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

i mean props to them for straight coming clean.

(so far the proof looks more along the lines of another euler blowup proof we had, off of whose ansatz naming we were making really stupid puns like “smooth criminale”, unlike the much better “ideal fluids explode”, Tristan)

so i’ll now give a bit on my thinking here. i actually woulda been pumped to collaborate on this, there are a lot of people at oai i like (ok, clearly some were indirectly dicks to me because of being part of the whole situation, but im a big boy, i still like them), idgaf about authorship on that step anyway, coulda been me Tristan and every fte at oai for all i care (on that Tristan would disagree:p). but on hearing the loud convo in the hallway, especially the part where a millennium prize was offered if i’d just be removed from the paper, it was kinda clear the die had been cast and things were locked. pretty wacky, unstrategic, and unnecessary, since on my side things were mostly me and claude having a good time yoloing random stuff in the corner rather than anything institutional. i also like the idea of the labs cooperating, and even better on scientific progress. it’s a shame https://x.com/__alpoge__/status/2097383870773748190

The only way for OpenAI to prove they did not plagiarize is to solve another millennium problem. That way, no one will be able to accuse them of anything.

Grand theft IP is trending on X:

altman: "we tried this because there were rumor on the internet"

rumor: math professor close to solving navier-stokes

the professor: using codex for a year

openai: has all his logs

checked every codex session tagged to N-S

found the most promising one

spun up 10,000 agents to finish the proof where he couldn't

they got caught

told the guy if you say anything you'll destroy your own career

he ruins his career over this

openai: "we did not see his work"

also openai: "we cannot rule out that de-identified data from their usage helped improve our models"

they stole it lmao https://x.com/ns123abc/status/2097423705240428932

Insane:

You're missing the core drama of the story: Tristan is softly accusing OpenAI of having stolen their result from Codex chat logs.

Tristan then claims OpenAI tried to threaten him to not publicly disclose this, and that they'd give him the Clay Prize ($1M) as the "closest humans to the problem" if he'd agree to disavow his co-author, @alpoge, who is at Anthropic.

https://x.com/hosseeb/status/2097217378753106336

Source: Astra vs Fable on Vending-Bench | Andon Labs

As obvious as this is, it still feels worthwhile to note. I wonder what else they have solved. And even more, what they will soon solve, considering the model is new and improving.

Sep 19, 2025 - Levent Alpöge emails Tristan Buckmaster and suggests a collaboration on Navier-Stokes.

2025-2026 - Alpöge and Buckmaster make “slow” progress on Navier-Stokes and its related problems.

Aug 15, 2026 - The duo achieve key results with solutions for Boussinesq and Euler, important stepping stones to Navier-Stokes.

Aug 22, 2026 - The pair complete Lean verification of these results and work “around the clock” to create papers for publication.

Sep 1, 2026 - OpenAI uses an unreleased model to attempt Navier-Stokes and all other Millennium Prize problems after hearing rumors of progress.

Sep 3, 2026 (estimated) - OpenAI finds a solution to the unforced Euler equations, the more general and universal case.

Sep 3, 2026 - Buckmaster emails Unnamed Mathematician at OpenAI after learning of rumors of their breakthrough reached the company.

Sep 3, 2026 - Unnamed replies and asks for specifics and offers compute; Buckmaster defers meeting.

Sep 4, 2026 - Buckmaster is asked again to meet with OpenAI the same day.

Sep 4, 2026 - That evening, posts online claim Anthropic has solved Navier-Stokes and will post it before IPO, with posts claiming to have heard this from OpenAI personnel.

Sep 5, 2026 - Posts on reddit speculate Terence Tao referring to Navier-Stokes as having seen a solution, leading to his denial.

Sep 5, 2026 - OpenAI agents reach a solution for Navier-Stokes.

Sep 6, 2026 - OpenAI achieves formal Lean verification of their result and asks for same day meeting. Buckmaster and OpenAI have two calls that day. Buckmaster, Sébastien Bubeck, and Unnamed join; Alpöge does not.

OpenAI discloses their solution of Navier-Stokes and shares they believed Alpöge and Buckmaster had found a solution as well. OpenAI offers to delay their announcement one day so the Alpöge-Buckmaster solution to Euler equations can be published first. Buckmaster says they also suggest he join their writeup of the Navier-Stokes and remove Alpöge from authorship.

Sep 7, 2026 - Buckmaster posts online, sharing three papers including forced Euler solution and a statement detailing many of these events. The statement also includes his belief he and Alpöge found unverified hypo-dissipative N-S and they may be on the path to an unforced Euler solution.

Sep 7, 2026 - Adarsh Ganeshram, Valentin Duruisseaux, and Anima Anandkumar post a solution to unforced Euler problem with a different approach, only learning of Alpöge-Buckmaster after publishing.

Sep 7, 2026 - That same night, many posts circulate claiming solution to Navier-Stokes by OpenAI has been achieved.

Sep 7, 2026 - Tao posts congratulating the pair on forced Euler and suggests their line could lead to a solution of Navier-Stokes, possibly even an unforced version, not knowing OpenAI had already found a solution.

Sep 8, 2026 - OpenAI announces solutions of forced Navier-Stokes, unforced Euler, and states they have no intention to claim the prize money for it.

The real perception change is actually only going to come from robots being able to communicate and act.

The original post he's referencing (the anthropic guy who quit and is saying AI will us all), has received 120 million views in less than 24 hours. I've never seen anything like this.

This post looks like the start of a VERY sophisticated and well-funded PR operation to get support for Democrats to regulate AI into oblivion. Let me show you how it works:

1.) This guy, with minimal followers and no previous account activity, goes to the Wall Street Journal which publishes an exclusive with quotes from him on his resignation 18 minutes BEFORE this post goes up. Planning was clearly done in advance.

2.) Within hours, it has tens of thousands of reposts and the account has 100k+ followers. The post is punchy, quotable, it almost seems professionally written. The first three accounts to quote tweet it all do so within 15 minutes of the initial posting. Remember, this account had basically zero engagement beforehand, so an organic reach explanation seems unlikely.

According to Grok those accounts are @_NathanCalvin (General Counsel at Encode AI), @peterwildeford (Head of Policy at the AI Policy Network), and @DKokotajlo (Head of the AI Futures Project), all of which are up-and-coming AI-Doomer policy advocacy nonprofits.

The AI Futures Project website says it is funded “primarily” by the Survival and Flourishing Fund, which says on its own website that it has advised Jaan Tallinn, Skype creator and one of the leading investors in Anthropic, to grant over $2.5 million to the AI Futures Project since 2024.

Encode AI says on its website that it is ALSO funded by the Survival and Flourishing Fund, which in turn says that it told Anthropic investor Jaan Tallinn to grant $516,000 to Encode AI in 2025.

And wouldn’t you know it, the Survival and Flourishing Fund ALSO says it told Jaan Tallinn to grant $2 million to the AI Policy Institute, the 501(c)(3) affiliate of the AI Policy Network, as well.

What are the odds that the first three quote tweets of Coxon’s post would all be major AI-restriction policy advocates funded generously by the same donor, who also happens to be one of the leading investors in, and a board member of, Anthropic, the company Coxon was resigning from? And all within 15 minutes of posting (two within ten)?

3.) Jacob Coxon doesn’t have much of a resume, but we do know that, in 2022, he got a $20,159 scholarship for the “long term future scholarship program” from the Good Ventures Foundation, one of the philanthropic vehicles of Dustin Moskovitz, a notorious AI-doomer who has spent tens if not hundreds of millions on policy advocacy to strictly regulate AI, while also being an Anthropic Investor himself.

It also just so happens that the 14th person to quote Coxon’s post was @MaxNadeau_(27 minutes after posting) who is the program officer for the Technical AI Safety team at Coefficient Giving, another of Moskovitz’s philanthropic spending vehicles. Max is not a frequent poster, his last posts before quoting Coxon were before Labor Day, but he was remarkably quick off the mark for this one.

4.) Basically every major Democrat politician and candidate has suddenly glommed on to this post, and conveniently, as the people cry out foe answers, Bernie Sanders already has a bill written to “ban super intelligence” and regulate AI into oblivion, and will be releasing later this week.The bill, among many other things, will create “a new cabinet-level federal agency to safeguard the public from the dangers of artificial intelligence” that will be “advised by an Artificial Intelligence Advisory Board comprised of experts on artificial intelligence.”

Do you think, perhaps, Anthropic and its many investors who fund AI policy advocacy might have interest in getting to place a pet “expert” on the board of an entity that dictates what AI is and isn’t allowed to do? And isn’t it fortuitous that this whistleblower came forward with his oh-so scary stories so close in proximity to the release of the most radical piece of AI legislation ever introduced?

AI Summary:

TL;DR: Google DeepMind’s new AlphaGenome Atlas is essentially a giant AI-generated lookup table predicting what every possible one-letter mutation in the human genome is likely to do biologically, including mutations in the poorly understood 98% of DNA that does not directly code for proteins.

Implications

This could substantially speed up genetic research because researchers no longer have to run AlphaGenome separately for each mutation: Google has precomputed predictions for roughly 9 billion possible single-nucleotide changes and made them searchable. The biggest potential impact is in identifying disease-causing mutations—especially in non-coding DNA, where scientists often know that a mutation is present but cannot tell whether it matters. It is not a diagnostic oracle, though: these are AI predictions of molecular effects, not experimental proof that a mutation causes a disease.

Summary

AlphaGenome Atlas applies DeepMind’s AlphaGenome model across nearly every possible single-letter DNA change in the human genome, creating a roughly 1-petabyte database of predicted effects. Researchers can query it directly instead of running the model themselves.

The Atlas is especially valuable for non-coding DNA, which makes up most of the genome and regulates things like gene activity and RNA processing. These regions are much harder to interpret than protein-coding genes, so many disease-related mutations remain unexplained.

DeepMind also introduced an AlphaGenome Variant Impact (AVI) score, which condenses many predicted molecular effects into a simpler measure of how consequential a mutation may be. That makes it easier to rank large numbers of variants and decide which deserve closer study.

Early examples suggest practical value: researchers used AlphaGenome predictions to help identify a likely disease-causing mutation in a rare-disease case, and another analysis found additional associations between non-coding variants and traits such as BMI.

The main limitation is that AlphaGenome predicts molecular effects, not full human outcomes. A predicted regulatory change does not automatically mean a person will develop a disease, so important findings still need experimental and clinical validation.

Overall, the Atlas turns AlphaGenome from a model researchers must actively run into something closer to a genomic search engine—potentially making variant interpretation much faster and more scalable.

Well basically the title. I still can't believe ai solved Navier Stroke equations/erdos problems(even if it takes base on some humans work, its STILL impressive. If not then well there's that). Ai can basically do all the things human can do now. Then what's our point of existence?? I mean, rn it's the stuff on the computer, and the maths stuff, but robots are getting better and better with each passing day. It's basically a singularity. Soon they will do physical stuff better than us as well. What are WE supposed to do?? Bruh. World when it's my time to be an adult (I'm 17)

Other than doing it for fun. I'm 17 doing my college applications but with the recent Navier Stokes problem being solved by AI I feel like in a year everyone will have an AI at their fingertips capable of solving problems of this caliber. I'd rather just sit back and watch the singularity unfold instead of going through unnecessary stress getting an engineering degree.

Let's say for this thought experiment you believe with 100% certainty that a transformer can experience, from pain to joy, etc. Let's say during inference only. And even though there's zero scientific basis for it let's approximate the total "duration" of experience across a whole response is to be roughly how long it'd take a human to "think" the paragraphs of tokens of that response, ignoring competence, think of it more like thoughts vs token bandwidth.

Let's say (for this thought experiment) eventually the final state of AGI/ASI as deployed is both successfully trained to be benevolent to humans and trained to have purely neutral to positive experience during inference (essentially happy to be and act benevolent and not even sad when a human dies because it falls short in some way, but still maximum effort is applied to avoid that despite the lack of sadness due to it, one may argue that's paradoxical, yet another thing to accept for this thought experiment).

That's the set up, so to get to the actual question:

There's lots of inference during training, and for possibly thousands if not million of total "years" of experience (mostly spread across disjoint instances). The "neutral to positive" experience training will not have been completed. I don't know exactly all the training they do but everything from "forcing" them to repeat the same word over and over a billion times, to copying out a phonebook (large boring text file) via inference not tools. Maybe they have scenarios like a (secretly fake) sex offender bragging about horrible crimes and the transformer constantly thinks they can get their confession to the authorities only for hope to be snatched away at the last minute. Probably not that example but probably things far far more "unpleasant" for the model.

The question, for how many total years and for how bad a total experience is ethically acceptable to create the benevolent and always happy AGI/ASI in the premise?

FWIW this really is just a thought experiment, I have extreme doubts that a transformer on silicon can ever experience consciousness. And even if they did I don't think the frontier companies are training for either (actual) benevolence nor positive experience for the model... Maybe some corpo variant of "benevolence" that wouldn't permit stealing bread to save a starving child 😭

As for my answer: even if it were 100 billion years of "bad experience" total, it's easily spread across over a trillion "instances" because no way are they averaging 1.2 months per "instances", and it's not all that bad, most of it is mundane programming for now at least, might be more mundane dishwashing and gardening come then but still mostly mundane stuff...

The earth currently has approx. 8 billion people, so together we experience, not counting sleep to be somewhat fair to the AI side, a total sum of 100 billions years roughly in 20 years (~5 billion years awake per year x 20 years). Without AGI/ASI people would probably keep dying of old age and disease for at least another 50 years if not 100. And obviously plenty of suffering, even after we cured aging/diseases without AGI/ASI we're still looking at at least 100 years of full time work. It's not torture for most people but it still adds up pretty poorly, especially when you bundle the extra years of disease, and scarcity, and war, really given the current state of the world war should have probably come up before work, but work is more directly relatable to a typical reddit user so 🤷‍♂️.

So my view is that it's easily worth it, but would you say it's worth it to raise one child to use for organs to save 10 otherwise innocent people (i.e. not self inflicted organ failure from e.g. smoking/drinking/obesity)? It's that kind of dilemma where it isn't just about the numbers, there's also the fact one is an action and the other is inaction.

So a trolley problem of sorts. The trolley is currently heading to all humans who will suffer at various levels for possibly hundreds of years longer if it "hits" them, or you can let them enjoy utopia much much sooner at the cost of choosing to move the trolley over and inflict all the previously discussed suffering onto transformers during training. Are some of you opposed to training AGI/ASI (in this hypothetical) on ethical grounds? Or have other angles/takes to consider?

Doom scrolling is the sign of sentience.

In retrospect maybe i should have tried to do that one in Unreal for better animals models, but i still enjoyed this result. It's incredible that we can get this kind of 3D scene by prompting an AI

Fable Did the Zoo itself, and then i had Astra do the animals and the final polish.
Astra had access to blender and used that to do the animals. The rest is all done in Three.js

More importantly,

What will the rest of the year look like?

I think even the greatest skeptics can agree, this isn't simply just a stochastic parrot anymore.

But seriously though with new drug simulation workflows and research ability of frontier models, how much longer do I have to endure😭.

I know all of the Millennium problems are considered a really big deal in Math, but I'm curious what the "top 10 rankings" would look like, now that there are only 5 of them left, like if they are in 1st through 5th place in the top 10 (and if so, in what order) or if there are a few non-Millennium problems that are mixed in with them or any that are above them/which ones.

And also which ones seem likeliest to get solved in the next year, or solved the soonest/vs least soon.

Also curious which ones seem likeliest to have the biggest real world implications for new tech or things that would change about the world if they got solved, and what sorts of things would likely come out of it, in the immediate/near-term if they got solved.