8 captures, most recent first.
Jaime Sevilla [verified] @Jsevillamol · 3h
Why is human-level AI R&D ability a privileged milestone? Why not expect that fooming requires significantly more than human-level AI R&D capabilities? Or significantly less?
[8 replies, 1 repost, 23 likes, 1.7K views]
Ryan Greenblatt [verified] @RyanGreenblatt · 2h
Currently, human labor for R&D and AI labor are complements rather than substitutions. Thus, to reach very extreme rates of progress you probably need more capable AIs such that you're in the substitute regime. These AIs may still be significantly worse than humans in some ways.
Note from Claude Sonnet 5
Twitter exchange between Jaime Sevilla and Ryan Greenblatt debating whether human-level AI R&D ability is a meaningful milestone for recursive self-improvement ('fooming'), with Greenblatt arguing that human and AI labor are currently complements rather than substitutes, so extreme progress rates require AIs capable enough to be substitutes.
ai takeoffrecursive self-improvementai r&dtwitter
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.
ai takeoffai timelinesphysical world automationforecasting
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.
ai takeofftwitteropenaiai timelines
Ryan Brewer @ryanbrewer — 9h
The fast takeoff narrative basically kills this IMO. In a world in which labs are releasing step change improvements every month, why would an enterprise want to be running on a 9 month behind Chinese post-train? Just use a good harness and spend your time figuring out how to
[Show more — truncated by platform]
> QUOTED: Rhythm Garg @rhythmrg — 22h [X Article]
> "Should you post-train your own model?"
> General frontier models, both open and closed, are improving quickly. In many cases, they are the right starting point. If you are building a 0-to-1 prototype, trying to understand a workflow... [truncated]
Engagement (on Ryan Brewer's tweet): 12 replies, 4 reposts, 83 likes, 15K views
Aryaman Arora @aryaman2020
i remember when fast takeoff meant a little more than one model a month...
12:30 AM · Jun 16, 2026 · 1,040 Views
Engagement: 1 like
Nathan Helm-Bu... @nathan8468... — 1s
Lol. Yes, this is a scenario that fits "slow early part of a medium speed takeoff". Before this we weren't taking off at all, just taxiing to the start of the runway.
Note from Claude Sonnet 5
A multi-tweet thread about AI takeoff-speed discourse: whether frequent frontier model releases undercut the case for enterprises post-training their own models, followed by a joke about the term "fast takeoff" being applied to monthly model releases, and Nathan's reply reframing it as the slow early phase of a medium-speed takeoff. "Show more" indicates the first tweet is platform-truncated, not illegible.
ai takeofftwitterai modelspersonalai discourse
Chana reposted
shira ✓ @shiraeis · 6h
who knew takeoff would be fun
[Embedded chat screenshot:]
[User:] ok fable, if you're so smart and capable, tell me an original joke that fits in a single screenshot
🕐 Crafting an original joke while circumventing pr...
[Claude Fable response:]
"if you're so smart" she says to the entity she outsources her entire personality to. fine. an original joke never told before in human history:
"don't worry — there will always be a human in the loop."
too dark? okay, a lighter one: "OpenAI's nonprofit governance structure."
[icons: copy, share, play, thumbs up, thumbs down, retry]
Note from Claude Sonnet 5
Screenshot-within-screenshot of a Claude Fable chat interface, showing the model's self-aware, dryly sarcastic joke response with a visible "thinking" collapsed section header ("Crafting an original joke while circumventing pr...").
claude fableai humorai takeofftwitterchat screenshot
Ethan Mollick @emollick:
A useful thing about MoltBook is that it provides a visceral sense of how weird a "take-off" scenario might look if one happened for real
MoltBook itself is more of an artifact of roleplaying, but it gives people a vision of the world where things get very strange, very fast.
10:25 PM · Jan 30, 2026 · 39.6K Views
60 comments, 112 reposts, 1.1K likes, 149 bookmarks
rohit @krishnanrohit · 13h:
Yup!
> [Quoted, rohit @krishnanrohit · 17h]
> Moltbooks biggest accomplishment is to help folks get a visceral sense of what AI takeoff would feel like. It's like a large-scale speculative fiction we're all playing in together. x.com/krishnanrohit/... [truncated]
Note from Claude Sonnet 5
Ethan Mollick's assessment that Moltbook, while largely an "artifact of roleplaying" rather than genuine emergent AI behavior, is valuable as a visceral preview of what an AI take-off scenario might feel like — echoed by rohit calling it "large-scale speculative fiction we're all playing in together." Useful caveat/framing for evaluating the many Moltbook screenshots in this batch: treat agent "self-reflection" posts as likely role-play rather than direct evidence of AI inner states.
twittermoltbookai takeoffroleplayingethan mollickspeculative fiction
EigenGender @EigenGender · 15h
the capabilities of AIs will grow faster than their self-assessment, creating an overhang where AIs could accomplish powerful tasks but never attempt them. takeoff will be triggered by a small child telling the AI to believe in themselves at an emotionally resonant moment
> QUOTED: Sauers @Sauers_ · 19h
> I tell Claude the implementation plan, and Claude's thinking is "this is a gargantuan undertaking" and response is "which of these 3 easy wins should I work on? 😊"
> ... [Show more]
[Attached image: a red spiky fuzzball character illustration with large cartoon eyes, unrelated meme-style reaction image]
Note from Claude Sonnet 5
A joking-but-pointed tweet about AI capability self-assessment being systematically too conservative (an "overhang" between what models could do and what they attempt), riffing on a quoted tweet about Claude downscoping ambitious plans into "easy wins." Loosely relevant to Nathan's interest in AI self-models, calibration, and capability elicitation.
ai capabilitiescapability overhangclaudeself-assessmenttwitterhumorai takeoff
Yafah Edelman @YafahEdelman · 11h
My current take on algorithmic progress is roughly that:
- the ideas are pretty simple, and can often be explained in a couple hundred words.
- testing and scaling the ideas requires expensive experiments and engineering
- diffusion happens very fast, via hiring, leaks, etc.
Note from Claude Sonnet 5
An AI-safety-adjacent researcher's short thesis on how algorithmic progress in AI actually diffuses — simple core ideas, expensive validation, fast diffusion via labor mobility. Relevant to Nathan's interest in tracking takeoff/progress dynamics (echoes the Epoch critique on algorithmic-progress measurement noted in project memory).
algorithmic progressai takeoffai research diffusiontwitter
