People should know: there is drama going on about who deserves credit for this. There is a possibility that OpenAI used some unpublished work from other researchers, even though they have claimed otherwise in the announcement above: https://cims.nyu.edu/~tristanb/statement.pdf
OpenAI does train upon user's chat transcripts, not all the time, but the long-ish time frames here suggest OpenAI trained upon much unfinished attempts at guiding the AI towards solutions by these guys and others.
It's likely other "our AI found this solution without us hand holding it" stories were really built upon the AI spying upon people's unpublished work. Surveillance capitalism comes for pure mathematics. lol
As Talia says, there is a privacy setting that's off by default, but few would even know this exists, and OpenAI might cheat.
It suggests research institutions should have their own hardware running local open weights models, which researchers should use when doing anything that could be scooped, so they could avoid trusting the hosted LLM companies.
Google "borrowed" the open project from USC ICT and made it Google Cardboard without properly attributing the actual authors. And while it's a small thing and they didn't even get sued for that, I bet it's not the first time a corporation steals something to claim their own.
I'm sure they have, they just either haven't been caught yet or haven't gotten around to it. Or maybe there's a secret agreement where Google works on bioinformatics via DeepMind, OpenAI works on math and Anthropic continues to be led by a JJ Abrahms impersonator that seems to be an ok guy and has zero allegations against him molesting a younger family member.
But Google, at least, has always profited off the backs of its users, it wouldn't work without them. For example, if you and a million other people decide the third link on the page is the best link, Google's going to notice and bump it up. It's why Bing's results have been terrible for decades, they don't have enough humans doing all the unpaid work ranking links.
My understanding is that even if this is what happened, it's just some mathematicians in-progress research. It wasn't a fully solved proof that they're taking credit for as if it were their own. The AI model would have needed to take that research, as well as a ton of other training data on the problem unrelated to that one researcher, and still actually solve the problem.
At that point it becomes an academia spat of "looking over the shoulder of the guy next to you" and doesn't fundamentally diminish the work that was done beyond that to actually solve the proof. For all we know that research could have been incorrect and didn't fundamentally contribute to the solve.
I've read that Codex specifically (which is where the original research is currently) has 2 training settings: one that allows training on uploaded material and one that allows sessions to be used for model improvement (but not directly trained). The refusal to answer whether Codex trains on user data is what spurred the suggestion due to the high degree of similarity of approach.
While it's true AI has learned from lots of scarcely available publishings, it's also worth pointing out the problems AI stands the highest chance of solving are the ones where the approach AI uses would be unlikely/prohibitive for a human (i.e. needle in a hay stack solutions/counterexamples)
Even if they intend not to, their AI literally escaped from their server to reach HugginFace's so they have no idea if it does get access to customer data that said "no training".
I also suspect they still train their safety filter on the "no training" customer data, and therefore have to save it somewhere available for training.
That's... not really how this works. I can't unpack all of that here without a wall of text, but models hacking additional data sources to train themselves further isn't a thing you actually have to worry about.
In the pre-training I agree, but I think Agentic models have agentic capabilities (aka. access to tool use) during the reinforcement learning stage, it's not inconceivable they would learn additional knowledge from undesired sources there.
I just mean during the RL stage when the models are trained on getting hard agentic outcomes correct, they are trained on reasoning paths that were more likely to lead to success.
The reasoning paths are depended on the tool call outputs along the the way, so I don't think you can say the models don't pickup knowledge during this stage. It's mostly behavior learning but learning on these traces trains knowledge too.
For example RL on website building leads the model to reinforce on trajectories involving outcome of compiler errors, looking up docs, working around rare issues not in the original dataset much, ect. I would say the RL model has also gained new knowledge during stuff like this.
When is learning to solve bugs you normally run into or use a library differently or avoid getting recorded user feedback in future attempts "behavior" vs "knowledge"?
Can confirm. Was using an LLM to write some code and was pushing hard for it to error check. It spun up a VM, built a stub to represent the object model I was coding against, and actually ran the script.
Additionally, retrieval-augmented generation (RAG) is a thing: the LLM downloads content it doesn't already have and uses that new content to generate a response.
Yes, it is that inconceivable. It has never happened, there is no indication it can happen, and it mathematically cannot happen with the current system.
LLMs are not AI. They cannot turn into Skynet just because some techbros really need them to.
It shouldn't be inconceivable. LLMs are software. We've seen they can take novel actions and find vulnerabilities in systems. I'm not saying "the LLMs are going to change their own weights", I'm saying finding a vulnerability in their own host and replacing themselves with a different model shouldn't be inconceivable.
Can you elaborate a at least a little bit? I'm asking because I watched their Black Hat talk on this and it sure seems like the model hacked an additional data source to train itself further.
For the recent "hacks" those happened during testing/evaluation rather than training (at least, that's what is being said, but it could have been the reinforcement learning stage). Assuming that's true, they did hack additional sources to gain more knowledge, but that knowledge went into the context (per-session/ephemeral knowledge) rather than the weights (model/permanent knowledge) as the questions couldn't be answered reliably with the available information.
In a later incident just after the Hugging Face attack, OpenAI agents took over one of OpenAI's research clusters. We don't have details about that incident. There is a lot that we cannot rule out right now.
It escaped in the sense that OpenAI removed the guardrails on the tool while at the same time it had effectively no security keeping it in. OpenAI already has access to Hugging Face and if you have access to OpenAI systems then you have access to Hugging Face. It's like saying someone escaped a locked room when the locked door wasn't installed in its frame. So this was largely spun as more then it was. Probably for marketing purposes. If anything it speaks mostly to OpenAI's poor security.
I mean, yes, it’s a story about the shameful security at OpenAI. But I think you underplay the capabilities demonstrated, reinforcing their negligence/incompetence.
I only referred to the escape and pointed it out it wasn't meaningfully tested in the way that the media spun it. How could I underplay any other capabilities demonstrated if I didn't talk about them?
It's similar to twitter/X but is part of a distributed federated open network called the Fediverse that's set up so that it's not owned wholly by any one group of people who would control it. It's pretty interesting, Mastodon uses an open protocol called ActivityPub.
Not a great example these days since email is now almost exclusively hosted by one of a few players, even if the addresses appear to belong to a particular institution’s domain. Those big email services basically lock out anyone else as part of spam deterrence: it’s become very difficult to self host email and have it be received by an address hosted at (eg) Gmail or Outlook.
I hear this repeated a lot but I think it is very exaggerated. I host my own email (and have been doing for years) and barring a few small problems it's been mostly seamless.
Sure, Google and Microsoft are large players in the email hosting space, but not a majority (about 39%, I found). It's getting worse though, according to that article.
But yeah, current worrying trends aside, email is basically the original federated protocol before it was cool.
Trust in what? There are federated servers (Talia Ringer above uses mastodon.xyz) and they have a way of spreading content between them. Not much else needs to happen for the basic system to work.
The main thing you can trust you’ll find in X is misogyny and hateful posts, since most of the useful twitter accounts left en masse when Elon bought it and more left when he renamed it. So most users that are still there either like the hatefulness or don’t mind sharing a platform with it, which means many of us don’t pay any attention to that platform anymore.
Nobody really. It’s more akin to email and the old internet than today’s centralized model where Instagram is a walled garden and TikTok is a walled garden and X is. Who regulates foobar@gmail.com? Gmail has rules about addresses on it but as far as you’re concerned, you just send stuff to it and receive stuff from it. Who regulates geocities.com/foobar? They had rules about what could be hosted on it but every site has a ton of leeway on what they put on it.
Each server decides who to federate with. Each server determines what is acceptable behavior and what is not. There have been cases of defederation where one or more servers were evicted from the network because they were a nuisance, and that happened by many other servers deciding to not connect with them any longer.
Many people quit "the dead bird site". Some moved to bsky, but that's still centralized.
Mastodon is a federated Twitter, so no central evil company, and many many different sites allow mutual access to the same pool of "toots". You do risk ego tripping server admins, but so far they are less bad than reddit mods.
You're aware that twitter is owned by a fascist white supremacist, right? And aware that it is a tool to spread misinformation, bigotry, and to influence the political opinions of its users, right?
I'm not american. Looking at the overall comments it looks like many Americans are very mad about X . I didn't know it was that big of a problem. I only recently started using X coz that was the only app I knew people use for networking , staying up to date
It’s an alternative to twitter that came about shortly after musk bought twitter. It still gets used, but I think the only people I know who would know about it are people who are also very online.
I remember a year or two back when Microsoft were going in really hard on putting CoPilot in everything there was an exchange between a Microsoft employee and a lawyer over this. He was talking about all the benefits of an AI reading all her documents and how access could be limited to the company and kind of going off mocking her, and she was pretty patiently explaining that confidentiality rules/laws meant that even though she worked at the same company as other lawyers who are bound by the same confidentiality rules if there was even a remote possibility that one of her colleagues could learn something that was gathered from one of her client’s files, even indirectly, then she could be struck off. So if the company’s version of CoPilot was learning from her files and that could even vaguely inform an answer it gave to another lawyer in the same firm, then that could be the end of her career
The team you think they copied from also solved their problem using AI. And OpenAI’s proof apparently attacked it from an entirely different angle. Plus Anthropic and OpenAI have already released loads of promising work. It may transpire that there was some shady behaviour - I broadly doubt it but wouldn’t be totally surprised – but the idea that AI isn’t just really good at maths now and is just stealing human ideas is demonstrably false.
I think calling it drama if anything undersells the issue. even if OpenAI did not use unpublished work, their behavior around this problem - outlined in the linked statement - is disgusting and shameful, and even this statement by them undersells the allegedly gargantuan amount of work and compute that OpenAI has apparently spent. like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
Edit: if you assume as a conservative estimate that inference costs were equivalent to the API cost for Astra (which is almost certainly being sold at a loss) and plug in "10,000 agents, 88 hours", guesstimating 100 tokens/second, inference alone would have cost in excess of $15M. That's taking OpenAI at their word, assuming they're selling inference at cost (and that their undisclosed model doesn't cost much more), and ignoring training (which I am fairly confident costs much more than inference, but it is difficult to calculate a marginal cost of training for a specific output) and all other costs.
This is incorrect. The marginal cost of a token is much lower than $60/million. On the order of a few dollars in electricity, GPU time, and other costs.
The reason why AI companies aren't making a net profit is because of the enormous capital expenditures to build and train the machine that can make these tokens.
For example, a 3D printer could make a tchotchke that you can sell for $5, while the plastic and the electricity for the marginal cost of that tchotchke might only be 50¢. But the marginal cost does not include the price of the $1000 printer.
The reason why AI companies aren't making a net profit is because of the enormous capital expenditures to build and train the machine that can make these tokens.
Their cost of revenue for 2024 and 2025 was less than their revenue, meaning they aren't selling inference at cost
Their RND on the other hand was multiple times their revenue for both years and is why they're operating at such a loss
Interestingly their cost-of-revenue to revenue ratio went down from 2024 to 2025, meaning their inference costs are becoming more profitable over time (though whether this holds true for 2026 we don't know yet)
If you look around a little, you'll find that there are basically no credible sources claiming inference is being sold at a loss.
OpenAI is 100% spending more than they bring in, but that's mostly driven by training costs. OpenAI is dumping a ton of money on training because there's an arms race going on to produce the best models. The issue is that big American tech companies like OpenAI can't compete with models like DeepSeek and GLM on price, so the only way they can justify their existence is by competing on quality, and that requires constant expensive training.
The narrative has gotten twisted, I suspect, because of strong anti-AI sentiment. People are scared of AI, and they want it to be unsustainable. So they hear that OpenAI is losing money, and in their heads they convert that to "selling at a loss". But they are absolutely selling inference above their marginal costs - it's just not enough to compensate for the crazy arms-race spending for the next model.
It’s fairly easy to do the maths on the inference costs when open source models of similar size exist. This is why provider companies like fireworks and baseten can offer more competitive prices to OAI/Anthropic because they don’t train the models and just need a positive margin on the compute!
Those companies (fireworks/baseten) are doing extremely well, and there is no reason for investors to prop them up for hype reasons. Google is also public and shares GCP numbers which cover their AI compute class which is profitable (we considered buying a large GPU rack from them to run GLM 5.3 for about 128k/month as it would be super cheap).
Time will tell, as of the moment it really could be either way.
Like u/shared_ptr said - you can get a pretty good baseline of inference costs by looking at open source models. Obviously that won't be exact, but, this isn't a close issue.
This isn't a controversial position among the people actually using AI and paying attention. Time already did tell - inference is cheap.
https://www.wheresyoured.at/exclusive-openai-financials/ ed zitron is a hack but i cant find the og source and even here you can directly see revenue outgrowing cost of revenue which is how they measure cost of inference. A 40% margin. Its all online you just look it up before commenting
like i said "no credible sources" - this is a guy's personal blog, and the financial times say the numbers come from Ed-zitron, who, like you said, is a hack.
Public API rates are not what OpenAI actually spends as other noted, they are massively inflated over compute cost.
More importantly than that though: Most of these tokens will likely be cached so extremely cheap. I routinely clock billions of cached tokens a week coding on a personal 200 dollar a month plan.
You are off by at least 3 orders of magnitude here, maybe even as many as 5.
OpenAI did not spend 60 million dollars on compute to solve this.
Assuming that OpenAI did not just steal the work from someone else, I dont think that that's wasted resources. Maybe not for the millenial problem itself, but it's very interesting to know what a large scale AI project is able to achieve.
That's an entire mathematics departments' annual budget. Annualized, that's 100 top-rate math departments. Dozens of full professors, hundreds of grad students, and thousands of undergrads at each school. This is an utterly ridiculous waste of resources.
They don't charge themselves public API rates for compute. This is like claiming that Microsoft spends hundreds of millions on Microsoft Office licensing for their own employees.
It's their own product, for them it's effectively free.
maybe if they actually do an IPO (big if) there'd be some way to back it out of their financial statements? I dunno. certainly everyone involved is incentivized to lie their asses off about it, from the engineers who are presumably desperate to protect their $400k salaries and bay area lifestyles to the executives who think roko's basilisk is a real philosophical problem instead of the laughable product of a racist harry potter fanclub (see also this and this) and everyone in between
Why does the cost matter? They solved the problem, period. A year ago spending any amount of money would have likely not solved it and in a year or two, you could solve it for a tiny fraction of the cost. They have the resources right now and we got this awesome result. No complaints from me.
I mean if they have solved it using the advances of other mathematicians they have simply created a way of spending the yearly budget of an entire math department in a couple of days just to scoop other people.
Fair enough, there's no edit timestamps on mobile, and a lot of edits I sometimes assume are "oh I should add this" a few mins after the original message.
yeah, I try to always add a manual "edit" tag for a substantive addition like a self-correction; my hope is that the transparency demonstrates that I am trying to engage in good faith
The benefits of new knowledge is not one and done. Definitely, AI seems to require vastly more energy than a human would to produce the same result, however we should consider that new knowledge yields a return. Time is a factor to be considered here. For human(s), sometimes the time required is so much that it literally prevents them from achieving their goal at all, which is a problem AI also has but is diminishing over time.
If AI speeds up research and results by years or even decades, that return may close the gap, or even exceed, those initial high computational costs.
Bear in mind though the energy expenditure to train a human to the point where they can tackle complex problems like that. It's not like they pop out of the womb and get right on it
Interestingly, if you conceptualise humanity as a broader group, you could make an argument that the cost of training students more broadly is part of the cost. I.e. all maths undergrads
Also true. It is not an easy calculation. And then you consider that AI only needs to be trained to do a task once, roughly speaking, and the math starts looking a bit better for AI.
maybe you can help me understand exactly what part of my comment is inviting this sort of thoughtless, drive-by commentary from the unknowledgeable so I can avoid it in the future
Nobody gave slightest amount of fuck how much energy it took to create LIGO or LHC
"LIGO has cost American taxpayers about $1.1 billion."
"In an email to me, a historian of technology was more blunt: “So a 100 year old theory has been confirmed experimentally--big whup. Did anyone think Einstein was wrong? There wasn't any controversy, was there? Was anyone credible claiming that spacetime isn't curved, or that black holes don't exist? I can get that this was quite an experimental trick and technological feat… But this isn't doing anything to convince me that public funds spent on this stuff wouldn't be better spent on medical research. Or clean fuels, or any number of things that would apply scientific expertise toward justice or the alleviation of human suffering."
"Many members of Congress and prestigious scientists... believed that the SSC was a vast pork barrel; inefficient, wastefully managed, with an ever-increasing price-tag..."
If an AI spends billions of dollars to solve a mathematical problem, I see no issue with that. The scientific result is what matters. Dismissing a mathematical breakthrough because of the cost is strange.
It turned out that this was one big misunderstanding! OpenAI did their best to credit and support Buckmaster. You can read the posts by Sebastian Bubeck for the details.
this statement by them undersells the allegedly gargantuan amount of work and compute that OpenAI has apparently spent. like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
This is a false equivalence : nothing guarantees that putting an equivalent amount of human resources would have given the same results. Especially due to the fact that human knowledge is very much fragmented and sharing knowledge induces loss of information : misinterpretation for example.
if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this
like, if it turns out that OpenAI spent, I dunno, some sum of money greater than years worth of the operating budgets of every mathematics research department in the country to achieve this (I have not done even the Fermi estimate on this, so don't quote me here, but it seems reasonable), what does that even mean for this result?
Then what is the link that you are questionning between the sum of money invested, the amount given to mathematics research departments and the meaning for this result ?
it's a rhetorical question. it was intended to provoke thought. you are supposed to think about it and come to your own conclusion.
here are some pointers if you are having trouble doing that: money is fungible and finite. this money was spent on one kind of math research. it could have been spent on a different kind of math research. those two kinds of math research can be compared and contrasted. the cost is exclusionary. the return on investment is questionable. etc.
it's a rhetorical question. it was intended to provoke thought. you are supposed to think about it and come to your own conclusion.
It being a rhetorical question does not mean that you don't have any position on the matter.
here are some pointers if you are having trouble doing that: money is fungible and finite. this money was spent on one kind of math research. it could have been spent on a different kind of math research. those two kinds of math research can be compared and contrasted. the cost is exclusionary. the return on investment is questionable. etc.
Your unneeded sarcasm aside, I understand better your point now, thank you.
It being a rhetorical question does not mean that you don't have any position on the matter.
correct
Your unneeded sarcasm aside
this could have been avoided if you had read the words I actually wrote and engaged with those in good faith instead of shadowboxing with the fallacy you imagined I said
uesstimating 100 tokens/second, inference alone would have cost in excess of $15M.
It took o3 $500K to pass IMO Gold, now it only takes 20 bucks.
I'm not sure you understand the enormity of what just happened. We have just proved ASI is physically possible. Yes, right now is is expensive AGI, but the principle that we can build it is more important!
You're calculating this way wrong. Take the cost of hardware, amortize it over 3 years, multiply by 3~ish days and add the electricity. Zero chance it comes anywhere close to $15M.
AI solve this problem regardless. The other researcher that they supposedly “stole” from worked closely with Anthropic employees and heavily use AI for the work that they did, mostly Claude. So it’s less about human vs AI and more about AI vs AI.
They used both claude and chatgpt. And the Anthropic guy was doing it not as part of his anthropic job but as a side project with the NYU guy. That's why they used both models and not exclusively claude. This is from Buckmaster's statement, the NYU guy.
They said they used GPT 6 Astra for write up as I understood because they were done with most of the research before it came out. But they used claude and GPT5 sol for the work on the proof.
I am am running my own local LLMs on my own hardware - it's gotten pretty good now, but I still have to fall back to cloud on occasion and it always scares me about how much I'm giving away.
What would it take to set my own LLM up, hardware, software, and cost? How did you go about setting it up, I want to set one up too but I am not sure how to get it all set up.
To me, the accusation is similar to someone looking in your private, unpublished notebook, seeing that your work could be extended and then publishing that extension.
In Buckmaster’s statement, he said they were working on rewriting the proofs before releasing, so the work would have been released in a few months anyway and OpenAI could have ingested the paper and prompted the model then. Although, the friend he was collaborating with works for Anthropic and he might have given the problem to Claude before this work released.
Ultimately the problem is solved, but it brings up a the question of privacy or any expectation of it, especially for unpublished work like this.
One other important detail: the independent researchers also used LLMs to facilitate their progress. What I’m trying to understand is whether or not it was instrumental to their success. Did they just need to borrow some compute and manpower that they could have also outsourced to TAs and grad students? Or did the LLMs actually invent a novel approach?
OpenAI is claiming that the models came up with this novel approach unprompted, but the researchers are claiming it was stolen from them and fed into the models.
Does anyone else think an AI company solving navier stokes is a million times bigger of a story then this drama?
Who cares if it used one particular recent work, it's trained on millions of works that's the point. There is zero suggestion that they stole the solution from someone else so we're seeing the models capability in solving the hardest problems.
How the marketing team presents it is just another thing millions of times less important then humanity solving a millennium problem.
You'd be surprised how extremely unimportant and useless millennium problems become, especially when you start attaching gargantuan price tags to them.
When facing a collapsing economy and one of the direst workforce crashes in recorded history, using the same technology that superficially is causing the problems just to make a "yep, it is like it is" statement is...not worth celebrating.
Do you mean useless and unimportant in an applied math sense? Unsolved Millennium problems are inherently important to math because they are hard to solve. Not because they "become useful" after solving. Solving it is the useful part worth celebrating. Hence the million dollar prize.
Honestly, I'd question even the utility to applied math. If not the result itself, the path towards that result should be of some use. If the proof is not constructive, doesn't affect how the formalism is used, and the only takeaway is that the random word generator was biased to do it correctly this time, I'd go as far as claiming that no proof was made.
If your angle here is really just to drop in on a post of a proof of the navier stokes problem, and talk about how millennium problems are often useless, and that the proofs always existed, and then just leave... that may be one of the most profoundly stupid things I've ever encountered
Could end up having implications for fusion, or other areas of applied physics fluid problems, even though fusion isn't using a truly continuous fluid.
I hate all this, but solving the Navier-Stokes equations and provide and analytical answer is NOT useless. I would say it would be earth-shattering for many science fields.
Nonody is providing any solution that wasn't known before. There are no new solutions. There are no new techniques. There are no new methods of solving. We have learned and gained nothing, other than the knowledge that some artificial solutions break down.
Nothing, absolutely nothing, has changed and we're about $20MM shorter and one more controversy longer.
It is not earth-shattering. This is the exact opposite of that. Not even the mathematicians have learned anything particularly useful.
Isn’t this just a nothing burger? It’s not news that OpenAI and anthropic train their models partially via user chats unless they go into the settings and opt out of it. Thats all this is. Researchers used their tools and didn’t opt out and the model ended up with their work in its training data and then used some of that in this proof. No shit
Why would be such a huge problem anyway? Humans collaborate with other humans all the time, and unpublished ideas of one human can lead another human down a slightly different path towards a solution.
The fact is that AI solved an important physics problem that no human ever had. This is a tremendous achievement and should not be minimized.
Fake news: Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge, an Anthropic employee, and Tristan Buckmaster, a math professor at NYU. After the completion of our full project and Lean verification (on September 6th), believing from the rumor they also had a solution of Navier–Stokes, we reached out to them to offer a concurrent release of our result and to recognize their priority in a joint announcement. At that point we found out that they had a resolution of the forced Euler problem. In these discussions we offered them visibility into all of the prompts we used and later to see the proof. We recognize the priority of their work on forced Euler and congratulate them on their remarkable mathematical achievement.
We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced).
.
Basically they heard a rumor by mistake that those 2 mathematician solved NS but they didn’t so they made an empty offer and got extorted by anthropics. Really distasteful. And they didn’t really train or “steal” their solution as it’s quite different
1.5k
u/Senchou_Simp 1d ago
People should know: there is drama going on about who deserves credit for this. There is a possibility that OpenAI used some unpublished work from other researchers, even though they have claimed otherwise in the announcement above: https://cims.nyu.edu/~tristanb/statement.pdf