we bought the dram and the interconnects and the CPUs and the inference players and the agentic winners. and now? well, imagine a world where simply being a part of the AI supply chain isn’t enough to make a stock go up 100% a year.

The regime shift means it’s going to be very easy for investors to get burnt applying frameworks that worked for the first part of the cycle to the next. Our 10th State of the Themes is about how we intend to avoid that.

This is a very textbook phenomenon in the market called “rotation out of all the names you specifically own and into names you don’t”. This happens when all the market participants have a meeting you aren’t invited to and decide they hate you.

“As of June 30, 2026, we hadraised an aggregate of  $141.1 billion of capital and had cash and cash equivalents of  $112.4 billion and short- term investments of $19.3 billion” I think Anthropic might be okay for a little while tbh.

There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused. The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc). The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays). Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false. This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology. Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions. Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible. This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use. How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months. I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.

What is AI really capable of in bio? To separate hype from reality, we spent 3 months working firsthand with @AnthropicAI's internal research model to see if it could create an experimental scorpion antivenom. Introducing Citrini Therapeutics.

Today, we’re announcing an open call for guest investigators in life sciences. Over 8–12 weeks, guest investigators will work directly with my team and other Anthropic researchers to produce analyses and tools for their work. We’ll run our models and harnesses, at no cost to them. We’ll work together to open-source the resulting data, artifacts, and code, and write preprints about what we find. We want humans to remain at the center of scientific discovery, deciding what questions to pursue, and how to pursue them. Those decisions are not ours to make alone. This open call is part of our ongoing efforts at Anthropic to support researchers and accelerate scientific discovery. I’m looking for principal investigators and their research groups at academic institutions, nonprofits, or government labs, working in causal human biology, structural and computational molecular biology, medicinal chemistry, drug discovery, and clinical science. Applications are open for about 14 days: