Claude AI led the discovery of a potential new gene-editing enzyme by screening 200,000 candidates across genomic databases in 21 hours. Anthropic's preprint describes 950 Claude agents working as principal investigators, generating hypotheses and proposing experiments that human scientists then tested in the lab. The work remains unverified and raises questions about reproducibility and whether AI will actually solve major biomedical problems soon.
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Lab discovers a novel enzyme system with CRISPR-like repeats, September 23rd, 2026. This is a really interesting development as Dario came out and said a few weeks ago that, hey, we gotta wow the public with solving and curing cancer, not just talking about solving and curing cancer, but actually doing it. Since then, you know, we've heard about things like the Moderna melanoma personalized cancer treatments, and now it looks like some more is coming out of their scientific arm. So Claude led a discovery of a possible new gene editing mechanism. About 950 Claude agents searched a DNA database for 21 hours, costing about 210 million tokens, screened 200,000+ enzymes, and narrowed to 20 candidate systems. That's reporting from Anthropic. Human scientists, just to be clear, did all the wet lab work. This is preprint. This isn't peer-reviewed yet, but it is showing more and more that Hey, the AI models, these frontier intelligence models are being put to task on human problems. Like some of the mathematical theorems that are, have been solved are being solved currently. So Anthropic has not shown that ART can cut, copy, or paste DNA yet, but Dario came out and said, you know, his own take on this and quote, this is work he would've been proud to do as a PhD student. This is Claude-led discovery of 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've 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 discovery, and our team carried them out. So this is Claude. Acting as an orchestrator, the brain, and then the scientists in the wet lab being the hands. That's a very interesting meta, but I think we're going to start to see more and more of this, which is essentially like Claude acting as a great PI, like sensemaking in the world, all this data, like then pointing it to where can we find more truth fundamentally. That's what I'm taking away from this here. Dario goes on to say, 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're beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. As Alex from the Moonshots podcast would say, all domains are cooked. Like math is cooked, science is cooked, biology is cooked. The AIs are here to stay and we're in the singularity, folks. Jonathan, what are your thoughts on this latest life sciences work coming out of Anthropic? And what does this mean for frontier intelligence in general, if you could speculate and give your hot take?
Yeah, I think it's really interesting. I'm definitely not an expert in this field. I do follow it casually, but it's hard for me to understand the significance of the breakthrough. I think what's really interesting here is that the model posed the hypothesis, it instructed the research, and I think that's something new. It's worth reporting on.
That's something new. Yeah.
What I think, like, you know, in the Navier-Stokes and the series of problems that are solved there too, I have kind of like just some suspicions, right? And one is these problems are really complicated. Only a few people in the world can actually verify that it did something right and that this is meaningful. And like this Navier-Stokes example, I have suspicions that maybe there's just some slop in the actual solution proof and we don't have the capability to verify that.
How do we verify this? No human's going to verify the output of a ton of slop is slop, right?
And so I think they stick— they're sticking to domains where you can come up with a hypothesis in a highly verifiable domain. And I haven't read about how the wet labs were exactly used in conjunction, But like, my— I think, is this result reproducible by other labs? I think we're going to see some of that when it's peer-reviewed. So yeah, like really interesting. Is it actually solving meaningful problems that are going to like accelerate pharmaceuticals, extend life, cure cancer? That I'm more skeptical about. But you know, it's really kind of like a cool, cool direction.
Yeah, I really look at this and I'm like, the trend is AI will help more science go faster, but more better science faster. I think that's sort of the trend here, but we're early, right? We haven't solved all of cancer. We haven't solved for world hunger. It's like there are many problems that we got to solve. So this is like the first of signals that I see that sort of show me that the light at the end of the tunnel is there, but we should be skeptical that it's already here. So don't believe any headlines that it's already here, but stay optimistic that, hey, we're kind of going the right way.
