In the Loop AI

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What's up guys, I just got to Japan, I am the most jet-lagged I have ever felt

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in my life, and I'm sat on my hotel room floor because I need to record this video.

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OpenAI just dropped one of their biggest releases and it's not an AI model,

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it's the announcement that they solved around 90 of the toughest problems in mathematics ever.

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And I know what you're thinking, you're wondering why on earth should I or you

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care about maths, That's something I haven't touched since high school or college.

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And the point of this video isn't that they solve these math problems.

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This will slowly and very quickly roll over into every particular field that

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you could think of that you probably studied to do your job today.

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It'll roll over into chemistry, into engineering, into physics,

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into whatever requires reasoning. And it's all happening much quicker than anyone can even imagine.

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So in this episode, I'm going to get into what exactly it's solved.

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But also more importantly, what happens next and what this means for you.

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All of that and more on today's episode of In The Loop, brought to you by Qualcomm.

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Let me just set some quick context. Three years ago, AI could not do math at

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all. Two plus two didn't equal four.

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It equaled 11. Strawberry had five R's in it.

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LLMs just weren't smart enough. And I remember commentary from Terence Tao,

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who is one of the leading mathematicians in the entire world.

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And he said, this is a reasonable attempt by AI, but I do not see how reasoning

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can eventually lead to solving maths.

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Reasoning is basically how you or I or any other human reasons about a problem.

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We don't just take it for face value, we apply different contexts to it.

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Back then, AIs could not do that, but they were at the start of their journey

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of figuring out this whole thing.

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Now, fast forward to six weeks ago, and OpenAI, who are back then and still

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are the experts in reasoning for training AI models, took that to its fullest

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extent when they announced that they had an internal model that was only 11 days old,

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had solved one of the Millennium Prize problems.

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Now, Millennium Prize problems, there's seven of them, and they're all in the

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field of mathematics. If anyone

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can go out there and solve any of these problems, you get $1 million.

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Now, for 200 years, these problems have existed. People have made progress,

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but the truth is, there are just too few mathematicians and experts in the world

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to try and figure this out, we are limited by human cognition and capacity.

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OpenAI came along with zero experience in maths, with zero help from math researchers,

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and they just decided, hmm, I wonder if I just use this powerful model that's

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more powerful than GPT-6 Astra and just let it go.

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Until it was able to solve the problems. There was infinite compute,

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infinite money, just get it done.

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And it solved it in 88 hours, 10,000 agents. It was one of the most costly experiments

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that they've done, probably that anyone's ever done for a research problem.

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It cost over $10 million.

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And there was a few interesting things about that. One, this was the first instance

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that an AI model had demonstrated that it wasn't just mimicking human intelligence.

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Up until then, people had been thinking, okay, AI models are smart,

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but they're just mimicking what it's learned on its data set.

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It's a next token predictor. What

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it means is it's trained on all human knowledge that we put out there.

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It's limited by the cap or the ceiling of how intelligent we already are.

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It solving Navier-Stokes was the first demonstration that it could go above

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that level, that it knew things that we as humans couldn't understand.

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And when Navier-Stokes got resolved, people weren't as excited as you might

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expect them to be, particularly mathematicians.

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They looked at this result and they weren't happy. They were perplexed.

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They were perplexed because they couldn't make sense of the solution.

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Now, fast forward to today and we have news that their new internal model has

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solved not just one, not two, but 90 out of 500 of the world's most hardest open math problems.

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That's 20% of all the hardest math problems that have existed for decades at this point.

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But there's a few differences between this and Navier-Stokes.

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It solved it in a much quicker way. On average, each problem was solved in three hours.

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And it also got it done at a much cheaper cost, costing only tens of thousands of dollars.

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So you can imagine or extrapolate in the future that these models will get faster

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or quicker and even cheaper to the point where the cost of solving problems

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that humans have spent decades trying to figure out becomes undeniable.

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You should just use AI and you don't need humans anymore.

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But I was critical. I saw this announcement from OpenAI here and I saw that

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they had dumped a massive GitHub, which was interesting. They open sourced the

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entire thing. So all the formal verification and proofs were laid out for anyone

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and everyone to critique.

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And it got me thinking, okay, it's solved a bunch of problems,

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but how is this useful for you or I?

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Like, how should I think about this being applied to the real world?

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And our friend Ole actually had a great breakdown in his tweet.

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He ran through a few of them, but being very high level here,

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because I'm not a mathematician, one of these problems is called Vlasov Maxwell.

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And if it's truly solved, this research can then result in a way to create virtually

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limitless clean energy to power civilization.

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Another example is the inverse elasticity problem that, if it's solved,

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can scan and map tissue stiffness, basically allowing you to evaluate and medicate

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and help individuals in the medical field.

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There's also another one called matrix multiplication. If you've heard that

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term before, it's because we've probably talked about it on this show.

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It's the foundation of how AI models run and how they run and compute all the

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important data to give you an amazing result. It's also how GPUs and any other

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modern graphics card processor works for videos, gaming, or whatever that might be.

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It's a very important fundamental mechanism of computing.

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It solved one of the most incredibly hard problems that that industry was facing.

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And if it's able to figure out, guess what? You get cheaper AI and bigger scientific

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simulations that you could use to map or design proteins that could result or

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end in the cure for cancer, for example.

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So the idea here is this isn't just a list of random niche, arbitrary mathematical problems.

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These are things that have stumped scientists, builders, engineers,

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and creators in the real world from innovating. It has prevented humans from

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innovating and building something that is marvelous and novel that would help

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world and humanity as it is today.

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So when you frame things this way, you start to realize that humanity's progress

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has always been dependent on the geniuses that they have in its civilization,

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in its society at the time.

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Who is smart enough to fix or figure out some of the hardest problems in the

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world so that we can progress as a human race?

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I loved this tweet from Will Depew, where he basically asked

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Fable 5.1 and GPT-6 Pro to rank all the discoveries that OpenAI's internal model

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had made over the last three years, just to see how important these discoveries

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actually were relative to that perspective that I just laid out,

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which is, you know, solving, you know, human innovation.

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And 81% of them were released on the day that OpenAI had solved these problems,

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meaning that a lot of these mathematical problems were fundamentally crucial.

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Just think about that for a second.

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The next Albert Einstein, the next Galileo isn't going to be a human.

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It's going to be a computer.

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It's going to be an AI because it's cheaper.

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It can work 24-7. And most importantly, it's now demonstrated that it's smarter

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than us. Framing it the way that Alex has here makes a lot of sense.

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He goes quasi RH. He's referencing one of the problems that it solved. Are you kidding me?

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If a human did this, it would be an instant Fields Medal. No questions asked.

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A Fields Medal is one of the highest honors you can earn in the mathematics field.

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PhDs, mathematicians make it one of their life goals to try and earn a Fields

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Medal. It is incredibly hard and a rare thing to do. It's very prestigious.

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And now the cost and effectiveness of that has been completely taken from mathematicians.

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And understandably, their reaction to this has mostly been mathematicians being

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very uncomfortable with the way open AI has approached this.

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Actually, it started off when they solved Navier Stokes six weeks ago.

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You might have thought mathematicians would have reacted positively to the news,

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but they really didn't. They were actually offended.

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They thought that the act of open AI using these LLMs to solve mathematical

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research, the act or thought of a computer doing their job that they had spent

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three to four years studying at a master's

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and bachelor's degree level and then and doing a PhD on top of this to,

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you know, their sole goal as a PhD student is to create net new information,

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can now be automated, that their prestige and their status that they've dedicated

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their life to has suddenly been taken away.

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And these are people that are performing at the top level of their field,

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suddenly automated overnight.

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Was a very uncomfortable feeling for them. And I totally get it.

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But at the same time, I'm grappling with this thinking, okay, well.

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The whole point about mathematicians doing their thing, chemists doing their

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things, Nobel laureates doing their thing, is so that we can push humanity forwards,

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that we can have a major invention or discovery

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that could make a lot of money, generate a lot of benefit for humans,

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and really help push us along.

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So why shouldn't we be happy about this AI model doing this for us?

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And I kind of tried to lay out my point in this tweet, where math PhDs basically

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dedicate their lives to trying to contribute in as small or as large a way as they can.

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But often, because we're humans, because we're limited in our ingenuity,

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because it's so hard of a field to figure out all these different problems,

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because our mental cognition and our context windows compared to AI LLMs

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are tiny, small, we need sleep, we're moody, we're irrational,

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we're emotional beings.

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Now we have a computer that can just work in the perfectly optimized way that we need to.

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And the thing that was holding back AI before, prior to this announcement,

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was that it was just trained on existing human data. It was dumbed down and

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held back by the limits of human capacity by even the smartest people.

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And now we see AI surpass that ingenuity. And that is a genuinely uncomfortable

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feeling, understandably.

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But you have to understand, this story isn't actually about the mathematicians.

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The way that they're feeling today is the way that experts across any single

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field are going to feel in the very near future.

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So I want to get into what that's going to look like and the major implications

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of that on today's show brought to you by Qualcomm.

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So the first thing we have to understand about this, aside from maths,

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is if reasoning is solving mathematics, which is one of the hardest fields, most complex

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to understand, learn, higher barrier of adoption to even enter the field,

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then it seems logical that this can apply to

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any other field that requires intense reasoning.

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The first thing that pops into mind that is closest to maths,

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it's kind of like the sister of maths, is physics.

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And then you kind of think of chemistry, all the numbers, equations that are

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required to create chemical compounds that could end up being a pharmaceutical

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product that cures a major ailment or illness. That is a big part of chemistry.

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And then it kind of rolls into systems biology and different aspects,

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but it's still very mathematical.

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And so those folks will probably push back and they'll say, but you do realize

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you need human intuition, you need judgment. And any feel that requires judgment

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is something that AI LLMs can't solve with just reasoning.

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And then you realize, well, actually, that's not strictly true,

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because you can technically take that logic and tell an LLM,

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hey, like, can you also try and reason and figure out human intuition and apply

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that? And it could end up doing that.

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But it's also about what OpenAI is not saying. And this is arguably the biggest

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point of the entire episode. This is post by Justin Drake. He is very renowned in cryptography.

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And he says, today I call upon the blockchain industry to calmly begin planning for bunker mode.

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What he goes on to explain is bunker mode is, we need to shift off of blockchain

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addresses because I think cryptography has been broken, which is weird because

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there's been no proof that an AI has done this.

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But he referred to OpenAI's announcement of solving all these mathematical problems.

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And he said the biggest problem about this is what they haven't said.

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He said, what is the coincidence of OpenAI's internal model being so smart to

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solve these niche problems that have existed for hundreds of years and somehow

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not been able to break the fundamentals of cryptography that has secured an industry

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worth trillions and trillions of dollars?

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And so the point he makes is, if an internal lab such as OpenAI has been able

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to do that, they wouldn't tell the public.

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And then I started to realize, oh wait, this model that OpenAI just used to

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do all these cool stuff isn't even accessible to you or I.

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It's accessible to OpenAI and they've had it for a while now, for months, for weeks.

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And look how much damage that they've done in that very little time.

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And then you start to think, well, if I were OpenAI and I had this model that

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could solve all these hard problems, I wonder what would happen if I pointed

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it at a problem that would give me a huge commercial advantage against my competitor

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Anthropic or Meta or Google.

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That's something that I would keep internally. And so we're starting to realize

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this gap being created where the model labs are forming these models that are

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very capable of self-improving, doing it in a very cost-effective manner.

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So the cost of human intelligence is rapidly declining, but it's not at the

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benefit of humans, it's at the benefit of these models, which are owned by these private companies.

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Have the incentive to not publicly release this for their own commercial or specific advantage.

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But I guess to challenge myself, on the flip side, if these models were to become publicly accessible,

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it creates another interesting challenge, which is anyone and everyone with

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access to this model who can think of a smart enough prompt can end up being as smart

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as Einstein, or at least achieving some of the greatest things that these former

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researchers have built over history in humanity.

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The future of human innovation is not really human at all.

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It's human and AI assisted. There is no future where we do not use this tool

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because it has now become cheap enough and more capable than us to be able to

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do this. So we need to work in confluence with this.

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But there is one major issue, which is humans, particularly the experts,

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have flipped from being the experts to becoming a complete bottleneck. Let me explain.

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With the math problems that have just been solved, there are 90 of them that

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have what's known as a lean resolution.

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So this is a formula that's been tested in a computer and it's come out with

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a big green check, 100 out of 100.

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But according to research practices today.

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You need a human to actually verify that it's true. We can't trust the computer.

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We need to trust the human. Problem is the humans, the smartest mathematicians,

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don't understand the formulas, don't understand the solutions that's in front of them.

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So it's going to take a lot of time.

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That time can cost us major innovation and leaps in the future.

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So I'm sitting here and I'm thinking about this and I'm thinking,

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hmm, what could help humans do this faster? AI.

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We should just use AI to check the AI solution.

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And then it becomes this weird thing where it's like, okay, well,

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why do we need the human in the loop anyway? Why do we need humans at all?

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Now, that is a very doomer and bearish take and I just want to be very upfront and clear.

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That's not actually where I think things will settle. I think to my earlier

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point, human intuition is a very important thing that is required to make the right judgment calls.

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I think there are plenty of fields out there that require this and it's not

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something that I think an AI model can intuitively figure out anytime soon.

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However, it's something that we should be wary of.

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And most importantly, that researchers of any other field should be wary of

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because it's going to slowly roll over.

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This is like a tidal tsunami that's going to sweep in very unexpectedly.

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And if you're wondering where I'm betting the next field is going to be,

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I think it's going to be biology, particularly focused on medical services or

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medical cures for the pharmaceutical industry.

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And there's good reasoning behind this. For the last six to 12 of months,

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major AI labs have been buying up as much scientific and biological data as they can.

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This data includes things like lab experiments and how to run these lab experiments

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and details on DNA and sequences that they can run through AI models.

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Anthropic has released a bunch of stuff on this. In fact, Anthropic two weeks

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ago announced that they have a automated wetware lab that they're building that

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you plug Claude into and it can operate robotic arms to try and run experiments autonomously.

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Now, the thing that a lot of critics very validly say is.

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AI models don't work in the physical world. You still need humans and intuition to go do the thing.

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But what happens when robotics accelerate and become good enough over the next

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three to five years, and these models are already vastly more capable than human

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intelligence, and you can just kind of plug them in and, you know,

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they create a cure for something the next day.

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That is a very weird world to kind of think of.

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And I fully think that humans are going to be heavily integrated into this.

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I guess I just don't know how. It's honestly hard for me to wrap my head around,

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if I'm being very honest with you.

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But I think it's an important one to talk about, obviously, as I'm sitting on

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this hotel floor and my fiance is trying to sleep in the bed next to me.

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So I hope you found this interesting. I felt very compelled to talk to you guys

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about this, because this isn't a story about maths at all.

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It's a story about AI finally being able to surpass human cognition,

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but doing it much quicker and cheaper than we could have anticipated.

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And so I'm scared or slightly nervous about how quickly this rolls into any

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and every other industry.

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And the ramifications that have on degree holders, workers, job roles that have

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existed for centuries that have been highly revered suddenly gets turned off overnight.

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Let me know what you guys think. Do you agree with my takes?

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Are there anything that I potentially missed?

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But thank you so much for listening. If you've listened this far,

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if you enjoyed this episode, you should definitely go check out some of our

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other episodes. We are back to posting day in and day out five times a week.

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If you have a friend that you think might find this episode or other episodes

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interesting, please share it with them. It helps us out massively.

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I'm anywhere and everywhere, by the way, on X in the description below,

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or you can just leave a comment here. I read all of them and I try and reply to all of them as well.

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But thank you so much for listening. This was today's episode on In The Loop

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brought to you by Qualcomm. See you on the next one.