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this is the kind of stuff I currently think about/work on
Gabriela 🦋❤️🔥 retweeted
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Gabriela 🦋❤️🔥 retweeted
I think it's important for OpenAI (and other frontier labs) to clarify and address these concerns in a considerate and thoughtful way, especially given how much value they are deriving from these announcements, and more importantly, the value they have derived from the work of the mathematical community, either through training data or otherwise.
Btw Andreas Thom is now also wondering outloud whether OpenAI used his personal user data to train the model to find a non-sofic group...
mathstodon.xyz/@andreasthom/…
Gabriela 🦋❤️🔥 retweeted
Too dark.
The AI companies must drop the doom narrative and remain what they are, helpful software. Pay for service tools.
They should see that we don’t want them to solve the problems *for us*. They don’t even need to do that to make their riches.
Instead, they pick the lowest hanging fruit to deceive us that from here, everything else will be automatically solved. They pick problems that are amenable to massively paralleled computing and search in spaces like math where the rules are clear and defined, and then falsely extrapolate that everything is just that, a search problem. It is not true. It is a dangerous lie.
In this way they will kill progress by creating an incredibly hostile and secretive culture where no one wants to share anything as we expect everything to be scooped by them.
The work to be done is to *help people solve the problems*. Academia has been broken for a long time. The literature is a muddled mess of contradictions and unreproducible results. No one wants to do the work to ‘clean it up.’ No one knows what to fund because the foundation itself is shaky. Rebuild the foundation.
OpenAI and Anthropic could do this *for us*. They could use agents to scan the literature in spaces where the uncertainty is highest and surface core contradictions to be tested, run automated experiments to figure out ground truths, and share new work that reestablishes clarity so we can then build new discovery on top. A clean knowledge base; a springboard of sorts for collaboration and prosperity. This is work that no human alone could do. It is a grand project.
Our AI leaders have lost their way. Look back to what inspired humans for millennia, wonder and awe at the universe, at beauty, pride for our culture, a shared sense that we can build the future together. We do not want idiocracy or fully automated space communism. We do not want you to take 1000s of years of our artistic work, our souls, and reproduce it.
We want to have a part in the future. We would once band together to defend our way of life against a common enemy. We have now made that enemy ourselves. If we stopped to take a breath, to see the forest for the trees, we’d realise that this is the best way of life man has ever known, and must be defended.
By stepping back from the superintelligence narrative to one of a defence of our prosperous way of life, the AI companies can make their money simply by charging for the services they provide and and in return, we will continue to feed them our out-of-distribution ideas, wants, and wonder.
It does not need to be this way.
Culture is becoming a dark forest
"The future of intellectual work is to be crouched behind a tree, frantically trying to complete your contribution as silently as possible. You’ll feel the pressure of being hunted, since all it takes is a prompt to produce an almost-as-good version of what you’re attempting to make great, and so to mine away the inspiration and insight and reward. Quite often, staggering successes are erected in the forest, but they are never done collaboratively or in the open. You’ll never see anything being built anymore. Instead you’ll find the results at dawn, a gleaming sculpture in a glade, of unclear origin, and with features brutally beautiful. And then once the sparkle fades everyone will rush back to their respective trees, to their fumbling amid the loam, to their slick-wet roots, to their dark."
open.substack.com/pub/erikho…
Gabriela 🦋❤️🔥 retweeted
I hear roughly 3 reasons people keep working at AI labs despite believing in ~10% extinction risk:
1) Techno-determinism: Someone will build ASI no matter what, and I can do it better & more safely than China/OpenAI/etc
2) Consequentialism: ASI might kill us, but it also might produce utopia/immortality/superabundance, so it's a +EV bet
3) Self-interest: I am personally having fun & getting rich working on cool tech with friends. I don't think about the macro stuff.
Notably, none of this is "I'm hyping up the risk for marketing reasons." People believe what they say, while being capable of a lot of internal dissonance / compartmentalization / self-justification.
(Personally, I think the public is better served by AI researchers talking about & creating consensus for the specific safety solutions — regulatory or otherwise — they want vs. vagueposting about extinction.)
Perelman will get an aura boost if he ends up the only human to solve a millenium problem
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Grisha the goat
Btw, this is the only mathematician who has solved one of the seven Millennium Prize Problems.
he rejected both the Fields Medal and the $1M prize:
> be Grigori Perelman
> Russian mathematician
> 16 years old
> go to International Math Olympiad
> score 42/42
> years later…
> there’s a geometry problem called the Poincaré conjecture
> been unsolved since 1904
> generations of mathematicians fail to solve it
> Clay Institute puts a $1M prize on it
> wrestle with the problem for years
> 2002
> casually upload the solution to arXiv
> don’t even care about submitting it to a journal
> mathematicians spend years checking the solution
“fuck
he actually solved it”
> 2006
> offered the Fields Medal
> most prestigious prize in math
> refuse it
> 2010
> offered the $1,000,000 Millennium Prize
> refuse that too
“EVERYBODY UNDERSTOOD THAT IF THE PROOF IS CORRECT THEN NO OTHER RECOGNITION IS NEEDED”
Gabriela 🦋❤️🔥 retweeted
This is one of my biggest questions right now. What EXACTLY does OpenAI commit to when you turn this off?
Gabriela 🦋❤️🔥 retweeted
not particularly important but "vortex" is a really good word. just really top tier. rolls right off the tongue. futuristic. not many words in math as good as vortex
Gabriela 🦋❤️🔥 retweeted
Synthetic data derived from production user data of consumer AI tools is used for training. I’ve heard this rumour from both large labs’ employees.
In particular, if you’re doing something “interesting” like working on complex math/business/software/bio problems you’re dramatically more likely to get trained on because they filter/up-weight towards those usecases where the model has the most to learn.
Even in ZDR and “we won’t train on you” regimes, derivative data is usually carved out. The promise is only not to train on exactly the data you put in, rewritten data is fair game.
We congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work.
We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem.
While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.
However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs. unforced).
Gabriela 🦋❤️🔥 retweeted
OpenAI's paper solving the Navier-Stokes millenium problem
drive.google.com/file/d/10Eb…
Gabriela 🦋❤️🔥 retweeted
"While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models." oh fun times fun times
We congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work.
We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem.
While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models.
However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs. unforced).
Gabriela 🦋❤️🔥 retweeted
OpenAI has solved the Navier–Stokes Millennium Problem using an 'internal model that is significantly more capable than GPT‑6 Astra.' They began work on it after hearing rumors that the problem had been solved, and threw 10,000 concurrent agents at it. They solved it in 88 hours.
I deleted my previous post. I'm just going to post the entire statement page by page. It's a complicated situation.
Statement: cims.nyu.edu/~tristanb/state…
cims.nyu.edu/~tristanb/euler…
cims.nyu.edu/~tristanb/ipm.p…
cims.nyu.edu/~tristanb/bouss…
github.com/tristanbuckmaster…
Gabriela 🦋❤️🔥 retweeted
Yes, this result cost millions of dollars.
But remember that when @OpenAI announced o3 it cost ~$500,000 to score 87.5% on ARC-AGI 1. Today, Astra scores higher for ~$20.
In 2025 it took us and GDM an enormous amount of compute to achieve IMO gold. For the 2026 IMO, anyone with a $20/month ChatGPT subscription could do it.
Massively scaling test-time compute gives us a glimpse of the future. I believe that a year from now everyone will have an AI at their fingertips capable of solving problems of this caliber.
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
Gabriela 🦋❤️🔥 retweeted
well, here we are. let's take a look back at this poll i ran about AI assisting with a millennium problem way back in the halcyon days of may, 4 months ago, right after erdos unit distance and before jacobian. phew
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.






































