The most ambitious AI project you have not heard about is not building the next chatbot or reasoning model. It is building a virtual human cell.
Mark Zuckerberg and Priscilla Chan’s science nonprofit, Biohub, just expanded its Virtual Biology Initiative from $500 million into a $1.8 billion effort backed by the US government, Google DeepMind, and Meta. The goal: collect enough cellular data to train AI that can simulate how human cells behave.
If it works, drug discovery changes forever. You test medicines on software before you ever touch a petri dish.
A universal virtual cell
Biohub’s end game is something they call the “universal virtual cell” – AI software that predicts how a cell will respond to a drug, a toxin, or a genetic change before any lab work begins. Think of it as a digital twin for the basic unit of human biology.
The National Institutes of Health is contributing datasets built on more than $500 million in past federal spending. The US Department of Energy is committing another $500 million over five years. Google DeepMind and Meta are chipping in $300 million alongside Isomorphic Labs, the drug discovery startup Demis Hassabis co-founded. All three private partners keep the resulting data private for a year before releasing it publicly.
Biohub’s head of science, Alex Rives, says accurate models will need data from trillions of cells. The biggest existing datasets cover only hundreds of millions. By Rives’s own estimate, Biohub is orders of magnitude short of what it needs — which is exactly why this partnership exists in the first place.
Why this matters
Fresh off AI making headlines by cracking open problems in pure mathematics, you might wonder which field the technology comes for next. Zuckerberg and Chan are making biology the target. They are throwing money, datasets, and talent at a moonshot that Biohub itself frames as “cure or prevent all disease.”
That language is ambitious enough to sound naive. But the structure here is worth paying attention to. This is not a single lab chasing a breakthrough. It is a coordinated effort between government agencies, big tech companies, and a well-funded nonprofit, all pointed at the same bottleneck: we do not have enough cellular data to train capable biological AI.
The private data exclusivity period is the detail that stands out. DeepMind, Meta, and Isomorphic Labs get first access to the data they help generate, a full year before it becomes public. That is a significant commercial advantage for companies already dominant in AI. Whether that arrangement accelerates or distorts the science is a question worth watching.
The bigger picture
Biohub’s virtual cell is part of a broader pattern. The largest AI investments are no longer about language or images. They are about building simulators of the physical world — weather, protein folding, cellular biology, materials science. Each one requires enormous amounts of domain-specific data, and each one has the potential to transform an entire industry.
The universal virtual cell may or may not arrive. But the machinery being built to pursue it — the datasets, the partnerships, the infrastructure — will produce useful science regardless. That is the quiet genius of projects like this: the attempt itself moves the field forward.
