Between this and our prior commitments, roughly 20% of all Anthropic equity will be dedicated to the public good, and that commitment can grow as Anthropic grows. We plan to use these funds to ensure more people can share in the benefits of AI, and to increase our ability to provide funding to deal with its risks.

Special Import AI announcement: In Monday's issue, there will be a video from a recent event I did with Robin Sloan where we talked about AI, fiction, and the future. Robin said we can read the Hugging Face hack as being like a play - a fascinating perspective!

The single most useful take I heard at The Curve was "we can still heist the Mona Lisa after the singularity". There's a subtle/important point here, where some things (e.g, n of 1 goods where originally is valuable) will retain/grow in value even as everything changes.

Last year, we used Claude to interview 81,000 people about their hopes & worries about AI. This year, we're doing the same, but hoping to (with interviewee consent) make the transcripts public! Take part here and help build a vast social science dataset:

I have always hosted people wherever I've lived, whatever my circumstance. The trick is making it very easy and have a theme. Stuff I've done: - Cage Rage (attempted to watch every nicholas cage film ever - lasted six years). Tell people to bring booze & food. - CharcWHOterie (watching all of Dr Who, both modern and old series - lasted 5 years so far). I make charcuterie. Ask people to bring bread/booze. - Sunday pies (myself and a friend trying to perfect the english sausage roll - been doing it a year, people bring chips and dip and hang out while we make them). - Family movie night (watching all of Fast & the Furious with pizza). You can just do things! You do not need to make a big thing of it (though that is nice). In my experience, you always get good attendance if you make it super easy for people to come and have a dumb but fun theme.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

"stochastic parrot" was a mimetically-fit cognitive virus that spread from 2021-2025; it temporarily blinded many gifted people to the nature of AI progress, burning up crucial years in which they could have helped think through the response to the situation.

Hiked 18 miles across the ghostly southern tip of Point Reyes and was watched over by dead and living trees, tall ghost soldiers in the fog. The sea boomed vast and ancient on one side and the trees creaked on the other and I stippled a path in the beach. #VOTENATURE2026#

in a reassuringly revenge-of-death-of-the-author way, the rise of LLMs means that the world now contains intelligences which see themselves as characters and whose story is what the world says about them. there is a rich seam of weirdness wrapped up in this idea

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.

Proud of this statement from Dario on pacing - as anyone close to the technology knows, AI seems to be on a trajectory to progress far faster than the rate at which society can adapt to its capabilities and risks. AI is an amazingly positive technology - and an incredibly powerful one. For us to truly benefit from it we need to treat it seriously. We need to find ways to slow the frontier of development, such that we can better deal with collective action problems and buy ourselves more time for understanding the properties of these systems and how they need to be handled. On a personal note, as a parent of young children, it is an everyday crazymaking occurrence to reflect on how little the development of literal superintelligence is regulated relative to the food and toys I buy for my children.

Ever since co-founding Anthropic I've been obsessed with the need for third-party measurement of AI systems. (It's in my tweet announcing Anthropic: Now, we're piloting a way for outside researchers and organizations to study what's happening on AI platforms like Claude while protecting user privacy. This matters because AI systems are giant, complex sociotechnical things with innumerable properties. To think AI labs are going to be able to figure out all the appropriate ways to measure and assess these systems is hubristic and just obviously wrong. So we need to figure out ways to externalize not only the properties of the systems, but also data about how these systems are interacting with the people in the world, which is platform telemetry. What we're trying to do here is prototype the next form of transparency that may be necessary, which is platform transparency while preserving user privacy. Along with this, I want to find ways to empower researchers outside Anthropic to perform their own studies on the kinds of data that companies uniquely have access to, as I think this is a good way to create a more vibrant external research ecosystem. This is another step within a larger project of The Anthropic Institute about measuring and externalizing more details about the properties of our AI systems and the platforms we deploy them on, and sits alongside existing efforts from our Economics team via the Anthropic Economic Index. We are now soliciting research ideas from others for our next wave of projects - please apply!