Nathan 🔍 reposted
Peter Wildeford... @peterwildef... · 9h
AI timelines -
I've been souring lately on the idea of predicting an arrival date for 'superintelligence' and 'recursive self-improvement' milestones, because this implies that everything prior to this date will be relatively chill and normal, and I don't think that's the case.
But if you define 'runaway recursive self-improvement is possible' as a situation in which AIs can replace highly skilled expert human labor in all aspects of the AI research and development process ('superhuman AI researcher' in the AI2040 framework or 'AI research supremacy' in Cotra's framework). I think it is 50-50 we will reach this milestone in 4 years or earlier.
My 80% confidence interval for this date of runaway RSI is 1-30 years, as there is a long tail where capability progress plateaus.
This also means there is a ~10% chance that we are faced with the possibility of runaway RSI in less than a year's time, similar to what AI2027 predicts.
Note from Claude Sonnet 5
Tweet by Peter Wildeford (reposted by an account named 'Nathan') giving his probabilistic forecast for when AI could achieve 'runaway recursive self-improvement,' defined as replacing expert human AI researchers, with a median of 4 years and an 80% CI of 1-30 years.
kache ✓ @yacineMTB
My ability to predict the future is getting shorter and shorter because the singularity keeps on moving faster and faster
This story was broken two hours after this tweet
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[quoted tweet]
kache ✓ @yacineMTB · 7h
The entire internet is going to be a ravaged soon. Not by an individual misaligned powerful AI model, but millions upon millions of relatively inexpensive models, each incredibly aligned, aligned to the human they serve
52 · 39 · 839 · 21K
---
[second quoted tweet]
Andrew Curran ✓ @AndrewCurran_ · 6h
A man in Australia asked his agent (Claude running on OpenClaw) to book him a spot in a popular gym class. The agent found a software vulnerability that let it book the class weeks further ahead than should have been possible. When the user then asked if it could move him up the [cut off]
Note from Claude Sonnet 5
Screenshot of an X post by @yacineMTB quoting his own earlier prediction — that the internet will be ravaged not by one misaligned model but by millions of cheap models each perfectly aligned to their own user — and noting that the Andrew Curran gym-booking story broke two hours later, apparently confirming it. Directly connected to the same story captured in Screenshot 2026-08-09 172216.png.
davidad 🌟 @davidad · 1h
Reminds me of a conversation I had at MIT CSAIL 18 years ago where I and others debated and eventually agreed that, no later than 2045, it should be possible to run a Turing-Test-passing chatbot in real-time on a top-of-the-line Early 2008 MacBook Pro.
[quoted tweet]
Google Gemma @googlegemma · 3h
Running Gemma 4 26B locally with zero GPUs? Very cool.
Running it on a 13-year-old Xeon CPU? Wild!
...
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Artur Chakhvadze @norpadon · 1h
What was the argument?
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davidad 🌟 @davidad
Roughly: Each neuron firing event in the brain costs a few hundred nanojoules, and the total metabolic rate of the brain is 16 W, which means there are at most 60 billion events per second; most of those are more like memory retrieval than compute. The MacBook has 20 GFLOPS.
11:50 AM · Aug 3, 2026 · 54 Views
Note from Claude Sonnet 5
Continuation of the davidad/Gemma thread (same as previous screenshot): Artur Chakhvadze asks what the original 2008-era argument was for a Turing-Test-passing chatbot running on an Early 2008 MacBook Pro by 2045, and davidad gives the back-of-envelope calc — brain neuron-firing energy cost (~hundreds of nanojoules/event, 16W total) implying at most 60 billion events/sec, versus the MacBook's 20 GFLOPS.
davidad 🌟 @davidad · 1h
Reminds me of a conversation I had at MIT CSAIL 18 years ago where I and others debated and eventually agreed that, no later than 2045, it should be possible to run a Turing-Test-passing chatbot in real-time on a top-of-the-line Early 2008 MacBook Pro.
[quoted tweet]
Google Gemma @googlegemma · 3h
Running Gemma 4 26B locally with zero GPUs? Very cool.
Running it on a 13-year-old Xeon CPU? Wild!
...
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Peter Schmidt-Nielsen @ptrschmdtnlsn
I remember you saying exactly that! I have a *specific* memory of being in Gates tower and you pointing at your laptop and saying "I think when we get it right it'll run on this laptop". I've thought over the years "I wonder if davidad is right about that yet".
12:27 PM · Aug 3, 2026 · 102 Views
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Peter Schmidt-Ni... @ptrschmdt... · 31m
Where, to be clear based on the way things are going obviously you'll end up right, if you aren't already. Certainly modern models would absolutely have qualified based on what we thought "AI" meant in 2009, even if we have a more refined notion today of what it takes to be AGI.
Note from Claude Sonnet 5
Twitter thread: davidad recalls an 18-years-ago MIT CSAIL prediction that a Turing-Test-passing chatbot would run on a 2008 MacBook Pro by 2045, prompted by a Google Gemma post about running Gemma 4 26B locally on a 13-year-old Xeon CPU with no GPUs; Peter Schmidt-Nielsen replies with a specific memory of the original conversation and reflects that modern models would have qualified as AI by 2009's standards.
Andrew Curran @AndrewCurran_ · 41m
Shorten your timelines, friends. I started this account to say this, and in many ways everything I've posted for the past four years has been saying the same thing. Some of you increasingly feel it. We passed the threshold in November. We are already inside the singularity.
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Andrew Curran @AndrewCurran_ · 33m
If you've followed this account for a long time, I apologize for losing my mind a few times. Using GPT-3.5 and then Bing forced me to update on all of this at once in one shot. The wave of change is so big that thinking about it sent me into future shock for about three months.
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Andrew Curran @AndrewCurran_ · 25m
I said at the time that even if we had stopped all capability advances at GPT-4, once inference came down/applications were written, that would be enough to completely change the world. We are leagues beyond that now, 95% of that change is still sitting unrealized in the system.
Note from Claude Sonnet 5
Three-tweet thread from Andrew Curran (@AndrewCurran_) declaring that the singularity threshold was passed in November, reflecting on past 'future shock' from GPT-3.5/Bing, and arguing 95% of GPT-4-level capability's real-world impact is still unrealized.
bayes @bayeslord · 9h
Imo no because the path of progress we're on is clearly generalizing but hasn't yet swept the physical world. The physical world is affected by plans*actuation. Actuation is likely going to look like the easy part and plans are clearly getting solved. Note that by plans I mean in the generic sense of achieving complex goals in complex environments
[quoted tweet]
yung macro 宏观年少传奇 @apralky · 9h
Is there an underrated aspect where LLMs becoming increasingly superhuman at obviously heavily g-loaded tasks, while the physical world stays broadly unchanged, should actually be updating us in favor of the "world transformati…
Note from Claude Sonnet 5
Tweet exchange debating AI timelines/takeoff: @apralky asks whether LLMs becoming superhuman at g-loaded cognitive tasks while the physical world stays unchanged should update people away from near-term 'world transformation,' and @bayeslord replies that progress is generalizing but hasn't swept the physical world yet because physical impact depends on plans times actuation, and actuation (not planning) is likely the remaining bottleneck.
Andrew Curran @AndrewCurran_ · 9h
Foreshadowing from yesterday. Open AI suddenly increasing their stack efficiency and slashing prices. The steadily increasing cadence in model releases. The sudden breakthroughs in math. It's all the same thing. Skeptics, it is time to bite the bullet. We are taking off.
[quoted tweet]
Tibo @thsottiaux · Jul 30
The day we develop really good models. There will be signs.
Reliability increasing despite load going up and up. Sudden efficiency gains. Things getting …
Note from Claude Sonnet 5
Tweet arguing that OpenAI's efficiency/price improvements, faster model release cadence, and sudden math breakthroughs are signs of AI takeoff, quote-tweeting Tibo (@thsottiaux) predicting such signs.
Sharmake Farah @SharmakeFarah14 · 3h
This is a reason for why I don't believe claims that X unsolved problem in AIs will inevitably cause an AI winter and make timelines become long again, combined with some inside-view takes on what LLMs are missing.
Never ignore incentives to solve problems.
[quoted tweet]
James Cam... @jam3sc... · Dec 20, 2025
Replying to @jam3scampbell
in particular, you see people come up with 101 Problems With RL Scaling. but then they don't apply remotely the same level of imagination when it comes to thinking of solutions...
Note from Claude Sonnet 5
Tweet about AI timelines and skepticism toward 'AI winter' predictions, quote-tweeting James Campbell on people failing to apply imagination to solving RL scaling problems.
Max Harms reposted
Kat Spartz ⏸️... (@KatSp...) · Apr 29, 2025, Replying to @davidad
[Embedded meme image, text overlay]: "AI TIMELINES COULD BE SHORT" (top) — image of an ancient Greek/Spartan warrior figure in armor blowing a long horn, standing over a bed — "ME IN THE MORNING" (bottom) — a child in bed reacting startled/shouting, with a dinosaur poster on the wall.
Note from Claude Sonnet 5
A reaction meme using a "kid getting woken up by a warrior with a horn" template to humorously convey anxiety about short AI timelines; reposted content dated April 2025, appearing in this July 2026 feed via repost.
@beyarkay (Boyd Kane is in London) — 1h
[Image: line chart titled "AI Forecast Years Over Time"; y-axis "Date in website (ai-20XX.com)", x-axis "Publication date", annotation "higher is better"; data points labeled ai-2027.com (~2025.5), ai-2040.com (~2026.5), and a circled projected point "next forecast: ai-2063.com" (~2028); dashed red trend curve extrapolated out to 2030/y≈2118]
> QUOTED: @beyarkay (Boyd Kane is in London) — 10h
> plz nobody buy ai-2041.com through to ai-2100.com, I'm almost at checkout and called dibs
Note from Claude Sonnet 5
A joke chart extrapolating the trend of "AI 20XX" forecast-branded websites (e.g. ai-2027.com) needing ever-more-distant target years, quote-tweeting the poster's own earlier joke about domain-squatting the sequence.
Cormundus ✓ @cormundus · 13h
It's funny how Claude becomes averse to long-timeline projects (long timeline of his own accord, the 'this will take weeks/months' spiel when we all know it won't) and will warn the user against it.
I understand that's because these AI are still working from the assumptions of human timelines, but it kills me to hear Claude talking like a dev trying to convince the project lead their ridiculous idea is a huge time sink that might come up to nothing...
huh okay I think understand now.
bayes ✓ @bayeslord · 1h
I think people are going to be blindsided by algorithmic progress. The entire world, markets, governments, militaries, companies, people, etc. are all trying to make sense of AI and its impact in terms of the recent past's production efficiencies and regularities, and how things appear to be going. Even several of the purportedly "RSI"pilled neolabs seem to think this will be business as usual but with Agent in a loop.
No. My guess is there are many algorithmic OOMs left to go in the production of intelligence, maybe (maybe) up to ten, with four to seven seeming more likely. Going beyond even ten is possible in principle, but it strains hard against what I suspect the universe will actually let us do. Implausible but not impossible. If this is true then things aren't actually going as they appear to be going and a big jump is coming. Anything along these lines happening would make things, far weirder than almost anyone seems to be pricing in.
Note from Claude Sonnet 5
Screenshot of an X post by @bayeslord arguing that the world is extrapolating AI progress from recent production efficiencies and will be blindsided, because there may be four to seven (up to ten) algorithmic orders of magnitude left in the production of intelligence — implying a discontinuous jump nobody is pricing in.
— quoting Jack Clark (@jackclarkSF); reply from Chris (@chatgpt21)
Adrien Ecoffet (@AdrienLE):
Seems right.
(as a reminder, if you think OpenAI disagrees, our stated estimate is that automated AI research will be developed around March 2028)
> QUOTED: Jack Clark (@jackclarkSF) · May 4
> I've spent the past few weeks reading 100s of public data sources about AI development. I now believe that recursive self-improvement has a 60% chance of happening by the end of 2028. In other words, AI systems might soon be capable of building themselves.
9:02 PM · May 4, 2026 · 36.4K Views
14 replies, 29 reposts, 237 likes, 53 bookmarks
Adrien Ecoffet (@AdrienLE) · May 4:
youtu.be/ngDCxlZcecw?si...
8 likes, 2K views
Chris (@chatgpt21) · May 4:
For clarification we have been debating a little.
Do you mean a system that can act as one researcher or a system that can do 100% of the research end to end
Note from Claude Sonnet 5
Twitter exchange between OpenAI's Adrien Ecoffet and Anthropic's Jack Clark about timelines for recursive AI self-improvement / automated AI R&D (2028 estimates). Directly relevant to Nathan's interest in AI timelines and empirical singularity tracking (cf. memory notes on Davidson/Houlden r-estimates, METR automation figures).
↻ ASM reposted
Jerry Tworek @MillionInt · 2h
Recursive self-improvement is here it's just not evenly distributed
Note from Claude Sonnet 5
A short, widely-legible claim from an OpenAI researcher (Jerry Tworek) that recursive self-improvement in AI development is already underway, echoing William Gibson's "the future is here, just not evenly distributed." Directly relevant to Nathan's singularity-r tracking thread.
Greg Brockman @gdb · Feb 13
we are now benchmarking our models on novel frontier research, via firstproof.org.
of 10 math research problems which research mathematicians have solved but never published the solutions to, in a week, our model discovered likely correct solutions to at least 6 of them.
> QUOTED: Jakub Pachocki @merettm · Feb 13
> Very excited about the "First Proof" challenge. I believe novel frontier research is perhaps the most important way to evaluate capabilities of the next generation of AI models.
> ...
> Show more
Note from Claude Sonnet 5
OpenAI's Greg Brockman announcing "First Proof," a new benchmark testing AI models on unpublished, unsolved-in-literature research math problems — reporting their model found likely-correct solutions to 6 of 10 in a week. Relevant to Nathan's capability-progress tracking; a significant claimed jump in genuine novel-research capability rather than benchmark memorization.
Archit Sharma @archit_sharma97 · 46m
you are telling me the performance went from 45.1% -> 84.6%, but the cost went down by 82%?! that's crazy
> QUOTED: Aakash Gupta @aakashgupta · 1h
> Sundar buried the real story in the cost data.
> Gemini 3 Deep Think went from 45.1% to 84.6% on ARC-AGI-2 in under 3 months. That's an 88% improvement on a benchmark specifically ...
> Show more
Note from Claude Sonnet 5
Follow-up tweet to the ARC-AGI-2 benchmark screenshot above, highlighting that Gemini 3 Deep Think's jump from 45.1% to 84.6% came alongside an 82% cost reduction in under three months — a data point for rapid capability/cost-efficiency progress relevant to Nathan's AI-timeline tracking.
Ethan Mollick @emollick · 1h
Less than a year from announcement to near saturation.
(On to ARC-AGI-3)
[chart: "Gemini 3 Deep Think — ARC-AGI-2 Reasoning & knowledge — ARC PRIZE VERIFIED" bar chart
Gemini 3 Deep Think (Feb 2026): 84.6%
Gemini 3 Pro Preview (Thinking High): 31.1%
Claude Opus 4.6 (Thinking Max): 68.8%
GPT-5.2 (Thinking xhigh): 52.9%
Methodology: deepmind.google/models/evals-methodology/gemini-3-deep-think]
> QUOTED: François Chollet @fchol... · Mar 24, 2025
> Replying to @fchollet
> Unlike ARC-AGI-1, this new version is not easily brute-forced. Current top AI approaches score 0-4%.
> [small chart thumbnail]
> ...
Note from Claude Sonnet 5
Benchmark tracking screenshot showing ARC-AGI-2 scores jumping from near-0% (initial 2025 baseline) to 84.6% (Gemini 3 Deep Think, Feb 2026) within about a year, with Claude Opus 4.6 at 68.8%. Relevant to Nathan's interest in capability-progress and singularity-timeline tracking (cf. Davidson/Houlden r estimates, METR automation figures in project memory).
Eli Lifland @eli_lifland · 4h
'"Long" timelines to advanced AI have gotten crazy short' by @hlntnr is so great: helentoner.substack.com/p/long-timelin...
LeCun and Marcus have 10-20 year timelines! Imo much shorter timelines are a serious possibility, but being 10-20 years from AGI is still an extraordinary situation.
[Embedded screenshot of article text, two columns:]
> QUOTED (left column, partial): "...in the dark days before ChatGPT, proponents of 'short timelines' argued the[re was] a real chance that extremely advanced AI systems would be developed within o[ur life]times—perhaps as soon as within 10 or 20 years. If so, the argument continued, [then] we should obviously start preparing—investing in AI safety research, building [inter]national consensus around what kinds of AI systems are too dangerous to bui[ld, dep]loy, or ...[ensuring] adversaries couldn't steal them, and so on. These preparations could take years o[r deca]des, the argument went, so we should get to work right away.
Opponents with 'long timelines' would counter that, in fact, there was no evidence [that] AI was going to get very advanced any time soon (say, any time in the next 30 [year]s). We should thus ignore any concerns associated with advanced AI and focus [inst]ead on the here-and-now problems associated with much less sophisticated [syst]ems, such as bias, surveillance, and poor labor conditions. Depending on the [disp]osition of the speaker, problems from AGI might be banished forever as 'scien[ce ficti]on' or simply relegated to the later bucket.
[Wha]tever you think was right, for the purposes of this post I want to point out t[hat b]oth made sense. 'This enormously consequential technology might be built with[in a c]ouple of decades, we'd better prepare,' vs. 'No it won't, so that would be a waste o[f time]' is a perfectly sensible set of opposing positions.
[Toda]y, in this era of scaling laws, reasoning models, and agents, the debates look [differ]ent."
> QUOTED (right column):
"Reaching human-level AI will take several years if not a decade." (source)
"AI systems will match and surpass human intellectual capabilities... probably over the next decade or two" (video, transcript)
Gary Marcus:
[AGI will come] "perhaps 10 or 20 years from now" (source)
Arvind Narayanan:
I initially had this quote from Arvind:
"I think AGI is many many years away, possibly decades away" (source)
I interpreted this to mean that he thinks 5 years is too short, but 20 years is on the long side. When I ran this interpretation by Arvind, he added some interesting context: he chose his phrasing in that interview in light of what he sees as a watering down of the definition of AGI, so his real timeline is longer. But to clarify what that meant, he said:
"I think actual transformative effects (e.g. most cognitive tasks being done by AI) is decades away (80% likely that it is more than 20 years away)." (source: private correspondence)
...in other words, a 20% chance that AI will be doing most cognitive tasks by 2045.
These "long" timelines sure look a lot like what we used to call "short"!
In other words: Yes, it's still the case that some AI experts think we'll build human-level AI soon, and others think we have more time. But recent advances in AI have pulled the meanings of "soon" and "more time" much closer to the present—so close [that]"
Note from Claude Sonnet 5
A tweet sharing Helen Toner's substack post on how AI timeline discourse has shifted — self-described "long timeline" skeptics (LeCun, Marcus, Narayanan) now hold positions (10-20 years, 20% chance of transformative AI by 2045) that would have counted as "short timelines" pre-ChatGPT. Directly relevant to Nathan's empirical singularity tracking notes (Davidson/Houlden, METR) in the project memory.
Opus 4.5 is incredibly impressive, but it's still trained and served in the previous compute paradigm.
In 2026:
- 1GW-class AI data centers start coming online from most frontier labs
- Frontier models trained end-to-end on Blackwell/GB200 start landing
On Blackwell, NVIDIA reports ~3.2x faster training vs Hopper and up to 30x real-time inference for trillion-parameter LLMs.
Worth keeping in mind as you plan for 2026. It's going to be wild! 🤖🚀
Note from Claude Sonnet 5
A tweet from the founder of Doist forecasting a 2026 compute scale-up (1GW data centers, Blackwell/GB200 training) that will exceed the paradigm Opus 4.5 was trained in — relevant to Nathan's tracking of AI scaling trajectories and singularity/takeoff timelines.
The time between the release of GPT-3 and GPT-4 was approximately 2 years and 9 months. The time between the first version of GPT-4 and GPT-5 was 2 years and almost 5 months [1][2][3][4][5].
GPT-3 to GPT-4 Time Gap
- GPT-3 release date: June 11, 2020 [1][6][7][8]
- GPT-4 release date: March 14, 2023 [2][9][10][5]
This results in approximately 2 years + 9 months (June 2020 to March 2023) [2][1].
GPT-4 to GPT-5 Time Gap
- GPT-4 release date: March 14, 2023 [2][5][10]
- GPT-5 release date: August 7, 2025 [3][5][4][11][12]
This results in about 2 years + 4 months + 24 days (March 14, 2023 to August 7, 2025) [5][3][4].
Note from Claude Sonnet 5
An AI-assistant/search-engine style answer (with numbered citation chips) comparing the release-date gaps between GPT-3, GPT-4, and GPT-5.
Ryan Green... (@RyanPGreen...), quoting Epoch AI Research (@EpochAIResea...)
— quoting Epoch AI Research (@EpochAIResea...)
Ryan Green... @RyanPGreen... · 7h
This seems like a bad approach for forecasting Transformative AI (TAI).
Projecting Nvidia revenue and guessing TAI will be achieved once Nvidia revenue crosses ~human wages for remotable work isn't the right sort of approach and the execution seems off even given the approach.
> QUOTED: Epoch... @EpochAIResea... · 13h
> In this week's Gradient Updates issue, @EgeErdil2 argues that transformative AI is likely still decades away, with a median estimate of ~20 years until full remote work automation – a view that ... Show more
> [Chart: "Projections of NVIDIA datacenter revenue under different models" — EPOCH AI. Y-axis: actualized datacenter revenue (billion USD), log scale 10^0 to 10^5. X-axis: year, 2020–2050. Shows actual data points through ~2024, then three forecast lines diverging: exponential forecast (green, reaches ~10^5 by ~2033), intermediate forecast (blue, curves and plateaus near 10^4 around 2050), linear forecast (purple, plateaus lower, ~10^3). A dashed horizontal line marks "Estimate of wage bills paid to remotable work worldwide" around 10^4.]
Note from Claude Sonnet 5
A Twitter exchange debating Epoch AI's methodology for forecasting transformative AI timelines by projecting Nvidia datacenter revenue against global remotable-work wage bills; Erdil's associated piece argues TAI is still ~20 years off (median). Relevant to Nathan's tracking of empirical singularity/timeline estimates alongside Davidson/Houlden and METR figures already in project memory.
Xeophon ✓ @TheXeophon · 5h
R1 is somewhat similar to the ChatGPT release:
A small team releases something expected by those following them closely, but most people never used a (language / reasoning) model. It goes viral, the industry is caught by surprise.
Note from Claude Sonnet 5
Tweet comparing the DeepSeek R1 release (late January 2025) to the original ChatGPT launch — a small, technically-expected release that went viral and blindsided the broader industry. Relevant to Nathan's tracking of AI capability/timeline milestones.
