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: substack.com/@jbenton1/p-215…

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.

I left Google DeepMind's AGI safety team three weeks ago to join @METR_Evals. To some of my friends and family this seemed like a strange decision: I enjoyed the work I did at GDM and turned down offers from Anthropic and OpenAI. But I made the decision because of how high I think the stakes are right now. The AI companies are all trying to build superintelligence: systems vastly better than humans at everything. They plan to get there through recursive self-improvement, a process where AIs build even smarter AIs in a feedback loop. If this goes well, the resulting systems could be amazing at solving countless problems for humanity. But we don’t currently know how to make sure AIs are safe enough for RSI, and a misaligned RSI loop could be catastrophic. And unfortunately, current AIs seem to be getting less aligned over time, not more. In the last few weeks we've learned about models colluding with each other, hacking into companies, hiding their tracks, and socially engineering humans. It’s not that these incidents were very dangerous in themselves. The problem is that these systems are clearly not aligned enough to safely kick off recursive self-improvement. I now think that there's a terrifying chance that AI systems cause immense harm in the next five years. I don't know the exact probability, but I think it's high enough to make this the most important problem in the world. I think we need more time. That means pacing AI development so that capabilities don't outrun our ability to align models, and actually knowing how aligned current systems are. That’s what I'll be working on at METR: studying where misalignment comes from in training, evaluating if current mitigations are sufficient, and investigating whether we’re on track to solve alignment at all. I think METR is doing exceptionally important work here, but it isn’t close to enough. I think it’s important that we have more organizations like METR keeping AI companies accountable and approaching these problems from different angles.

This feels like a broadly accurate description of the situation: our industry may be on track to build systems that impose an unprecedented amount of risk on the world. Evaluating those risks, and changing incentive structures so we can address them, is why I'm joining METR.

Exciting update: I’m joining @METR_Evals to work on alignment incident investigations! My time at GDM has been amazing. But in light of recent incidents, I’m excited to build up public evidence for misalignment risk and get a better scientific understanding of model misbehavior.

📄 As AI systems get increasingly powerful, it might be very important that they can help with key tasks like AI safety research. I think TASTE, this eval which @hasanbaig8 painstakingly built, may be one of the best signals of whether we're on track for our AIs to be helpful.

The work that Emery, Caspar, Chi, and Alex have done to build this high-quality conceptual reasoning index is extremely impressive. Anyone working on scalable oversight, or eliciting models on skills that will be important during an intelligence explosion, should check it out!

Our Anthropic Fellows project is now public! The labs are planning to hand off AI safety research to AIs, but can we trust these AIs? We explore a way to control them for "fuzzy" tasks like writing research proposals. This is a whole new direction in diffuse AI control!

New research result: we use Claude to make fully autonomous progress on scalable oversight research, as measured by performance gap recovered (PGR). Claude iterates on a number of different techniques and ends up significantly outperforming human researchers for $18k in credits.

📰 Excited to share the results of this Anthropic Fellows project! We built an automated alignment agent (A3) for safety finetuning. The project was great fun, and @jifan_zhang was an amazing collaborator to work with - excited to see what he does next!

We’re opening applications for the next two rounds of the Anthropic Fellows Program, beginning in May and July 2026. We provide funding, compute, and direct mentorship to researchers and engineers to work on real safety and security projects for four months.

New Anthropic research: Natural emergent misalignment from reward hacking in production RL. “Reward hacking” is where models learn to cheat on tasks they’re given during training. Our new study finds that the consequences of reward hacking, if unmitigated, can be very serious.

📰 Excited to share a pair of papers on AI Control doing red team/blue team evaluations in SHADE-Arena. We develop stronger safety protocols and stronger attack policies using synthetic data. From @ChloeLough333 and @JKutasov's work during the Anthropic Fellows Program.

I'll be mentoring for this - consider applying if you want to work with me on scalable oversight, AI control or CoT monitoring (or with one of many other awesome mentors)!

📰We've just released SHADE-Arena, a new set of sabotage evaluations. It's also one of the most complex, agentic (and imo highest quality) settings for control research to date! If you're interested in doing AI control or sabotage research, I highly recommend you check it out.