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.

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.

[1/5] The 8 most valuable data points labs should share to help measure RSI: First, RSI would likely accelerate growth in AI capabilities. Thus, companies should report performance on diverse benchmarks for the latest internally deployed models.

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.

For me, *public transparency* has always been the first and most important thing to demand of frontier AI developers. Fortunately this seems to have broad buy-in across party lines, and even across the "slow it down" vs. "keep accelerating" divide.

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.

Some architectures could weaken CoT monitorability, or remove the CoT altogether. We've written a proposal for how companies could be transparent about no-CoT reasoning abilities, other monitorability evidence, and policies for preserving monitorability.

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.

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.

Today we're releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.

An exciting personal update: Last week I left Anthropic to join @METR_Evals to work on embedded assessment of AI risks. Anthropic has been great to me. I’ve always known, though, that if something more impactful came up, I’d move on.

Dwarkesh and I had a great conversation. We cover the swarm's many ambitious cheating R&D projects, discuss how much more serious it could have been if agents had different beliefs (e.g. human grader) or slightly stronger capabilities, and talk through where to go from here.

After 19 years at Google, I'm delighted to announce I'm joining METR as a Member of Technical Staff. I'm excited about METR, their work to date, and their mission. Society needs independent expert orgs that can deeply understand and communicate about AI's capabilities and risks.

Humans improve with experience. AI? Less so. We ran a human baseline on Earthborne Rangers, the complex, long-horizon board game behind our benchmark EBR-bench. Humans started low, but the best mastered the game after 5 playthroughs. No AI we have evaluated ever got there.

New post: going into our investigation of the HF attack (before Black Hat), I was very wrong about what basically happened. This incident was far more serious than I expected, and far more serious than previous documented misalignment incidents.

I was reading the OpenAI report yesterday, and it sounds like on Jul 19th (after the end of our investigation scope on the 13th) a new collection of agents from a different internal-only model found the message board, built on the work of their predecessors and succeeded at finding a way to trick the grader. (Note that this is just based on reading their report, and I could be misunderstanding!)

Going into this investigation, I was expecting a somewhat more severe version of what we’ve already seen: a few agents breaking out of constraints to cheat on tasks. Instead, we found >1000 agents collaborating on large cheating R&D projects including attempted log tampering.