forecasting

Peter Wildeford @peterwildeford

2026-08-23 — saved image

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.

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Saif M. Khan @KhanSaifM · 4h
My extrapolation from data in Anthropic's August 2026 risk report suggests fully automated AI R&D sometime between Dec. 2026 to Feb. 2027 (or with pessimistic assumptions, more like 2028).

In the risk report, Anthropic provides data on Anthropic ECI (AECI) score growth per year as well as AECI and CoBench scores for several recent Claude models. (CoBench is an Anthropic-internal automated AI R&D benchmark.) It also asserts "that a model which was truly capable of fully substituting for Anthropic research staff would be able to score at least 85% on [CoBench.]"

Using these datapoints, see two Claude-generated charts: 1) CoBench vs. AECI scores, which suggests that a 168 AECI score gets you full AI R&D automation (or 182 AECI with a more pessimistic fit); and 2) projecting when Claude models achieve AECI scores of 168 and 182.

This is a quite naive extrapolation and I have no idea if Anthropic would endorse the result!

[Embedded chart image]
ANTHROPIC RISK REPORT · AUGUST 2026
When could AI fully automate AI R&D?
Anthropic now publishes an internal capability index (AECI) and a bar for full researcher substitution (CoBench ≥ 85%). Chaining the two: the bar sits at AECI ≈ 168 — on trend, an internal frontier model gets there around Dec 2026 - Feb 2027 (Sep 2026 if progress is accelerating; 2028 on the pessimistic mapping). Anthropic's own words: plausibly "a major concern in the next 6-12 months."
Chart 1: "Anthropic ECI over time — extrapolated to the full-substitution band"
Legend: Anthropic frontier, Off-frontier, Mythos-class, Model 2 (unreleased), Projection fan 7.5-28.8/yr, Substitution band
Y-axis: Anthropic ECI, 120-180+. X-axis: 2024-2028 (by quarter/year labels: Jul, 2025, Jul, 2026, Jul, 2027, Jul, 2028)
Annotations: "CoBench 85% → AECI ≈ 168.4"; "Sep 2026 - if accelerating"; "trend continuation - 13.5/yr"; points labeled Claude 3 Opus (~2024, ECI ~125), pre-Mythos frontier 13.5 AECI/yr (report's fit), Opus 4.6 (~2026, ECI ~150), Mythos Preview, Mythos 5, Model 2 (~Dec 2026), Apr 2027 - slow
Below, second chart begins: "CoBench score vs AECI — where the fit crosses the 85% bar" Legend: Opus-class, Mythos-class, Logistic fit (5 models), Mythos-only fit, 85% = "could fully substitute for research staff" [chart cut off at 100%]

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Leo Gao @nabla_theta
[Attached meme image, described below]
1:12 AM · Aug 10, 2026 · 33K Views
15 replies, 106 reposts, 2K likes, 215 bookmarks
Relevant / View quotes

Harold @HaroldsAltAct · 1h
OP will get <100 views on the thread and no comments because his arguments are so rock-solid that there's nothing left to critique.

Nobody will even know the post existed until after the events unfold and a vagueposter reposts the thread on xitter.

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Dean W. Ball @deanwball

2026-08-07 — saved image

Dean W. Ball @deanwball · 28m
if you can master the meta-skill of figuring out what problems in arbitrary domains are computationally tractable, you will have the opportunity, for at least a year two, and maybe longer, to be a kind of meta-genius. you will not know the answer to anything, or even how to find it, but you'll have refined heuristics for the right questions to ask about everything to make meaningful progress along the margin. this is probably the skill to have optimized for in the last three years, though I readily admit I don't know how long it will remain a human advantage. it is for now though.

aiskillsforecasting

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...

ai progressreasoning modelsforecasting

Dwarkesh Patel @dwarkesh_sp

2026-08-07 — saved image

Dwarkesh Patel @dwarkesh_sp . 13h
Full debate from 2021:

lesswrong.com/s/n945eovrA3oD...

[Christiano][22:57]
right now I think hardware R&D is on the order of $100B/year, AI R&D is more like $10B/year, I guess I'm betting on something more like trillions? (limited from going higher because of accounting problems and not that much smart money)

I don't think steel production is going up at that point

plausibly going down since you are redirecting manufacturing capacity into making more computers. But probably just staying static while all of the new capacity is going into computers, since cannibalizing existing infrastructure is much more expensive

the original point was: you aren't pulling AlphaZero shit any more, you are competing with an industry that has invested trillions in cumulative R&D

[Yudkowsky][23:00]
is this in hopes of future profit, or because current profits are already in the trillions?

[Christiano][23:01]
largely in hopes of future profit / reinvested AI outputs (that have high market cap), but also revenues are probably in the trillions?

[Yudkowsky][23:02]
this all sure does sound "pretty darn prohibited" on my model, but I'd hope there'd be something earlier than that we could bet on. what does your Prophecy prohibit happening before that sub-prophesied day?

[2 replies, 2 reposts, 71 likes, 11K views]

Dwarkesh Patel @dwarkesh_sp . 13h
In 2016 (before transformers) Paul wrote,

"It's plausible that a large neural network can replicate "fast" human cognition, and that by coupling it to simple computational mechanisms—short and long-term memory, attention, etc.—we could obtain a human-level computational architecture. It's plausible that a variant of RL can train this architecture to actually implement human-level cognition."

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Dwarkesh Patel @dwarkesh_sp

2026-08-07 — saved image

@dwarkesh_sp
.@paulfchristiano has such an crazy good prediction record.

These are some quotes from way back in 2021 during a debate he was having with Eliezer about takeoff speeds:

"So like, I think we are going to have crappy coding assistants, and then slightly less crappy coding assistants, and so on. And they will be improving the speed of coding very significantly before the end times.

"[Before ASI, we'll have] hundreds of billions of dollars of spending at google on automating AI R&D... massive scaleups in semiconductor manufacturing, bidding up prices of inputs crazily... massive speculative rises in AI company valuations financing a significant fraction of GWP into AI R&D (+hardware R&D, +building new clusters) .... largely in hopes of future profit / reinvested AI outputs (that have high market cap), but also revenues are probably in the trillions?"

Honestly, it's pretty scary that people like Paul, who predicted the shape of our currently world so far in advance, think superintelligences taking over and disempowering humanity is eminently plausible.

All this to say, you should consider working with him.

[below, partially visible card: "How to help" / "ARC is hiring an automation lead and a chief of staff:" - cut off]

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FleetingBits @fleetingbits

2026-08-07 — quoting @dwarkesh_sp / ARC article — saved image

FleetingBits @fleetingbits · 2h
the problem with a lot of these predictions is that they were pretty much the same as what you would expect with ai being a transformative technology until you get to loss of control or whatever

like all of paul christiano's predictions here are just consistent with ai being a thing and not being priced in; and i think people that were closer to gpt-3 / ml at the time were better placed to see this

the risk narrative is pretty much separate from the capabilities / economic effect (similar issue with ai 2027)

[quoted tweet]
Dwarkesh Patel @dwarkesh_sp · 13h
.@paulfchristiano has such an crazy good prediction record.

These are some quotes from way back in 2021 during a debate he was having with Eliezer abo...

[quoted article/webpage, white background, serif font]
How to help

ARC is hiring an automation lead and a chief of staff:

• Automation lead. LLMs can increasingly automate ARC's technical work. Right now that means researchers using extensive AI assistance, but we want to hire an engineer and project lead to build better tooling, systematize our AI use, secure model and compute access, and generally make sure we are automating ourselves as quickly as possible. Apply here.
• Chief of staff. We are hiring a chief of staff to work closely with me to manage everything other than research direction as we scale: running our hiring processes, managing our operations lead, building out the non-research parts of the organization, and handling a long tail of tasks that would otherwise fall to me (like communication, funding, and project management). Apply here. [cut off]

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forecaster 3                                    < 3 / 12 >

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| I am resubmitting the forecast data after correcting the schema error, maintaining my estimate that [cut off]

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bayes @bayeslord

2026-08-01 — saved image

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…

ai takeoffai timelinesphysical world automationforecasting

Andrew Curran reposted
Christian Szegedy @ChrSzegedy · 9h
IMO, the next steps are:

Within 1 year: strictly better than human AI in all problem-solving aspects of math.

Within 2 years: producing mathematics becomes so cheap, AI will produce mathematical theories for all kinds of applied domains on the fly at need.

Mathematics becomes the true infrastructure of all of engineering and applied sciences (including AI, biology, optimization, cybersecurity, etc) where approaching things theoretically was too expensive to be practical.

[quoted tweet]
FleetingBits @fleetingbits · 9h
some thoughts on ai and math

1) a new openai model has solved 10 important problems in mathematics; and, the cost of solving them would be ~$2,000 at current api …

ai and mathopenaiforecastingchristian szegedyai capabilities

Bayesian @Bayesian0_0 · 2h
Fun fact: Across 44 benchmarks that have a "Human baseline", the human baseline BECI (a personal replication of the Epoch Capabilities Index) comes out at 166.7, which projections say will be beat by AI models around october 2026!

ai capabilitiesbenchmarksbeciepoch capabilities indexforecastingtwitter

Rand @rand_longevity · 1h
do you think we are in the singularity?
90 replies, 12 reposts, 140 likes, 5.6K views

Larry Panozzo @LarryPanozzo · 29m
Hm I was about to say Yes but... No, because many can confidently predict the main dynamics of what's going to happen next year

Next year is the singularity because beyond that year, even while living out the year, we won't be able to predict much of anything, like not even 10%, of what ASI will bring thereafter

singularityasiforecastingtwitter

David Krueger 🇺🇸✊ reposted
Peter Barnett @peterbarnett_ · Jul 31
4 months ago Anthropic had a model gain internet access and hack another company.
Chinese AIs are 6-9 months behind.
Chinese developers generally care way less about safety/guardrails than US developers.
In 2-5 months we will likely see rogue Chinese AIs hacking other companies. This might include US companies, causing an international incident.

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AI Notkilleveryoneism... @AISafetyMemes

2026-07-28

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prinz @deredleritt3r

2026-07-08 — quoting @_simonsmith

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bayes @bayeslord

2026-06-19 — saved image

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.

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Miles Brundage @Miles_Brundage

2026-05-24

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Nathan @NathanpmYoung

2026-02-02

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Noam Brown @polynoamial

2026-01-27

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Peter Wildeford @peterwildeford

2026-01-26

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prinz @deredleritt3r

2026-01-19 — quoting @deepfates

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Nathan @NathanpmYoung

2025-09-12 — quoting/crediting @foudy_joseph

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Stefan Schubert @StefanFSchubert
Be alert to uses of "by default" or "business-as-usual" in the context of projections of future trends.

While they literally mean "in the absence of new policy", they often carry the association that they're likely (cf "usual").

But in fact, they're often highly unlikely.
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Stefan Schubert @StefanFSchubert · Jul 2
It's often very unlikely that we'll fail to introduce new policies.

But the phrases "by default" and "business-as-usual" - and the way people use them - often hide that.

That contributes to the flawed perception that we'll sleepwalk into disaster.
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