... we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors ...

I've observed this exact effect last week. I made a discovery regarding a stepwise performance improvement in a codebase. I shared the benchmark results with a peer and within 12 hours they replicated the same. We had both been looking for this for years.

I think giving someone hope that an answer exists might as well be the same thing as giving them the answer these days. Competition is a hell of a drug, and frontier LLMs aggressively compound that energy.

While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.

What do you mean, as OpenAI employee, you cannot tell that his work has entered the training data ?

But also correct me if I'm wrong, if the two mathematician were really close to finish this problem, and their conversation were used by OpenAI, shouldn't the Agent have succeeded way faster/efficiently instead of using "4.9 million messages and used about 300 billion output tokens."

- They threw compute on a problem another team/company was rumored to have solved to see what their secret model could do.

- The texts I read do make it seem like OpenAI wanted to talk and share credit generously.

- Imagine working on a frontier math problem with someone at Anthropic and not only do you use Codex but also through a non-business account that allows training on your data.

- Timeline-wise, if they mainly used GPT 5.6 it's unlikely any meaningful data made it into an model that's being internally validated right now.

If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt an LLM would be useful at all on their own. Is this the «ultimate ASI test»?

My two favourite hypothetical questions regarding this used to be:

If I'm running Codex and one of my API keys accidentally gets consumed in the context, what are the chances that someone else might ask for an API key in the future and get mine back? (I asked someone at OpenAI once and they called this the "regurgitation" problem and assured me that they take great pains to prevent that... but wouldn't describe how.)

If I brainstorm with ChatGPT about potential new directions for my company, what's the chance that information might be exposed to a competitor in six months' time who asks "what might company X plan to do next"?

My new preferred hypothetical for this is:

If I use ChatGPT to help me partially solve a Millennium Prize problem, what are the chances that my work will influence training such that a later model helps someone else solve it first?

I say that as a mathematician (on paper) who perhaps surprisingly doesn’t give a crap about the problem itself.

What I cannot reconcile is the timeline and the concern in this specific case.

I don’t think training pipelines are anything close to the level of continuous training needed to incorporate Aug 15th ideas into a model that generates a breakthrough early Sept. Either OpenAI nakedly had someone with mathematical understanding dig into a specific user’s chats (a massive red flag) or this really is poor handling of a more classic parallel discovery situation (with one party clearly having worked on it longer)

People talk about the "compute" but what about the "storage"? Is storage exponentially greater, or soon to be, than the compute? Is the storage going to slow down growing to some constant rate, i.e. all people on earth using chatgpt, or no, on the contrary, it will keep growing?

If there were any shady business, I do not condone it, but technologically we are not there yet for said shady business to happen.

"Allow your content to be used to train our models, which makes ChatGPT better for you and everyone who uses it. We take steps to protect your privacy."

But apparently there is also an entire completely different route "Do not train on my data"?

Does this mean that before I submitted the "Do not train on my data" request, my data was used for training in spite of "Improve the model for everyone" being turned off?

We are getting to facebook/meta-levels of privacy settings obfuscation.

While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models

AKA everything you send to them (and I bet it's the same for any other lab) will be used, no matter what are the TOS, the law or what they publicly say.

The discovery is somewhat overshadowed by accusations of skulduggery from Tristan Buckmaster [...]

I think you should not say that. Buckmaster did only state his version of events and was very clear on that he did not make any accusations at all.

To quote from his statement pdf:

I am not accusing anyone of anything.

OpenAI can easily identify these outstanding human behind their accounts. Human in OpenAI constantly check their logs for breakthrough. When they find something interesting, they brute force the result using their massive computing power.

No LLM is even needed.

How can openai do this, what is being claimed, at the scale of their entire userbase? If they do this only for particular sessions then how do they sieve through sessions for the good stuff?

How are sessions stored, how are they processed, how much storage and how much compute is used in these pipelines, how economical is it, how fast are the requirements on the storage on the compute growing as userbase grows and generated data grows.

All these questions are far more pertinent than the navier-stokes, but i can only imagine all at openai doubling down on this "very important" mathematical milestone.

While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models

I mean... yeah? What do you expect? What else can they say? How could you prove a negative in this case? I do not want to comment on specific OAI employee chat messages, but on the actual OAI discovery here.

The amount of millions available to do those kind of research cases is practically unlimited.

There are an unlimited amount of cases to work on.

What a huge development would this give to both humans and the world in general. Because in the end better understanding gives new options.

It's deeply interesting that those things now get a concrete economical price which seems to be viable to extrapolate. The enormous additional "production" of knowledge will inherently increase the speed of all pieces of research and development.

Taken into account that it's used wisely, the risks with a strong force are always huge as well.

[1]: https://www.sqlai.ai

Showing of the model's capabilities - okay, but it's not like it solved the problem on its own, and apparently not particularly efficient. Are there practical applications that justify the investment?

A) That we're at the point where SOTA models can, on their own or guided, be used to solve such monumental problems

B) That EVEN if they exist, they're still so cost prohibitive that they are completely out of range for pretty much everyone. Yes, yes, if the costs drop like a stone the hoi polloi can access this power in a year or two - but fundamentally it will divide cutting edge resource into two groups: Those with money, and those without.

That sort of latency, in turn, could lead to some feedback loop where research centers / groups that break barriers get more resources, and those who do not, are starved of resources. This sort of stratification can seriously lead to more centralized research. Do we want a future where only the chosen few get to make progress? For no other reason than that they are the ones with enough resources to spend on the required compute.

Instead, all I read on HN about N.-S. is human soap opera, told from every possible angle.

In 100 years we won't care about the soap opera. The N.-S. result itself will still matter.

Someone, anyone, please, submit articles on the result itself.

If this doesn't show you that the fantastical claims of their LLM's ability to solve problems on their own are bullshit, I don't know what will.

It's pretty clear all of this is a marketing effort only, and they pass human results as LLM findings.

We already know that the supposedly industry-changing Mythos and Fable results were actually complete BS and they're just your run of the mill model. There's nothing at all to suggest this is any different, and once we get this "unreleased model" (aka bob from the math department), we'll see it was all lies again.

08 Sep 2026 07:50:24 UTC | I talked to deep buddy about AI solving Navier Stokes rumors | http://www.echohive.ai/deep-talk-buddy/navier-stokes | https://smackernews.com/item/49606955 HN | 0 comments

08 Sep 2026 14:40:37 UTC | Major breakthrough made on Navier-Stokes Problem | http://www.newscientist.com/article/2588115-major-breakthrou... | https://smackernews.com/item/49610993 HN | 3 comments

08 Sep 2026 15:02:35 UTC | Has OpenAI model solved 80-year-old Navier-Stokes problem? | http://www.firstpost.com/tech/has-openai-model-solved-80-yea... | https://smackernews.com/item/49611362 HN | 3 comments

08 Sep 2026 17:13:21 UTC | On the NavierStokes Millennium Prize Problem | http://openai.com/index/navier-stokes-solution/ | https://smackernews.com/item/49613262 HN | 1129 comments

08 Sep 2026 17:26:44 UTC | Navier-Stokes and the Future AI Acceleration in Science | http://blog.valency.io/posts/navier-stokes-ai-science | https://smackernews.com/item/49613469 HN | 0 comments

08 Sep 2026 18:41:41 UTC | We're Sharing a Solution to the Navier-Stokes Millennium Prize Problem | http://twitter.com/OpenAI/status/2097374640582668336 | https://smackernews.com/item/49614788 HN | 1 comment

08 Sep 2026 19:35:46 UTC | Improve the model for everyone: OpenAI and the NavierStokes problem | http://twitter.com/danimberman/status/2097379292367802672 | https://smackernews.com/item/49615732 HN | 1 comment

08 Sep 2026 20:28:33 UTC | Show HN: Browser viz of OpenAI's "spaghetti" NavierStokes vortex (illustration) | http://3d-retro.com/experiments/vortex | https://smackernews.com/item/49616564 HN | 1 comment

08 Sep 2026 21:31:38 UTC | Global regularity problem for the incompressible three-dimensional Navier-Stokes | http://terrytao.wordpress.com/2026/09/07/finite-time-blowup-... | https://smackernews.com/item/49617372 HN | 0 comments

08 Sep 2026 22:56:30 UTC | Navier-Stokes Proof | http://medium.com/@m.alfaro.007/open-sourcing-the-universes-... | https://smackernews.com/item/49618321 HN | 0 comments

Them being assholes, trying to exclude an author just because he worked at Anthropic, shows the kind of culture within (that part of) their organization. The focus wasn't on supporting academics or expanding research. It was on getting great marketing.

If they had to burn millions of dollars solving a problem _that they thought was already being solved_ to do so, they'd do it.

That aspect alone would/should be unthinkable to any serious academic. Authorship reflects who did the work and changing it for business competition reasons should be a red flag for multiple different reasons. They include, the sheer tactlessness of treating a major theoretical advancement as a competitive posturing first, the norms of academia second, and all the misunderstandings of the culture of the disciplines culture that people will now suspect are hiding beneath the visible surface (insert topography joke).

Math as a field is fairly unique even in how they list authorship. It was long the norm that authorship to be alphabetical because the idea of first, second, senior etc authorship is harder to define than many other fields.

“The stated rationale for alphabetical order is that it treats co-authorship as intellectually joint work: every listed author’s name carries equal weight, and no one has to negotiate, or be seen to negotiate, over billing. That is a genuine advantage over position-coded conventions, where disputes over who is “first author” are one of the most common sources of authorship conflict in fields that use them” [0]

That norm is changing, slowly, but one option people are pursuing is notable: randomized author order. Their is a perception that alphabetical is too biased…that’s the world OpenAI is stepping into when they make that offer of authorship to one scholar with a demand that he exclude his partner.

I can’t speak to the facts of anything else in this, but if a grad student came to me and said someone made them that offer, I would tell them to run and if they were brave report it.

[0] a to the point lay description of the history of math authorship can be found here: https://casrai.org/guides/mathematics-alphabetical-authorshi...

This imbroglio looks like a point in the journey for a firm that is realizing it is not going to make money selling shovels during a gold rush, and that it has more to gain from just… being vertically integrated across an industry.

Open models have definitely hampered the ability to sell tokens at a premium, so mass market adoption is impossible.

But then take someone like Jane Street, for example. They self report making $30bn leveraging LLMs. It’s a defensible assumption that they are making profit on it.

Perhaps it’s more profitable for OpenAI/Anthropic to build their own funds. They have the capital, compute, they can afford to recruit teams and buy any IP/Data required.

This isn’t a fully fleshed out argument, but it is the first time it feels like the winds are changing.

09 Sep 2026 03:02:22 UTC | Navier-Stokes Sebastien Bubeck | http://twitter.com/SebastienBubeck/status/209737941169151631... | https://smackernews.com/item/49620327 HN | 3 comments

09 Sep 2026 07:04:58 UTC | Sam Altman's statement on the Navier-Stokes dispute | http://twitter.com/sama/status/2097385167002415140 | https://smackernews.com/item/49622377 HN | 8 comments

09 Sep 2026 13:08:45 UTC | The Navier-Stokes solution for the legal profession | http://lexifina.com/blog/navier-stokes-solution-for-the-lega... | https://smackernews.com/item/49625957 HN | 0 comments

09 Sep 2026 13:59:20 UTC | Navier stokes- Problem solution and the math for absolute dumbfucks | http://x.com/kingofknowwhere/article/18 Jun 2036 22:40:17 UTC640886376 | https://smackernews.com/item/49626706 HN | 0 comments

09 Sep 2026 14:39:38 UTC | OpenAI's 165-page Navier-Stokes proof failed a basic physics test | item?id=49627395 | https://smackernews.com/item/49627395 HN | 0 comments

09 Sep 2026 16:28:57 UTC | To Serve Man: AI, Math, and NavierStokes | http://ml5885.github.io/writing/navier-stokes.html | https://smackernews.com/item/49629132 HN | 2 comments

09 Sep 2026 17:33:47 UTC | Lean certificates accompanying Navier-Stokes and Euler results | http://github.com/openai/NavierStokesAndEuler/tree/main | https://smackernews.com/item/49630159 HN | 0 comments

09 Sep 2026 18:54:55 UTC | NavierStokesAndEuler | http://github.com/openai/NavierStokesAndEuler | https://smackernews.com/item/49632068 HN | 0 comments

OpenAI even wanted to respect that part but now you’re too focused on them being able to leverage the treasure map at the same time instead of the treasure for humanity being found at all, a completely different treasure where you both have to deal with getting paid by the bounty provider anyway

Goofy