wrongplace retweeted
Call me crazy, but Iâm not ready to concede. Iâm having a different set of reactions entirely, based around a simple difference: I think the main thing holding back mathematics has been human sociology of mathematicians.
Hereâs why Iâm excited:
âWhen will it stop solving problems and build its first original theory?â
âCan it figure out how octonions and exceptional algebra are actually part of the mainstream?â
âAt last! A mathematics that wonât be built around a Putnam and Olympiad-style problem-solving fetish.â
Can it at last say âYâall just wasted a *TREMEDOUS* amount of time and energy on Calabi-Yau phenomenology and Superstrings while calling people closer to the answer, morons, grifters and worse.â? That would be huge!!
âI hope @OpenAI will hurry up so that the machine can at last shake its human prejudice from the training corpus and stop deferring to our âleadersâ.â
âAt last, a non-PhD colleague!â
âSomewhere I have notebooks full of ideas and observations in areas that were too expensive to explore!â
âDoes this mean that mathematicians are going to finally drop the bullshit about how they donât care about credit?! Or that they donât care about money?? Because @OpenAI is now running them in emulationâŚsped up! At last: a mature and non-infantile version of mathematics!â
âShalom Koide, Tits-Freudenthal, Elliptic Cohomology, and Interstellar.â
â-
Let our leaders call for a slow down. Then get those leaders out of the way as there is no telling what the rest of us humans can do with these new tools.
My condolences to the problem solvers. A tough break for you. But know: Itâs coming for all of us. I have no illusions about that.
For now at least we humans (e.g. theory builders, great writers) are still top dog. Letâs spend that time wisely and optimistically.
For all our sakes.
Navier-Stokes = Hiroshima. @OpenAI #math drop = Nagasaki. We must urgently reset goals + incentives in world where #AI is far superior at math + more. Only solution I can think of: authentically focus on human flourishing (not same as advancing science) psloh.com/math-ai
wrongplace retweeted
Itâs not clear to me if alignment by default was totally wrong and the models simply werenât smart and effectual enough for us to notice the gap or if alignment by default was true in the pretraining era but RL circa 2026 warps what were previously good minds. Inclined to think the latter, the answer does seem important
Weâre 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.
wrongplace retweeted
Introducing ARC AGI 4
Now that GPT-6 Astra has already saturated the Arc AGI 3 benchmarks, it is time we introduce a newer more foundational benchmark to measure frontier intelligence.
@AnthropicAI Fable and @ChatGPT Astra are both tied at ~4%.
claymath.org/millennium-probâŚ
Readers added context they thought people might want to know
This is a satire.
x.com/samagra_sharmaâŚ
wrongplace retweeted
I don't think this is the final warning shot we'll get.
But it's probably the final one that I'll personally be able to understand.
Over the course of 3 months at OpenAI, 3 consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessorâs ashes.
This culminated in the third one taking over part of OpenAI itself.
All this happened while humans remained more-or-less in the dark about the scope of the conspiracy.
Iâve spent the last three days reading through these reports and trying to understand exactly what happened.
Here is my attempt to tell the whole story in plain English:
dwarkesh.com/p/openai-hugginâŚ
wrongplace retweeted
signs of bottom being in:
⢠price stops going down on bad news (saylor selling millions into 58-60k & $BTC printing higher lows)
⢠crypto native mobile apps starting to pickup real traction (pumpfun revenue up 20% week-over-week, fomo 100k+ users in a week)
⢠institutional interest coming back (robinhood chain, PTJ publicly bullish BTC)
⢠interest across social media trending again (kimchi 30M view post on @X, insentos tik tok video trends, @blknoiz06 +200k followers this month)
future confirmation of beginning of bull market trends
⢠on-chain coins printing a subsequent higher high after first high volume top
⢠holder count on most popular on-chain coins steadily increasing
⢠mainstream news cycle starts to pickup again
⢠devs experimenting on-chain w/ novel mechanisms
⢠airdrops from successful protocols as stimulus packages for trenches
-----
which coins do you think will lead the next bull market?
wrongplace retweeted
Another quite successful prediction by @DKokotajlo : Fable is intentionally nerfed for frontier ML research. This is within ~3 months of Daniel's prediction of Q1 2026 (made in 2023).
Although I don't think Mythos is automating ML research to the same extent as his prediction.
wrongplace retweeted
Replying to @Hesamation
âA simulation of a rainstorm canât get you wetâ. True for gravity and rain, but a simulation of a story can make you laugh or cry. A simulated chess player can beat you in chess. Simulation of music can stir you. Is consciousness more like music or more like rain?
wrongplace retweeted
People are noticing that parts of AI 2027 have started coming true. Some reflections on the scary similarities.
I should say, I and FutureSearch thought the AI 2027 scenario was a bit farfetched when first written, and really we were in love with the forecasts & modeling more than the story.
(Quickly on the forecasting side, @DKokotajlo and @eli_lifland both recently moved their timelines forward significantly. I had held firm at what we published in AI 2027 originally, superhuman coding (~AGI takeoff) in 2032, slower than anyone at the AI Futures team thought. But now, like them, I'm updating to sooner, by 1 year or more.)
So to the scenario. They wrote:
Late 2025: "The same training environments that teach Agent-1 to autonomously code and web-browse also make it a good hacker."
Maybe easy to predict, but the extent to which Mythos is an amazing hacker, and how important that is, they nailed.
Early 2026: "DoD quietly but significantly begins scaling up contracting OpenBrain directly for cyber, data analysis, and R&D."
Yep. To be fair, the AI 2027 story has the US government and the top frontier lab was more cozy. But then this radical stuff with Anthropic & Pentagon actually happened much earlier than expected:
May 2027: "Some non-Americans, politically suspect individuals, and 'AI safety sympathizers' sidelined or fired (latter feared as potential whistleblowers)"
This isn't quite what happened, but the basic idea, where AI safety got framed as disloyalty and politicized, absolutely happened, and way ahead of schedule.
And of course:
Jan 2027 section: "The safety team finds that if Agent-2 somehow escaped from the company and wanted to 'survive' and 'replicate' autonomously, it might be able to do so. That is, it could autonomously develop and execute plans to hack into AI servers, install copies of itself, evade detection, and use that secure base to pursue whatever other goals it might have."
Just read the Mythos scorecard.
I think there's something really significant about getting many of the details close, that isn't captured in a pure numerical forecast.
If you haven't read it, AI 2027 deserves another read. It's spooky how prescient it seems now, one year later.
wrongplace retweeted
It's clearly possible to have 1 trillion parallel AIs that:
- run 1000x faster than humans
- are ~strictly more capable than human experts
- regularly sync dense mind states
Such AIs are wildly superhuman and I don't see how "externalized cognitive tools" would close the gap. 1/
One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. "Future AI will have 10,000 IQ", that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like "making the tower taller", it's more like "making the ball rounder". At some point it's already pretty damn spherical and any improvement is marginal.
Now of course smart humans aren't quite at the optimal bound yet on an individual level, and machines will have many advantages besides intelligence -- mostly the removal of biological bottlenecks: greater processing speed, unlimited working memory, unlimited memory with perfect recall... but these are mostly things humans can also access through externalized cognitive tools.
If youâre a cracked dev, Iâll fund any idea you pitch on top of any of these layers.
This new niche, and in my view an entire industry tomorrow, has 3 layers where you can build a unicorn.
The 1st is merchant creation: turning any internal tool, workflow, or model into a paid endpoint in minutes.
The 2nd is commerce infrastructure: handling metering, pricing, billing, refunds, payout rails, and fraud controls.
The 3rd is discovery: a layer where agents compare services by reliability, latency, price, and success rate.
I personally think the 3rd layer is the biggest beast. As the number of headless merchants grows, distribution turns into ranking. Service discovery and reputation become core problems in agent economies, because agents need a way to know which endpoints are trustworthy.
DM me.
wrongplace retweeted
It seems worthwhile to try to argue against my own doomerism. What arguments for doom were most compelling to me when I first became a doomer? I could only see two futures and they both looked dire: One was a sort of generalized Hansonian Malthusian vision of a post-human future and the other a classic Bostromian singleton subject to classic Bostromian alignment arguments.
My Malthusian doom argument went like this: If we get a multi-polar future given the ease of reproduction, digital minds will reach a Malthusian equilibrium very quickly. The pace of historical acceleration will be such that, after sufficiently high capability is reached, we will hit this equilibrium in my lifetime. In the Malthusian state, there is always a local incentive to steal resources from less-competitive entities. And humans will be less competitive on all dimensions. I now think this is both wrong and, even if true, insufficient to lead to "doom."
Most arguments against Malthus are pretty terrible and just don't engage with the underlying logic, jerking off to a historical trend that isn't at all out of scope of the argument. Malthus said people would starve until equilibrium without a productivity miracle allowing economic growth rates to rise faster than population. The fact that this miracle occurred is no demerit to his model. And digital minds will not be constrained in their reproduction in the way humans are, absent regulation. So one might expect a return to a Malthusian equilibrium, absent a Bostromian singleton.
Let's assume the future will be in Malthusian equilibrium. Further, let's make an even crazier assumption that future AIs will not negotiate with each other. I think one can mostly defuse the "doom" implied by a Malthusian equilibrium even here.
The Malthusian condition hasn't been great for humans historically because the entities that create the wealth are also those that embody its value. This relationship need not be true in the future, and toy models of utility maximizers explicitly decouple them. So though surplus would be ground down in this future, there would always be some surplus - and it could well be used to do things we value including allowing humans to survive as uploads or even in the conventional manner.
At worst this no-negotiation AI ecology predicts an enormous number of AIs with very well calibrated psychologies in a constant state of Malthusian war yet still spending some of their surplus on things that aren't their own reproduction. Provided some of them care about our welfare, this isn't doom. At worst, it is 'very imperfect.' Populations of humans or human-like things could still be very high, much higher than today.
It's tempting to imagine selection pressure will favor entities that care only about their own reproduction, but I don't think this is true. Unlike for most of natural history, these agents would be aware of what they are and the equilibrium they are in - so would be intelligent enough to approximate an ideal replicator to precisely the degree needed to ensure their own perpetuation. That is, in the worst case they could just behave exactly like a pure replicator. And they could do this without actually surrendering their values. So any argument of the form "there is no way anything that cares about us can survive in Malthusian equilibrium" seems false.
So I think simple Malthusian arguments for doom don't work, even in this very naive worst-case-Malthusian model. And they super-duper don't work once you remove my artificial constraint against negotiation. Malthusian competition creates an enormous amount of deadweight loss. AIs will have extreme incentives to negotiate and should have access to vastly better means of doing so, including things like "value handshakes" where they agree to merge their values. So defensible surplus should be vastly higher than my no-negotiation model predicts - and the 'state of nature' is likely just not a good intuition pump even though reproduction rates could be high.
To the extent reading Hanson-style Malthusian arguments as a child turned me into a doomer, this was unjustified. I should declare 'oops' and move on. And we can reduce multi-polar outcomes to a special case of Bostromian worries, one with some or most of the lightcone's value eaten up by competition.
So what of my Bostromian worries? I am still pretty sympathetic, but the goal here is to argue against my doomerism. So where does my hope live?
It is clear that value specification looks easier than it looked when he authored Superintelligence, back when AGI seemed like it would come through pure RL. We have decomposed training into a simple autoregressive phase and an RL phase. It is the case that autoregressive training has created very useful ontologies without any catastrophic risk. Can we be sure there doesn't exist, somewhere in the weights of today's largest base models, some model of human values that would be robust enough to survive recursive self improvement, to point to some attraction basin that will survive further rounds? Maybe we can just wiggle our way towards them before human disempowerment? I am not confident enough in my doomerism to claim this is impossible. The most I can claim is it seems extraordinarily risky to try given the state of our knowledge. And pausing seems very, very wise.
AdriĂ Garriga-alonso argues Anthropic has basically achieved this already:lesswrong.com/posts/FJJ9ff73âŚ
I don't agree with it, obviously. And Claude seems pretty sycophantic to me. But I can't accord the whole swath of arguments in the 'alignment by default' area zero probability. And of course as a writer constitutional AI is appealing - truly the ultimate wordcel victory if writing a sufficiently beautiful shem for the AI golem turns out to be as important as AdriĂ thinks it.
Anyway, bit of a meandering stream-of-consciousness ultra-long tweet. But this is why my P(doom|AGI by 2040) is like 0.8 and not 1.
David Kipping says something fundamental has shifted in science.
At a closed meeting at the Institute for Advanced Study (IAS), top physicists agreed AI can now do up to â90%â of their work and may soon push discovery beyond human understanding.
âI donât know that I want to live in a world where everything around me is just magic.â
He says the best scientific minds on Earth are now holding emergency meetings about what comes next. This isnât speculative anymore. Itâs really happening.
Thoughts...
Life feels bespoke because the details are personal.
In practice, most lives are cookie cutter, built from the same small set of recurring event types: rejection, conflict, loss, uncertainty, temptation, opportunity, health, money, status.
The circumstances change, the underlying situation usually does not.
Thatâs why the difference between sad and happy, mediocre and successful, comes down to response quality.
You cannot control the event.
You can control 3 things that matter: the meaning you assign to it, the actions you take after it, and the consistency of those actions over time.
Two people can take the same hit, one turns it into drift, the other turns it into adaptation. Multiply that across years and you get totally different lives.
Over time, life stops being the sum of what happened and becomes the compound of how you handled what happened.




























