OpenAI’s Unreleased Model Solved Math: Is Biology Next?

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.

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Timestamps

0:00 OpenAI’s Math Breakthrough
1:53 From Impossible to 90 Problems
4:39 Why This Changes Everything
8:06 Mathematicians Feel the Shock
10:30 Reasoning Spills Into Other Fields
11:37 The Cryptography Alarm
14:19 Humans Become the Bottleneck
15:56 Biology Is Next
17:26 Beyond Math, Toward AI Supremacy

#openai #maths #physics #chemistry #ai #science 

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Creators and Guests

Host

Ejaaz Ahamadeen

Investor & Host of the Limitless AI Show. Former Product at Coinbase and ConsenSys

What is In the Loop AI?

Exploring the frontiers of Technology and AI

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

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

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

Ejaaz:
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.

Ejaaz:
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

Ejaaz:
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,

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

Ejaaz:
It's a next token predictor. What

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

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

Ejaaz:
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

Ejaaz:
expect them to be, particularly mathematicians.

Ejaaz:
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.

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

Ejaaz:
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?

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

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

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

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

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

Ejaaz:
limitless clean energy to power civilization.

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

Ejaaz:
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

Ejaaz:
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

Ejaaz:
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.

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

Ejaaz:
world so that we can progress as a human race?

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

Ejaaz:
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.

Ejaaz:
Just think about that for a second.

Ejaaz:
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.

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

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

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

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

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

Ejaaz:
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

Ejaaz:
very uncomfortable with the way open AI has approached this.

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

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

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

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

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

Ejaaz:
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,

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

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

Ejaaz:
their life to has suddenly been taken away.

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

Ejaaz:
suddenly automated overnight.

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

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

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

Ejaaz:
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.

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

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

Ejaaz:
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,

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

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

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

Ejaaz:
we're emotional beings.

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

Ejaaz:
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.

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

Ejaaz:
feeling, understandably.

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

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

Ejaaz:
field are going to feel in the very near future.

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

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

Ejaaz:
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.

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

Ejaaz:
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.

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

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

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

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

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

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

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

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

Ejaaz:
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?

Ejaaz:
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.

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

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

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

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

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

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

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

Ejaaz:
Anthropic or Meta or Google.

Ejaaz:
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

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

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

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

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

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

Ejaaz:
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

Ejaaz:
researchers have built over history in humanity.

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

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

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

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

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

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

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

Ejaaz:
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

Ejaaz:
a big green check, 100 out of 100.

Ejaaz:
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.

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

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

Ejaaz:
We should just use AI to check the AI solution.

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

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

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

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

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

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

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

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

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

Ejaaz:
because it's going to slowly roll over.

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

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

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

Ejaaz:
medical cures for the pharmaceutical industry.

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

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

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

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

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

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

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

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

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

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

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

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

Ejaaz:
they create a cure for something the next day.

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

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

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

Ejaaz:
if I'm being very honest with you.

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

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

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

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

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

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

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

Ejaaz:
and every other industry.

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

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

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

Ejaaz:
Are there anything that I potentially missed?

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

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

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

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

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

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

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

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

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