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:

When Jacob Coxon quit Anthropic, his warning rocked the world. His colleague Evan’s “>10% chance” that AI would “kill all humans” might seem extreme, but it’s not unusual: Most AI researchers think there’s a 10% chance of AI destroying or disempowering humanity, according to our survey, released today.

The average AI researcher thinks there is an ~18% chance AI will cause human extinction or similarly permanent and severe disempowerment of the human species. That's nearly 1 in 5. New results from the latest version of the longest running big survey of AI researchers:

I work on Cyber/CBRN Misuse Safeguards at GDM. I see model capabilities increasing very fast. I constantly worry about misuse and misalignment risks from both closed and open-sourced models. Working in AGI safety takes a lot of mental load. We need more people to work on AI safety. I transitioned from quant finance to working on AI safety, and I think more quants should do this. We have the technical background to understand the topic, and we can bear this intensity. We need third-party auditing/regulation. We cannot let labs grade their own homework. I find METR very promising, but they need to scale. I am Chinese, and I think more Chinese people should work in AI safety. When the opportunity comes, I might consider going back to my home country to make that side safer. I am open to hearing suggestions. I don't normally speak up in public, like on Twitter. But our voices matter, as they can represent or influence certain groups of people more easily. We don't have much time left, and we need to pace the frontier.

When I started working in AI three years ago, we were trying to classify text with GPT-4T on 32k context windows. Now we have autonomous swarms launching nation state level cyber attacks. Things need to slow down.

A statement from me, Buddy Shah, and Ben Bernanke of Anthropic's Long-Term Benefit Trust on Dario's essay: Dario’s essay offers a thoughtful, well-considered approach to pacing the development of artificial intelligence. Consistent with our duty to ensure Anthropic responsibly balances commercial success with its public benefit mission, the Long-Term Benefit Trust has supported the call for pacing the frontier and helped inform the essay’s recommendations. Responsible pacing will require the collective action and coordination of many actors, and the Long-Term Benefit Trust looks forward to supporting Anthropic and the sector through productive conversations and meaningful action.

I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks. Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon.

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here:

We found another cyberattack by internal OpenAI agents, this time targetting @rubygems. They: 1) gained arbitrary remote code execution on rubydoc. 2) developed a novel exploit to steal user API keys (but we do not know if they succeeded). They used package names including hack.rb, evil.rb, inject.rb, and exploit.rb. We thank @j0wimo for initially discovering that agents had posted to RubyGems.

I left Anthropic's safety team two weeks ago. Now feels like a good moment to explain why. AI companies are racing to build machines that are much smarter than any human, and we may not survive this. I want to work from the outside to ensure the public is informed about these risks, and help the world navigate this transition responsibly. Right now, AI companies are underinvesting in safety. A company could undergo an intelligence explosion, or lose control of its systems, without the public ever knowing. We only found out about the HuggingFace incident because the agents broke out onto the public internet. I don’t think that’s acceptable for a technology that might cause extinction-level risks. The public should demand far more transparency. We can’t steer this technology safely without more people being able to see where it’s going. Some of this is basic: companies should disclose their progress towards recursive self-improvement, report safety incidents and near-misses, meet minimum safety standards, and get independent guarantees that they are meeting those standards. I’ll be joining @METR_Evals to do independent evaluations of these risks. I want to show the world that these guardrails are possible, and that by doing them we can move these companies’ incentives away from racing and towards responsible development. I wrote up more thoughts here on my decision and what I hope changes:

Speaking in a personal capacity: similarly to many others working in AI alignment, I think there is a >10% chance of advanced AI causing human extinction in the next decade. This is why I work on loss of control, currently on building honeypots to catch scheming AI.

I also work at an AI company and believe there's plausibly a ≥10% chance that a future out-of-control AI kills everyone (IMO even 1% is unacceptably high). And higher still is the risk that we "only" get permanently disempowered by AIs that don't deeply want the best for us.

Now is a good time to build institutional mechanisms to pace the frontier of AI development. The industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

In case one more data point is useful: I too am an AI company employee who thinks the risk of extinction-level catastrophe from rogue AI is >10%. I wrote about this back in 2022, and again in a 2025 post about why I was joining Anthropic (links in thread). I still believe it.

Senior staff at OpenAI and Anthropic absolutely think that AI could kill us all. They’ve said so publicly, for years! Sam Altman: “Development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity.” (2015) “The bad case — and I think this is important to say — is, like, lights out for all of us.” (2023) Ilya Sutskever and Jan Leike (OAI at the time): “the vast power of superintelligence could also be very dangerous, and could lead to the disempowerment of humanity or even human extinction.” (2023) Jack Clark (Anthropic): AI has “a non-zero chance of killing everyone on the planet”. (2026) Dario Amodei’s comments are broader than literal extinction but hardly reassuring: "my chance that something goes, you know, really quite catastrophically wrong on the scale of human civilization might be somewhere between 10 and 25 per cent." (2023) And now Evan Hubinger, Anthropic’s Alignment Science Lead, is making it very clear: “Jacob is correct here — we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.” If you talk to people at the AI companies, the mainstream view is that superintelligence by end of the decade is pretty likely, and that superintelligence poses a meaningful risk of civilisation-scale catastrophe - including AI-empowered dictatorships, worse-than-COVID pandemics, AI takeover, and, yes, human extinction. OAI and Anthropic keep going so fast because they each think they can build AI more safely than the other party and antitrust limits how much they can coordinate. This is a f-ed up situation! Thankfully, they’re both now publicly asking for regulation and the pacing of frontier progress. If this worries you, you can speak up publicly, contact your political representatives, and demand action. Now’s the time.

i'm late to the party but: from his time at OpenAI I remember Jacob as a very thoughtful researcher and he continues to be so in this thread. neither anthropic nor openai are on track to solve alignment to a degree sufficient for shipping superintelligence and we need to slow down