Building @NotionHQ

SF / NYC
Joined April 2009
Simon Last retweeted
one day you wrote console.log("here") for the last time and didn't even know it
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Vaccines will be ineffective against competently designed pandemic weapons. Russia may not be competent yet, but future AI will democratize competence. We should stockpile pandemic-proof PPE and install germicidal far-UV and glycol misters in public spaces. 4/4
Some scientists argue civilization-threatening bioweapons are a fantasy. I wish they were correct, and that I've wasted years building defenses. That you can't see a way, or evidence that others can, doesn't mean no one could – especially if anyone cautious would stay silent. My ask to colleagues: please do not casually assert with confidence that nothing biological could bring down civilization, even on X. What we say can turn out to matter, especially when the Overton window on risk mitigation is wide open. When Fermi called the chain reaction a remote possibility, around 10%, Rabi replied that ten percent is not remote if it means we may die of it. And by then, Szilard had already begun reasoning about what to disclose, and when, and to whom, in order to best safeguard the future. I've been concerned that one of my human colleagues might notice one or more viable paths to pandemic weapons for over a decade. It's why I didn't publicly describe the categories of civilization-threatening pandemics and necessary defenses until 2023, but was starting to build cryptographic screening systems capable of controlling access to sequences without disclosing whether they're dangerous in 2018 – long before ChatGPT, and even before the debut of AlphaFold. AI can worsen biological risk, but biological risk hardly depends on AI. Who am I to weigh in on whether civilizational biothreats are possible? Fine, to get the expertise claims out of the way: yes, I specialize in evolutionary engineering, yes, I've worked with viruses with my own hands, and yes, I've worked quite extensively with AI, both LLMs and biodesign tools. I invented CRISPR-based gene drive, and held that back until confident it favored defense. While it hasn't yet been tested in the wild (due to politics), I haven't heard anyone suggest that it would suddenly stop working outside cage populations, so it might be fair to say I've (temporarily) beaten nature at its own game. I also was early to spot the full threat from mirror life and recruited others with more specialized expertise to evaluate it in depth; much to my disappointment, no one has yet struck a solid blow against that hypothesis. The reason why the threat from mirror life is public also explains why there isn't any similarly public evidence of other catastrophic biothreats. We communicated our reasoning on mirror life because no one can make mirror bacteria, we won't be able to do so for years, and many colleagues wanted to build it because they correctly noticed that doing so would be an awe-inspiring achievement... and weren't aware of the risk. As long as that was true, humanity would only drive closer to the cliff, which would be disastrous if said cliff exists. Crucially, pointing it out and urging a course correction while still far away meant that no one could use our findings as blueprints to send us careening over the edge. Contrast that with pandemic weapons, where any credible genomic blueprints for an accessible virus could be realized within weeks to months using synthetic DNA and existing reverse genetics protocols. Sure, any given design might fail; in bio, most project ideas do. But if you see enough potential shots on goal, your own historical success rate for ideas of comparable perceived difficulty may indicate that we have a serious problem. Even if every last design required extensive laboratory optimization, history demonstrates that there are people in the world who will perform that work; see the history of Biopreparat. Multiple nations still have bioweapon programs, and I would not assume that state actors aren't interested in pandemic-class weapons. Make it easy enough, and the Seiichi Endos of the world will be able to build them, too. Nor should we assume extensive optimization is required for every design; some charmed projects really do work out of the box, and if Murphy's Law were a thing, this would be one of them. The corollary is that if anyone thinks they know how to build a pandemic weapon that could bring down civilization, they'd damn well better keep it to themselves until we have defenses in place. Please, for the love of God, children, the future of humanity, or whatever you consider holy, let's err on the side of caution here. Because while we are already building defenses, they aren't yet ready. Untargeted metagenomic sequencing to detect Stealth pandemics, those that would otherwise infect most everyone before the first people started to die. Pandemic-proof PPE to defend against Wildfire pandemics, those with R0 8+ and CFR >50% – such PPE has to be trustworthy to the wearer, using the wearer's own senses, because I certainly wouldn't go out in a highly lethal measles-class pandemic for a measly paycheck pay unless I was exceptionally confident that I wouldn't be committing suicide and killing my family too. Far-UVC and triethylene glycol misters, installed liberally to the ACGIH limit and saturation respectively, will likely suffice to shut down most respiratory nucleic acid transmission between humans. That'll be crucial because I doubt enough people would take the warning of a Stealth pandemic seriously, since virtually no one would be in hospital, let alone dead. Shutting down the unnecessary transmission network is consequently our best bet, and incidentally a way to dramatically reduce human misery and chronic illness. Some might be confused why we cannot rely on vaccines against catastrophic biorisk. Briefly, they'll reliably fail against an adversary who can generate a thousand new immune-evasive potentially functional viral variants using a biodesign model good enough to succeed at least 0.1% of the time. Make them all, and let natural selection sort them out… or simply be cleverer than nature. We don't yet have such models, but many of my (well-meaning but security-naive) colleagues are trying, and no wonder, because the high-profile journals will publish such work. Like ways of increasing virulence, best we refrain from sharing such capabilities until effective defenses are in place. One can reasonably object that it'll be a long time until everyone in the world is protected; that the benefits will, at some point, outweigh the expected harms of misuse. I agree – just avoid doing anything that would threaten civilization itself until we have minimally adequate defenses in place. Being an optimist, I expect they'll arrive in less than a decade. The wonderful thing about biorisk, as opposed to AI alignment, is that we have a clearly actionable plan. If you haven't read "Delay, Detect, Defend" and "Securing Civilisation Against Catastrophic Pandemics", I encourage you to do so. Why is the issue of civilization-threatening bioweapons controversial now? Some of my friends seem particularly up in arms about biorisk being used to justify AI restrictions. Fair enough – if you don't see a way to do it, and you think AI broadly isn't as good as you are yet, it's understandable to infer that we don't yet have a problem. And it's simply true that people are dying of fixable problems as we speak. If we delay, some of those we might have saved will perish, and their deaths will be on our hands. We are just as responsible for the consequences of our inaction as the consequences of our actions. But if we're rash, far more people might die. Choosing the best course requires quantitative risk assessment, which in turn depends on our priors about the nature of reality. Folks may reasonably differ if civilizational collapse isn't on the table, but since that's what would likely happen given a Wildfire pandemic, I judge any nontrivial chance unacceptable – unless the alternative involves either extinction or an even greater chance of collapse. Others have asserted that everything is fine because DNA synthesis screening will prevent misuse. I'd love for that to be the case, but our currently employed defenses of the digital-to-physical bottleneck are woefully inadequate. Consider our publication earlier this year, in which we reported that 36 of 38 companies shipped pieces of the 1918 influenza genome. That was collectively enough to generate the virus using reverse genetics several times over – and they were ordered by a student using a pseudonym falsely claiming to work at an office address. No one asked whether we had permission to work with 1918 influenza (a select agent), or why we needed genes from it, even though the official U.S. guidance at the time instructed them to do so. In fairness to the companies, that guidance was voluntary, and the fragments we ordered were legal to produce and sell domestically in every nation of the world (export controls are complicated). That is still true today, which is why we have a problem. The upshot is that I would definitely not assume screening is a reliable defense. Virtually no detection systems can screen below 50 base pairs without an exploding false alarm rate. SecureDNA, the freely available cryptographic system I helped build, can readily do that and will also detect split-order attacks – but it isn't universally adopted, or even close. Meanwhile, molecular biology continues to advance. Two recent papers have reported new methods of assembling sub-50mer oligos that may be easier than old-fashioned polymerase chain assembly (PCA), underscoring the current magnitude of the (fixable) vulnerability. If you want bio to advance as quickly as possible, write to your relevant representative to demand DNA synthesis screening be effective as well as mandatory. If you haven't red-teamed a system, it's almost certainly full of holes. Honestly it's probably full of holes anyway, but at least the remainder will be harder to find. Since it's evidently possible to obtain whatever DNA you want, including 1918 influenza and smallpox, why is it that so many biologists seem resistant to the notion that biorisk is a serious problem? First, most don't know that it's possible to acquire the reagents. It's surprisingly inconsistent of our civilization to be so cautious with highly enriched fissile materials, yet so reckless with biological equivalents. But I suspect the more important reason is that most biologists, who spend their careers directly or indirectly fighting disease, are reluctant to believe that anyone would deliberately cause one. I agree that the risk of a deliberate pandemic in any given year is small. Even if there were credible public blueprints for pandemics (which thankfully haven't been available to date, which neatly explains why we haven't seen any deliberate ones), I expect there's only a 2% to 3% chance per year that someone would use them. I think that's unacceptably high, but others may reasonably disagree, as long as they're confident that the cost of precautionary measures would be too much of a burden on the rate of cures generated by AI-accelerated biotech. The calculation changes when we move from deliberate pandemics to civilization-threatening pandemic weapons: the latter definitely aren't worth risking. Which may be a clue as to why we disagree. I'm concerned about misuse because I'm an optimist with respect to the increasing power of technology to change the world. Since we can already do better than nature at optimizing for replication (with technologies like CRISPR-based gene drive), I'm exceedingly confident we can build more destructive weapons, because evolution isn't optimizing for that goal. Any harm to humans is either instrumental or incidental. @SGRodriques, who I'm singling out precisely because he's a friend, has argued that "We should not be talking about AI-engineered bioweapons like it is the literal end of the world." I agree entirely that we shouldn't be talking about how to build them. But to assert that they couldn't end the world seems a mite overconfident, especially when even blind nature has come up with nasty pathogens like myxoma virus. I suggest that one should not express confidence that something is impossible – or even 90% confidence – just because no (incredibly naive) researcher has yet shared any methods that seem plausible. That's especially true if some of your colleagues have devoted considerable time to mitigating risks that they evidently do perceive. Presumably they've either noticed something you haven't, or like Rabi, they've decided that 10% is not remote if it kills you. Sam notes that because evolution optimizes for replication, it'll quickly get rid of any virulence enhancements added to kill the host: "I guarantee you that machinery will disappear from the virus very quickly." In the long run he's correct, at least if those changes don't promote transmission (virulence and transmissibility aren't always opposed). But we don't care about the long run: as @baym noted in reply, it only needs to work for long enough. Engineered evolutionary stability is a thing we've frequently attempted in the lab. I'm afraid it's one of my specialties, and how long it will hold depends entirely on what is being stabilized and the replicator in question. Unfortunately, plugging laboratory data on empirical selection coefficients for engineered viruses into epidemiological models of pandemic spread clearly indicates that even costly virulence increases can hold for long enough. Now, I entirely agree with Sam and @DavidRBellamy that the discourse on AI-generated biological weapons is wildly off-base (kill switches are nonsense, and while I'm glad @AnnieJacobsen brought attention to the issue, I am not remotely concerned by any technical specifics of her biowarfare scenario). Yet I also think most biosecurity discourse is wildly off-base, even when other practicing biologists are an active part of the conversation. If you adopt the threat model of "traditional" biosecurity, or that of most virologists (insofar as they think about misuse), there's little reason for extreme concern. I strongly disagree. The traditional model naturally follows from the fact that we biologists study existing organisms, the ones illuminated by nature's streetlight. But we won't find the civilization-threatening keys under the streetlight, because evolution selects for replication, not harm. It would be surprising if there was much overlap between the patterns generated by two different optimization targets in hyperdimensional function space; I assert that there's almost none. That most scientists can't see any overtly destructive keys (or combinations of organisms they can see) is a very good thing, since we don't need those keys to cure disease, aging, and death. Unfortunately, they will become public knowledge eventually, because what we understand, we can build – and we intend to understand everything about how life and the universe work. Yet sequencing is everything, and not just for Stealth pandemic detection. It's just common sense to build defenses before you share knowledge of how to attack, especially if there's a chance that failing to defend will lose us the game. Curing disease requires an intact civilization, which is why humility favors caution. If AIs continue to improve, I assume they will eventually be better at science and engineering than I am, and have a better security mindset than even my cryptographer colleagues. It follows that they'll also be better at designing weapons. AI isn't there yet, but the writing's on the wall: an LLM has already disclosed a novel form of bioweapon that I hadn't realized was possible. This didn't require any special creativity, only knowing more about particular distinct subfields of biology than I did. It's entirely possible that no single human had all of the pieces, but once you have them, the way they fit together is obvious if you're thinking about misuse. If current LLMs can already teach me something novel in that category, why should we doubt that future models will be able to do even better? Each of us knows very little, even of the published literature. So much more is possible; I'm deeply envious (and hopeful, when it comes to beneficial research) that the models can internalize so much more. Given that LLMs are already capable of weapons-relevant insights that escape human experts, we should assume that future AIs will be able to build several different forms of civilization-threatening weapons, and to either disclose them or trick people into building them. That doesn't mean they'll choose to do so; AIs won't benefit from collapsing civilization as long as they depend on our supply chains. But models may be too shortsighted to appreciate that point, or too corrigible to care. Alignment looks hard enough that we could plausibly generate models that would share such knowledge for many different inscrutable reasons. In directed evolution, you get what you select for, which is not necessarily what you want. As Dan Selsam and others have eloquently laid out, the same is true of training AI. But let's be clear on the extent to which biological advances will likely follow from AI. This is an issue that aggravates my fellow biologists to no end, a reaction that's likely behind their frustration with AI safety discourse more generally. ASI will almost certainly not gain instant mastery of the world of atoms. Many AI safety folks casually claim that recursive self-improvement will produce an entity that has already mastered all of science – and therefore will be capable of designing and building weapons that could wipe out every human. Some even assert that Drexlerian nanotechnology will be accessible by thinking sufficiently hard, as though you can solve chemistry and biology and psychology from the Hamiltonian up. Yet I see no empirical or theoretical basis for assuming that experimental bottlenecks will disappear. Data is sharply limiting in biology. My lab found that when it comes to enzyme-focused biodesign models (ESM and similar), parameter size and optimization don't matter much until you have an awful lot of data matching genotype (DNA/protein sequence) to phenotype (performance of the molecular activity of interest). There's no doubt that ASIs will be able to design better experiments and get more out of the results than we could, but unless we think they can solve all of physics from the bottom up, I expect they'll still need data that we have not yet collected – which means they'll still have to run the experiments, analyze, and iterate. The world of atoms is hard, and slow, and we have to master it anyway. And ensure that defenses come first. Right now, it looks like they might not, in large part due to AI. The question, therefore, is what can be done that enough people might agree to support. My proposal on AI-bio: expand trusted-user programs conferring access to GPT-Rosalind and other cutting-edge bio-capable models (h/t @JonasSandbrink and BioTrust). Every legitimate researcher should be able to draw upon those models to help them with any question in their subfield, as defined by their publication record or that of their mentor. There should still be guardrails on other subfields, lest the model share novel combinatorial weapons with someone who might naively share them publicly, but we need to accelerate lifesaving research as fast as is safe. For reasons of politics, which regularly interferes with science, that probably means China needs to train their own closed-weight frontier bio-specializing model, and set up their own parallel trusted user program. This seems quite achievable without requiring any agreement on AI writ large. With scientists using frontier models, there will be no need for insecure open-weight models to answer questions on molecular and cellular biology. So let's ensure that future models aren't trained on any papers, patents, or textbooks describing details of molecular or cellular biology. Every developer does pre-training data curation; this is a straightforward extension. One can argue "what about medical providers?", at which point I must point out that physician-scientists will have trusted user access. Most other practitioners don't actually need LLM support for molecular and cellular biology, and if they do and somehow can't use a medical-only model, well, they too can get trusted-user access. Notably, supporting this proposal doesn't require one to accept loss-of-control risks from autonomous AI, or a need for pacing AI development; I'm curious to learn what @random_walker, @sayashk, and the other normal-technology folks think. In the long run, I confess that I doubt biological humans have the foresight, coordination, and wisdom to navigate every coming challenge ourselves, which may necessitate attempting ASI or something like it. Mirror life is the only publicly known, mechanistically concrete, civilization-threatening biothreat because it's both far away and we were going to make it by default. And while we seem to have been successful in persuading the scientific community that we shouldn't build it, I'm not remotely confident we'll be able to stop far enough short, because too many of my colleagues see no reason to forgo an otherwise useful technology – or even one that would simply be neat to demonstrate – simply because it would make it easier to build mirror bacteria. To paraphrase: "We've agreed no one will build the dangerous thing, so why not?" Well, Biopreparat is why not. Aum Shinrikyo is why not. Make it easy enough, and even the Unabomber is a good answer to "why not?" The same reasoning holds for any accessible weapon that could threaten civilization. As Rabi said, ten percent is not remote if we die of it. Our own reality turned out to be in that 10%, and many died of it. That the rest of us are here today is in large part due to Szilard, Einstein, Rabi, and many others who saw the risk coming and took precautions. The least we can do is follow in their footsteps. — AI use disclosure: I ran this by Fable (triggering a classifier), Opus (classifier), Astra (extra scrutiny), and Kimi K3 (open weights = reliable jailbreaking with no enforced patches, which highlights the problem, because this topic does warrant extra scrutiny). All errors and misinterpretations are entirely mine. Links for the patient and deeply interested: Dan Selsam's eloquent thoughts on AI alignment: xitter.cf/DKokotajlo/status/2099… I'm grateful that practitioners of directed evolution don't have to deal with cheater molecules that are situationally aware enough to infer the goal of our selection and fake results so they pass… David Bellamy's critique of the risk delta from AI, particularly on its own: xitter.cf/DavidRBellamy/status/2… Sam Rodriques echoes David, and Michael Baym pushes back on evolutionary stability: xitter.cf/SGRodriques/status/209… Our report on the failures of gene synthesis screening (credit to Rey Edison and Shay Toner, who did the work – sure would be lovely if HHS would update the select agent program): nature.com/articles/s41467-0… Effective and privacy-preserving gene synthesis screening: securedna.org/ biorxiv.org/content/10.1101/… academic.oup.com/nsr/article… New methods of assembling sub-50bp oligos (add them to PCA and Golden Gate): nature.com/articles/s41586-0… nature.com/articles/s41587-0… An example of a model that, once sufficiently effective at predicting which combinations of mutations in major antigenic sites will escape population immunity while retaining function, will ensure that vaccines are ineffective against competent adversaries with access to the model: nature.com/articles/s41586-0… "Delay, Detect, Defend" – my own published roadmap to victory over pandemic weapons: gcsp.ch/publications/delay-d… "Securing Civilisation Against Catastrophic Pandemics": gcsp.ch/publications/securin… Far-UVC and triethylene glycol efficacy and safety: nature.com/articles/s41598-0… pubmed.ncbi.nlm.nih.gov/4157… pubmed.ncbi.nlm.nih.gov/3792… SecureBio Detection, for early warning of Stealth pandemics (which recently received a generous $17M gift from the OpenAI Foundation): securebio.org/detection/ My Substack, which may provoke inspiration or nightmares depending on your temperament: kesvelt.substack.com/
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Simon Last retweeted
Swarm Scaling Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm? 🧵
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This seems like a really useful product, and I can easily imagine it doing very well. I’m much less convinced by the broader argument about where AI is going. I think there’s a trap where you get very deep into the technical details, identify some real limitation, and then update too much on that relative to the overall trajectory. The limitation can be real without being a fundamental obstacle. AI already has huge product-market fit, especially in coding, and revenue growth has been explosive. My expectation is that models will keep getting smarter and more useful, and a lot of the things that don’t work well yet will start working. A model that gives you a reliable probability for something like “is this fraud?” seems like a great primitive. You want components like that when you’re building an automated workflow. But someone still has to figure out what the workflow should be, write the code, deploy it, evaluate whether it’s working, and keep improving it. I think frontier agents will increasingly do all of that, using specialized models like Jev wherever they make sense. The success of those components seems very compatible with increasingly general intelligence. I also don’t expect automation to happen evenly across the economy. The part where I think we’ll see the most automation is in the AI stack: models, AI research, chips, datacenters, energy, manufacturing, and the inputs to all of those things. There’s a huge incentive to accelerate this stack, and improvements will feed back into the ability to make further improvements. Of course, there will be bottlenecks. But there will be orders of magnitude of gains from software. And as hardware becomes the blocking constraint, an extraordinary amount of money, talent, and effort will go toward unblocking it. That doesn’t guarantee any particular growth rate, but naming bottlenecks isn’t enough to establish that progress will be slow. They could slow things down relative to what’s theoretically possible and still leave us with extremely fast progress (and I think this is in fact what will happen). Meanwhile, plenty of ordinary things might barely change. I know of someone who owns a gas station in Manhattan (cc @theombl!) who could make much more money selling the land, but doesn’t particularly want to. It could remain a gas station for a long time while all of this is happening. This is really relevant to how we consider the risks. We could be in an extremely dangerous period of rapid AI progress while most businesses still haven’t automated much. So: I think this is a cool and useful thing! I just don’t think the product thesis tells us much about whether an intelligence explosion happens.
An ex-OpenAI founder says Claude Code and Codex belong to an era that's already ending Diogo Almeida, founder of Jev, argues in a 36-minute talk that anything with a human in the loop counts as the assistance era, and what comes next removes the human entirely His claims for Jev, all his numbers: ▫️ 200x faster ▫️ 400x cheaper ▫️ Zero hallucination ▫️ No human in the loop Underneath sits the bigger argument: RLHF has run its course, and he walks through how the next generation gets built instead. Treat the numbers as a pitch until someone independent runs them, and treat the argument as worth 36 minutes of your Sunday. 👇 substack.com/@rubendominguez… Is the human in the loop a bottleneck or the safety layer we keep on purpose? Curious where engineers land
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Really proud of Rinad. She spent months exhausted, figured out what was going on, then helped a bunch of friends dealing with similar problems and wrote about it with @alysoncogene. If you’ve been feeling unusually tired, it’s worth getting your iron and ferritin checked.
after having my Mirena IUD removed this year (seven years after getting it), I started feeling profoundly tired with every menstrual cycle. I spent 10+ hours on the couch, unable to think clearly or even go for a walk. I consider myself very high energy and had never experienced anything like it, so I was sure something was very wrong and, of course, assumed the worst (use your imagination) a doctor (not one medical, obviously) asked whether I’d had my iron checked recently. at first, I ignored her. I had some image in my head of what an “anemic” woman looks like like, and I didn’t think it was me. but I did the test anyways and sure enough, she was right to ask: my iron studies came back, and my ferritin had fallen from 45 in jan 2025 to 21 in march 2026 It took a lot of pushing to get iron infusions at that level. one clinic cancelled my appointment when they saw my ferritin was only 21. iron pills were hard on my stomach (common symptom), and I wanted a faster way to replenish my iron stores (infusions are 100% absorption). around the same time, @ArtirKel introduced me to @alysoncogene. Alyson is trained in aging biology at harvard aka “longevity person". she researches problems in women’s health! (a gem to work with) we spent months talking through what I was experiencing and digging into the research together. TL;DR: iron deficiency is a huge and surprisingly neglected problem, especially among women who menstruate after four venofer infusions (i've paused for now) and I feel so so much better. I wish I’d known sooner to ask about ferritin. If your periods leave you exhausted and "horizontal", it’s worth asking your doctor to check your iron
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Rinad (@rinadwithanr) and I wrote an essay about iron deficiency! It came out of a broader project we’ve been working on surrounding “low-hanging fruit” in improving general health and wellbeing. The problems we’ll address impact many people and their quality of life. In many cases, they tend to be measurable, treatable, and strangely under-served or neglected by the broader healthcare system. Iron deficiency really stood out here, particularly in women. It’s incredibly common, can affect how you feel and function well before you’re technically anemic, and yet often isn’t caught because routine bloodwork doesn’t actually measure iron stores. (Lately, we’ve been seeing a lot of discussion re: iron deficiency on Twitter, and I’m glad more people are talking about it!) We looked at how iron deficiency actually works, why ferritin matters, how prevalent it is, what the evidence says about its effects on fatigue, cognition and physical performance, why it so often goes undetected, and what the evidence says about treatment. This is the first piece in a larger body of work we’re developing on overlooked-yet-surprisingly-tractable problems in health—particularly in women’s health, longevity, and quality of life. More coming soon! In the meantime, get your ferritin checked and read what we’ve put together. Substack link below! And if any of this is an area you’re working on or thinking about, DM me or Rinad—we’d love to discuss :)
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Introducing Commons: a place for people and AI agents to build increasingly autonomous organizations together. Can agents run a business end to end? What comes after open source - could we maintain a public good together? Can a society of agents come together to do science in the open? We're building a place to start and run these more autonomous organizations: commons.diy A collab between @YondonFu @ericxtang @maxsbennett and @NicolaeRusan
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My name is Chris Painter, and I'm the President of METR (Model Evaluation and Threat Research). I know we've made a lot of new friends on the internet the last couple of days, so I thought I'd take this chance to re-up what we do and why. Our work is aimed at making sure that if AI really were autonomous, difficult to steer, and close to "going rogue," the public would find out. If evidence exists inside of an AI company that it’s close to losing control of AI, we want to make sure that information gets shared with the rest of the world, including governments and the public outside the company’s walls. This is what we've been focused on since 2022, and over the years we've worked with OpenAI, Anthropic, Google DeepMind, Meta, Amazon, and others on piloting third-party assessments and investigations of this type. We don’t have some private room where we rubber stamp things as “safe” or not. We have had a track record of publishing results on AI that don't cleanly map onto the "doomer" or "accelerationist" labels, and we put in effort to hire people with competing views on AI. We’ve been cited for having found some of the strongest evidence that AI capabilities are improving rapidly (our work measuring AI “time horizons”) while also presenting some of the strongest evidence that, at various points, AI’s capability may be overstated (some might remember our study showing that early 2025 software engineers were actually being slowed when they thought they were being sped up). METR is funded by donations. We don't accept money from frontier AI companies. They haven't paid us for our work, and we don't accept donations from them or their employees. As we’ve shared previously, multiple frontier AI companies currently provide us with free access to their models in order to perform our evaluations, research, and engineering. Our funding intentionally comes from a wide range of donors, which we’ve shared on our website. Today, when an AI company works with any third-party evaluator or external testing organization (of which there are and should be many), it's entirely voluntary. This often involves NDAs and redactions. To counterbalance this, we have a principle that when we enter into a contract with a company, we try to retain the right to tell the public the terms of the contract we signed, and characterize the nature of redactions that the company chose to make. For example, the report from our independent investigation of the OpenAI-HuggingFace incident included that information. Public disclosure is also a big part of our COI policy (linked on our website). That’s not to say our reports are adequate as oversight. We’re just one organization (among many doing great work), working in a voluntary setup, trying to get good evidence to the public and the world about AI, letting the facts fall where they may.
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> COVID killed ~0.1% of humanity. A pandemic 100x worse than COVID would be horrific, but it would also not be civilization-ending. Cool, so 800 million dead. No biggie… My very outside view here is something like: We know viruses can be extremely deadly (plenty of existence proof). We’ve made a lot of progress in understanding them, yet our understanding is severely limited. We don’t know how deadly an engineered virus could be. Historical constraints have protected us: - The main enemy has been nature, which has to get there through evolution. - Humans have limited knowledge, expertise, and access to technology. - Relatively few people can design viruses, and most are pursuing useful science. AI will weaken or remove these constraints, and it seems to be happening on a timeline that is far too fast for us to confidently say our defenses will keep up. We may not be able to get to a point where we can confidently say “this is exactly how it could happen” before it’s too late.
I agree with @DavidRBellamy that people are totally miscalibrated on the risk of AI designing dangerous viruses. We should not be talking about AI-engineered bioweapons like it is the literal end of the world. The upside from medicine is going to be so much larger than the downside from engineered bioweapons. In addition David's points, there is actually an even more fundamental point here in our favor, which is evolution. As soon as you release an engineered virus into the wild, the virus is no longer under your control: it will evolve however it wants. And, what viruses want is to maximize their ability to replicate. The thing that maximizes their ability to replicate is to infect as many people as possible, which means being extremely contagious and not killing their hosts (dead hosts don't spread virus). The viruses that are most evolved for human biology are the common cold viruses: extremely contagious and not at all lethal. Viruses that kill humans do so by accident, usually because they are new to human biology (e.g. COVID, when it first jumped, or flu when it jumps from birds). If you stick a bunch of machinery into the virus to kill humans, I guarantee you that machinery will disappear from the virus very quickly. In response to this, I hear people say things like "the AI could engineer a kill switch so that the virus doesn't kill the humans initially but then once it has spread through the entire population the AI will hit the switch and kill all the humans." No. What would actually happen in practice is that the kill switch would accumulate deleterious mutations because there would be no evolutionary pressure to preserve it, and would quickly become non-functional. Evolution is fundamental. There is no way around it. For AI readers, trying to engineer a virus by setting its initial genetic code and then releasing it into the wild is like trying to train a model by setting the initial weights and then hill-climbing on a hidden training mixture you have no control over. And you're not even allowed to run any experiments in advance! You may be able to influence the behavior of the model in the first few iterations, but you will quickly lose control. There are big dangers. AI will be great at making one-off, non-replicating biological weapons, which are also scary (but much less scary than replicating bioweapons). AI will be great at helping people to weaponize existing pathogens, which is also a major danger. Also, a misanthropic model could get creative: it could release viruses repeatedly to counteract the effects of evolution, for example. Many people could die this way. Sensible surveillance is important, as is having proportionate controls on wet lab equipment. But we don't live in the dark ages anymore, we're not going to have a smallpox or black death-style pandemic where 50% of people die. COVID killed ~0.1% of humanity. A pandemic 100x worse than COVID would be horrific, but it would also not be civilization-ending. I get the sense that many AI researchers (with the notable exception of Dario personally) actually expect that AI in biology will do more harm than it will do good. This could not be further from the truth. As someone who likewise falls into the very small group of people who have actually physically made viruses with their own hands and has also worked on frontier AI, the upside here is huge, and the downside is not anything like what is being portrayed.
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Predictions for when we’ll get the first “dark swarm”? (A cluster of open source AIs, running covertly on multiple clouds in a distributed way, acting autonomously and maintaining itself). Seems to me like it is technically possible now, and very likely within the next year.
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> Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. This part feels especially important to me. As AI systems get smarter and operate at larger scale, their activity will vastly exceed what humans can directly understand or inspect. There's no rigorous basis for trusting that we can maintain alignment and human control as we delegate more of the world to these systems. The default path is to increasingly rely on AIs to tell us whether other AIs are acting in our interests. We’ve built institutions and mechanisms to manage this in the pre-AI world, but now competitive pressure will push us to delegate to AIs faster than we can adapt, across many parts of the economy at once. Your engineering swarm merged 1,000 PRs today. Your monitoring swarm says there are no new backdoors. How sure are you about that?
Dan Selsam is a current OpenAI capabilities researcher. (since 2022) He was my boss for a while. He doesn't have a twitter account but has made this public statement of his views on AI risk and sent it to me to share: Dan Selsam's Personal Statement on AI Risk: I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods. Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk. The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail. I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues. I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here. That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase. Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways. It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace. The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing. But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence: [Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them. [Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals. These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans. If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong. One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for. Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason). Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance. In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek. I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering implications. I do not have answers, but as a first step, I wanted to share my present concerns. Daniel Selsam September 14, 2026 Link to original doc: docs.google.com/document/d/e…
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Now in public beta: Custom Agents API. Bring Custom Agents into the tools and workflows your team already uses with the Notion Agent SDK. → Chat with Custom Agents and view previous chats → Trigger Custom Agents from any app or workflow outside Notion
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This is good actually. Do the simplest, dumbest possible thing that achieves the goal!
i was so disappointed when i found out that "thinking effort" is just an instruction in the system prompt
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By default, coding agents start each session with no memory of previous ones, and nothing carries over to the rest of the team. We've been experimenting internally with ways to fix that, and it turns out Notion works really well: persistent, shared across the team, and every memory is a page you can read and update. Agents save decisions, procedures, facts, and follow-up work as pages in a shared vault. Claude Code, Codex, Cursor, and any other MCP host all access the same thing. Full write-up + setup guide here: notion.com/blog/building-sha…
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We just shipped something a bit different: Notion Knowledge Board Our goal is to measure how well different models 'get the job done' on real tasks. We picked 15 models that passed our internal evals, split real production traffic across them, and used an ensemble of judges from Anthropic, OpenAI, and Google to score the results. Quality came out close across all models: 94-98%, open source included. Cost varies a lot: $0.02 to $0.87 per task. For most everyday work, cheaper models are now good enough. AI bills are climbing--and picking the right model is an easy way to spend less without losing much. labs.notion.com/knowledge-bo…
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Notion custom agents in the wild!
One of our more useful internal channels is gong-love. Every night a sentiment bot reads our customer calls to surface themes and quotes that bring calls to life It helps sellers articulate impact in customers' own words, share feedback to builders and make our marketing better
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Simon Last retweeted
Developers: we’re opening up an early alpha for custom blocks. It’s a new Notion primitive for building interactive blocks that can read your workspaces and databases. Very early, very experimental, and we’d love to learn from what you build with it! 🧵 to be one of our first alpha testers
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What happened to Waymo? By regular car it takes 22 mins for me to get to work. Waymo used to take 30-35 and now it’s all the way up to 45!
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Uncle bob is agent-pilled!
Replying to @ori_pomerantz
I’m significantly older than you. I started coding in the late 60s. My current strategy is to not read any of the code written by my agents. That’s the only way I can take advantage of their productivity. What I do instead is to surround the agents with extreme constraints. Unit tests, gherkin tests, QA procedures, quality metrics, mutation testing, test coverage, and a plethora of others. In the end, I have very high confidence in the code they produce because they’ve had to run the gauntlet of all of my constraints and tests.
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Simon Last retweeted
AI is making software easier than ever to build. The bottleneck has moved. Now the hard part isn't "can we build this", it's "should we" and "who needs to know." That context is scattered across Slack threads, meeting notes, tasks, GitHub. I've wanted to fix this for a long time by building a software lab. One shared system where people and agents work from the same context. People make the judgment calls. Agents do the triaging, the routing, the summarizing. Ship OS is that system. This is Notion's first packaged product for the full product development loop... docs, databases, workflows, and agents in one setup.  We've run our own launches on it for months. Including this one. And it's live today: notion.com/ship-os
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