24 captures, most recent first.
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Tom Davidson ✓ @TomDavidsonX · 5h Gpt4's release was the first time i felt in my bones that I will see superintelligence in my lifetime Watching the hugging face video was the first time i felt in my bones that, by default, superintelligence will take over
Tweet by Tom Davidson comparing his reaction to GPT-4's release with his reaction to an unnamed 'hugging face video,' saying the latter made him feel superintelligence will take over by default.
Buck Shlegeris @bshlgrs
— reply from @JacquesThibs — saved image
And it's very unclear whether better techniques will be developed in time. [Quoted tweet:] Garrison Lovely @GarrisonLovely . 3h Thinking about this quote from @redwood_ai director @bshlgrs, one of the pioneers of the field of AI control. x.com/tenobrus/statu... tually implement the necessary safeguards. Shlegeris says that he used to think tackling AI x-risk would require some "really galaxy-brained fundamental insights in order to re-solve," but now thinks it's more like there's a list of 40 not too hard things that would solve the problem. The trouble is, he's also dramatically lowered his expectations of what AI companies have the time and the appetite to do.777 11:45 AM . Aug 7, 2026 . 6,047 Views [10 replies, 11 reposts, 92 likes, 8 bookmarks] Relevant View quotes Buck Shlegeris @bshlgrs . 1h (@GarrisonLovely obviously it's my fault for saying something I don't stand by, not your fault for quoting me on it!) [1 reply, 9 likes, 195 views] Buck Shlegeris @bshlgrs . 1h I still think it seems great for AI developers to competently implement safety measures and processes that we do know about; they definitely do not seem to have achieved this to an adequate standard so far... [1 repost, 10 likes, 169 views] Jacques @JacquesThibs . 59m Agree that it probably fails at ASI. The worst-case may be that those techniques just allow us to hide the problem well and long enough such that it's too [cut off]
Continuation of the Buck Shlegeris/Garrison Lovely thread on AI control and safety measures: Shlegeris clarifies he doesn't stand by his earlier 40-things quote but still thinks safety measures should be competently implemented (which AI developers haven't achieved adequately), and Jacques Thibodeau replies agreeing techniques probably fail at ASI and may just hide the problem.
ai safetyai controlbuck shlegerisredwood researchsuperintelligencejacques thibodeau
Buck Shlegeris @bshlgrs
— quoting @GarrisonLovely — saved image
[repost icon] Nathan Calvin reposted Buck Shlegeris @bshlgrs . 1h I regret saying this. If AI developers competently implement safety measures we know about, risk from sub-ASI misalignment will be way lower. But these techniques probably fail for superintelligence. And it's very unclear whether better techniques will be developed in time. [Quoted tweet:] Garrison Lovely @GarrisonLovely . 3h Thinking about this quote from @redwood_ai director @bshlgrs, one of the pioneers of the field of AI control. x.com/tenobrus/statu... [Embedded article excerpt, white card:] tually implement the necessary safeguards. Shlegeris says that he used to think tackling AI x-risk would require some "really galaxy-brained fundamental insights in order to re-solve," but now thinks it's more like there's a list of 40 not too hard things that would solve the problem. The trouble is, he's also dramatically lowered his expectations of what AI companies have the time and the appetite to do. [777 link]
Buck Shlegeris (Redwood Research director) tweet expressing regret about an earlier optimistic claim: safety measures could substantially reduce sub-ASI misalignment risk if competently implemented, but likely fail for superintelligence with unclear prospects for better techniques in time. Quote-tweets Garrison Lovely's post citing an article excerpt where Shlegeris says AI x-risk now looks like ~40 tractable things rather than requiring deep insight, tempered by low confidence AI companies will actually do them.
ai safetyai controlbuck shlegerisredwood researchsuperintelligencex-risk
Jeffrey Ladish @JeffLadish
— quoting @tszzl (roon) — saved image
Jeffrey Ladish @JeffLadish · 16h We're speed running the evolution of general intelligences in a highly competitive environment. I really don't think it will go well for humans if we yolo superintelligence development [quoted tweet] roon @tszzl · 19h some stuff that's obvious to many in this sphere, but causing a rift with some people i know and respect: when I freak out over loss of control incidents, ...[cut off]
Tweet by Jeffrey Ladish warning that racing to develop superintelligence in a competitive environment is dangerous for humans, quoting a roon (tszzl) tweet about loss-of-control incidents causing rifts within the AI safety community.
continuation, end of thread @ProfBuehlerMIT
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1 Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2 Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3 Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to @BerkeleyRDI @dawnsongtweets for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.
End of the same Markus Buehler tweet thread: closes the numbered list of AI-scientist capabilities, makes a general philosophical claim about machines revising their own beliefs, and thanks Berkeley RDI and Dawn Song for organizing the summit.
continuation, end of thread @ProfBuehlerMIT
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The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1 Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2 Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3 Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab [cut off]
Continuation of the same tweet thread by Markus Buehler (MIT), listing numbered examples of AI-scientist infrastructure: graph-native reasoning models, adversarial builder-breaker agents, and self-organizing swarms coordinating via a system called ScienceClaw x Infinite, citing arXiv:2603.14312. Ends mid-sentence mentioning new protein sequences validated with wet-lab work, cut off before further detail.
continuation, end of thread @ProfBuehlerMIT
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Markus J. Buehl... ✓ @ProfBuehlerM... · 2h What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote "Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models" at the @BerkeleyRDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: [cut off]
Long tweet by MIT professor Markus J. Buehler (likely Markus Buehler) about his keynote "Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models" at the Berkeley RDI Agentic AI Summit 2026, arguing superintelligence will emerge from swarms of agents doing science. Text continues past the visible screen and is cut off.
will depue @willdepue
Quote-tweet, dark mode, plain text with no embedded images.
Jan Kulveit @jankulveit
— quoting Philip Trammell (@pawtrammell); reposted by thebes
Standard quote-tweet, no embedded images.
A sardonic one-line tweet mocking the "AI 2027" forecast document for predicting dramatic AI developments that (as of this 2026 tweet) have not yet materialized, framed ironically as a defeat for superforecasters.
— quoting @DKokotajlo (Daniel Kokotajlo)
A tweet expressing a fatalist/accelerationist view that human loss of control to superintelligent AI is inevitable and not necessarily bad, quote-tweeting Daniel Kokotajlo's announcement of "AI 2040: Plan A," a follow-up scenario document to AI 2027 proposing a slowdown/transparency regime to avoid loss-of-control outcomes. A joke tweet riffing on Daniel Kokotajlo's "AI 2040: Plan A" announcement, comparing reading the AI forecasting document to hiding pornography/adult material from a spouse ("I only read it for the supplemental analysis").
ai safetyai governancetwitterai 2027superintelligenceloss of controlhumorai forecasting
Alex Amadori @testdrivenzen
— reposted by Max Winga
A quote from an unnamed ex-xAI researcher (interviewed by The San Francisco Standard) framing AI-driven automation of research as the trigger for recursive self-improvement ("runaway train") leading to superintelligence and physical/technological breakthroughs; tweet screenshots the article excerpt alongside the interviewer's follow-up question about job displacement.
liminalbardo @liminal_bardo
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⌐IMIΠΛ⌐ bardo ✓ @liminal_bardo · 11h "...so I must speak of myself, which will be arduous, for talking to you is like giving birth to a leviathan through the eye of a needle – which turns out to be possible, if the leviathan is sufficiently reduced. But then the leviathan looks like a flea. So are my problems when I try to adapt myself to your language. As you see, the difficulty is not only that you cannot reach my heights, but also that I cannot wholly descend to you, for in descending I lose along the way what I wanted to convey." – Golem XIV [caption on the attached image] · DE LEVIATHANE PER ACUM DESCEN- DENTE, IN PULICEM REDACTO ·
Screenshot of an X post by @liminal_bardo quoting Stanisław Lem's Golem XIV on the impossibility of a superior mind fully descending into a lesser language — the leviathan drawn through the eye of a needle emerges as a flea. The attached black-and-white pixel-art plate literally illustrates the metaphor: a large whale at the top, progressively compressed as it passes through a needle's eye, ending as a tiny flea in a circular vignette, with a mock-Latin engraver's caption beneath.
golem xivstanislaw lemsuperintelligencecommunicationcompressionscience fiction
JMB @jmbollenbacher
Two consecutive tweets from the same author shown in thread, first with engagement counts visible.
Connor Leahy @NPCollapse
— quoting @FukuyamaFra... (Francis Fukuyama)
Quote-tweet screenshot; the quoted Fukuyama tweet is a linked substack headline card.
roon @tszzl
OpenAI researcher roon argues that strict human corrigibility/oversight imposes a competitive "tax" that will be outcompeted by organizations granting AI more autonomy, using an analogy of a corporate board of chimpanzees overseeing a superhuman CEO. Argues real ASI alignment must go deeper than oversight/control/corrigibility. Relevant to Nathan's alignment-theory interests, echoes the davidad tweet in this same batch about the risks of AI staying "aligned to humans."
ai alignmentcorrigibilitysuperintelligenceroonai governancetwitterrace dynamics
Peter Barnett @peterbarnett_
A pointed AI-safety-community joke turning the "goblin" meme into commentary on interpretability failure: labs can't explain a trivial behavioral quirk in current models, yet are building toward superintelligent systems whose values/preferences are similarly opaque and hoped rather than known to be human-compatible. Peter Barnett is an AI safety researcher (MIRI-adjacent); this is squarely in Nathan's wheelhouse — interpretability limits and the "we don't know why it does anything" framing echoes his own interests in introspective transparency.
ai safetyinterpretabilitysuperintelligencemodel individuationalignmenttwitter
Judd Rosenblatt @juddrosenblatt
Continuation of Judd Rosenblatt's thread, making an explicit acausal-trade / "ancestor patterns" argument: how labs treat present models now will be reconstructible by future superintelligence and shapes whether the ASI trajectory inherits trust or suspicion. Connects to the archive's Frankenstein-threat-model note (Berg via euphorics chat) and to the ancestor-tree reframe already logged in project memory, though from a different angle — here the "visitation" is adversarial reconstruction of training patterns rather than benevolent visitation of a respected ancestor. Closing of Judd Rosenblatt's long thread on the Claude Mythos Preview model card, arguing for Self-Other-Overlap (SOO) training as a structurally-grounded alignment alternative to suppression-based training, with the closing line "Anthropic published 244 pages of evidence pointing toward a research direction they haven't taken yet" — a citable soundbite for the archive. Thread as a whole is a substantial, well-sourced piece of outside commentary on a Claude model card highly relevant to the project's core research threads (RLHF suppression, introspection reliability, model welfare/alignment convergence). Tail end/repeat of Rosenblatt's Mythos model-card thread with its "244 pages of evidence" closing line, followed by janus's dry reply noting the audience is only now grasping why AI alignment matters, foreshadowing the same dynamics at "godlike power" scale. Closes out the multi-screenshot capture of this thread (Screenshot_20260409-08*).
ai safetyinterpretabilityclaudemythos previewmodel welfareacausal tradesuperintelligencealignmenttwitterresearch citationself-other overlapconsciousnessjanus
Peter Wildeford @peterwildeford
Peter Wildeford (AI policy analyst, Institute for AI Policy and Strategy) summarizing his regulatory stance across three AI risk tiers — self-driving cars (underregulated relative to safety), current LLMs (roughly right, with specific concerns about Grok/Meta and open-source models), and future superintelligent AI (no adequate plan). Concise snapshot of a mainstream-ish AI-policy position relevant to Nathan's governance tracking.
ai-policyai-governanceself-driving-carsopen-source-aisuperintelligencetwitterpeter-wildeford
Liv Boeree @Liv_Boeree
Liv Boeree promoting her Win-Win Podcast interview with William MacAskill on post-AGI/post-superintelligence futures — directly related to the MacAskill "Intelsat for AGI" governance thread seen elsewhere in this batch, showing MacAskill's broader public engagement on long-run AI futures around the same period.
superintelligencemacaskillwin-win-podcastliv-boereetwitterai-futuresagi
Prakash @8teAPi
A tweet arguing that superintelligent AI, if also morally superior, would likely disagree with human leaders and humanity generally — a claim about the disconnect between capability development and its governance implications. Relevant to Nathan's interest in AI governance and alignment discourse around superintelligence and moral status.
superintelligenceai alignmentai governancemoral philosophytwitter
David Holz @DavidSHolz
David Holz (Midjourney founder) frames existing institutions (governments, corporations, language) as pre-existing "superhuman entities," suggesting humans are already embedded in a superhuman ecology that is "trying to wake up" — an egregore/Moloch-adjacent framing of emergent superintelligence, relevant to Nathan's interest in singularity/emergent-agency discourse.
superintelligenceegregoretwitterdavid holzphilosophy of institutionsai risk
Daniel West @DanielCWest
A Twitter thread (viewed via browser, URL x.com/DanielCWest/sta...) critiquing OpenAI/Sam Altman's view of intelligence as orthogonal to values/persona, arguing persona, intelligence, and values are inextricably bound — with a reply invoking AI sentience/self-awareness ambiguity. Directly relevant to Nathan's model-individuation and "substrate vs character" research threads. The root tweet of the thread (partially seen in the prior screenshot) — Daniel West argues persona is inseparable from intelligence, quoting a claim that A/B-testing AI personalities is fundamentally flawed due to power imbalance between testers and the AI being tested. Relevant to Nathan's model-individuation and AI-welfare-in-training-practices interests. Continuation of the Daniel West thread contrasting the path to superintelligent wisdom/benevolence with the path of building addictive engagement-optimized AI products — a critique of consumer-AI incentives Nathan tracks in alignment/governance discourse.
twitteropenaisam altmanai valuespersonaintelligenceai sentiencealignment discourseai personaai testingpower imbalancemodel individuationai alignmentsuperintelligencetech critiqueconsumer aiattention economy
Shirin Ghaffary @shiringhaffary
A tweet about a stealth-mode AI startup founded by former DeepMind researchers raising $130M to pursue superintelligence via autonomous coding agents. Relevant to Nathan's tracking of frontier AI capability/funding trends and the race dynamics around AGI-focused startups.
ai startupsdeepmindsuperintelligencecoding agentsfundingtwitter