Mathematician Tristan Buckmaster asserts OpenAI cribbed the work he and a collaborator had done, which included significant progress toward solving the equation in August. OpenAI announced its team had solved the equation this week.
Posted by Leslie Eastman

In May 2000, to inspire mathematicians and mark the start of the new millennium, the Clay Mathematics Institute in Cambridge, Massachusetts, launched its seven Millennium Prize Problems. Each challenge carried a $1 million award for a solution.
One of the seven challenges was the Navier-Stokes Equation. The Navier–Stokes equations are among the most useful mathematical tools for describing how fluids move (including liquids such as water and blood, and gases such as air). Their practical value is that they connect pressure, velocity, density, temperature, viscosity, and external forces, allowing scientists and engineers to predict or simulate flow.
Essentially, the Navier–Stokes equations express conservation of momentum for viscous fluids. The Navier–Stokes Millennium Prize Problem asked a very specific question: If you start with a perfectly smooth three-dimensional flow of an ordinary fluid, do the equations always keep producing a smooth, physically sensible flow forever, or can the flow become infinitely extreme at some finite time?
This week, OpenAI announced that it had solved the equation.
OpenAI announced September 8 a solution to the Navier–Stokes existence and smoothness problem, which is so monumental that a $1 million prize has been offered for its resolution. “It’s one of the guiding problems for the field. It is a huge deal to know the answer,” says mathematician Dallas Albritton of the University of Wisconsin–Madison, who was not involved with the new work.
However, New York University mathematician Tristan Buckmaster disputes this claim, as he and another collaborator were making meaningful progress on related work. The mathematician asserts that OpenAI copied his collaborator’s work.
…[P]rofessor Tristan Buckmaster, a British-Australian researcher who trained in Germany, has come forward alleging OpenAI may have copied his work after asking him to collaborate — with one of the company’s scientists allegedly warning he might “ruin his career” if he refused.
Buckmaster had been working with Long Island-raised math prodigy Levent Alpöge, a researcher with OpenAI rival Anthropic, on a different elusive fluid dynamics equation, known as the Euler problem, when he found a solution using AI tools from both Anthropic and ChatGPT.
He said scientists from OpenAI approached him and tried to convince him to jointly announce the solution, claiming they had independently solved Navier-Stokes.
Buckmaster questioned the timing of their finding the solution in a blog post, noting OpenAI had only done so “in the past few days” — after the company became aware of his work.
Buckmaster indicates he feels rolled over in the battle of two AI giants. It appears that when Anthropic solved one or two of math’s “Millennium Prize problems”, OpenAI’s teams were to work on the mathematical challenges themselves.
Dr. Buckmaster said he did not care about the $1 million or even winning the race to come up with the answer. “I love math, and I just wanted to be part of the story,” he said during a two-hour recounting of the events in his N.Y.U. office on Tuesday afternoon.
He is now part of a different story: the acrimonious rivalry between OpenAI and Anthropic, which was founded in 2021 by former OpenAI leaders who said OpenAI had become cavalier about the dangers of artificial intelligence.
“When they are two of the biggest companies in the world, they need to get over it,” Dr. Buckmaster said. “I am just a bystander in these petty games.”
The tension between Dr. Buckmaster and OpenAI also reflects a chasm of distrust between the swashbuckling culture of high-flying, high-tech start-ups and the usually cordial and communal collaboration of mathematicians, who nonetheless increasingly rely on the tools of A.I.
There was an attempt at a compromise, but OpenAI claims it denies that its team ever saw the pair’s work before publication.
Tristan Buckmaster, a mathematics professor at New York University, alleged that OpenAI moved on the problem only after word of his unpublished results reached the company. He had worked for nearly a year with Levent Alpöge, a mathematician employed by rival lab Anthropic.
Both men leaned on OpenAI’s Codex assistant while assembling their own proofs, and the company can train on those sessions unless users opt out.
The pair chose a narrow route through the problem that almost nobody else was pursuing, which made the timing look suspicious to Buckmaster. He says OpenAI mathematician Sébastien Bubeck proposed a compromise that would strip Alpöge of credit, then asked why he would ruin his career.
OpenAI denies that its researchers or agents saw the pair’s work before publication. It says no specific user data went into the effort. The company concedes that de-identified data drawn from product use may have improved its models, though it argues the two proofs differ significantly.
Whether the fluids are smooth or not, the human drama surrounding its alleged solution to the Navier–Stokes equations is anything but.
Whether OpenAI’s proof was truly derived independently will determine if this revelation becomes a genuine scientific milestone or a cautionary tale about an increasingly ruthless race for AI supremacy.
In the meantime, a million-dollar prize has exposed a far more troubling equation: the unintended consequences of pairing extraordinary computational power with intense corporate rivalry, devoid of the spirit of collaboration that has long been central to scientific and mathematical progress.
x sub (n+1) = r x sub n (1 – x sub n)
is the discrete logistic equation. Sorry, but I don’t know how to do subscripts in these comments.
As far as I know, no AI is prepared to state that future values can be calculated from an initial known input for an infinite number of input values. These are values which result in chaotic outputs.
At around 3.57 chaos occurs, and the only way to find, say, the 100th value is to iterate through the outputs. However, there is no calculation which allows one to say “this will result in chaos” or “this will not result in chaos” at the boundary between chaos and non-chaos. It is always possible to find a smaller number.
Chaos rules. Determine future climate states is impossible. You can’t even forecast accurate wind speed and direction 30 seconds hence. Or temperature or barometric pressure, for that matter. Accurately, mind.
Chaos rules KO.
It’s what gives us our normality.
Long may it rule
Plagiarize
Let no one else’s work evade your eyes
Remember why the good Lord made your eyes
So don’t shade your eyes
But plagiarize, plagiarize, plagiarize
Only remember always to call it, please
Research!
-from “Lobachevsky” by Tom Lehrer
Wonderful talent … & quite good at maths as well !!
Wait a minute …. hasn’t all AI output been copied?
Do you mean, all AI INput is copied from its creators and from its work doing its research and calculations.
Unless it has been instructed to do so, it does not keep its working notes nor print them out.
The recent report including the instructed outputting of its notes is part of the story which raised some alarm. Some acts they were told not to do, it did, and some things it reported did not exist.
Buckminster and Alpöge solved the Millennial Prize question for the Euler system unquestionably before OpenAI claimed the same result shortly later for Navier-Stokes, resulting in the controversy described here. In both cases the answer is no— not smooth. Singularities can arise in finite time.
A fun aside. I carefully studied Feynman’s famous Lectures on Physics (my precious 3 volume copy was bought at the Stanford University book store), his infamous only once taught introductory two year ‘undergrad’ course at CalTech. Couldn’t do all his math, but grasped all the uberlying physics concepts.My own patented physics work on energy storage materials partly depended on several of those lectures (electrical fields, electrostatics, barium titanate capacitance).
Feynman spent four years studying Navier-Stokes both theoretically and experimentally after finishing his quantum chromodynamics theory. He had two consecutive lectures, the first on Euler (describing fluid flow without viscosity), the following second on Navier- Stokes (fluid flow including viscosity). In true Feynman fashion, he titled the first ‘Flow of dry water’ and the second ‘Flow of wet water’.
Yes, but the real question is: What do you get when you multiply 6×9?
But which real answer would you like today ??? …
10.
or
60.
or
70.
or
120.
or
200.
or
110110.
There are lots of others (:-))
There is only one answer.
Not true … you’re not very bright, are you? Open your mind !!
an integer, under any other base, is still the same integer
“Now, if a six turned out to be nine
I don’t mind, oh, I don’t mind “
J. Hendrix
“And if all the hippies, cut off all their hair
I don’t care, I don’t care”
Or something like that…
Humans made AI. Humans plagiarize, steal, and take credit for other’s work. Why would we expect AI to behave differently? I think there is a high probability that AI will replicate all human failings at some point.
That’s proof that AI has become sentient 🙂
Congratulations!
But it’ll be hell of a task to make AI act like a drug addict, watch football,suffer from bipolarity or throw paint at Mona Lisa to protest the climate change 97% of AI ‘s agree on.
But If the AI now insists on keeping the million it’s already on the right track.
Or blame everything on the JOOOOOS.
There are lots of youtube videos about this:
https://www.youtube.com/results?search_query=Navier-Stokes
I am in full support of Tristan Buckmaster’s effort to expose OpenAI’s plagiarism.
Otherwise no one’s work will be safe from being ripped off wholesale by zealot AI activists.
The above article appears to be great fodder for mathematicians that might salivate over closed-form solutions to complex equations.
However, to the extent needed for practical, everyday purposes in applying the Navier-Stokes equation, scientists and engineers learned several decades ago that numerical analysis (under the broad category of “computational mathematics”) provided all the accuracy they needed for such. Computer-aided design (CAD) programs developed back then handle the Navier-Stokes equation just fine for gas and liquid flows . . . to the extent such have been used to design high-performance aircraft, spacecraft, submarines and marine vessel hulls, including critical details such as wing shapes, propeller shapes and re-entry heat shields. The advent of high performance supercomputers has only made the application of the Navier-Stokes equation via numerical analysis that much more easy and ubiquitous.
But catch this from Google’s AI bot:
“Yes, general circulation models (GCMs) of Earth’s climate use the Navier-Stokes equations as the fundamental physical basis for simulating the movement of fluids like the atmosphere and the oceans.”
And, of course, we all know that just using the Navier-Stokes equation (via numerical analysis) does not automatically confer any specific reason to trust the accuracy of modeling climate. There is NO reason to expect this will change if a closed form solution to the Navier-Storkes equation is obtained.
One can now buy Computational Fluid Dynamics (CFD) programs that handle the Navier-Stokes equation and that can run on a personal desktop computer, such as:
— SOLIDWORKS Flow Simulation
— Autodesk CFD
— Ansys Fluent, and
— SimFlow CFD.
Note that the same numerical analysis approach has been used with great success in astrodynamics for handling the “no closed-form solution” three-body problem of combining gravitation fields of more than two celestial objects in relative motion. It’s the fundamental reason that we can very accurately navigate spacecraft to the Moon, and even to Pluto, comets and asteroids.
“Note that the same numerical analysis approach has been used with great success in astrodynamics for handling the “no closed-form solution” three-body problem of combining gravitation fields of more than two celestial objects in relative motion. It’s the fundamental reason that we can very accurately navigate spacecraft to the Moon, and even to Pluto, comets and asteroids.”
But no real accuracy on whether it will rain on Tuesday afternoon.
That’s because gravity is much easier to understand and analyze than is weather, let alone climate.
As far as models go, there is dry water (Eu) and wet water (N-S) and turbulence (watch this space for x years)
Copying from an unpublished work is deeply immoral.
So, are we now asserting—or just hoping—that Artificial Intelligence has ethics and acts on “moral” principles?
Good grief!
which is exactly the nub of the problem or the danger
Guaranteed psychopaths like Daleks Exterminate