A World Model of Life: Biohub's Alex Rives on Virtual Cells, Measurement, & the $1.8B Data Push

Hello, and welcome back to the Cognitive Revolution! Today my guest is Alex Rives, formerly co-founder and Chief Scientist at Evolutionary Scale, makers of the ESM series of models, and currently Head of Science at Biohub, the Chan Zuckerberg Initiative backed moonshot with the inspiring mission to cure or prevent all disease, which just announced a $1.8B expansion of the Virtual Biology Initiative, a data-generation effort that brings together the Department of Energy, the NIH, Google DeepMind, Isomorphic, and Meta. I opened by asking Alex what "curing all disease" means to him, and if I should expect to live much longer than the standard human lifespan. Calling himself "a practical person" in response, Alex described, not immortality, but a world without cancer, without infectious disease, and without chronic or degenerative conditions. From there, we get Alex's perspective on why today's virtual cell models haven't revolutionized medicine. The heart of the issue, Alex explains, is that our ability to observe what matters in biology is still very limited by the measurement technology we have available, and that as a result, we simply don't have the quality of data needed to represent the fine-grained causal structure that models presumably would be very happy to learn. Better architectures built on clever inductive biases can make the most of scarce data, such as in the extremely thoughtful use of physics in AlphaFold style models, and we do see real progress in next-token modeling of the biological code, but the available data is failing to capture critical information about how cells and tissues work. It's on this basis that Biohub is investing heavily in data, by opening new modalities, as they did with laser phase plate, an advance in microscopy that is allowing scientists to begin to directly see how individual proteins and other macromolecules interact inside the cell, and also by systematically building cell atlases that link molecular and imaging datasets across hundreds of billions of cells, representing both healthy and diseased states. The goal, over the next few years, is to build an information theoretic model of the cell, which effectively encodes the physical programs that run life, starting with a transition function that takes a cell or tissue state and a set of inputs and predicts a distribution of cell states at the next timestamp, ultimately building toward what Alex calls "a world model of life." This, of course, would support the use case I always dream of, which is answering the question: how do I push a pathologic state back to a healthy one, or in other words, effectively treat disease? Along the way, Alex describes how the LLM training paradigm applies to biology, his vision for "RLVR for biology", with lab verification, reacts to my proposal for "a million Bryan Johnsons", and ponders whether biological and language latent spaces will become deeply fused or remain separated by tool-calls, noting that the things we most want to know about biology are "novel concepts for which we don't yet have words and names." Finally, I asked Alex how he tracks his own health. He answered that he's so focused on Biohub research that he barely thinks about it, which surprised me, until I considered that, if he and the Biohub team are successful in creating the data layer needed to train highly generalizable biofoundation models, a little bit of timely, relevant personal health context might indeed be … all you need. With that, I hope you enjoy this look ahead toward a world model of life, with Alex Rives, Head of Science at Biohub.

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Thank you for being part of The Cognitive Revolution,
Nathan Labenz