Google DeepMind is pivoting from attacking hard biological problems to joining the race for an "AI scientist." This will likely harvest a lot of low-hanging fruit, but I think it will stall our actual understanding of biology. If we think of biological knowledge and drug discovery as a distribution, we currently observe only a slice of it, along only a few dimensions. That is why drug discovery remains largely a matter of luck rather than optimisation over known parameters, and luck is not something LLMs with better reasoning can fix. What we need is new biological data, generated specifically to expose the missing dimensions and the unobserved mass of that distribution. Above all, we need causal data and models with strong inductive biases, AlphaFold2 being the obvious example. Almost everything since has been reaping the fruit AF2 planted. If this resonates with you and you have a strong AI background, come work with us.