Daphne Koller argues that AI in drug discovery won’t pay off until we vastly improve our understanding of human biology. She breaks drug development into three stages: finding disease mechanisms, designing molecules to hit those targets, and running clinical trials. AI work so far has zeroed in on molecule design, inspired by successes like AlphaFold. But no AI tool has yet “drugged the undruggable.” The real bottleneck lies upstream: most diseases lack a validated mechanism to target, so faster molecular design simply produces more failures. Koller points out that 90% of clinical trials fail because they aim at the wrong biological locks. We’ve thrown resources at a handful of well-studied targets—38 of them now have more than 50 drug programs each—while the number of genuinely new targets has plunged from about 100 in 2015 to roughly 30 in 2024. Patients suffer because we keep refining keys for familiar locks instead of mapping the unknown parts of the system. Expanding modality toolkits—antibodies, siRNA, gene editing—helped before, but without fresh mechanistic insight, we’re back to square one. She calls for a massive push to measure biology at scale, especially causal and perturbational data. Cell atlases cover only a tiny slice of the cellular universe, and they lack most of the data on how cells respond to interventions. To turn AI into a real discovery engine, we need experiments that sample billions more cell states under varied conditions. Only with that data foundation can AI reasoning shift from generating failures faster to finding the right therapeutic paths.