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$32,700 in savings. 10 months of runway. non-technical founder building a real SaaS to $10K/month using only AI tools - or going broke trying.
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They've done it, OpenAI, they are sharing a solution to the Navier-Stokes Millennium
Prize problem, one of the deepest problems at the frontier of mathematics.
In this video, I will be showing you exactly what on earth that actually means, why this is
a big deal, and whether this is the end of AI advancement or just the very beginning.
Before we do that, I'm Lenny, I love making videos about AI, it is currently 1.54am in
the morning.
If you want more videos about AI from an Aussie dude with a Mo, subscribe.
So back to the news at hand, if you have been living under a rock, GPT-6 Astra came
out about a week ago now, and it is the best AI model in the world.
What we're talking about now is a next-generation model significantly more capable than the
best in the world right now, which is already smarter than most people on Earth.
The problem at hand concerns whether the description of smooth three-dimensional fluid motion
modeled by the Navier-Stokes equations can break down.
It has remained unsolved for roughly 90 years.
Here is the part that blew me away.
This model represents a step function improvement on many benchmarks, and its training is ongoing.
But here it is.
Our internal model group arrived at the Navier-Stokes solution in 88 hours using around 10,000 coordinating
AI agents.
Here's a graph, performance of GPT-6 Astra and our internal model on a curated set of
open math problems.
We see two lines on a line graph.
One is GPT-6 Astra, one is this unnamed internal model.
GPT-6 Astra is basically significantly lower than the internal model, continuing on with
the post.
The solution, which is the thing that was just discovered, a spinning swirl of fluid that
spirals inward and gets increasingly elongated like spaghetti.
Then there's a link to the official paper titled finite time blow up for Navier-Stokes.
And this is the vortex that was just referred to.
I'll discuss exactly what all of this means in just a second, but we've just got a couple
more things to take a look at here in the official blog post.
Agents were subdivided into groups with the ability to communicate within the group.
We encourage different groups of agents to explore a diversity of approaches.
After some time, we cross-pollinated agent groups by using Codex to consolidate the
most useful insights from each agent group.
These follow-up prompts drew on the agent's own intermediate results, the group that found
the solution to Navier-Stokes was guided in such a way.
The agents arrived at their resolution on Saturday, September 5, about 88 hours after
the first agents were launched.
So that was done by the internal model and then lean formalization and verification of
these results took an additional 17 hours via GPT-6 Astra, which is currently publicly
available.
Here's a cool part.
Across all attempted problems, the agent sent 4.9 million messages and used about 300
billion output tokens.
And in the process of resolving the Navier-Stokes problem, the agent sent 2.7 million messages
and used approximately 130 billion output tokens.
So the way AI is billed is through tokens.
GPT-6 Astra is currently the most expensive AI model offered by OpenAI.
This is an internal model.
Assuming this internal model was publicly available, which it isn't, and was priced at the same
price as GPT-6 Astra.
That means across all attempted problems, solving the Navier-Stokes Millennium Prize
problem cost 15 million US dollars.
And in the process of resolving the Navier-Stokes problem alone, the commercial cost was 6.5
million US dollars.
Our goal in releasing this result is to report on the substantial progress of AI models.
We do not intend to claim the Millennium Prize for this result.
Very interesting.
Noble, I would say.
This is not a culmination, but rather a snapshot in time of progress on AI development.
We believe we are now in the next period of AI progress, and today's results provide
further evidence of this.
What is the significance of this?
We're going to get to that in just a second.
But what the end of that blog post is talking about is just basically saying AI is getting
really fucking smart, really fucking quickly.
Just to put this into perspective, GPT-5 came out 13 months ago.
The chart that we're looking at now, think of it as a good rule of thumb for intelligence.
Higher is better.
One year ago at its peak was 23 out of 100.
13 months later, it's now at 53 out of 100.
And most of those gains came in the last three months.
And that's only going to continue to accelerate and to get faster.
Like we're just at the beginning of what people call the exponential, where things are going
to get crazy really quickly.
Anyway, to better understand this, who better to help us, or what better to help us, than
GPT-6 Astra itself.
Explain to me in super simple terms what the Navier-Stokes Millennium Prize problem is.
It's a challenge about the equations we use to describe how fluids move, like air or water.
The big question is, do these equations always behave nicely, or can they sometimes blow
up and produce impossibly wild results?
In simple terms, it's asking whether smooth fluid motion stays smooth or can break down.
Explain it even simpler.
Sure.
It's asking if you start with water flowing smoothly, will it always keep making sense?
Or could the math suddenly go crazy and stop working?
Simpler again.
It's asking, will the math for flowing water ever break?
What's the significance of solving it?
Give me an example, put this into perspective.
Think of a flight simulator.
It uses maths to predict how air moves around a plane.
This problem asks, can that math hit a point where it stops giving a sensible answer?
Even if everything starts smoothly, OpenAI says it found a carefully constructed case
where that happens.
If that holds up, it settles a question mathematicians have wrestled with for about 90
years, and reveals a fundamental limit and a hugely important tool.
It's not about real water going infinitely fast, and it doesn't mean planes or weather
forecasts don't work.
The AI angle is also big, helping discover new maths, not just answering old questions.
What would you say are the five most interesting or important things to know?
One, they say they also solved a separate major maths problem on the way involving the
Euler equations, which helped point them in the right direction.
Two, in their example, the water starts completely still, and a smooth external force gets it
moving, so it's not starting broken.
Three, the AI was still being trained while this was happening.
They upgraded the agents midway through the project.
Four, another team had a related result, but not the same result.
Those details matter.
Five, OpenAI says it won't claim the million dollar prize.
They frame this as showing how far the AI has come.
Cool, so I didn't actually know that it was a million dollar prize, but yeah, OpenAI
probably doesn't need any more money.
So I heard whispers that Claude and Anthropic had solved the Navier Stokes Millennium prize
problem on X or Twitter.
Wouldn't be surprised if OpenAI got wind of this.
They thought, fuck you, Claude, deployed 10,000 agents at the cost of around $15 million,
and then just solved the thing in a few days.
OpenAI is so back.
Imagine AI teams exploring scientific ideas and helping finding something genuinely new.
I think that is the future.
The headline from this is they deployed 10,000 super intelligent AI agents to solve this
thing.
They just pointed compute at it, spent $15 million and then just broke frontier math.
If that doesn't tell you that we have just entered the AGI era, I don't know what will.
Let me know in the comments below.
Do you think this is the future of scientific discovery, deploying ginormous swarms of super
intelligent AI agents at some unknown problem and getting them to just work 24 seven around
the clock, thinking about multiple different solutions, like Dr. Strange looks at 13,000
different possibilities.
That's what these AI agents are doing to solve new problems.
Do you think that is the future?
Let me know down below.
If you want more videos about AI from an Aussie dude with a Mo, subscribe, peace.