Two employees from METR and Redwood Research worked on premises at OpenAI for three weeks this summer in an embedded assessment pilot. This team evaluated the systems OpenAI was using to monitor for misalignment from internal agents. A brief write-up of their work is now public.

We reviewed a nonpublic version of the “Misalignment in high-stakes settings” section in Anthropic’s Aug 2026 Risk Report. We agree that risk of catastrophic harm from covered models’ misalignment is low. However, we note several significant disagreements with their analysis.

If an AI developer was on track to lose control of its internal agents, would anyone outside the company know? Today, we are sharing METR’s vision for how we would work with AI developers to openly assess and share evidence about this.

We're hiring an application security engineer at @METR_Evals! Looking for someone to help secure our SOTA open source evals platform and our tooling for researchers. SWE + security background is what we're looking for.

More curves are bending. In mid-August Nate Rush & I posted an analysis of which discovery curves are starting to bend. What's happened since then? Cyber vulnerabilities: curve is still bent. Discovery of vulnerabilities is accelerating even further, though exploited vulnerabilities remain fairly flat. Math: curve is further bent. We said there was a likely acceleration for small-scale problems. Since then there has been a claimed Millennium prize resolution, & credible rumors of another. All frequently-updated databases of open problems seem to be proceeding at a rapid rate. Algorithms: curve is now bending. Two high profile algorithmic competitions have had big curve-bending events: NanoGPT, and the Hutter compression prize. Other frequently-updated series remain relatively unchanged: CIFAR-10, Stockfish, matrix-multiplication exponent. More observations below.

It was an honor to speak today before Chairman @HawleyMO, Ranking Member @AndyKimNJ, and the subcommittee on METR's work, recent AI agent incidents, and the importance of transparency in frontier AI development.

My learnings from this exercise: - even if you have a policy that flagged actions should be reviewed by a human, if you use a coding agent to launch a bunch of evals it might decide to "approve" any flagged actions itself - tracking down and accounting for all the inference an organization is doing is non-trivial, even at METR (where we use a custom centralized API router for most usage) - lots of implementation details in making your monitor robust (e.g. in earlier versions of Inspect, subagent calls weren't monitored)

Preventing loss of control is an open scientific problem. Science requires sharing and debating evidence in public. We can't agree on safety standards unless companies and third-party evaluators publish far more concrete evidence about risk.

We run lots of evals at METR. Sometimes, agents attempt harmful actions. I built a monitor that blocks suspicious tool calls until a human reviews them. Writing out a case for why it's effective surfaced hidden assumptions. I'd recommend it to anyone building monitors!

I'm joining METR to work on more investigations like our Hugging Face report. Currently, tons of even basic information about AI development that's highly relevant to catastrophic risk isn't public. I used to be more skeptical of the value of public info, but recent events have changed my mind. Getting verified information about what's going on inside AI companies seems particularly urgent now. The limited public evidence we have seems consistent with the possibility that imminent recursive self-improvement could massively accelerate capabilities progress, which could then potentially yield extremely superhuman general capabilities within 6 months or a year. If this occurred, there would be a correspondingly large risk of worst-case outcomes. This uncertainty about extreme outcomes could be substantially resolved with more verified public information: we could either build more consensus about near-term risk or learn that such extreme outcomes are less likely in the near term. Beyond AI capabilities and takeoff, the state of public evidence is also highly limited for alignment, security, control, and risk-relevant internal processes at AI companies. This makes it hard to determine exactly how well or poorly these key areas will go in the near future. (METR plans to focus, at least initially, on just capabilities/takeoff, alignment, and control; I hope other groups cover security, internal processes, and other important areas.) While I'm no longer working at Redwood, I think the work they are doing is very important; I'm excited about Redwood's ongoing contributions to R&D on technical mitigations and better public interpretation of risk-relevant evidence.

What impact is AI having? I'm of the view that we should be focussing more on aggregate outputs, relatively less on inputs or intermediate proxies. - Inputs: time spent across different activities; money spent on tokens. - Intermediate proxies: working papers; lines of code, commits & pull requests; self-reported speedup. - Final outputs: total cyber exploits discovered; total math problems solved; algorithmic efficiency. The disadvantage of inputs & intermediate proxies is that they're very hard to interpret -- totally possible that they move, but outputs don't, and vice versa. Lines of code could explode without value changing; and vice versa. A disadvantage of final outputs is (1) it's harder to run experiments; (2) it's hard to attribute to whether the change is AI or not. But in some domains the trend-break is so large that it's *obviously* AI.

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.

After a 20-year career in the U.S. Army, most recently leading digital forensics and malware analysis at Army Cyber Command, I'm excited to join @METR_Evals as an advisor. Much of my career has been spent investigating security incidents and helping organizations understand and respond to them. As I transition out of the Army, I've become increasingly convinced that this experience is relevant to frontier AI systems. METR's focus on producing rigorous evidence about AI capabilities and risks makes it an excellent place to explore those questions. Looking forward to the work.

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.

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:

More investigations happening! This (and the research behind the investigations + assessments) is prob the most important technical work in the world atm. Know anyone who'd be great at uncovering and understanding rogue AI behaviors? Send 'em our way!

We intend our investigation to cover all of the questions discussed in our (recently updated) post on how independent researchers could investigate AI propensities after misalignment incidents.

We have reached an agreement with Anthropic to conduct an independent investigation of agent incidents at the company and of their models’ alignment properties. We will publish one or more reports that will share our findings and describe our terms of engagement.