A Grammar for Biology

Thomas Clozel on building superintelligence that reads life

Sometime in the middle of the nineteenth century, Rudolf Virchow looked at blood under his microscope and found cells that should not have been there. He gave the disease a name that was, in the manner of the age, a direct translation of what he saw. Leukämie. White blood. The naming was not an act of understanding. It was an act of description elevated to the status of a category. And for a hundred years afterward, an entire field built its taxonomies on Virchow’s original visual habit.

This is how the grammar of medicine has always worked. We look. We name. We reify the name until the name becomes the thing.

Thomas Clozel, MD, an oncologist and the co-founder and CEO of Owkin, has spent the last decade trying to write a new grammar for biology, the deep structure of how disease is described, categorized, and understood at all.

This is the substance of the latest episode of Precision Signals.

Chemistry is a confined problem. Biology is an open one.

Thomas is the son of two French physicians who left the clinic to build their own biotech and put several drugs on the market. His grandmother was one of the first women physicists in France. Science was the first language he learned. Medicine was the second. Coding, which he took up under Dr. Olivier Elemento at Weill Cornell Medicine during a research year, was the third.

His first rotation at Mount Sinai was under Dr. Valentin Fuster, the great cardiologist. What Fuster taught him, more than any clinical technique, was the discipline of I don’t know. Three words, Fuster insisted, that separate the good doctor from the excellent one. Three words that any serious reasoning model will also, one day, have to learn how to say.

In late 2016, with his co-founder Gilles Wainrib, a machine learning researcher trained at École Normale Supérieure, Thomas founded Owkin. The contrarian bet was that chemistry was a confined space. It had rules: Molecules bind, and proteins fold. Given enough compute, an AI could, in principle, learn chemistry the way it had learned to play Go. Biology was different, and it was where causality lived. The question was not how to design a better molecule. The question was what disease actually is.

The mesothelioma paper

To do this work, Owkin needed patient data at a scale no lab could provide alone. Its first bet was on federated learning. Rather than pulling data out of hospitals, Owkin built infrastructure that let its models travel to the data and train there, in situ, without ever taking the data out. Hospitals in Germany, France, the UK, and the US signed on. The engineering was hard. The trust was harder.

In 2019, Owkin published a study on mesothelioma histopathology that, to me, was the first real signal that something was different. Most digital pathology work at the time was trying to replicate the taxonomies human pathologists had already invented. The AI was being asked to imitate the human eye. Owkin’s mesothelioma model was asked to do something else. It was asked to look at the slides without a taxonomy and to organize what it saw. What it produced was a set of categories no human had ever named, corresponding on later analysis to distinct survival curves. Disease entities that had been sitting in front of pathologists for a century without ever being recognized as such.

This is the real grammar of biology and a demonstration that the units of disease are not fixed.

The AI scientist

Over the last two years, Owkin has consolidated its tools, its models, and its data assets into a single architecture called K Pro.

K Pro is not a wrapper on a general-purpose model but a reasoning system trained on real patient data, orchestrated through workflows that Owkin builds by having Claude write code for it, and validated through Owkin’s own wet lab and clinical partnerships. Thomas describes it as an AI scientist that is beginning to have ideas of its own. He tests it in two modes. In research mode, he asks it to find a new target or a new molecule. In clinical mode, he asks it what to do for a fourth-line bladder cancer patient. He says the answers, when checked against his network of experts, have begun to surprise him.

Success

I asked Thomas what would need to happen for him to say, someday, that Owkin had succeeded. He gave the answer I already knew he would give. It will only count if he treats something important for the world. Not the billion-dollar valuation, their approved diagnostics, or their programs in the clinic. Only a treatment that changes a patient’s life.

Everything else is preamble.