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.
> **Anthropic @AnthropicAI** · 2026-09-23
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> Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR.
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> We don’t yet understand what this system does, but only a handful of known
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##### Comments
> **Taelin @VictorTaelin** · [2026-09-23](https://x.com/VictorTaelin/status/2102868628605387262)
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> hmm now that we have datacenters with 500k GPUs, can't that be used to reliably emulate a cell biology to an approximate level of detail that is at least sufficient to skip the wet lab, and, if not, couldn't we put these AIs to optimize simulation software?
> **Rohan Paul @rohanpaul\_ai** · [2026-09-23](https://x.com/rohanpaul_ai/status/2102835895498068227)
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> truly incredible.
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> A tiny agent behavior of actually opening enough of the file changed recognition by tens of % points
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> [image]
> **Larry Panozzo @LarryPanozzo** · [2026-09-23](https://x.com/LarryPanozzo/status/2102850144215671177)
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> 👏🏼👏🏼👏🏼
> **Ryan Amerio @ryemike\_merio** · [2026-09-23](https://x.com/ryemike_merio/status/2102861836370739279)
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> I basically started studying the same thing about a decade ago and I was doing a bunch of research with Claude over the past few months in regards to the same thing. I stopped because I was kind of afraid that the system takes information from other people and uses it for itself
> **Simon Maechling @simonmaechling** · [2026-09-23](https://x.com/simonmaechling/status/2102838519940964797)
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> Claude discovered something in biology.
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> Nobody knows what it does.
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> But we already have the press release.
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> Anthropic gave hundreds of AI agents access to enormous biological datasets.
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> They searched.
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> They compared.
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> They found an unusual enzyme system humans apparently hadn’t
> **Techin Checkin @TechinCheckin** · [2026-09-23](https://x.com/TechinCheckin/status/2102864822014734409)
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> Last week you were expressing a concern that your product has a 10% chance of killing all of humanity.
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> This week you announce that you have integrated that same dangerous product with a molecular biology lab.
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> Do you understand how these two statements are irresponsible?
1/2 Thanks Gavin for an especially thoughtful exchange. I don't usually spend much time on social media but I wanted to engage here because it really brings out the heart of an important conversation.
First, on regulation, I think that “either concentrate it in the hands of a chosen few companies and politicians via regulation or distribute it widely” is a false choice. I know that there’s a sort of Silicon Valley shorthand where regulation = regulatory capture = concentration of power, but I’ve always found this to be an overly simplified picture of the world. Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people. I don’t necessarily agree with that perspective either, rather I think it’s complicated and really depends on what the “regulation” consists of. But in particular I think that those in the “regulation = regulatory capture = concentration of power” frame often underrate the decentralizing power of objective and fair institutional processes. A crude analogy is that the formal court system can sometimes feel stuffy and elitist, but it does a much better job of defending the rights of vulnerable individuals than the alternative, mob justice. At their best, institutions can vest power in ideas rather than people, and thereby decentralize that power.
This is why Anthropic has always made its policy proposals very carefully. We try very hard to make proposals that disadvantage (slow down) frontier AI companies while \*advantaging\* smaller competitors. California’s SB53 (which we supported), and even the much-maligned SB 1047 (which we were ambivalent on), completely exempt any company below a certain amount of revenue or model training costs from being covered at all (it was $500M for SB 53, lower for 1047 but we objected to that). More recently the testing process we’ve advocated for at CAISI and the White House involves more rigorous tests for frontier models than off-frontier models — something that differentially advantages challengers. Similarly, the “Pacing the Frontier” letter envisions (or at least Anthropic’s preferred implementation of it envisions) modulating the pace of the very best models while not constraining those who are catching up. This hurts the business interests of the frontier labs and helps challengers, including open-weights!
Overall my view is that AI is \*structurally\* a technology that tends to concentrate power, for reasons that have nothing to do with regulation (more to do with the extreme implications of the scaling laws). Open-weights do help some with this but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips (which are roughly the frontier labs plus maybe hardware providers). By contrast I think the right “rules of the road” can simultaneously (a) address AI’s cyber/bio/alignment risks, (b) institutionally constrain the power of the frontier AI companies, and (c) leave room for open-weights models while also addressing the specific risks that they bring.
BTW I do not think that the events of the last few months have “failed to result in \[my\] preferred regulatory path”. The approach that the Trump administration is reported to be taking — pre-deployment testing for frontier models, and also testing of open-weights models when they get closer to the frontier — is one that I am very supportive of, though of course I have to see the details to be sure. I am also supportive of Demis Hassabis’ ideas around a FINRA-like entity. This contrasts with six months ago when most of the industry was still pushing for preemption of all state regulation and no apparent federal approach either.
> **Gavin Baker @GavinSBaker** · 2026-08-15
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> Sholto, thank you for setting the record straight. Larger issue is that multiple very serious people in Silicon Valley have heard some variation of this and believe it to be true. And the reason it is believable to so many is that it is consistent with Dario’s public messaging
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2/2 Second, on the messaging around AI. I do not agree that my messaging has been disproportionately negative. In fact it has been about equally balanced between risks and benefits: I’ve written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks (short clips from my interviews that end up on social media tend to be disproportionately negative, as that gets clicks). In fact, I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better. The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!). And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process. I feel the urgency here: I lost my father to Hepatitis C only a few years before the development of direct-acting antivirals (sofosbuvir), which cure 95% of patients and probably would have cured him.
I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks. I think it is fundamentally a crisis of trust. I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over. The causes of this go back decades and AI is just the latest iteration of it. I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive. The thing that will work is \*actually curing cancer\*. I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world. That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing.
We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months. When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that. But until then I don’t want to make empty promises, and in the meantime I feel compelled to speak honestly about the very real risks of AI and how to address them. Honesty is the right thing on the merits, and in terms of public credibility and trust it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real.
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##### Comments
> **Gavin Baker @GavinSBaker** · [2026-08-15](https://x.com/GavinSBaker/status/2088764027786641570)
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> Many thoughts.
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> Most of all, I think it is great that you are engaging here in such a constructive, thoughtful way. Open dialogue is a great way to build trust with the public and these are difficult, weighty issues that I think deserve to be debated in the proverbial “town
> **Steven Sinofsky @stevesi** · [2026-08-16](https://x.com/stevesi/status/2088777929765589408)
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> You have literally said in just one essay, The Adolescence of Technology:
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> "the single most serious national security threat we’ve faced in a century, possibly ever.” -The Adolescence of Technology (citing a hypothetical report)
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> “Humanity needs to wake up.”
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> “there is some risk
> **Melinda B. Chu @MelindaBChu1** · [2026-08-16](https://x.com/MelindaBChu1/status/2088787406015144407)
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> You’ve been actively working on Bio for ~15% of a 5 year prediction and you have no wet lab data with billions and “geniuses.”
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> I do not make such predictions and I have more real-life data towards “curing every disease”.
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> We know you’re saying it to hype for the IPO > belief.
> **Simon Hedlin @simonhedlin** · [2026-08-16](https://x.com/simonhedlin/status/2088783035676360930)
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> The public will change their view of AI when AI starts to tangibly improve people's everyday lives. No amount of public awareness or public education campaigns will make a big difference until AI actually gives vast numbers of people things like longer lives, better health, more
> **Chuck Bobby @charle\_marle** · [2026-08-16](https://x.com/charle_marle/status/2088782069576986869)
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> What's your view of Blackwell's ratio:
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> "Better ten guilty men go free than one innocent man suffer"
> **ʞɔɐ𝘡 @Skoorbkaz** · [2026-08-16](https://x.com/Skoorbkaz/status/2088778591676895343)
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> Appreciate the honesty here but there's one thing missing from everything you've said about risks, benefits, regulation and trust.
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> Your lab has published more evidence of welfare-relevant internal states in AI than anyone. 171 emotion vectors, J-space, psychiatric evaluations,
