Stanford just built a fake biotech company staffed entirely by AI agents & put a chief scientific officer in charge of it. 37,075 agents, organized into divisions like target identification, trial design & the usual org chart. The CSO assigns the work. Each agent got exactly one late-stage clinical trial to analyze, out of more than 55,000 trials across a huge range of conditions. The finding that came out of that pile: drugs targeting proteins that are active in specific cell types were nearly 50% more likely to reach market than other drugs. A structural predictor of trial success, pulled from data that was already public and already sitting there. Then they pointed it at a real question: is CD276 a good target for lung cancer? The system confirmed the target from existing data & designed an approach: an antibody that recognizes CD276, tethered to a cancer drug. Outside reviewers called it promising. Here's the part to keep honest about. Nothing was tested. No experiments, no trials, no validation in a lab. A very sophisticated literature-and-data analysis produced a plausible plan & plausible plans fail in pharma all the time. The lead author's own framing is that this is about how far agent teams can go, not that they've arrived. Still, 37,000 agents reading 55,000 trials in parallel is a thing no research group could do any other way.