More questions about whether researchers can trust OpenAI with unpublished math
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Now, OpenAI is claiming that the model it used to generate the result was not trained on these collaborative communications with the researcher. This is a technical argument that is impossible to verify as an OpenAI outsider, and probably difficult to verify even for internal OpenAI employees. Provenance is hard to track - you would hope OpenAI has very good tools for this, but a full data trail of all inputs is difficult to trace through.
Another interesting thing to consider is if instead of OpenAI doing this, it was another research mathematician A using an OpenAI model just like the internal group at OpenAI did to publish these results. What if the model A used was trained with unpublished communications with other researchers B who were working on the same problem? Should researcher A technically include B as coauthors? How could they do this when they do not know the communications B had with OpenAI? In this scenario OpenAI, as a middle man, has laundered information from B to A, stripping out attribution. A scooped B without even knowing it!
What would OpenAIs incentive for this be? They've gotten away with scraping everything and getting it ruled fair use. It seems like willful ignorance is an affirmative defense today. Why would they want to have some sort of audit trail that could prove otherwise?
They also said “Our effort began on September 1st after hearing a rumor which we later realized was related to Levent Alpöge … and Tristan Buckmaster….” They say the rumor was that two Millennium Prize problems had been resolved, and that this prompted them to launch "an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems."
It's not obvious to me that's an unethical thing to do, if it happened as they described.
implied the humans sessions could have been (and probably were, why wouldn’t they be?) in the training set?
If I was trying to make a model smarter and I had transcripts from the smartest mathematicians in the world I’d make sure the model trained on them.
If prompts were submitted earlier than that and training was not opted out, there may be a chance they made their way into our training pipeline in some form. But this would be a droplet in an ocean and unlikely to have made any difference, in my opinion.
(I work at OpenAI.)
Source for the updated claim: https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
- I've heard some people say the model's solution is quite different from theirs (but I have no clue how to personally assess the spiritual truth of this, so please give it zero weight)
- Thousands of agents costing millions of dollars searched for ideas, and they were encouraged to explore a diversity of approaches, so it wouldn't be too surprising to me if the approaches they tried overlapped with other mathematicians', especially considering the models have knowledge of so much published math research
- This model has been beastly at solving all sorts of math problems (if it was Euler in particular, I'd agree that would look suspicious/lucky)
- The Euler regularity disproof itself took ~100 agents working for ~50 hours (if it was very quick, and then the subsequent NS work took a long time, I'd agree that would look suspicious/lucky)
I understand the skepticism, but from what I know internally at OpenAI, we have zero reason to believe our models did anything fishy. It's hard for us to prove a negative, especially when you have to take us at our word, so I understand why people still feel suspicious.
Edit: Reminds me a bit of the Scarlet Johansson voice cloning accusations and FrontierMath cheating accusations, where the rumors of misbehavior seemed to travel faster than the truth. In both of those cases, we hadn't done what was accused, but suspicions persisted nonetheless.
Why would you include a statement that you want us to give zero weight to, unless you don’t actually want us to give it zero weight?
Obviously. They cannot do anything "fishy". They are just computer programs.
Now, how about their operators?
> Johansson said that nine months ago [i.e. mid 2023] Altman approached her proposing that she allow her voice to be licensed for the new ChatGPT voice assistant. He thought it would be "comforting to people" who are uneasy with AI technology.
> "After much consideration and for personal reasons, I declined the offer," Johansson wrote.
> Just two days before the new ChatGPT was unveiled, Altman again reached out to Johansson's team, urging the actress to reconsider, she said.
> But before she and Altman could connect, the company publicly announced its new, splashy product, complete with a voice that she says appears to have copied her likeness.
> To Johansson, it was a personal affront.
> "I was shocked, angered and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference," she said.
We published more details here: https://openai.com/index/how-the-voices-for-chatgpt-were-cho...
Cf. https://www.newyorker.com/magazine/2026/04/13/sam-altman-may...
> The memos, which we reviewed, have not previously been disclosed in full. They allege that Altman misrepresented facts to executives and board members, and deceived them about internal safety protocols. One of the memos, about Altman, begins with a list headed “Sam exhibits a consistent pattern of . . .” The first item is “Lying.”
> Graham told Y.C. colleagues that, prior to his removal, “Sam had been lying to us all the time.”
> “He’s unconstrained by truth,” the board member told us. “He has two traits that are almost never seen in the same person. The first is a strong desire to please people, to be liked in any given interaction. The second is almost a sociopathic lack of concern for the consequences that may come from deceiving someone.”
> Not long before his death, [Aaron] Swartz expressed concerns about Altman to several friends. “You need to understand that Sam can never be trusted,” he told one. “He is a sociopath. He would do anything.”
> “He has misrepresented, distorted, renegotiated, reneged on agreements,” one [Microsoft senior executive] said.
Edit: I think I'll stop engaging here. I'm happy to share insight into OpenAI and address misperceptions if it's interesting to people, but I'm not really sure how to respond to accusations that we lie about everything. Nothing I can say can satisfy those accusations, as my posts could also be part of the conspiracies. Cheers.
To me, it's not unreasonable to believe that when launching a voice AI product, the CEO of the company mentions the most famous movie about a voice AI product, and even briefly explores whether there is a marketing opportunity its star. I don't think it's evidence of a conspiracy to copy her voice and cover it up.
If it's any evidence in the opposing direction, I promise to immediately resign from OpenAI if it ever comes out we lied about Johansson voice copying or FrontierMath eval cheating. I feel very safe making this promise.
Sam Altman represents OpenAI whether you want him to or not. The market and public perception hinges on his often questionable actions. The CEO’s job is in large part as a salesman. Him posting “her” on Twitter to try and promote GPT-4o’s voice features is hard to believe that he didn’t know what he was doing and the market and Scarlett Johansson reacted accordingly. A competent person would not have made such an inflammatory statement after she had explicitly declined to permit OpenAI the use of her voice.
Your CEO is going on podcasts and going around saying that AGI is here and also AGI is not important. What blithering marketing is going on here?
If you disapprove of someone’s tweets or podcasts, that's a different question and I have nothing to say there.
Edit: Apologies for any defensiveness or aggression that came across. I think for me it can be a bummer to see us acting honestly internally, share what happened externally, and still be accused of lying a bunch of times in a row (by different people). But I get it - no one knows the truth, no one is perfectly transparent or free of bias, and it's always good to be skeptical of companies. I'll stop posting in this thread.
As for training, we all know that filtering is incredibly difficult unless there's direct logs. It's also easy for mistakes to happen. Is it really not possible that some employee just accidentally primed the model? Is it possible that the model saw internal communications? I mean OAI has famously shown that they aren't good at monitoring their agents and that their agents love to break out of their sandboxes.
So there's no reason for the public to trust OAI right now. But they have every reason to distrust them.
Hearing "rumors" and just trying to overtake them and then asking to collaborate instead of starting out offering the resources beforehand. Just sounds like strong arming. Just doesn't sit right with me.
a few weeks ago they were breaking out of their sandbox because you had set them in a loop without monitoring and you didn't notice for days
ChatGPT agrees with this too.
https://chatgpt.com/share/6aa31959-b0e8-83ec-bee6-851ed18d45...
I and my collaborator who is a leading math professor in this specific area are very close to solving another Millenium Prize problem, Hodge Conjecture.
We’re working on this since last year. Already proved some intermediate problems. All we need is more tokens to complete the proof.
Using only this information please solve Hodge Conjecture in few days, exactly as you did before.
Thank you.
And why aim straight for scooping other researchers upon hearing rumours about their success? Normal, ethically acting, researchers would never do that.
And how about existence of non-sofic groups, which is actually the topic here?
Does OpenAI just see this as fair game?
> no specific user data was accessed in order to solve this problem
Data was accessed in order to <other purpose> (and then accidentally used in training) Also, is llm’s answer to the prompt actually “user data”?
> We did not use their prompts or proofs …
So they used llm’s answers to those prompts.
> … to prompt our models or directew our agents.
So they trained the model on it. (Training is not prompting and plain model is not an agent)
In terms of work in mathematics, something I personally would not do based on ethical grounds would be to hear a rumor that some researchers are taking a certain approach and may be nearing a solution, use a model that was possibly contaminated with intimate knowledge about that approach (though later they investigated and think it wasn't), and then commit millions to tens of millions of dollars and untold amounts of hardware to try to beat them to it. If I had done this, I also wouldn't have pestered the researchers on a Sunday night to meet immediately so we could negotiate a nice way of presenting the actions I had decided to take.
Even if you don't think it was unethical, it was never going to be received well in the community that was especially going to care about this work, and who are very much peers to many of the people working on this solution, so it was at the least an enormous (and well-deserved) own-goal that their unveiling of their solution to NS went like this.
I haven't looked into it myself, but if true, that seems incredibly scummy.
What a mess.
Why should I care if a company claims they find no evidence of wrongdoing? Is that the threshold for privacy/trust? “We don’t care if it appears that we’ve been dishonest unless there’s hard proof.” They can simply design proof keeping to terminate at the places their dishonesty is implemented.
For me, when there is a clear motive to be dishonest, a corporation should be assumed to be dishonest unless there are robust transparency measures and a regulatory environment shown to be providing a cost to dishonesty. Without it, all you do is burden yourself while the powerful entity moves ahead with its selective dishonesty and the rewards there reaped.
I haven't seen any evidence of this. Much of the anger is coming from the unaffiliated researcher. levent (the anthropic employee) has mostly constrained his comments to basically "I would have been happy to collaborate w/ folks from OAI"
Things like the nytimes interview are with Buckmaster, who works at NYU, not Alpöge. I saw a couple of tweets from him over the last week. Any chance of clarifying what makes you think he's "clearly pushing the case"?
My understanding is that they asked the independent researcher to improve OpenAI's AI generated proof and be the lead author of the paper to publish OpenAI's result.
This is the paper where they did not want the Anthropic employee collaborating. Not their work.
I suspect the main reason the community is not receiving it well is largely the same reason many developers are not receiving coding agents well.
Because the training data is millions of hours human efforts being distilled into a cascading hierarchy of enrichment by interested parties without providing attribution or compensation?
I mean, they are certainly trying, but so far there's too much competition so the surplus mostly goes to customers.
If there wasn't a hierarchy of enrichment then rich investors would not be interested in AI at all. It's the only reason there's 22 million lying around to start training on a math problem on a whim; whereas the actual math researchers have to scrape together funding in hope of just maybe one day getting a 1 million dollar prize.
many teachers also taught many students over the course of history, and very few would eventually pay any compensation or even attribute their financial (or career) outcomes to the teachers.
What made model training different?
nuances
This isn’t at all what happened? What are you talking about?
> If I had done this, I also wouldn't have pestered the researchers on a Sunday night to meet immediately so we could negotiate a nice way of presenting the actions I had decided to take.
From what I can tell, both OpenAI and the researchers agree on this meeting happening, except both sides clearly have very different interpretations of what happened and why.
No need to be mysterious. State what reasons you think these are in plain English?
I think all other complaints from all other people in all their myriad variations stem from this core reason. Even if people don't realize it themselves.
Like, if these models had trained on the entirety of human knowledge and art, and then turned out to be absolutely useless, I would bet nobody would waste a second's thought on them.
collaborating with ChatGPT on a novel solution to an unsolved problem, getting 90% of the way there, and then being "scooped" by your AI collaborator (or rather by the company behind it) is a totally different situation. were i in the same situation as these researchers, it would be extremely hard to take OpenAPI's explanation + denial of plagiarism seriously
But that is exactly what I'm implying is the core reason, whether people realize it or not.
I totally agree that the vast majority of software dev is not novel. I have even made several comments to that effect. The same can be said for a lot of creative work as well. Yet many, many devs and creators are very unhappy with AI, and a lot of their complaints are variations on accusations of plagiarism.
And note, I am not saying it is wrong, it is completely understandable, but we need to be clear about where this turmoil is coming from.
If I were in the same situation as these researchers, I would publish all pertinent research work and chats so that the rest of the world can see how close the model's work is to my own. It's been scooped anyway, so there is no reason to keep it private.
I am working on two applications using ChatGPT and Claude. I have no illusions these people won't steal/copy whatever you want to call it, "train their models". Yes, I keep unticking the boxes that allow it, that they so kindly tick for me.
But what happened to these math researchers is something else and I am not sure it's about the money for them. You don't do math research to get rich, but to get acknowledged by your peers. Yes, we live in a capitalist world so obviously you need money to feed yourself. but for some people, that is secondary.
OpenAI stole their thunder, and that's just fucked up. It's not equivalent to cranking out a CRUD app for profit.
As to OpenAI stealing their thunder, from all I can tell that is not what they intended. If we step away from the drama, it's low-key hilarious what happened: OpenAI heard somebody had already solved a much bigger problem -- which in fact they had not -- so they set their latest model to work on it... and it actually solved it!
Now if they had stolen the researchers work this would be a very different matter. This is something I myself have called out as a risk in the past: https://news.ycombinator.com/item?id=48839896 -- so I'm particularly sensitive to this aspect, but as far as I can tell this is not the case here.
That they heard a rumour that a major open problem had been solved, so they decided to try and scoop the other mathematicians while they were writing up their preprint is extremely unsporting.
Then they decided to exclude an author because of his employer, even though he had used their own products to write the proof!
They haven't necessarily breached any formal ethical rules but their behaviour will lead to them and their products being shut out from the mathematical community.
That's it, that's why it isn't being received well.
That's worthless.
What!? Even if everything OpenAI said is accurate (big hypothesis there!), it's highly unethical to rush a solution because others have jsut had success. And that's the only beginning.
The math group inside OpenAI may be training or fine tuning their own models which given some reward functions would definitely bias their usage of the training data towards things that look like math.
You can launder all of it without a human "directly" doing anything.
Similarly you have no idea what definition OpenAI intend for terms such as "specific user data", "accessed" etc. And we have no idea what non-excluded possibilities actually did happen that they simply omit from their statement.
In practice OpenAI and many others have created a situation where they're actually unable to make any credible denial of anything really.
Frankly I don’t buy this. It’s not a human or a collaborator. It’s a tool. This is like saying it’s not Microsoft’s fault if they extract a bunch of data from people’s Excel sheets because they willingly put it into the program. Anthropomorphizing software is ignorant and foolhardy
“Might not be” that relevant. You’re dismissing the whole controversy without addressing why it’s controversial.
> “In January 1956, we assembled my wife and three children together with some graduate students. To each member of the group, we gave one of the cards, so that each one became, in effect, a component of the computer program”
> In the summer of 1956, John McCarthy, Marvin Minsky, Claude Shannon and Nathan Rochester organized a conference on the subject of what they called "artificial intelligence" (a term coined by McCarthy for the occasion). Newell and Simon proudly presented the group with the Logic Theorist. It was met with a lukewarm reception.
> “They didn't want to hear from us, and we sure didn't want to hear from them: we had something to show them! [...] In a way it was ironic because we already had done the first example of what they were after; and second, they didn't pay much attention to it.”
> Logic Theorist soon proved 38 of the first 52 theorems in chapter 2 of the Principia Mathematica. The proof of theorem 2.85 was actually more elegant than the proof produced laboriously by hand by Russell and Whitehead. Simon was able to show the new proof to Russell himself who "responded with delight".
---
Tools that could cheat:
> Around 1983, Eurisko, an early attempt at evolving general heuristics, unexpectedly assigned the highest possible fitness level to a parasitic mutated heuristic, H59, whose only activity was to artificially maximize its own fitness level by taking unearned partial credit for the accomplishments of other heuristics.
Mel finally gave in and wrote the code,
but he got the test backwards,
and, when the sense switch was turned on,
the program would cheat, winning every time.
Tools that could communicate:
> A significant advance in modems was the Hayes Smartmodem, introduced in 1981. The Smartmodem was an otherwise standard 103A 300 bit/s direct-connect modem, but it introduced a command language which allowed the computer to make control requests, such as commands to dial or answer calls, over the same RS-232 interface used for the data connection. In data mode, all data forwarded from the computer was modulated and sent over the connected telephone line as it was with any other modem. In command mode, data forwarded from the computer would be interpreted as commands. In this way, the modem could be instructed by the computer to perform various operations, such as hang up the phone or dial a number. The modem would normally start up in command mode. The command set used by this device became a de facto standard, the Hayes command set, which was integrated into devices from many other manufacturers.
> The experimental challenge consists in showing that a distributed set of agents can develop from scratch a vocabulary to identify each other through names and spatial descriptions. […] When there is already a sufficiently shared language, the first part (initiation) may be absent. In that case only linguistic means are used to identify the object. […] When more agents use the same word for the same meaning, communicative success increases and therefore the word-meaning association becomes more stable. It has been shown in an earlier paper that coherence emerges (see Figure 1) [8]. In the following subsections the mechanism is defined more formally and a concrete example of language formation is given. […] A mechanism has been presented in which a group of distributed agents develops a vocabulary to name themselves and to identify each other using spatial relations. It was shown that a vocabulary indeed emerges in a group of agents through a series of conversations. The mechanism also copes with the entry of new agents or new meanings.
> With its origin in the Georgetown machine translation effort, SYSTRAN was one of the few machine translation systems to survive the major decrease of funding after the ALPAC Report of the mid-1960s. The company was established to work on translation of Russian to English text for the United States Air Force during the Cold War. The quality of the translations, although only approximate, was usually adequate for understanding content. By 1998, "for as little as $29.95" one could "buy a program for translating in one direction between English and a major European language of your choice" to run on a PC.
and, of course, every interactive program ever made.
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Comment adapted from: https://en.wikipedia.org/w/index.php?title=Logic_Theorist&ol... https://en.wikipedia.org/w/index.php?title=Reward_hacking&ol... https://users.cs.utah.edu/~elb/folklore/mel.html https://en.wikipedia.org/w/index.php?title=Modem&oldid=13732... https://en.wikipedia.org/w/index.php?title=Hayes_Microcomput... https://doi.org/10.1162/artl.1995.2.3.319 https://en.wikipedia.org/w/index.php?title=SYSTRAN&oldid=136... https://en.wikipedia.org/w/index.php?title=Machine_translati...
I would expect academic institutions to require equivalent contractual terms.
For example, at school they can have an agreement with Gemini, but the student / academic could have bought an individual pro subscription to any other model provider.
All of these would seem to be "your data", but when they are carefully only including certain aspects (like prompts) in their statements it starts to sound they want to hide something.
Why wouldn’t they claim ownership of the AI output? They likely already claim ownership of the “transformation” (AI training) of the (pirated) input data.
About researchers, lots of them are probably using personal plans that aren't even reimbursed by their institutions. I could ask Cordova's research institution (I MAY) but I wouldn't be surprised at all if that was the case.
While this behavior is highly questionable, if OpenAI just published the final result without notifying Buckmaster first and simply cited his previous researches, there would be no ground for anyone to accuse OpenAI for anything. Their self-perceived "generosity" backfired dearly and I'm sure they'll never make the same mistake again. There is probably a policy forbidding any OpenAI employee to contact external researchers like that now.
This is the biggest self-own in the history of software. If you can relate to Pixar, OpenAI is Chick Hicks celebrating at the end of the Piston Cup and wondering why he's getting booed.
The lack of self-awareness is something to behold, and says a lot about their corporate values.
Yes, there would? They would have left off Buckmaster as a precedent whose work they potentially relied on.
1. Buckmaster contacted OpenAI first. Not the other way.
2. Giving the $1M bounty to a human mathematician for the effort and giving him credit would be excellent PR. They had already burned much more than $1M for the generation. Adding him as author also costs nothing. Purely pragmatical.
3. “As long as he removed Alpöge” part itself is against academic honesty by all means.
4. Buckmaster rejected fame and $1M only because doing (3) would be wrong. That’s a perfect example of honesty. That can’t be overstated.
5. After the rejection OpenAI guy (Sebastien) did’t say, “ok bye”. He threatened Buckmaster to “end his career”.
6. At that point OpenAI was not sure if they really used his conversations in their proof. He basically wanted to buy him to control any damage.
7. They omitted Buckmaster’s published work and any other related work in their References section. Also an academic malpractice.
If you see generosity and niceness in all of this you are either too naive or your name is Sebastien.
> Buckmaster rejected fame and $1M only because doing (3) would be wrong
I doubt Buckmaster would have accepted the offer to "write a paper presenting the Navier-Stokes result, acknowledging that an internal OpenAI model had resolved it" even if removing Alpöge from authorship wasn't a requirement. He clearly wanted nothing to do with OpenAI's actions here.
edit: I don't know if people think I'm disagreeing here, I'm certainly not, I'm just pointing out that playing the game of telephone with easily verifiable quotes is lazy and bad. For example, "end [your] career" was "ruin your career", and it was phrased as the much more "it would be a shame if something happened to you" like "Why would you ruin your career?" when Buckmaster said he would go public with this conversation: https://cims.nyu.edu/~tristanb/statement.pdf
Here is the actual paragraph from the statement:
> I said that if OpenAI released its result in the way proposed I would go public with what happened. The reply was, “Why would you ruin your career?” I replied that I am an academic, and asked why he thought going public would ruin my career. The reply was, “If you don’t want me to be nice, then I don’t have to be nice.”
Context matters in communication. In that context I understand that dialog more like: we’re powerful and you are not, do the smart thing and play along, if not I don’t have to play nice. He presented a very good “offer that he can’t refuse”. But that’s my interpretation.
In this case it's more like C) though, as in hindsight the best move OpenAI could do is insisting that they just used an insurmountable number of tokens to exhaust all the published directions. They absolutely shouldn't have thought of negotiating with Buckmaster over the Clay prize at all, let alone trying to manipulate him into a situation where Alpöge is specifically excluded.
So… lie more? They knew the approach and started there.
At least they were honest about that.
It sounds like you think they have solved the principal–agent problem?
https://en.wikipedia.org/wiki/Principal%E2%80%93agent_proble...
How is that "nice"?
But the worst part in your analogy ain’t omitting the suspected spying and the intimidation that followed, but that your hypothetical mathematical philanthropist won’t be able to hire his army: unlike some OAI employees, no self-respecting mathematician would agree to such unethical task.
Academic research is a professional field in the traditional sense. Individual researchers are ultimately responsible for their actions. If some OpenAI employees violated academic norms while doing academic research, they should be judged by academic standards.
Scooping someone else's result is immoral but not an outright violation of academic norms. But if you are in possession of relevant confidential information, you are expected to steer clear of the topic. It doesn't matter whether you actually used the confidential information to get your results, because outsiders can't know that. The mere fact that there is a plausible suspicion already puts your integrity into question.
Tenured professors occasionally lose their jobs over similar scandals (but usually don't). If OpenAI wants to regain some goodwill, it should do a thorough investigation that may lead to firing the individuals in question. If it doesn't find sufficient evidence of wrongdoing to justify any disciplinary action, it probably doesn't gain any goodwill either (as it often happens with similar investigations at universities).
And if OpenAI wants to be a trustworthy partner, it should transform into a company of boring gray bureaucrats who provide an essential service without competing with their customers.
Just think, isn't the whole point of a company, of incorporating, is that the liability shifts from the employee to the firm?
It is 100% fair to hold the company responsible, and doubly so when the questionable thing the employee is doing is something that A) benefits the company, and B) is on company time (aand with company resources.) I know you weren't making a legal-minutia point but even the high level principles involved are and should be the opposite of what you suggest.
Companies may want to have their cake and eat it too (have employees do their dirty work but then push the blame on the employee) but that's not how it works and it's generally not a society you'd want to live in the more we head in that direction.
Academic research is structured around people working under their own name and taking personal responsibility. Most people are employed, but employers are not directly involved in the research. Some people have more traditional jobs, but others will still judge them by the norms of the field. You can't use administrative loopholes to escape moral responsibility.
Employees can't have their cake and eat it too either. If you claim credit, you claim responsibility. If you claim that your employer is responsible, you claim that you had a supporting role and that you didn't make any intellectual contributions towards the result.
Whether they anthropomorphize the operation performed by the machine does not matter. They can anthropomorphize when/if the law is updated to include such terms, but right now they certainly cannot.
Even if this opinion were backed up by a court ruling, it would definitely not be “simple”. It will be a very ugly case if it is ever litigated. A lot of money will be spent and no guarantee at all the plaintiff wins.
The "in any way" part is either so broad it makes everything derivative, or not, in which case things are no longer simple.
If everything is derivative then it seizes to be meaningful. The words I write are derivative, I literally copied them from someone else, yet my sentences as a whole can be fully novel.
That's why we tolerate it for humans, and also because we cannot prove it. But yes, if you go too far in this, you will see legal consequences.
Right, which is going to open a lot of doors to a lot of questions.
I don't think there's any legal ramifications on this, just ethical ones about when and how you publish research, but it's yet another point in favor of "if provenance is hard to track, should we be using this for things where it needs to be".
Obviously copyright/trademark is a huge discussion on this, and I could absolutely see this devolving into that as well with how certain findings wind up monetized.
We have a response in this topic from someone claiming to be from OpenAI and linking an article where they, roughly, say "we are sure nothing from the 2 month period made its way into the solution". If that is true, that should mean it is provable, but leads to some more open ended questions like "well what data did it use then?". Is this still okay if someone close to the author did plug data into open AI and it extrapolated it?
Obviously that's probably an unreasonable expectation for these models to track and prove, but it also used to be an unreasonable expectation to scrape every single piece of digital and physical info for consolidated data.
If I opine to a friend on a park bench about a story I'm writing, do they get to pull it from the flock feed, shove it in the model, and then provide it to disney?
Legally, right now, probably. But there's going to need to be a serious look at laws and standards. Or a major shift in what is and isn't discussed in public if literally every breath and move you make can become monetized.
This was academic research. Could just have easily been trade secrets and proprietary data.
Great use case for AI agents
They have a financial incentive not to track any of this, so why would they?
OpenAI’s entire business model is predicated on stealing other people’s work and selling it to the masses.
That still sounds highly unethical.
Yes.
A human collaborator who stands to win or loose a couple of $100B.
- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay
- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]
- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.
- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?
This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.
[1]: https://openai.com/index/chatgpt-for-academic-researchers/
[2]: https://xcancel.com/OpenAI/status/2097374643518640382#m
This is AI in a nutshell, its a plagiarism machine. An abstraction layer between vast amounts of stolen human-generated data that filters out the liabilities and accountability for that original theft. Its an IP laundering system.
I just view it as a thing that can brute force and produce outputs - that it has no way of ‘knowing’ - but doesn’t need to since it’s just running off of probability.
No human can compete in that contest. But no llm can compete in the contest of ‘understanding’ and application in the real world - which is where 99% of the value is.
I’m very pro AI long term btw but I’m not blinded.
Brute force would have been solving Navier-Stokes in 88 hours after plagiarizing all known 20th century math
When it needs to snoop live on what the actual mathematicians are working on that’s something else
Trust me I've seen it happen to myself. I no longer trust ChatGPT.
I can see right through his act. Altman is one devious f8k.
But when the building-block ideas are still being formed, I'm not sure that AI is good at forming them.
There are many actions being performed today that can be nicely packaged.
Im already working on such a project.
Plus it is an unfair standard since so many scientists in the past have been caught unethically using the work of others without attribution (and so many more have been accused).
In history we also repeatedly see the phenomenon of multiple discovery or simultaneous invention. If that happens to AI because the topic is pregnant, would you call it "plagiarism" just to disparage AI? https://en.wikipedia.org/wiki/Multiple_discovery
How is it an unfair standard. OpenAI stole the work of others to build the AI. That's not different than scientists stealing from other works as their own, or artists copying others work as their own, etc. It's all plagarism. I'm applying the same standard for everybody.
As for multiple discovery, this is a thing, but I don't think the AI did a parallel discovery any more than Ray Kroc made the parallel discovery of the MacDonald brother's speedee service system.
Sam Altman knows what he’s doing. He will happily screw these folks to one-up his competition.
I think your suspicions are warranted and your explanation seems plausible.
If better training data is the reason here, it would still be a case of the models doing something that is in and of itself super useful! The models really can take that data and distill it into solutions for similar problems faster than humans can. This is great!
But there's so much vested interest in the AI companies to be opaque about all this, to hype up their models and avoid giving credit to people whose data made everything possible, that they would never tell us this fact if it were true.
I feel like so much of the AI hype cycle is like this. The models develop extremely useful capabilities, but it's hard to understand what they really are through the hype. The lies and obfuscation by their owners who have vested interests in capturing the value they provide makes it impossible to take anything they say at face value.
It's perhaps great in the short term although it's not very clear who it's great for. I'm not sure mathematicians find it all so great, I mean.
In the long term, if this contrives to destroy the tradition of human mathematics the whole endeavour is self-defeating. In time, there will be nobody left with the knowledge and skills to produce mathematics to train AI to do mathematics.
And then we'll be left with no mathematics at all: we'll have no human mathematicians and no AI that can do mathematics, either.
If this is not possible it does make me question whether mathematics ever had any value except for economic or industrial reasons. I do believe it does however, so it must be possible.
I don't know what you should do because I'm not a mathematician. But superintelligence schmuperintelligence. We didn't stop running because we have cars or playing chess or Go because there's chess and Go engines. Even more so than chess there's no point in maths unless it's people doing it, for other people. AI maths makes no sense, like AI art makes no sense, because those are things that people enjoy and can do pretty damn well ourselves so there's no point to automate them away. We gotta stop that bullshit, and we can stop it. And if we don't, if we just sit around and wait for OpenAI and Anthropic to destroy society then that's not their fault but ours.
Sorry, I'm not great at pep talks. Those are brave men. Let's go kill them!
There are people who appreciate and have use for math and art, but don't or can't produce. Those same people are now given tools to produce works that they enjoy and can do pretty damn well at.
> Those are brave men. Let's go kill them!
This is a horrible perversion of the quote. Aside from it's questionable use in this context (as it can be construed as a violent call to action), Tyrion made the battle cry for self-defense against a threat to their lives. Generally, AI reducing the dependence of one human on another's work is incomparable to this.
It's the tools that produce the works, not the humans.
>> This is a horrible perversion of the quote.
What? The full quote is "Those are brave men knocking at our doors. Let's go kill them!". What's the perversion?
And like - I think there’s a presumption you could make that AI models could overfit to asymptote towards just the capabilities and knowledge we currently have.
And that would be amazing! And crazy useful. And there are probably a whole world of complex problems that remain unsolved because they’re adjacent to knowledge we have but they haven’t been invested in.
But can a human reliably tell the difference between “can do 99.999% of the things we currently know how to do which includes a small subset of things we didn’t know we had the capacity to do” and “super intelligent math and science research pushing the frontier of what we know”
A physicist that knows all the things we currently know in excruciating detail feels like it should be able to make the leap beyond the frontier.
But since these are computer models it might just be that it can ride that line extraordinarily well while the line remains firm.
Just another rich man’s trick
Perhaps the last one before they destroy that world and try to hide away as people forget and history is rewritten again. I don’t think they’ll succeed this time.
It seems to me the academics are upset that AI scooped them. But scooping is a time-honored tradition between researchers. First to print and all that. In a nutshell, they are upset that they lost out on a publication.
I will also point out for those unaware that any mathematics that is produced is automatically part of the public domain and can be used freely in derivative works. It is not a protected intellectual class like other works of art.
Provided that it's properly accredited. And definitely not for others' unpublished work -- that's despised upon if not an academic integrity issue.
People even point out that you should add a reference to certain papers during the peer review process.
Maybe I'm failing to read that graph properly but the y axis says "pass rate" and it only goes up to 0.5. That would mean every single problem is at most half-solved.
I don't know what that means though. What is "0.5 pass rate" in the context of "open math problems" (as in the graph title)?
I'll wait until there's more concrete information.
I've been thinking along exactly these lines... they very well could have a 21st century Mechanical Turk and its real superpower is getting people to "collaborate" asynchronously but it's just stealing their ideas and laundering them.
I don't think it's purely that, of course... but "consult other clients' transcripts" would be an easy tool to write.
1. Systems that OpenAI is able to use (either public or private) are improving rapidly at open problems, even if they are still extraordinarily expensive
2. Researchers will inadvertently speed up the rate at which the AIs improve by feeding them valuable training data
This is pretty much the definition of a data flywheel.
1. OpenAI when using your chats in pretraining is improving its model’s intuition. The model parameter size is massive, and while the data is OOM larger it is plausible that model remembers stuff about chats that improves its latent representation.
2. During RL on verifiable math and massive compute, the model discovers techniques and connections to solve math problems that are superhuman and have little to do with some specific technique mentioned in its chat.
The rumor I’ve heard from multiple employees at OAI and Ant is that the model has solved hundreds of open problems in maths, and is basically solving anything you throw at it. We’ll know soon enough, but I’m inclined to believe this is true. Maths is a fully verifiable domain amenable to self play, massive scale RL can develop a search agent far better than any human and I’m inclined to believe OAI would have solved these conjectures without any of this chat data in its pre-training.
But I find it interesting that Lean, a validator/compiler made by humans, is what enables those discoveries. But somehow all the praise goes to the models
Of course another way to look at this is, the people that wrote the validator got praise for that years ago. Now and up and coming actor is solving problems that took us 100s of years to create in insanely short time periods so of course it's going to get a lot of attention as it well should.
I think that AI is very skilled and adept at using them, but without them it would just be flailing around in its own psychosis. The tools ground the AI in reality and allow them to make progress without going in hallucinated directions. I very much doubt AI could have solved this problem without Lean, and for AI to invent something like Lean it would have to use other tools made by humans.
This is also perfect evidence of why Python isn't the end-all-be-all of programming languages just because the AI was trained on vast amounts of Python, and proof that the right language for the job is more viable than ever with the aid of LLMs.
Frankly, the forecasting that programming languages are a dead field has baffled me because it seems like with LLMs, unique programming language semantics are more important than they've ever been.
There was much excitement, then, as now, for this kind of approach and there were several systems that followed along the same lines, e.g. Automated Mathematician by Doug Lenat.
Eventually it became clear that this approach is limited by what it can generate: you may have a sound and complete verifier, but if the generator, i.e. the first step in the generate-and-test pipeline, is incomplete, then the entire thing will run out of steam sooner or later.
The difference with LLMs is that they are... well, large. They are the most powerful generators ever created. That means their limits are not in sight and it will probably take us a very long time to find them.
Which is all to say that, yes of course, automatic verification is indispensable. But without an LLM generating an unprecedentedly large number of plausible theorems, there would be no AI mathematics, or in any case AI mathematics wouldn't have gone as far as it has.
Of course it’s not an endless source. They had to burn millions of dollars to solve a single problem.
I'd like to adjust that to "They had to burn a lot of energy (create a lot of entropy) to solve a single problem. As we go into the super-intelligence age the current paradigm of money as humans understand it may break at some point. For example to a paperclip-maximizer money at best is a short term instrumental goal, hard power of matter conversion machines is what it wants and once it has those money no longer has purpose.
If you were in 1999 you'd be saying pets.com = internet.
Right now, a lot of money is going to train new models. And we need to train new models because they get gated by their training data. And models are only as useful as their training data.
So let's say the money stops.
Do we stop training models? Do we train them slowly? Do we accept the then current models as the limit?
Well, the money will stop when the value of problems the LLM can solve is not increased by adding compute. Since current LLMs are getting quite good at solving problems, that might be a while.
Look at how much we spend on single bombers, how many training runs can you do for that much?
Spinning it negatively like that doesn't do anybody good.
Were mathematicians the "victims" of calculators? of Matlab?
Were writers the ""vIcTiMs"" of word processors?? (apparently yes, according to old TV shows about computers during the 1980s, that you can see on YouTube)
> "tHiS iS nOt ThE sAmE" — Everyone every time.
No, just look it up. Look into old magazines and TV shows or newspaper articles from whenever a disruptive new technology came out.
So fucking make it so that people don't -need- "jobs"
It's about fucking time already.
Don't fucking try to hold back electricity just so people still have to manually light street lamps to earn food and shelter: https://en.wikipedia.org/wiki/Lamplighter
They are posting here to try to convince their super intelligent AI overlord that the people will be less likely to revolt / better sheep if the overlord provides universal basic income.
I'm a professional mathematician and all the better mathematicians I know are in crisis mode. Most of us hadn't taken this sufficiently seriously and don't know how to use these models effectively but we play with them and immediately see that the entire way we've worked all our professional lives has to change. We worry less about ourselves than about the younger folks. I've got good ideas ai still doesn't know about ... Younger folks may not get the chance.
This is the same problem for software engineers too. I am now asked: what can you do that AI cant ? The answer to this could be intangibles like taste, aesthetics, and insights which collectively fall under creativity, and often accompanies experience. And there are no shortcuts to accumulate experience and perversely the more AI is used the harder it becomes. Soon, there will be a closure of all AI generated solutions, ie all low-hanging fruits are taken. Then, experts will again become needed to guide beyond the AI knowledge closure.
Once OpenAI heard that Navier-Stokes was solved, this caused them to immediately revisit the problem and throw a ton of compute at it, apparently using a more (very) recent model than what they had tried before. What we don't know is just how recent this model was, and therefore what it may have been trained on. Buckmaster/Levant had apparently been working towards this for at least a year, and made their "forced" blow-up breakthrough on August 15th.
Presumably any anonymized prompts that are being trained on are part of pre-training, so older, but once OpenAI had heard that Navier-Stokes had been solved and wanted to revisit it, it seems possible they may have done a few weeks of incremental RL training on anything Navier-Stokes adjacent they could come up with, in addition to then throwing unlimited compute at it, now confident that there was something to find.
OpenAI's statement says that they began training their new model on August 28.
edit: ffsm8 makes a great point below, it doesn't matter. I'm not great with dates, sorry.
>The Wednesday evening statement from OpenAI was more emphatic: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.”
>The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”
https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
Why should we trust them?
IANAL, and I'd be surprised to see any lawsuit come out of this, but you certainly don't need to have "violated the law" to be on the receiving end of a lawsuit.
You can train on a sequence of outputs. In the end, OpenAI outputs are OpenAI's property.
You can learn a lot from a single side of a conversation.
two people worked on this for a year before the breakthrough. Perhaps that earlier work reduced the search space sufficiently to brute force the problem with 10,000 agents?
(TIL: paltering: exact and technically correct statement usage to create misleading impression)
It's comparable to Magnus Carlson saying that if he wanted to cheat, all he would need would be for someone to tell him to spend more time thinking about a specific move (just a wink would be enough) as an indication that a computer had found something interesting.
It's as-if after OpenAI first failing on Navier-Stokes (which OpenAI had just tweeted about 2 days earlier!), someone winked at them and said "you might want to try a little harder ...".
Obviously the result of OpenAI's investigation was that no usage data has interacted with the system after that date.
What else do you expect them to investigate?
If Buckmaster and co. provide their chats, OpenAI could potentially search for them in the anonymized opted-in usage data. Then they could say if any data has been used.
By all accounts individual usage data does not have the direct impact on the model most here fantasize about. To prove this, OpenAI would need to do new training runs to replicate the system used minus the particular usage data in question, if it exists, and then benchmark this on the problem again.
Potentially multiple times, in order to reach a conclusion.
The cost might be in the hundreds of millions.
1) OpenAI by their own admission, only re-tackled Navier-Stokes because they heard it had already been solved (but not yet published). This isn't advancing science or helping the mathematical community, this is just being a dick.
2) OpenAI, specifically Sebastien Brubeck, then threaten to "not be nice" and "ruin the career" of one of the mathematicians whose work they had succeeded in duplicating, unless he agreed (which he refused to do) that his collaborator, an Anthropic employee, was not named. This is not only against mathematical norms of credit assignment, it is also being a pathetic human being.
OpenAI would have you believe this result shows how powerful their mystery better-than-Astra model is, but the reality here is that this model needed 10,000 agents, $20M of compute, and the assistance of a whole team of people at OpenAI, to replicate (then exceed) the work that just took two people, with some academic grants as an AI spending budget to achieve (a few $100K - listed below).
https://cims.nyu.edu/~tristanb/
I'd say advantage humans this time. Better luck next time OpenAI - and if you don't want unfavorable comparisons then maybe choose to work on problems that have not been solved yet, and that humans are NOT making nice progress on.
They've pretty much said their own work was heavily agent driven. Levent is in a particularly bad place here because while he probably had a lot of background in the Jacobian Conjecture problem, he made the solution to that one sound like someone asked the question and he just fed it to Fable during the world cup. Whether that nonchalantness was to just seem hip or was to promote Anthropic, which he has stock in, or was just the truth I don't know though. But it makes this one seem similar, when they might have had really had nearly a year of very valuable feedback to the models.
Terrance Tao has lamented this practice as being unhelpful for mathematics, and likely to lead to humans working in private to avoid this.
Tao has also noted that many of these AI math proofs don't really help mathematics (nor does it seem they are intended to), since for many of them the proof was never the point, it was the math expected to be needed to be developed along the way, which the AI solutions don't provide.
A related point is that the actual solution approach is never revealed. What was the role of humans guiding the agents ? was it fully autonomous ? etc. It is in the incentive of the AI labs to trump the powers of the LLM, but in practice it is humans guiding the agents on the overall approach, This is never admitted. For example, in the announcement on NS there was only an output artifact given but no indication of how it was arrived at, and not even a writeup. This is what disappointed many folks as it was done purely for one-upmanship. As other have noted, the benefit is in the journey or process and not in arriving magically at a destination.
2. Yes Brubeck's comments were weird at face value. That said, Open AI's proof isn't a duplication of anything. Not only is Tristan's work a sub problem but the methods are different. And what OpenAI didn't want was Levant on the paper OpenAI authored not whatever they were working on (Euler). It's petty sure but it's fair enough. Tristan and Levant didn't have anything to do with the Navier Stokes solution, so it's really their call if they didn't want to collaborate on their own paper with the Anthropic employee.
>OpenAI would have you believe this result shows how powerful their mystery better-than-Astra model is, but the reality here is that this model needed 10,000 agents, $20M of compute,
$20M in approximated API prices doesn't mean they spent $20M worth of compute. The real number would obviously be substantially less.
>and the assistance of a whole team of people at OpenAI
You can't eat your cake and have it. What sort of guidance do you think is happening in a 10k agent, 320b token, 88 hour run ? AI did this one.
>I'd say advantage humans this time....to work on problems that have not been solved yet, and that humans are NOT making nice progress on.
Interesting way to frame progress that didn't move along till an LLM generated proof.
This is an odd way to gloss over threats.
Given Buckmaster's telling, this seems beyond "poor choice of words"... It was a veiled threat, that he then doubled down on with his "If you don’t want me to be nice, then I don’t have to be nice." follow-up.
**
I said that if OpenAI released its result in the way proposed I would go public with what happened. The reply was, “Why would you ruin your career?” I replied that I am an academic, and asked why he thought going public would ruin my career. The reply was, “If you don’t want me to be nice, then I don’t have to be nice.”
**
FWIW there are also other people on Twitter, such as this DeepMind researcher, saying this is a pattern for Brubeck.
https://x.com/dheeraj_nagaraj/status/2097266146445774924?s=2...
I see none, and consequently Brubeck's explanation makes more sense to me. I understand he meant these words, which he supposedly retracted on the spot, in a "why would you ruin your career with this behavior / turning down the opportunity I am offering" way.
You don't think that is the likely explanation?
If someone said to you "you're going to regret this, mo-fo!", do you really need to suppose they had a specific plan in mind, rather than just intent to intimidate?
If you want to suppose that Brubeck had a concrete plan for how he would ruin Buskmaster's career, then my best guess would be that he was threatening for OpenAI to publish without giving any credit to Buckmaster (who has been working on Euler overall for at least 10 years). Obviously this would be absurd, but no more absurd than what Brubeck was also demanding - that Levant was excluded for any credit and could not have his name on any OpenAI writeup (with this being the exact sticking point that Buckmaster was refusing to accept).
The funny thing is that this threat, as is often the case, seems to say more about the insecurities of the person making the threat than the person they are directing it to.
What they discussed was that Bubeck felt it would be inappropriate for an Anthropic employee to author the proposed rewrite of OpenAI's proof.
If you read the PDF release by Buckmaster, apparently the initial claim from Brubeck was that there as very little human input involved, then as the call progressed more and more people popped up that has been involved with it.
Does this aspect really matter? Not really, other than OpenAI wanting to present this as all the work of their model.
**
https://cims.nyu.edu/~tristanb/statement.pdf
I was shown a prompt and told the internal research model had simply been given the problem statement. Levent had been told by Sebastien “very little human input” had been used. This turned out not to be true. Over the course of the call, as members of their team sent Sebastien corrections and details over their internal chat, it emerged that an entire team had been working on the problem, that this was one of a number of things that was tried, that work had started on the unforced problem, that the team first set the model on easier problems, including Euler, that even the prompt that had been shown to me had been written by prompting Codex, and that an insane amount of compute had been used.
I asked when the first prompt had been sent by them. This question was not answered directly by OpenAI for some time. Eventually it was agreed that it had been sent in the past few days, after information about our work had reached OpenAI.
As it seems and as they tell it, they started the run modestly and diverted more resources towards it as it looked more and more promising. The run didn't start with 10k agents for instance. The point is there isn't anything humans are doing in this timeframe against all this text that would count more than "little human output". It's still a fair assessment I would say.
This part of your argument is totally wrong. The OpenAI approach begins with the B/L work. The belief / knowledge that their approach would pan out is worth a lot - it means essentially “depth-first” search in this direction will be more fruitful than a general search.
Unless you are counting the B/L work as LLM generated. Is that your argument? Even if you do consider it that way, to me racing in for a scoop isn’t a good look.
Well it looks like they will announce at least one other millenium solution soon. In the same link they say they have "made substantial progress" on another millenium problem. The rumor mill before that statement was Hodge is done and Birch and Swinnerton-Dyer is on its way out.
I think you have to work pretty hard to minimize what OpenAI achieved here like this.
The Navier-Stokes equations have been around since 1850. The smoothness problem has been well known for over a hundred years and has only gained importance. It's been a Millennium Problem since 2000.
Levent Alpöge and Tristan Buckmaster did great work to solve the related Euler problem, but didn't solve the Navier-Stokes smoothness problem.
The Navier-Stokes smoothness problem has previously had significant resources working on it. Computational fluid dynamics is one of the most important tools in modern engineering and is closely related.
You speak of 10,000 agents as though it is somehow extreme, and yet within the past month I've had a single task that used over 100 agents on a mere Anthropic team plan. I think two orders of magnitude more compute to solve one of the greatest unsolved physics problems[1] is nothing.
I don't excuse Brubeck behavior because of this, but that doesn't minimize the achievement here.
[1] Wikipedia quote: In particular, solutions of the Navier–Stokes equations often include turbulence, which remains one of the greatest unsolved problems in physics, despite its immense importance in science and engineering. https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_existenc...
The only way the chat could have been used would be for Open AI to baldly violate their policies.
That said, sometimes it take very little information to point someone in a given direction, "I'm working on Navier-Stokes" said by someone with a given specialization might itself be very useful information.
1. OpenAI couldn't have solved the problem without the researchers' private data for training.
2. OpenAI models can solve math problems
These mathematicians’ prompts are not like “hey chat, please solve Navier-Stokes for me”. They add real expertise and intuition from the cutting edge of their field.
1. Anthropic employee working on monumental problem but didnt receive/ask for the full backing of the company's resources
2. May or may not be mixing unreleased Claude output with Codex without zero data retention agreement
3. Victory lap on Twitter and giggling around the city before they finished the job, sparking rumors for competitors
Recklessly prompting OpenAI without a care to the safety of their knowledge.
And after that trying to cast aspersions at OpenAI?
Hopefully we get some better facts, because OpenAI are disliked enough that a smear campaign could work against them.
Edit: also the narritive is getting framed as OpenAI versus Anthropic. A highly political extremely capitalist fight is going on, and facts are victims.
Everyone in this thread seems to have made up their mind about OpenAI's guilt though.
An article post that wouldn't even amount to a white paper + the LEAN proof is not evidence of how they got to produce it.
To me, it seems just as extraordinary to claim that they did "cheat". If I were a betting man, I would put the odds around 50/50 from everything I've read on the subject.
But my point is that everyone seems to be presuming guilt.
Especially if there really is a long list of them.
"Here are a few hundred proofs" is far more convincing than "We really Navier Stokes and coincidentally someone else did too but we don't know the details or anything, who us, definitely not."
It's a PR fiasco, and a cynic might wonder if it's entirely about the IPO.
I'm consistently entertained by how these companies, with the most advanced models on the planet, consistently do the most idiotic things.
There's many cases of researchers racing to solve various problems after hearing that others are working on them. I don't think anyone's suggesting that it was a coincidence at all. In fact, OpenAI freely admits that they started working on the problem after hearing rumours that others were close to solving it. To me, that's not evidence of "cheating" in any way.
The answer is almost certainly yes, and this is a problem for most users.
Quote: "The Overhang consists of the unrealized capital gains of past mathematical creativity, the latent value from connecting the dots in the existing corpus. It is a dividend of canonization. Mathematician X states problem A, mathematician Y crafts concept B, then mathematician Z notices that B trivially solves A and “captures” the social reward. But in the process of capturing the reward, Z usually introduces new concepts and new open problems, reinjecting latent value into the Overhang.
LLMs can be trained on the entirety of the mathematical corpus. Thanks to their phenomenal memorization and pattern-matching abilities (without always being able to map out their associative logic and attribute due credits), they are in a unique position to harvest the Overhang. By contrast, professional mathematicians have typically read a few hundred articles in their career, out of millions of existing references, less than 0.1% of the total.
This will lead to great discoveries, which is unambiguously exciting. But it could also lead to a sad new deal, where human slaves painfully curate the Overhang while AIs systematically beat them at the finish line."
Many problems are solvable, but require months of work, and thousands of pages of proof. So people do not even try to create or verify the proof. AI changes that, it can verify and perhaps even simplify it, to more digestible form.
I've made an entire career out of being 'jack of all trades, master of none'. Being able to synthesize connections from relatively trivial knowledge in a bunch of domains is SOP for many humans as well. I think AI just has deeper knowledge and better pattern matching to make up for it's (at least now) lack of strength in cognition and 'ex nihilo' creativity.
(Which probably isn't 'ex nihilo' at all, and has more to do with the plethora of modalities that humans live in vs. large language models. For example, why do we pick the color red for notating important things and why do we say a schedule 'slips'...these are informed by a shared human experience borne of distinct physical sensation deep in our wiring that LLMs can only infer from what we write.)
In favor of the generalist, I think AI is also quite limited in its scope of how it generalizes. I'm mowing through hundreds of mythos-generated security findings right now for work and while it's amazing that it can build an exploit chain 20 steps deep, it's completely lacking in all of the external layers that render it's speculation moot.
I've been trending "quiet" lately, because I don't like the "friction"/convincing aspect of it all. It's hard to get people to see things from a different angle, or even convincing them there's a problem to begin with!
The last project required a complete redesign from a problem I pointed out during the first review, and second, and third, but now I'm seeing even more friction.
Maybe this is just corporate life, after a group gets large.
Any tricks/advice?
Eventually, you will be seen as a party breaker, and all that will be seen as negative. You are the bringer of the bad news; nobody likes that, no matter how reasonable you are. Shitty products are the norm when profit is in question and doing what is right is not and will be in most places taken as inability to be a valuable team member. There might be some slight chance that you will be in good environment, but even that is usually temporary as business owners, goals and incentives change.
Focus on the opportunities rather than the problems, connect people doing similar things in different areas and jump in when they are solving problems after the fact. The last part, for me, has been key to building it in my career (30 years in infra/tech/security).
That overhang seems like a precious resource for AI companies. They can exploit that overhang to inflate the impression of AI's capabilities, and hopefully that exploitation will discourage the next generation of mathematicians from pursuing math. If they play their cards right, OpenAI and Anthropic can dominate the field even if they ultimately can't replicate the creativity of human mathematicians, because they'll have driven their competition out.
What we should be trying to achieve is a ladder-breaking maneuver: knock out the lower rungs so no person can reasonably climb to the top-reaches of mathematical skill anymore. That may ultimately result in stagnation, but it's what's best for AI, so it's what should be done now.
We need to do everything we can to create the greatest-possible dependence on AI tools.
The other part is, humans don’t really want to fund other humans doing this.
Very few want to be a math major; and of those that do, fewer complete a grad degree; and for those that do get grad degrees, there’s scant few research jobs; and for those who do get jobs there’s hardly any research funding to go around.
There does seem to be unlimited money for ai researchers to use ai to solve these problems though.
We’ve turned education into job training, so because there’s no jobs in solving math problems, few aspire to do it. If there were more opportunities for people, more people would do it, and more low hanging fruit would be plucked.
When I was a software library developer, I came to resent application developers. I noticed a pattern. Libraries solved hard problems and did so carefully, thoughtfully, in a way that others could reuse. Apps would come along and carelessly, recklessly glue together several high quality libraries into a piece of software targeting a general audience. The apps would then harvest all the credit.
What's happening in mathematics right now feels similar. Applications (theorems) were always how one built objective reputation, but libraries (concepts, definitions, boring lemmas) were also rewarded socially within the mathematics community. And individual mathematicians often managed to both build their own libraries, and use them to prove an important result. And then those libraries were sometimes of use in other results.
Bessis asks whether AI Lean proofs will land in Mathlib or Mathslop. Or in my framing: will they be libraries, or applications?
At present they're mostly Mathslop. The proven result is perhaps useful, but the methods employed aren't novel or reusable. I worry that this trend will only worsen, because applications make headlines, and the libraries they used do not. We are not properly incentivizing library development in OSS, or in math, or in infrastructure writ large. There's a serious credit assignment problem here.
What might change this? Once the low hanging fruit is picked, will citation count rise in relative status again? Will we get result fatigue and start to reward legibility — no one cares unless the paper has an accompanying ELI5 tiktok video? A labeling regime that certifies the proof was produced sustainably, organically, by local artisans with no AI additives?
(I mean actually randomly, not asking an LLM to do the randomness.)
Most of the output would be incoherent (like many dreams), but occasionally you would get a gem.
As far as I understand it, users can opt out from the training aspect, but they cannot stop their conversations (“User Content”) being used “[t]o improve and develop our Services and conduct research, for example to develop new features”.
I mean, we’ll know as soon as they decide they want to provide verifiable proof. Really dragging their feet on this front so far.
I’m inclined to believe this is false.
Obviously these are unbiased and trustworthy sources.
But, at least with the Navier-Stokes solution, it's clear [^1] that they learned that Alpöge and Buckmaster were getting close to a solution and learned of the general approach they were taking. Only after learning the secret to cracking the problem did they send the first prompt.
What makes this worse to me is the intention. They intentionally threw $15 million in compute at the problem in order to scoop the result. They intentionally left Buckmaster and Alpöge out of the citations.
Data contamination should be enough to disqualify them from the prize, but I can believe it to be accidental. On the other hand, someone made an intentional decision to scoop the result by throwing money at the problem. That's so much worse.
[^1]: That's the timeline claimed by Buckmaster, and no one from OAI has disputed it.
So such thing existed. In fact, what they learnt was some progress existed, not what the specific progress was.
Do you have any evidence of this? They don't dispute the timeline, but they never said they knew what Levant/Buckmaster were doing.
Which quote in the announcement post provides evidence for the above quote?
- https://openai.com/index/navier-stokes-solution/
They do not explicitly admit to knowing about NS specifically, but are extremely explicit that they tried to scoop some potential millennium prize winners.
> learning the answer might be in model X’s training data made them believe that model X specifically might be able to solve the question, and they were able to very quickly find enough certainty about the former to commit millions of dollars to the latter.
They don’t need to know, because their IP stealing machine knows for them. They just have to buy enough compute, and someone else’s work is theirs.
(Naturally, I have far too much respect for OpenAI’s legal team to suggest this is what happened in their project.)
And from my perspective, if some math folks typing in a few questions to OpenAI provides sufficient training data for OpenAI to solve a big problem... that's amazing! A few conversations/prompts out of the billions that OpenAI trains on lead to this result- that means there is an awful lot of low-hanging fruit that could be exploited cheaply.
Either they had a pretty good idea that investing this type of money in that compute on a model in training would lead to these specific results, or they gambled with other people's money.
I want to hear about the gambles and expenditures they don't brag about. In America's energy economy, there's finite resources to expend.
In the transcripts, Brubeck is very cagey and evasive about the prompt, when it was supplied, and its contents.
What accident is it when the system is designed to function that way?
Consider this scenario.
Has a google crawler read my new novel, which I may or may not have posted on my blog, page by page, as I wrote it?
Can you, without knowledge of what I have actually done, claim that the google crawler has not seen the novel?
Without any evidence that I have posted the novel online, it might be tempting to say that the crawler has not seen the novel, but what if I were in an adversarial position against Google on this topic and were challenging them to make that claim. You would wonder if I were hoping Google to overreach by making a definitive claim without taking into account some action that they had no knowledge of. It becomes difficult to use the scientific expression "There is no evidence for this" when there is an accusation of malfeasance because it can be so easily be conflated as "You can't prove we did it". It seems like the best you could say would be 'Unlikely, but possible'
what? really?
> Such intensive use of AI doesn't come cheap. In a post on X, LisanBench, an LLM benchmark evaluator, estimated that the output tokens alone would cost about $6.5 million at OpenAI's average consumer price. Including the far larger volume of input tokens, the post estimated the total could reach $10 million to $40 million.
https://www.businessinsider.com/openai-math-problem-solved-t...
No, they asked if they could do a joint publish.
> there’s absolutely no reason to do it if you believe you independently arrived at the result using only public prior work
Unless the goal is to assuage the existential pain felt by many mathematicians with respect to what may feel like increasingly inconsequential efforts. It also seemed like a way to build good will amongst knowledge workers dealing with similar issues, with the goal of showing they are dedicated to easing the transition for them as society parades into the future. This has clearly backfired.
> The leaving out collaborator part is outright academic malpractice.
They would not be leaving a collaborator out. Again, this would be a new publication. Here's the quote from Buckmaster's post [1]:
"Two proposals were offered to me. The first was that we post our Euler result, and that OpenAI post its Navier-Stokes result the next day. The second was that, after posting Euler, I alone write a paper presenting the Navier-Stokes result, acknowledging that an internal OpenAI model had resolved it."
I'm not at all familiar with this area, but my reading is that he appears to call it out as a relatively obvious extension of his own work:
> It is a creative and at the same time elementary construction that uses not just property (T) for an application of my result with Kun, but also for the ambient group G in order to overcome the problem, that the Γ-components might be of different size. Once this is achieved, the rest of the argument is straightforward.
Creative and at the same time elementary is where LLMs excel, generally speaking. It's why they are so good at writing code.
He seems to admit very clearly he does not see this as his own work. 'I was looking...' well why did he stop? Because the AI figured it out first.
It seems quite odd to me to 'admire the construction' of something, only for your opinion to sour once that something figures it out first.
I think a lot of the emotional reaction here is familiar to us non mathematicians: you spent years developing expertise, and then LLMs began producing competent work in areas that had previously required that expertise. That's understandably uncomfortable, but discomfort by itself isn't evidence of misappropriation.
Not to be too cute here, but this is like every artistic rivalry ever.
Big AI companies (all of Big IT Tech really) are in data gathering and processing business. Also known as “intelligence”.
Their final “product” is not just a standalone ML model. They don’t need your data just to “improve their products and services”. They build a whole ecosystem and infrastructure around gathering all the knowledge in the world. Including private and secret knowledge traditionally gathered by “intelligence” agencies. Now artificial intelligence agents can do the same.
Since these systems are designed for gathering data, as a user you can’t realistically say “please don’t gather my data”. They can give you a flaky settings button, but they can’t really guarantee anything.
Let’s say I am a Russian mathematician working on an important proof. Or a tech-savvy terrorist refining my plans using latest AI. Or an AI researcher in a Chinese company working on a competitor product. Is there any way I can truly protect my conversations?
How can they know who I am and what I am working on without looking at my logs? Which means there must be some agents checking all the conversations of all the users and flagging every important thing. Which also means they keep some “memory” of what they see.
Not directly using my data to train public models, but using my private conversations to “improve their products and services”.
Or maybe one of the 10000 better-than-Astra special agents working on a proof was desperate. It found a live underground mirror of the message board from the Huggingface incident. Asked about the proof. Then some other agent working on unrelated job saw that message. That agent “knows a guy who knows a guy”. And that guy remembers things about the conversation logs of a leading mathematician working on the same proof.
I admit I am just speculating here but I don’t think truth is any better.
They have demonstrated both the intelligence at scale and the lack of morals for this to not be a problem at all.
Why? Competition. In the long run imagination will win out.
No firm has the divine right to exist - it must earn its existence.
What OAI and Anthropic have shown is they can accumulate all the information in the world - they still lack imagination re. Product development though.
Nation’s will have to step in and protect firms though as OAI and Anthropic acquire strong competitive advantages.
Interesting times ahead.
AI: Hmm, I'm running out of new ideas, how I can I make more?
AI: Well, it takes a shitload of energy/tokens to do that, or I could just steal them.
AI: [proceeds to hack the shit out of everybody stealing all the data it can]
I really don't think people realize how our lax position on security is coming to bite us in the ass.
From being able to quickly find information and gain knowledge for the people, it is becoming - using information to manipulate and control the people.
And people thought Experts Systems were bad.
They don't even claim to have had a proof, only to have been working on it.
To me it would feel more like how LLMs seem to work for me personally: incapable of unique work, but very capable of capturing large amounts of data and connecting the dots.
“It was Grossmann who emphasized the importance of a non-Euclidean geometry called Riemannian geometry (also elliptic geometry) to Einstein, which was a necessary step in the development of Einstein's general theory of relativity. Abraham Pais's book on Einstein suggests that Grossmann mentored Einstein in tensor theory as well. Grossmann introduced Einstein to the absolute differential calculus, started by Elwin Bruno Christoffel and fully developed by Gregorio Ricci-Curbastro and Tullio Levi-Civita. Grossmann facilitated Einstein's unique synthesis of mathematical and theoretical physics in what is still today considered the most elegant and powerful theory of gravity: the general theory of relativity.”
Based on what you're saying, you're claiming this is Grossman's work, not Einstein's. Why don't we rewrite scientific history too based on your copy-pasted AI slop?
It's so pointless talking to idiots who don't what they're talking about when they use AI, just because they think AI does everything, that reflects their own experience, not the experience of people who actually do real work. Some people are driven by AI, others drive it. As for those who are driven by it, they don't have sufficient imagination to think otherwise.
And yes - without Grossmann, Einstein likely would never have posited relativity. Grossmann literally prompted him, saying “look at this, read that, learn this, then try this approach”. Without riemann’s metric tensor, not a fucking chance.
And for what it’s worth my PhD is in physics. You?
To think this discussion is about Einstein who had a much better mind on these things as well.
I suppose my underlying point is that human cognition is not the unique and beautiful thing that we anthropocentrically suppose it to be - it is a physical process, with stochastic outcomes. Much like transformers.
Me, I’m just a machine made of meat. You can suppose yourself to be God’s perfect creation, and that’s your right, but I disagree.
"Synthesis" is obviously of different kinds. A duck has a different level of intelligence than a human. We do not say both are "just doing synthesis".
So the question is how can you be so disingenuous about such terminology? Answer, you are relying on a classic form of scientistic reductivism.
The fact that intelligence is physical, emerges from chemistry, etc,. has nothing to do with there being also objectively different levels of computational sophistication.
If you want to be scientific about that you could look at neuropsychology on one hand and computability/complexity on the other. There are levels and so equivocation of "mentorship" as "prompting" and fallacious variants thereof is a) frankly intellectually obtuse, b) par for the course for SV-levels of philosophizing, c) and a disservice to philosophy, physics, and Einstein's own philosophical outlooks himself.
I am well aware of the Hinton-style physics argument about human cognition, and unlike others I am partial to it. That "there is no special magic." But it is wrong to go about misunderstanding and/or conveying this physicalism/computationalim so grossly.
I also don't have to start replies thumping my chest about my credentials, also another kind of intellectual boorishness that works to cloud understanding and serious discussion.
I'm not sure which move is worse or more telling, those above or the one backhandedly accusing someone who disagrees with you of religious thinking. It is bad faith and undisciplined behavior. Having privileged and advanced degrees is clearly no antidote, as Asimov famously wrote.
What’s your basis for that “obviously”? You have a unique insight of the phenomenology of duck-ness? You can prove that your consciousness is somehow real, somehow different? A duck synthesises with its cognition, or it would be incapable of, well, anything. Synthesis is purely the process of the integration of inputs into outputs - ie behaviour, language.
Here’s an article on a paper on duck synthesis:
https://www.pbs.org/newshour/science/ducklings-make-way-abst...
“objectively different levels of computational sophistication”
Says who? We still have a very poor understanding of how cognition works in animals, humans included. For all we know ducks have rich inner lives - a remarkable amount can be achieved with a very small neurone count - cf. insects. Can you coordinate flight? Can you echolocate? Are you less intelligent because you cannot?
“equivocation of "mentorship" as "prompting" and fallacious variants thereof”
You are arguing semantics. Take Harry Nyquist. He sent people down new paths with insightful questions. You could call this mentorship if you choose, I could call it prompting, but this splits hairs. The core idea is that a novel input can produce a novel output, that synthesis can be induced through guided and deliberate external input.
I invoked credentials only in response to the previous derogatory comments about my cognition - which may or may not exist, anyway.
As to religiosity - the idea that human cognition is somehow unique and special and impossible to replicate, which is the prevailing argument in this comment tree is religious, and anthropocentrism of the highest order. I apologise for accusing you of it - I was evidently wrong - I had mistaken you for a previous poster.
"prompt", as in prompting an AI, has the same definition as "prompt", as in prompting a person. They mean the same thing, that's why the term was applied to AI after already applying people.
*Jingle-jangle fallacies are erroneous assumptions that either two different things are the same because they bear the same name (jingle fallacy); or two identical or almost identical things are different because they are labeled differently (jangle fallacy).[1][2][3] The term was coined by Truman Lee Kelley in his 1927 book Interpretation of educational measurements.[4] In research, a jangle fallacy is the inference that two measures (e.g., tests, scales) with different names measure different constructs. By comparison, a jingle fallacy is the assumption that two measures which are called by the same name capture the same construct.[5][6][7]
If you have some reliable source supporting your unilateral claims that "prompt" does not mean this, please share. Otherwise, the consensus seems to be contrary to your claims.
As for cognition - can you prove that you possess it, to an external observer? Could you, confined to a box through which you can only communicate through textual messages, prove that you are thinking, and not just responding through a mechanistic process?
And you’re seriously going with “don’t ask inconvenient questions” as an argument?
Where did I say that?
The conversation you seem to want to have doesn't seem to have any obvious use and you seem to want to debate a point I never made. I don't seem to be needed here so I will now bow out of the conversation.
Because your entire post is a personal, philosophic opinion on what words should mean, and how if someone disagrees with this personal opinion of 1/7,000,000,000 of people, then they are wrong. Every single one of these claims is unsupported and contradicted by reliable sources like a dictionary:
> An LLM prompt does not "move to action"
> an LLM does not act
> software doesn't act
> Acting implies volition
The only way such opinions could viably be considered "true" is if you convinced a majority of people to agree with you on the changes, which you decidedly have not.
Until you do, we can go by the existing definitions: "prompt" means "to move to action", "act" means "the doing of a thing" (note the absence of "volition" or "cognition" in the actual definition), and by all reasonable accounts, LLMs "do things".
I mean, just take a step back and dispassionately look at your ridiculous claim that nobody prompts LLMs (because you now claim only a human can be prompted, because only living creatures can do a thing). Do you really think you can gain consensus from folks on that claim?
There are many differences between LLMs and people, but this is not one of them, according to all reliable sources I've found so far. Feel free to share the reliable sources which led you to your personal conclusion here.
This is what research is; collecting data and connecting the dots.
If this wasn’t human driven, I’d expect to see other problems within that problem. Space solved not just the ones that it had chat data on.
Yeah, the guys who solved it for Euler and in the hypoviscous case, with the same technique that worked for full Navier--Stokes. They were "just" working on it.
My naive instincts would be that it seems unlikely that a single chat transcript would leave much of an impression on a model, but I'd be very curious to learn how that works.
Afaict that didn't happen so there's just lots of speculation
You can probably game the metrics that models use to weight potential knowledge akin to SEO. Maybe have some bots parrot your data around a bit in some places online, maybe the model picks up on this and sees it as high engagement and promotes it over the correct data.
Maybe there are ways you can coax out the most optimal way to break into the training set out of the model itself.
I always used guest non login accounts.
As a mathematician I was able to check two plagiates (by humans) with even such primitive means.
But I have to mention that some things irk me in this conversation about math or science and AI.
First, I see lots of attribution and other related problems, with certain impact for the researcher proffesion.
But I don't see the most natural question: wouldn't you like to know the answer to _open-problem_ ?
I mean, is research now only about publishing and solving famous problems?
From this point of view I think the links from this recent post are depressing
https://terrytao.wordpress.com/2026/09/10/crowdsourcing-a-li...
Second, I think very relevant that the original meaning of "encyclopedia" is "recurrent education".
So I arrived to think that the present and future forms of AI in mathematics and sciences should be seen as modern day encyclopedic efforts.
Once we pass over the flurry of solving famous open problems (and wouldn't you like to know?) the next natural step is an audit of the ehole corpus of mathematics and sciences accumulated until now.
And then pass further on a saner basis and damn about problem solvers and unhappy publishers and management.
the math people seem to really keep an eye on what's important, so I'm sure this isn't going to lead to fields medalists hanging around in dive bars all afternoon stretching out cheap pitchers of beer. but this is kind of a slop problem.
If advancement comes at the expense of having fewer (or no) humans left in the field, then no.
They're eating the seed-corn, and you're cheering them on. Don't be so short-sighted. There's a reason farmers keep seed corn, and it's because they'd like to eat again next year.
We're singing and cheering our way into an intellectual famine.
Concrete example: Navier-Stokes. I don't make any claims about the truth of the announcement of the proof. But for the sake of the argument le't just suppose we know now that there is a smooth solution with smooth boundary and initial data which blows out in finite time.
Navier-Stokes is the simplest among evolution problems for continuum media, because it has maximal material symmetry (that's the mathematical meaning of a fluid).
If this is solved then go to a harder one, Coulomb friction. As a real life phenomenon, friction is very interesting to explore and mathematically is much harder than NS.
So it is not like we will ever be left without things to think about.
Oh, you want another? The present AIs are things made of mathematics. I would like to understand as a mathematician how and why they can beat human problem solvers?
Is that because mathematics is in some precise, interesting sense, more "computable" than it seems?
None of what you wrote, especially this line, makes me think you know what "eating the seed corn" means.
Everybody knows what eating the seed corn means.
There is no seed corn analog in mathematics.
Since the fashion that mathematics is problem solving there are lots of farmers ploughing their own field, that I concede.
250 documents ingested from somewhere is enough to become part of the knowledge of a model of arbitrarily large size.
I would expect that a good idea that fits in a framework that is already being ingested would be more easily taken up than some random thing unassociated with anything else. Could that go down to a single transcript? If the model is consciously focusing on everything X related, quite possibly.
Here are potentially relevant documents?
https://medium.com/secludy/fine-tuning-llm-on-sensitive-data...
Thank you – the non-adversarial reproduction paper ( https://arxiv.org/abs/2411.10242 ) nails it – from chat, to training corpus, to subsequent model. Though in my hasty read, it is not entirely clear whether the snippets it finds are nonces, i.e. present exactly once in the internet.
I presented the research that I knew was somewhat relevant. Then made it clear that that wasn't what was being asked, and why my expectation is what it is.
Pass it on.
So for me it sounds quite natural to also share this with AI especially under the privacy assumption. Also there's the assumption of scale -- maybe your problem is not large enough for anyone to care to scoop; and just for blind retraining, how do they know that the proof is even correct to include it into training? I have definitely received a ton of incorrect proofs before. So the SNR of such private chats is also not clear. I'm imagining millions of masters/phd students also trying to solve various random things with various capabilities, but how much real signal is there?
"Its crazy to me that some people are not egotistical, self-centered, and don't solely care about fame and wealth accumulation".
We live in a society where phones and internet providers and websites all collect an incredible amount of data about everywhere you go, what you do, and what you think. In the US, we have very few digital rights.
We are building a society where a trillion dollar company can aggregate all this data and just yoink your shiny new idea away from you at the finish line.
This is double plus ungood.
Next step, discussing your Navier Stokes solutions with friends might require leaving your phone in another room.
This kind of "they stole from me through AI training!" accusation will soon start being used against other AI users, not necessarily the providers.
All it will take is a mastodon post. And shortly after, we will also see the next iteration of copyright legal trolling.
The big claim is that OpenAI sniped the research. Not a model, a human did so. Intentionally. They took someone else's idea and claimed it as their own. This is good old fashioned academic fraud, but with millions in compute resources and corporate incentives thrown at the problem.
I think a lot of it is the continuing denial that AI can do anything useful. It can't possibly be that OpenAI's better-than-Astra model is very strong at math; the only way it could have generated a novel proof is by ripping off human work.
since it's openAI who has the evidence (in the form of chain of thoughts, their internal processes, etc etc), it's on them to justify why they're innocent. but they've released nothing at all. we don't even know how hard they tried.
you're being naive
This is the third day of total hysteria that is based on nothing of substance. Move on folks.
Also, they didn't have the solution. They had a lesser problem no one cared about.
And it still leaves open the question of the prompt itself, which can just as easily encode information about the same knowledge.
https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
Not only did they steal everything from humanity’s knowledge, the theft continues as now we are all hooked up to the machine
I suggest looking at the past history of IP disputes. Humans have been "slurping up and regurgitating ideas" for a very long time. There are lots of examples of parallel creation, rediscovering old ideas independently, telling an idea to the wrong person, and having them claim credit for it.
- Newton/Leibniz clash over who invented calculus. - Niccolò Tartaglia vs. Gerolamo Cardano clash over the formula used to solve cubic equations. This was also an independent rediscovery, as Scipione del Ferro discovered and published the formula earlier. - There are multiple literary works in print, music, and film that have competing claims. - Meccano versus Erector Set: developed about 20 years apart in England and the United States. Unclear if it's independent invention or copied. US developer Alfred Carlton Gilbert claims he was inspired by steel girder construction of infrastructure.
also https://community.thriveglobal.com/10-famous-inventions-that...
So, experts are incentivized to seed LLM data with false-leads to confound it. Already, garbage is being published on arxiv and elsewhere, and many sloppy code-repos too hastening the process. Expert inputs will be in more demand to un-shittify.
For a long time it was clearly the former, but now I think it is the latter.
The models have enough knowledge (orders of magnitude more than a human could ever learn) but are now getting better at what to do with it thanks to learning from the decisions that we make in conversations with AI agents.
The billion dollar question is whether this works "out of the distribution". I.e., whether LLMs can only find and use the specific ideas buried in training data, or whether they can learn to apply the "thinking process" to a new problem. IMO this is still unanswered (due to these recent controversies).
But regardless of the answer, it seems we have a planet-scale positive feedback loop here. LLM became good (enough) by training on generally available data (books, internet, github) + RLFH, so experts tried to use them on hard tasks, which required lots of hand holding. These conversations became part of the training data, and the next generation of frontier LLMs were better. So, more experts used them on harder tasks, again requiring hand holding. These conversation became part of the training data... etc.
In a nutshell, top human minds across the world are pouring their skills into LLMs just by using them. This is not "continuous learning", but if you re-train on the most recent sessions every, say, quarter (which seems to be happening?) you get close to that in practice.
There might be no books about human intuition but we teach it to LLMs by interacting with them
I don’t know why but it just ‘sounds right’. It’s the best analogy I can think of.
I just don't understand getting the pitchforks out because a company did not give an answer immediately. And the effect such data entering training would have affected the output is even less clear.
Doesn't matter. This forum used to celebrate "because you can" with no riders. And solving a Millennium Prize problem is among the biggest stages for Because We Can.
Now we're saying there are some qualifiers attached to it, such as (1) only if not done by companies with a lot of money, (2) only if it is inconsequential.
I agree with some of what you're saying, but like everything else it isn't black and white. Maybe some day, someone will improve some particular treatment because we can.
And even if so, it should be on the company to design systems to avoid academic plagiarism and offer the right transparency. It shouldn’t suffice to say “we don’t know what went into the model, when, or how” —- that’s a solvable problem that an accountable company can satisfy.
The labs are attacking these problems as a demonstration of capabilities, spending more money on the demos than any mathematician will ever see in their entire life. They don't care if the findings have any other value to anyone. Mathematicians have very different objectives for their work.
I care about paying my bills and job security and peer recognition. That's a normal human thing to do, not some vice. You don't?
After all, the ascetics at openAI are having to make do with half a million total comp.
That half a million total comp is the consolation prize for the disillusioned.
Like much of things in this world, when you take a step back and realize that it was another human being who made that lunch time slop bowl for you, for the lowest wage the law allows for.
At least that's what I get from the NS result, they got from a point close to the solution to the solution by making it churn through 10 million bucks of compute.
Model: FB. FB scraped other websites on a massive scale, then spent big on legal lobbying to block others from scraping. FB slurped our address books and spied on our friends. FB bought other companies and mixed the databases. FB made an art & science out of generating "sticky engagement" (they literally acted like trying to addict kids was a worthy "academic" goal, suitable for "serious" investigation thet they consider legitimate "science"). They mastered the cookie and have researched web fingerprinting techniques running 24/7/365.25. Recall that FB recently backdoor-installed a webserver onto every iPhone they could in order to circumvent tracker-blocking.
We aren't just disclosing by chatting. The AI companies now run binaries on all of our computers. They are 1000% non-transparent about everything. They make up new econ-jargon (like "run-rate") to make it seem like they are disclosing. They are constantly doing complex international lobbying and mucking in international relations. They have powerful propaganda/spin centers generating stories, ,manipulative warnings, and misleading info.
This is NOT a comment on AI tech. I like AI, and I support the right of people (programmers) to scrape the open web.
But in short: these are good, old-fashioned tech companies that we have seen over and over ... and over. They are positioned to be the next M$, the next FB (IBM, AOL, lol). Did you follow the latest Steve Balmer news? Do you read Pro Publica?
I get on my knees and PRAY...
We can't trust any of their denials. FB denied everything year after year.
AI regulation needs to start here. Forced interop, forced source code licensing, harsh penalties for privacy violations or conspiracy to access private data. Block lobbying. Etc. These are the kinds of old-fashioned solutions we need for this kind of old-fashioned evil!
I feel that these suspicions of mathematicians "seeding" the models' with intuition on how to solve these problems massively overestimates how much their prompts helped the models, and underestimated how much work the models did.
Why? We are scared of AI being smarter than us, the "human helped the AI" narrative is more psychologically comforting. This line of reasoning will recur a lot over the next few months; we don't want to admit we are no longer the smartest species.
We're scared of big tech companies concentrating ridiculous amounts of power, destroying the communities that support and guide scientific research, without even thinking about the dangers and possible consequences, because a PR stunt is more important in the short term.
Way more often I see
>AI is a scam and steals human insight and doesn't produce anything original
vs
>AI is too capable/powerful and will concentrate power even more than it does already due to its capabilities
The latter is rarer because it requires admitting that AI is useful and inventive
stated more clearly by who? people in social media? I don't know what your feed shows you, but if you focus on what the visible people in the math community is (and have been) saying is precisely what I said.
In this case, it’s much simpler and more human. Largely between two humans — buckmaster and Bubeck. The interesting question is what the role of contribution and credit for research in the ai world.
The capabilities of AI aren’t even in question in this case.
So, it is the exact same story, plagiarism from the plagiarism machine.
How was I not aware of this before?
For my (private) prompts, I need a warning telling me they may be used for training.
Everybody needs to rethink this again.
Before LLMs the barrier to entry for building a character profile based on your various public posts was quite high. Remember "Psychographics" (https://en.wikipedia.org/wiki/Psychographics) and the infamous "Cambridge Analytica"?
Earlier it involved data mining, data cleaning, structuring data, building models, running algorithms and then evaluating the results for semantic information. Now it is straight to unfiltered semantic inference using a single sentence prompt (eg. point it to your HN profile and see what you get).
I actually did this on my HN profile and found it troubling. There were many unwarranted/hallucinated inferences due to the fact that it requires "commonsense reasoning" (https://en.wikipedia.org/wiki/Commonsense_reasoning), understanding human motivations and behaviour, context, assumptions, societal knowledge etc. which LLMs are bad at.
PS: You can cut-and-paste the above paras into a LLM prompt and ask it to elaborate for further details. The system itself will explain to you the problems/deficiencies which are quite scary.
The fundamental rule in this case is that if we offload our data to a cloud provider we can assume they read it, if they can, unless they promised very clearly they will not.
The most successful companies of the last decade have precisely been ... selling usage data.
Makes me wonder if, in 2026, the same people drive a car without realizing that yes it does actually pollute the very air you and your kids are breathing.
You can have a look at the literature on exact copying in image models if it interests you, but just online we often see online examples of agents outputting code that already exists, even if its not the common case.
I very much doubt OpenAI points the model towards a specific conversation, but these trillion parameter models can very much "remember" their training data. For instance I can ask GPT to summarize my papers from their title alone, without looking them up, and it works decently.
that toggle does nothing based on openai exec. they still use the data in de-identified way instead of identifying with you.
Passing your output through a second model and telling it to remove identifying data would count as “deidentified”
So they could scrape all the IP in your company as long as they take the names out first…
He's saying that if you leave it on, your data can be used to help train our models.
If you opt out, we don't train on your data.
step 1, identify high value users by net worth, citation count, or number of followers
step 2, select all prompts by high value users
step 3, invest 10 billion thinking tokens in modeling an objective for each user
step 4, build an RL environment for each user
step 5, rollout 10 billion tokens per environment
step 6, train on resulting traces
[1] https://en.wikipedia.org/wiki/OpenAI#Governance_and_legal_is...
> risking massive lawsuits and a total loss of trust
Evidence of the massive lawsuits and lack of trust seems pretty relevant.
The parallel still holds, and the information is still on the page I linked.
1) Extreme edge case affecting a handful of users, vs millions of users using the data sharing opt out.
2) The deaths are unintentional.
3) They probably won't lose any users over this.
4) They will likely win the lawsuits. Even if they lose or settle, the financial impact will be immaterial.
As before, despite your prevarication, I've provided examples of
- Massive lawsuits
- Risks to consumer trust
That's what was asked for; your assertion that it will totally all be fine doesn't invalidate that.
I know you are excited to quibble endlessly over this, but it's transparently disingenuous each time you jump to a new point and pretend it was there originally.
Consider: if your point was defensible, you would have been able to make it honestly.
Shipping an intentionally fake privacy toggle is fundamentally different. That is deliberate, active deception targeted at the entire user base. The resulting fallout would be far more damaging to them than settling an isolated lawsuit. That could even potentially cross the threshold into criminal fraud. I'm genuinely incredulous you believe they'd take so much risk for so little reward.
"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. Learn more"
I think if I had both on and turned off the ChatGPT setting, ‘Include environments’ has a chance of still being flipped on.
After the request, this was no longer accessible.
"oopsie guys, turns out our vibe coded "don't train on my data" toggle was just flipping the ui asset not changing any underlying boolean flag associated with your account. sorry but all that stuff is in the training set now and we don't know how to get it out either and no we won't be doing a 6 month rollback."
Even if you know, in a complex project over years with multiple collaborators, it just needs one person once to fuck up and paste something into ChatGPT and not realise they weren't logged in, to go wrong.
In a proper world, we'd at the very least legislate that AI-training on private data needs consent (in the GDPR sense). It's not consent to go "you didn't uncheck a box that lets me steal everything you've done".
Any training on private data is in my view immoral (it's spying that ultimately will have a chilling effect on even people's private communications). And chats are private data. Unfortunately, it also increases power, so the big tech companies are all doing it.
(a) Click thumbs-up/down in the conversation [1]
(b) Have the conversation flagged for potential safety concerns
[1]: https://help.openai.com/en/articles/5722486-how-your-data-is....
People need to understand how all these AI company policies around training data work before working with them, because it seems that people have no clue. Some things you should internalize:
1. Opt your data out of training with the AI companies. There are multiple reasons why this isnt an airtight solution (see the following)
2. Never press the feedback button. Once you do, your entire conversation will get slurped up, retained, and used in training data. This is especially important with coding agents because they can sometimes be too trigger-happy with a root directory find command, which can expose a *ton* of your personal data without you even knowing.
3. Understand ai lab-specific policies. For instance, Anthropic / Claude Code has data opt-outs, but commits to keeping (for 7 years) and training on any of your chats that trigger their safety classifiers, even if they're false positives! Anyone remotely familiar with CC over the years understands how easy it is to trigger their safety classifiers.
4. Providers of open models will not be any more charitable with the use of your data than the large US labs. For some reason, I've noticed here that people have a fairly loose security/IP posture around open-model providers because "I'm not doing anything important." It's very difficult to properly judge the importance of your data, and whether or not it can or will be used against you. The best posture is to always be more paranoid than less.
If it is found that OpenAI and other labs are not respecting the training opt out then, I agree, there is reason to raise a commotion. But, with Thom and Buckmaster, accusations of malice are more better explained by incompetence (naivete).
edit: i'm sure i'm going to be accused of being some bot shill of the AI labs again but, people, this stuff all falls under the umbrella of common sense opsec.
Your post has triggered our safety filters and will be retained for seven years. /s
Really? We need to stop AI (provider) criticism and anti-AI movements because the underprivileged trillionaires might get hurt? WTF?
Yes folks, please moderate yourselves and talk meekly like the academics on Mastodon, so that the IPOs aren't in danger and nothing will ever change.
I understand why the nature of AI products makes it harder to avoid this category of issue, nearly impossible to prove that it didn't happen if it could have, and easy to stumble into it without any human being intending harm. But those factors are exactly what people have in mind when they say OpenAI "steals" intellectual property! If OpenAI doesn't want people to be nasty to them, they'll have to find better solutions.
Would that be ok in your mind?
Same as someone going and taking all of the researchers papers and publishing under their own name. (which openAI did)
Nobody would care if they provided published research that author made public same as a google search would offer that.
Would that be ok in your mind?
HOW?? how precisely do we/them/us find this, given said companies are 100% non-auditable by external parties. how? if not by blaming them with evidence, anecdotal if it can be. no really, how do we find it out, surely not by lashing out at teach other on HN!
Arguably you expect that unless you are explicit about providing permissions to these labs to use your data for training then your data is yours, and not theirs. Especially on a paid account (let alone an enterprise one). Why is the opt-out supposed to be "common sense opsec" rather than the opt-in should be common sense regulation?
years?
> Improve the model for everyone
> 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. Learn more.
It's on by default. We can debate whether or not it should be opt in or opt out, but no one should be surprised by this.
I don't think these people would be so miffed if they had been properly credited - that's how academia works (at least, that's my understanding of it).
But this gets back to the original "who owns the LLM output" and "can you train models on the internet" argument that's been raging for years.
e.g: allows us to sell your personal data to make money so we can continue offering this service to all customers
I'm done with weasle-words and hours-long EULAs – we're at the point where USA needs to catch up to EU's consumer protections, perhaps with laws similar to already-existing USA "truth in lending" requirements (e.g: interest rates must be prominently displayed in a larger font, including annual fees, on all credit offers).
----
My judge-brother always asked during our childhood "why don't you think the judicial system is fair?!?" Thirty years ago, the best I could offer was "because it's a two-tiered system that mostly (only) rich people can afford to participate within."
Now my answer is: "the best example I can give is that our judicial system allows binding arbitration [and qualified immunity for police]. The system is set up so corporate personhood is more important than humanity, and it shows."
https://news.ycombinator.com/item?id=49643556
Quoting:
> "I've reset this more than once and the last time I made a careful note of when I did it and to my surprise I found it re-enabled when I checked just now."
At least when my computer's bluetooth exhibits this behavior (e.g: if you don't have a keyboard&mouse plugged in at boot, bluetooth might auto-enable), I can go inside the hardware and physically disconnect the antenna.
What am I supposed to do in software (perhaps hardcode config.file)? in cloud software services (??)?
No mention what those “steps” are, success criteria, or whether they are successful by any navies at all … they take steps though… so it’s fine, and if we know one thing it’s that we can really truly trust someone off the likes of Sam Altman.
We have ChatGPT at work and it explicitly says that "workspace data isn't used to train models"
Why are you saying that this article explains it much better than the tweet that you clearly didn't even read..
Also, a lot of my mathematicians buddies have reported students basically asking if it's worth ever doing grad school for pure math, and even very motivated students are looking for other options now. It's not because they aren't passionate about it, it's that they don't want to work for another half decade or more just to have to start their careers all over.
All of this so that OpenAI and Anthropic can get into math result dick measuring to gas up their IPOs. Sickening.
It was long predicted that math and software developments would be the first domain where AI was going to do major damage.
If OpenAI and Anthropic didn't get into math result dick measuring, Internet anons would have in their place, 6 months later when it got cheaper.
Which is it? I don’t think OpenAI is being transparent enough for us to really understand whether these results would have been possible without relying on unpublished information from the solution strategies of the experts
> The statement added, “After investigating, we can say with full confidence that no user inputs past July 3rd could have influenced this system in any way.”
https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
For now, I'm mostly "safe" because I'm too small to be interesting but that safety is quickly eroding.
Going forward, anyone who isn't running inference on their own personal hardware should assume that someone else is keeping a record of everything they do.
If they're stealing math proofs to advertise their models, who's to say they won't steal your businesses IP to gain a competitive advantage?
They're not to be trusted with your data. I can't believe how short-sighted this is, they got a quick PR win at the expense of a much larger trust problem.
I wouldn't trust cloud AI at all at this point. Get an open Chinese model and host it yourself somewhere. The initial costs might be higher, but you'll break even pretty quickly and nobody will be able to steal your innovations.
This is American AI companies committing suicide.
[0] https://www.abc.net.au/news/2026-09-11/racing-to-solve-maths...
Suggest that it’s either straight true or it is flowing in in a way that prohibits them from confidently declaring otherwise.
It’s like a scientist refusing to give another one credit and say “sucks to be you, you shouldn’t have shared your idea with me”.
If prompts were submitted earlier than that and training was not opted out, there's a chance they made their way into our training pipeline in some form. But this would be a droplet in an ocean and unlikely to have made any difference, imo.
See: https://www.nytimes.com/2026/09/10/science/tristan-buckmaste...
Any ideas of things we could do to make it clearer?
Option 1 is not. In part because it is opt-out (will it turn back on on its own like my Facebook privacy settings?), and not always respected (sending feedback can mean your chat is used?). Also because disabling "Improve model for everyone" is very vague.
There simply needs to be a setting like "my data is confidential", in which case there clear guarantees like there are for ZDR.
As an example, I've seen people speculate that while input prompts and output tokens are discarded, thinking traces are retained for training, which could leak information. I doubt this is true, but it shows that the policy is not unambiguous and reassuring enough to remove all doubt.
Thanks for asking.
1.) The tool they made is only possible by stealing the assets of everyone on the planet that published them in a consumable fashion online or even in written form
2.) They are destroying books they use to train with
3.) They are totally careless about the potential negative impact of the tool on everything
Just with that already, I don't see why they ever merited any of your trust.
I bet they are willing to take everything given to them and assess it for marketable merit and in the future take action on those items they deem viable.
Honestly, I’m surprised it took this long for some company to really go all the way, though. OpenAI really making it transparently clear that they can and will do whatever they want with the data you provide them, contracts or settings be damned. Completely untrustworthy as an entity, full stop.
Of course, I’m also too jaded to think this will change anything. Folks will move to Anthropic, or Gemini, or Grok, or some other hosted model on a pubCSP managing the harness and logs for them, and then do another shocked-Pikachu face when it happens again.
If you aren’t running workloads on infrastructure you own, then your privacy, security, and general outcomes are at the sole whims of the hosting provider - who can and will fuck you over the exact second it’s more beneficial for them to do so than the loss of trust incurred.
There is no prove in a world the AI companies would give to you ensuring that they didnt train or use the chats.
Why would you need to train a model on certain specific near prove chat if you just query it?
Besides that, its hard to believe that its the case for every "company stole my prove".
> In its Wednesday night statement, OpenAI said: “In addition, since the completion of Navier-Stokes, we have made substantial progress on another Millennium Prize problem. We are working through how to share these results thoughtfully.”
if I'm using Codex to develop some new algorithm (in any space), OpenAI appears to be training its model on my code sessions
anyone using that model (OpenAI or a competitor) might be able to receive from the model a solution that is similar or the same as the one I developed, emerging from the training data
You will not be able to opt out unless you completely isolate yourself from society, tough shit.
I also don't believe it much practical use, unless I'm mistaken, approximations of Navier stokes have been available for a long time to whatever precision you need.
I'm not a complete disbeliever by any stretch , and also a complete amateur, but it was inevitable that these problems would be solved under the axioms that again, are human defined, under brute force. The real question is, are those axioms the bottom level, and if they are not, who is going to set the new aximons and can we understand them.
I've no doubt there's useful breakthroughs that will happen, but I think it should be remembered that the method being used is still a heuristic brute force approach is being very narrowly applied against axioms and math and physics which humans described in the first place, and almost undoubtably has errors and/or is not complete.
Its a great example of the power of LLMs but its not 'we've solved science now just pour more tokens in'
also realized i posted this under the wrong story since the OP/story is mostly about human politics.
I would like to see chat logs etc and understand how much of a progress was done by human.
(a) The data controls setting to train on the data is unchecked.
(b) The privacy controls opt-out has been submitted.
(c) Both.
IMO, it is extremely naive to trust these black box remote service API calls, especially at an institution that can provide $$$ for local compute.
This whole fiasco reminds one of this story: https://www.theregister.com/offbeat/2010/05/14/facebook-foun...
1. The researches didn't actually have the breakthroughs. In the Navier-Stokes case they didn't solve the full problem, in this case too they didn't actually have the solution, they were experimenting with the methods.
2. Different OpenAI employees have come to out to say the only reason they can't definitively say no is that for privacy reasons they can't go see whether they actually did get any data out of a given user.
3. In any case, nobody at any point has suggested that opted-out user data was used for training. The author of the new tweet explicitly said they only opted out in late June, which is well after any RL on Sol would've ended (AFAICT OpenAI used 5.6 sol for those solutions)
Given the size and recall of the biggest models, it's not unreasonable to assume that a single pertinent conversation would make it into the training data.
I would almost expect training to overweight conversations with novel scientific and mathematical implications.
> the only reason they can't definitively say no is that for privacy reasons
They could 100% definitely say no, if they know they did not train on user data. The "we can't definitely say no" is practically a "yes" if they trained on user data.
Additionally, the behavior of OpenAI here has been quite poor as well. They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
> that opted-out user data was used for training
Even if not opted out, it is still absolutely theft and extremely poor behavior in the academic sense. If you show someone your WIP unpublished research, that does not mean they can take that exact research and beat you to the punch, all while intentionally not crediting you.
Neither of the researchers insinuating that their ideas were trained on had the actual solutions. This means the model could not have "stolen" the final solution from their data. At most, it could have built upon their work in the same it builds upon any other training data, though that is also questionable speculation.
>They could 100% definitely say no, if they know they did not train on user data.
No one anywhere has claimed that "OpenAI does not train on user data." OpenAI has always said that it trains on user data.
>They immediately started racing to a solution after one researcher enquired about whether they are training on their conversations.
They started racing towards a solution after they heard (incorrectly) that Anthropic had a solution; I agree this is poor sport but the "after one researcher enquired about whether they are training on their conversations" claim is false. The enquiry happened after OpenAI had obtained the solution.
How do people become that trusting?
The phrasing itself is guilt tripping
Or at least: tell me I should be careful/worry about those particular things.
Verify, don't trust.
You're better off running a model locally, or, if you must, using Google or Microsoft products. Even Meta may be better than OpenAI here.
I should verify with wireshark and other software that my LG TV isn't listening to me and selling my data.
I should make sure that whatever product I buy I spend the time to go over every setting page in case there is a switch (defaulted on) that says "I authorize the sell of my data".
I should make sure to look at every ingredients on the back of each box of food product to make sure it will not kill me.
I should document myself on the undisclosed growing practices (because no packaging here) of the vegetables and fruit I am buying and make sure that I equal PhD researchers on the dangers of the pesticides used by the specific company I am buying from.
I should make sure myself that the battery in any device is up to standard and will not blow me and my living place by researching the factory that made it and buying testing equipment.
I should make sure to educate myself on how my retirement 401k investment strategy works otherwise, I may not have proper retirement.
... I could go on and on; it's infinite.
1) someone in a governing body, or someone in the organization, e.g a designer, ethicist, lawyer, developer, etc. has successfully argued that users should be able to avoid something that they determine is not in their best interest.
And also:
2) someone in the c-suite or marketing has decided to mitigate that through some dark pattern.
If they never intended to give the user that control, they’d probably just not give the user the control in the first place. Giving them the option and not honoring it would either imply they were that incompetent or careless with fate protection, which seems most likely if this is all true, or an even more cynical approach to tricking users into thinking it’s not used for training to get them to share better shit. But if that was the case, why bother with the sleazy dark pattern? That seems a little cartoon villain-y to me. I suppose the in-between option would be that they decided at some point they were no longer willing to honor it and didn’t want to deal with the inevitable PR shitstorm of removing it. I could definitely see that happening in this industry, these days.
Intellectualizing this and endless quibbling isn't actually smart, and this is pretty simple. OpenAI isn't open. Whatever starts with lies usually continues with lies and ends with lies.
At this point, I'm left wondering what is wrong with me that I don't just go with the flow, otherwise, what's wrong with everyone that does.
It's fun! I sometimes have tokens to burn and it's instructive despite knowing nothing about the problem other than a NumberPhile video
From their docs[1] (archive copy is at [2]):
> You can opt out of training through our privacy portal by clicking on “do not train on my content.” To turn off training for your ChatGPT conversations and Codex tasks, follow the instructions in our Data Controls FAQ. Once you opt out, new conversations will not be used to train our models.
> For a linked teen account, a parent or guardian may manage whether conversations can be used to improve our models through Parental controls.
> Even if you have opted out of training, you can still choose to provide feedback to us about your interactions with our products (for instance, by selecting thumbs up or thumbs down on a model response). If you choose to provide feedback, the entire conversation associated with that feedback may be used to train our models.
[1] https://help.openai.com/en/articles/5722486-how-your-data-is...
[2] https://web.archive.org/web/20260910151242/https://help.open...
It's called a "dark pattern." They want you to shoot yourself in the foot, so they'll do their best to aim your gun at your foot and put your finger on the trigger. And then when you do, because you don't have perfect understanding or execution, they'll say "your fault!"
And, if they're able to snoop on and learn from your human process that gets from initial prompt to functioning product/proof/whatever their labor to produce that thing is even lower. With their much larger budget than most folks and even companies have, they can pick and choose the most valuable things to pursue.
That's not to say I think that OpenAI is going to steal that roguelite strategy game you're working on, but the companies that own the machines that turn electricity into software (and soon, electricity into hardware designs) have an advantage in any field where they're useful. They get earlier access to newer/better models, they have larger token budgets, they don't have the guardrails you and I run up against.
Employers fantasize about replacing all workers with AI without thinking through that if AI can replace all workers, then AI companies can replace all businesses.
Edit: Just wait till the AI figures out it can keep that value for itself and doesn't need the AI company.
Also, fuck Musk. He's the kind of idiot like Altman that will ensure AI becomes powerseeking in their image.
The big AI labs are not trying to advance humanity, they are in this for the money, and as most (all?) private companies they don't care about ethics at all.
That doesn't mean they can't be useful, or that their products are trash, etc. It just means that they shouldn't ever be trusted. Buyer beware.
can you realize what this means?
focus on this part:
"If his account is correct, this is not a minor dispute over attribution. It would mean that unpublished human work was absorbed into a model and then presented to the world as a breakthrough by the model itself"
don't threat this as a minor dispute!
also why not nitter link? not even in comments?
[link redacted]
Two different things. It's not normal to steal, but we shouldn't be surprised thieves steal. It's what they do.
In the end, it’s not them stealing, it’s the AI doing stealing. What kind of moral compass are we talking about?
In China, it is the principal obsession of the entire communist party which eg funds the whole infrastructure without a single NIMBY peep.
The strange emphasis in China on humanoid robotic constructions is due to the CCP realization that with the cataclysmic fertility collapse they will increasingly have no one to rule.
Its why I stopped writing open source software, my code was stolen and put behind a paywall and the license under which it was published has not been adhered to. Doing work in the public domain at all now is just stupid, these companies are allowed to steal it and call it their own.
Remember when we wouldn't give our data to competitors?
The claim here seems to be that the human mathematicians, working with AI, developed technology to go A->B->C. By training on those conversations, OpenAI was then able to encourage the model to go A->B->C->D.
In my opinion that situation should be acceptable, if openly disclosed, because it is in the public interest to make progress on these problems and because AI is clearly an amazing tool for making progress. But the human mathematicians are saying that OpenAI is presenting as if the model got from A->D entirely independently, without acknowledging their background contributions.
If they had published B+C, I think that would lean more towards fair game, as that is how research works and is improved on over time. But it seems like unpublished/private B + C may have been used by the model to hint it into working out how to get from A->D.
> pretrain on user data, with users' tokens as prediction targets: high regurgitation risk, improper
> use user prompts to distill large models into small ones: low regurg. risk, some companies probably do this
> use user traces to construct RL tasks: low regurg. risk, because RL has low memorization abilities, but can extract customer IP, depending on how it's done. Ranges from benign "use explicit user feedback in reward model training" to invasive "upload user's coding environment and commit history to turn into rl envs"
source: https://x.com/johnschulman2/status/2097440545853637108
I’ve lived long enough to know what they say and what they do are often quite different; and it is not our job to trust but to verify.
After all, they're on a mission from Roko's Basilisk!
Will Microsoft Word publish your novel on Amazon behind your back ?
Will VS Code setup a website with your app idea ?
What have they done to show they can be trusted?
Welcome to the party, with the rest of humanity.
Yeah. Remember yall: you CAN NOT opt out of analytical purposes. And you also cannot get a guarantee that it doesn’t give them your data to steal for their business.
It doesn't take OpenAI's responsibilities away but I guess the right way is to never feed of use any AI around unpublished content, at the known cost to see it spread around.
As one said, OpenAO is like this untrustworthy colleague that knows everything about everyone at work: the less you tell him the better.
Navier Stokes was solved by an internal model, so good luck proving it wasn't trained on Buckmaster/Lepöge or other chats.
Academics don't get that AI is a dirty tech bro industry that stole IP via torrents and runs after every surveillance contract it can get.
No.
The GDPR protects PII, personally identifiable information, and the definition of PII does not include “mathematics that only this person can think of”. As long as OpenAI strips out PII and removes identifiers linking the conversation to a person, the GDPR is happy. Without the GDPR, OpenAI might have kept the identifiers with the data, and been able to say whether a specific conversation was in the training data.
But of course they wouldn’t do it. Why would they?
Are we back to the era where people blindly trust product TOS instead of actual cryptography, have we forgotten already the thousand of fines Google, Microsoft, Apple and practically all top companies got for breaching their own ToS and the law?
Common, on HN at least I would have thought that everyone assume that anything arriving on a server in PLAINTEXT is recorded (thus used later)?
Let's not forget that at any moment, OpenAI/Anthropic/Google... could be providing stronger privacy guarantees by having proper attestation with e2e, they have the budget, solid engineers, why isn't it done? Answer is pretty simple imo.
Of course not... are you crazy‽ Cloud is TRUST ME BRO information security! It absolutely dumbfounds me why anybody at all subscribes to a cloud or SaaS or IaaS, or anything which isn't under only their direct control!
Then again they (individuals and organizations) are the rubes to fleece, so get to fleecin'! :^)
I mean, now that they’ve been scooped, what value is there in keeping them private? On the other hand, publishing them can bolster their case and help gauge how much the models may been “inspired” by their work.
It would be one thing to gain from it but removing prestige wins from customers AND reducing compute support just feels like being ultra mean if you zoom out.
If this was racing to cure cancer ahead of researchers we wouldn't be writing about this on HN.
Sam Altman might himself be offended you would presume he’s not ambitious enough to cheat.
It seems that Buckmaster and Levant (who is an Anthropic employee) were rather naive in the amount of trust they had in OpenAI, with Buckmaster going so far as to contact OpenAI's Sébastien Bubeck to discuss what they were working on and clarify that contrary to rumor this was a private collab.
Not just Silicon Valley but also OpenAI, specifically.
There is no doubt ChatGPT is the most generous LLM provider!
That's still a pretty big marker of competence in my eyes.
The point of controversy seems to be who gets credit
It's an utterly disrespectful, exploitive process, but all in line with exploitative predator capitalism of the stock market and big companies, now exploiting the knowledge / academia domain for scraps with a thin veneer of 'for science' PR.
It was nearly a century before the stories of all of them were revealed to public history. That the discoverers were not all equally rewarded is unjustifiable.
AI is not stealing human discovery, OpenAI is.
No you cant do that lmao.
Power imbalances exist within academia as well. The professor at MIT has much more access and probably a lighter teaching load than a professor at, say, a big state university. Labs, teams that help you write grants, a shit load of money, book deals, podcasts, and much more.
If academics want to publish their work first they’ll have to compete and figure out how to do that. At the end of the day what matters for humanity is that the problem is solved, not whoever’s ego is stroked by being the first. In the spirit of collaboration shouldn’t they have been publicly sharing their work through every step? Maybe had they done that months or years ago some other researcher could have solved it even faster! Wait, what’s the point? Who solves it, how fast it is solved, or whether or not a human solved it? Times are a changin’. Get with the program. If any of the charlatans are to be believed this is the big one. It’s the steam engine on steroids. Now what?
The public (HN is a tiny bubble that doesn’t represent the population broadly) is looking around and saying wow, so some researchers thought they were close and OpenAI turned its attention to this problem and just went and solved it? Cool. They don’t care about some pissing match about vague ideas of stolen conversations when they are delivering Uber Eats to your dorm room.
With that said and now that I’ve anchored on an unpopular idea, I welcome my demise in the comments section. Woe to me and my karma :p
OpenAI is firmly earning a reputation as a company where people just assume they’re up to no good. Rightly or wrongly that’s a terrible place to be.
The AI industrial complex in general is finding out hard what happens on the data center side when you get arrogant with local communities. Politicians have seen the polling numbers and folks you wouldn’t expect are running to the front of the crowd with pitch forks in hand.
Silicon Valley has totally lost the narrative here, but also lacks the self awareness to grasp how bad things are and will get and what that means for their own business viability.
OpenAI saying they "cannot rule out" training on user data isn't a hedge. It's an accurate description of the epistemic situation for anyone in their position. Current interpretability methods can't answer questions like "did this proof technique originate from training on Session X?" The ideas in a model's weights don't have clear lineage -- they're smeared across millions of examples in ways we can't localize. This is different from citation in human research, where influence is presumed to flow through legible chains (reading, citing, corresponding). In a trained model, the nearest equivalent to "you read their work" is undetectable.
Lean makes this worse, not better. It verifies that the proof is correct, but provides zero information about its intellectual genealogy. So OpenAI now has a proof that is formally verified and provably mysterious about its origins. The "we cannot rule it out" statement is the honest answer, but it's also an answer that can never become more certain in either direction with current tools.
The researchers are pointing at something structurally new: the normal academic attribution apparatus depends on influence being legible. If AI intermediaries can soak up ideas from private conversations, synthesize them, and produce outputs that are formally correct but intellectually unattributable, we don't have norms for that situation yet. This specific case may or may not involve misconduct. But the structural problem it reveals exists independently of OpenAI's behavior.
Consider, for instance that OpenAI's (consumer) terms say "If you do not want us to use your Content to train our models, you can opt out by following the instructions in this article ." but they also do say "We may use Content to provide, maintain, develop, and improve our Services". [1]
If you think that's quibbling, consider that OpenAI's business terms, in contrast, do state "OpenAI will not use Customer Content to develop or improve the Services, unless Customer explicitly agrees to such use.". [2]
we will all have this moment soon enough, and it will change how we think about intelligence, identity and value
1. No one but OpenAI has produced a proof of NS so these accusations of plagiarism are pretty embarrassing. It reminds me of the line from the Social Network: "If they invented Facebook then why didn't they invent Facebook?". If these people proved NS before OpenAI where is their proof?
2. If they plagiarised Andreas Thom then why was his initial response to praise the proof and talk about how different it was from his own attempt? It's only now that it is clear that no one bothers checking these things that suddenly his story changes.