I read all of them and I try and reply to all of them as well. But thank you so much for listening. This was today's episode on In The Loop, brought to you by Callcom. See you on the next one.