29 captures, most recent first.
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gfodor.id @gfodor · 12h I'm begging people to realize that takes about "LLMs" are transitory. If you're hinging your beliefs about the near future based on ostensible limitations of large language models, you're going to have a bad time.
Tweet by gfodor.id arguing that takes about LLM limitations are transitory and shouldn't anchor beliefs about the near future.
Ed Leon Klinger @edleonklinger
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Ed Leon Klinger [verified] @edleonklinger · 16h Hardly anyone on Earth understands the magnitude of this. It's actually happening right now. Crazy that we get to witness it close up. [Attached chart: y-axis "Intelligence", x-axis "Time" from 100,000 BC through 50,000 BC to Sep 2026. A flat blue line labelled "Human intelligence" runs across; a flat red line near zero runs along the bottom and then shoots vertically at Sep 2026, labelled "Artificial intelligence". A circled zoom inset at the crossing shows a stick figure with an arrow labelled "You are here".]
Dark-mode X post with an attached schematic chart showing human intelligence as a flat line over 100,000 years and artificial intelligence as a near-vertical spike at Sep 2026, with a magnified inset marking 'You are here'.
ai progressintelligence explosionsingularitychartyou are here
Ethan Mollick @emollick
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Ethan Mollick [verified] @emollick · 21h We have come pretty far from "every AI reference is hallucinated" (which still happens a lot if you use cheaper models): when I had an AI go over my new book to check for errors, it found an error not in my reference itself, but in the original article my reference referred to.
Tweet by Ethan Mollick on AI error-checking of his book catching an error in a cited source rather than in his own reference.
Andrew Curran @AndrewCurran_
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Andrew Curran @AndrewCurran_ · 18m OpenAI confirmed to the New York Times that they have made "substantial progress" on another Millennium Prize problem in the last five days, and are preparing to announce. The rumors for the last 48 hours have been OpenAI solved the Hodge Conjecture, and that Anthropic has solved the Birch and Swinnerton-Dyer Conjecture. Since Navier-Stokes rumors abound, so I was reluctant to post about either. However, OpenAI's statement to the NYT now gives the Hodge rumors some very serious support. In general people have not updated yet that the new unnamed OpenAI model, the one that finished training about two weeks ago, which I believe will be named Aeon, is massively better at math than Astra, which two weeks ago was the best in the world. Aeon solved Navier-Stokes in 88 hours, start to finish. Follow the trend line. That means everything is on the table. Literally everything. And this does not end with math. Please update. We are taking off. [Embedded image of New York Times article text] The next chapter of A.I.'s ascendancy in cutting-edge mathematics may come soon. 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."
Tweet by Andrew Curran relaying OpenAI's statement to the NYT about progress on another Millennium Prize problem, with rumors (Hodge Conjecture for OpenAI, Birch and Swinnerton-Dyer for Anthropic) and his claim that an unreleased OpenAI model, possibly named Aeon, solved Navier-Stokes. Includes an NYT excerpt image. Posted in an urgent, earnest register.
millennium prize problemsai mathopenainavier-stokesai progresshodge conjecture
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Aidan Clark @_aidan_clark_ · 4h Replying to @_aidan_clark_ For the first time I am asking myself if things are moving too fast. I'm honestly not sure, but I am sure that it would be good for us to have an answer to "what would a successful pace look like?". I am hoping in the coming weeks and months a clear proposal is painted.
Dark-mode tweet screenshot: Aidan Clark (@_aidan_clark_), replying to his own earlier post, says that for the first time he is asking whether AI progress is moving too fast and hopes a clear proposal for a 'successful pace' emerges.
Yuchen Jin @Yuchenj_UW
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Yuchen Jin @Yuchenj_UW · 44m If you want to understand how fast AI is improving, look at intelligence per dollar. 1.5 years ago: o1 Pro: $150 / $600 per M tokens Today: GLM-5.3 Flash: $0.15 / $0.50 That’s a ~1000x collapse in price in under 1.5 years, while GLM-5.3 Flash is more intelligent than o1 Pro. [18 replies, 14 reposts, 268 likes, 7.5K views] MTS @MTSlive · 2h SITUATION DETECTED: OpenAI has announced that it has reached its goal of an automated AI research intern, and is on track for a fully automated AI researcher by March 2028.
Two stacked X posts. Yuchen Jin (@Yuchenj_UW) compares o1 Pro pricing ($150/$600 per M tokens, 1.5 years ago) with GLM-5.3 Flash ($0.15/$0.50 today) as a ~1000x price collapse; below it MTS (@MTSlive) reports in capitals that OpenAI has announced reaching its goal of an automated AI research intern and is on track for a fully automated AI researcher by March 2028.
intelligence per dollartoken pricingglm-5.3o1 proopenaiautomated ai researcherai progress
François Chollet @fchollet
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François Chollet @fchollet · Sep 3 Side note: when we released ARC-AGI-3 in March, and frontier models scored <1% on it, a few Singularitarian poasters took it as a personal insult, and got very worked up about it. They argued the benchmark was fundamentally broken, that it could not even be solved by the smartest humans, that the max reachable score was actually 40%, etc. We had to deal with a torrent of insults and hate poasts since because we had released an unsaturated benchmark. As it turns out, the benchmark is perfectly calibrated. It is straightforward for a human to score 100% if they do better than average people – all you need is to use fewer actions than our human baseline (which is not a strong baseline, as we used unfiltered human testers). And naturally as a result it's also very feasible for AI to score 100% once real progress towards agentic general intelligence has been made. The trajectory of AI from <1% to 100% over the course of 6 months shows that the benchmark was able to snapshot the recent rise of agentic capabilities. And that rise has happened faster than most people expected, including us. [quoted post] François Chollet @fchollet · Apr 15 Any smart human giving it real effort should score >90% on ARC-AGI-3
Dark-mode screenshot of a long post by François Chollet about the ARC-AGI-3 benchmark's trajectory and calibration, quoting his own Apr 15 post.
arc-agi-3benchmarksfrancois cholletagentic capabilitiesai progress
will depue @willdepue
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will depue @willdepue · 9h rumors i've been hearing on the rate of progress inside anthropic and openai are truly bonkers. i think we'll see a jump at the size of one from o3 to fable again in the next 8 months
Tweet by will depue about rumors of the rate of progress inside Anthropic and OpenAI, predicting another capability jump the size of o3 to Fable within 8 months.
Paata Ivanisvili @PI010101
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Bogdan Ionut Cirstea reposted Paata Ivanisvili @PI010101 · 22h Seeing what is happening on the frontlines, I would say this: if we humans want to keep understanding the knowledge AI is producing at the current rate (new proofs, ideas, and approaches) we may need 10× more mathematicians within the next year than we have today. [quoted tweet] Daniel Litt @littmath · Aug 14 We're going to need a lot more mathematicians! x.com/PI010101/statu...
Paata Ivanisvili (@PI010101), reposted by Bogdan Ionut Cirstea, saying humans may need 10x more mathematicians within a year to keep up with AI-produced mathematics; quotes a short Daniel Litt (@littmath) post from Aug 14.
ai math resultsmathematiciansverification burdenai progressfuture of mathematics
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Jaime Sevilla reposted Cheryl Wu @cherylwoooo · 6h If we look at ECI, it doesn't seem like the gap between the US and China is closing yet. Chinese frontier models are still 4-5 months behind. But why do people feel Chinese models are catching up (or even, have caught up)? I think it's because they just started to satisfy most coding needs. Opus 4.6's release was the moment that I felt a huge jump in AI's coding capability and reliability. Kimi K3's ECI is 157, just surpassing Opus 4.6 (ECI 155). Maybe the ECI ~155 is a threshold above which AI is perceived useful and reliable. [embedded chart: "Epoch Capabilities Index (ECI)", y-axis Score (120 to 160), x-axis Release date (Jan. 2025 to July 2026), "50 Results"; legend by Country: United States of America (pink), China (blue), EU, United Arab Emirates, United Kingdom (greyed). Stepped frontier lines per country. Labelled US points: o3, o3-pro, GPT-5 Pro, Gemini 3 Pro, GPT-5.3 Codex, GPT-5.4 Pro, Claude Fable 5 (highest, about 162). Labelled China points: DeepSeek-R1, Qwen3.7-Max (about 157 at the right edge).]
Cheryl Wu (@cherylwoooo), reposted by Jaime Sevilla, arguing from Epoch's ECI that the US-China frontier gap is still about 4-5 months and that Chinese models only recently crossed a threshold (ECI ~155) where coding feels useful and reliable; attached is Epoch's ECI step chart with US lines above China lines.
epoch capabilities indexus-china ai gapkimi k3claude opus 4.6claude fable 5benchmarksai progress
Jack Clark @jackclarkSF
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Jack Clark @jackclarkSF · 3h Rough eras of recent AI progress in terms of what research community is collectively hillclimbing on: 2018-2022: Basic capabilities (summarizing, coding, etc) 2022-2026: Norm & time coherence (rlhf/CAI, longer context, agents) 2026 - ?2028?: scientific intuition / independence
Tweet from Jack Clark (Anthropic co-founder) sketching a timeline of rough eras of AI research progress, from basic capabilities (2018-2022) to norm/time coherence via RLHF/CAI and agents (2022-2026) to a projected era of scientific intuition/independence starting 2026.
will brown @willccbb
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will brown @willccbb · 2h i don't think "progress multiples" is really the right framing of RSI "how fast is LLM progress moving vs if we didn't have LLMs" isn't really coherent we're doing things that make no sense without good LLMs, like judge rewards and synth data what's the counterfactual?
Tweet by will brown arguing against framing recursive self-improvement (RSI) in terms of 'progress multiples,' since comparing LLM-era progress speed to a counterfactual without LLMs is incoherent given that current techniques like judge rewards and synthetic data only make sense because good LLMs already exist.
ai progresstwitterrecursive self-improvementrsiwill brownsynthetic data
Andy Masley @AndyMasley
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Andy Masley @AndyMasley · 22h "The prediction that AI would radically transform the world has turned out to be a complete flop. Instead, we're see-" NO IT'S BEEN 3 YEARS. THAT'S ALL. 1993 WOULD HAVE BEEN A BAD YEAR TO DECIDE THE INTERNET WASN'T GONNA BE A BIG DEAL. WE WENT FROM GPT-3.5 TO FABLE IN 3 YEARS
Tweet by Andy Masley arguing against premature declarations that AI has failed to transform the world, comparing it to dismissing the internet in 1993 and noting the jump from GPT-3.5 to Fable in three years.
Jerry Tworek @MillionInt
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Jerry Tworek @MillionInt · 6h First time we figured out any reasoning method with neural networks: - AI progress moves decades forward - new trillion dollar companies started popping out almost overnight - all exams and competitions got solved by AI - any notion of cyber safety gets shattered Discovering new, different, more efficient method of reasoning does not seem impossible...
Tweet from Jerry Tworek (@MillionInt) speculating that discovering a new, more efficient reasoning method for neural networks is plausible, given the disruptive effects of the first such discovery (reasoning models).
Captain Pleasure, André... @Algomancer
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Captain Pleasure, Andrés... [verified] @alg... · 11h How have LLMs surprised you recently? Anything new they're capable of you've actually seen with your own eyes up close you'd like to share with the class? :-) [engagement: 8 replies, 29 likes, 2.8K views] liu grey [verified] @Liu_eroteme · 4h simply how good at long-running tasks they have become. I've never trusted agents with more than 30-ish minutes of work at a time because they just kept drifting off into nonsense territory.. today I'm reviewing a 9-hour 10k loc PR by fable & opus, and it's close to flawless. [engagement: 1 reply, 2 likes, 109 views] liu grey [verified] @Liu_eroteme · 4h nothing too complex, just a real-time map overlay i built on the side for one of our web dashboards, but still lots of gpu stuff, SABs, bitops, weird buffer layouts... quarter of a billion tokens and now it's fully ported to webGPU with a massively improved data pipeline [cut off]
X thread: Andrés (Captain Pleasure) asks how LLMs have recently surprised people. Liu Grey replies that agentic long-running task performance has improved dramatically — describing a 9-hour, 10,000-line-of-code pull request produced by 'fable & opus' (AI models) that was 'close to flawless,' a real-time map overlay for a web dashboard involving GPU work, SharedArrayBuffers, bitops, ported to WebGPU over a quarter-billion tokens.
Aryeh Kontorovich @aryehazan
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Aryeh Kontorovich [verified] @aryehazan · 2h losing our jobs will be the least of our problems [quoted] John Carney [verified] @carney · 2h I'm going to admit that I don't understand the mathematician takes on AI. It seems like they sound like they have some "problem" they've been thinking about so long ... [cut off]
X reply by Aryeh Kontorovich (@aryehazan) to John Carney's post, terse retort that 'losing our jobs will be the least of our problems' — quote-tweeting Carney's puzzlement over mathematicians' psychological reaction to AI solving open problems.
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John Carney [verified] @carney I'm going to admit that I don't understand the mathematician takes on AI. It seems like they sound like they have some "problem" they've been thinking about so long that they consider it their own. And now AI has solved it, and that has triggered a psychological crisis. Not an employment crisis. No mathematician lost their job because AI solved a math mystery. It seems purely mental. The mystery they pondered is no longer mysterious! I guess I hadn't realized this was how the mathematician mind worked. And somehow "the fate of mathematicians will be the fate of all of humanity." But most of humanity has nothing like this in their lives. Lawyers? Actors? Doctors? Landscapers? I can't think of anyone who would experience a psychological crisis over AI figuring out a new thing (or answering an old question) relevant to their field. [quoted] Aryeh Kontorovich [verified] @aryehazan · 9h I saw this all coming 2 years ago, about when @GSalafatinos solved my problem using Gemini I experienced the existential crisis and wrote about it here, albeit perhaps in more emotionally muted ... [cut off] 6:36 AM · Aug 3, 2026 · 4,335 Views
X post by John Carney expressing puzzlement at mathematicians' psychological reactions to AI solving open problems, arguing it's not an employment crisis but a purely mental one, and questioning why mathematicians uniquely generalize this to 'the fate of humanity.' Quotes Aryeh Kontorovich describing his own existential crisis ~2 years earlier when a colleague solved his problem using Gemini.
davidad @davidad
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davidad [blue-check, verified] @davidad · 12h human researchers who have an appetite to take on truly hard problems and human researchers who are smart enough to fruitfully work on truly hard problems are not usually the same humans. this does give humans a somewhat unfair disadvantage [quoted] neppy @plumnotes · Aug 2 as an insider, my experience with AI for math is that when it's a problem not in my field i'm like, "holy shit math is so cooked", and when it's a problem in my field i'm like, "lmao an AI mogged dan" (dan is the only one who seriously tried th... [cut off]
X post by davidad (verified) commenting that the human researchers willing to tackle hard problems and those capable of solving them are often different people, quote-tweeting @plumnotes's observation about mixed feelings on AI progress in mathematics depending on whether the problem is in their own field.
Prakash @8teAPi
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Prakash @8teAPi · 4h we are getting closer to discontinuity.. all it takes is a single algorithmic improvement on the scale of transformers... remember the idiot index on intelligence is the difference in power consumption between the 20 watt human brain and 14,000 watt B300... 700x more to go
Tweet from Prakash arguing AI progress could see a sudden discontinuity from a transformer-scale algorithmic breakthrough, citing an 'idiot index' comparing the 20-watt human brain to the 14,000-watt Nvidia B300 GPU as evidence of ~700x remaining efficiency headroom.
JMB @jmbollenbacher
Self-threaded tweet (reply to own earlier tweet), same author as several other tweets in this batch.
Miles Brundage @Miles_Brundage
Simple quote-tweet, no additional images.
will depue @willdepue
— reposted by Eric Schmidt
Plain text tweet, no images beyond profile pictures.
Ethan Mollick @emollick
Ethan Mollick observing that multiple well-funded startups are betting against pure LLM scaling and pursuing alternative architectures, framed as increasing overall AI progress redundancy. Relevant to Nathan's tracking of AI progress/timelines and architecture diversity (parallels his own brain_graph_1 work as an alternative-architecture bet).
twitterai progressai architecturetimelinesethan mollickllm scaling
@__paleo... (Gappy / Giuseppe Pale...)
A reflection on the pace of technological change from 1990 to 2026, using AI coding agents as the capstone example of "unbelievable" progress, closing with the "Everything is amazing and nobody is happy" line (a nod to the Louis CK bit). Relevant to Nathan's tracking of public sentiment on AI progress and capability timelines.
Brangus @RatOrthodox
A joke tweet capturing the sense that AI progress is moving fast enough that near-future extrapolation feels like sci-fi. Light context for the same fast-takeoff mood as the adjacent METR/Noam Brown screenshot.
TechGringo @TechGringo
A short, jokey tweet observing that AI models are increasingly exhibiting apparent preferences rather than random behavior (the "tomato angst" line likely riffing on some emergent-preference anecdote). Light general commentary tangential to Nathan's model-welfare interest in whether emergent preferences reflect something real.
model preferencesai progresstwittermodel welfare (tangential)
Aidan McLaughlin @aidan_mclau
A tweet with an Epoch AI chart arguing (contra popular narrative) that GPT-4→GPT-5 benchmark gains were larger than GPT-3→GPT-4 gains, especially on hard math/reasoning benchmarks (MATH Level 5, Mock AIME). Relevant to Nathan's tracking of empirical AI capability progress/scaling trajectory (cf. his singularity-rate tracking notes, Davidson/Houlden, METR).
twittergpt-5gpt-4benchmarksepoch aicapability scalingai progress
David Holz @DavidSHolz
A poetic tweet from David Holz (Midjourney founder) framing technological progress as a recursive tool-building process culminating in simulation of mind. Reflects the kind of big-picture AI-progress commentary Nathan tracks.
Noam Brown @polynoamial
Noam Brown (OpenAI researcher) argues LLM eval saturation claims are overstated, listing tasks LLMs still fail at; follow-up tweet on grad students contributing via better evals. Relevant to Nathan's interest in AI capability evaluation and benchmarking.
ai evaluationbenchmarksllm capabilitiesnoam brownai progress