some thoughts on Claude’s protein design work, and implications for the future of AI drug discovery: 1. the optimal biotech company will get shockingly small. a couple great scientists will supervise hundreds of agents doing literature work, modeling, design, analysis, and experimental planning. 2. the future looks like a human+AI scientist orchestrating many specialist models, not one giant biological foundation model. the moat shifts toward the data and infra needed to know which models to use, how to route between them, and how to combine their outputs. 3. value may sit in the harness, not the model. orchestration, tool use, QC, and failure handling may matter as much as raw model capability. 4. the huge 16k+ word prompts are basically an attempt to make tacit scientific expertise executable. a major source of leverage will be converting the knowledge currently trapped in scientists’ heads into rules, examples, tools, and evals agents can use. 5. candidate ranking may matter more than candidate generation. generating 100k plausible molecules is only useful if you can identify the 20 worth testing. 6. surprisingly little of the prompt is actually about biology. most of it is model selection, compute allocation, controls, provenance, scoring, and managing an unsupervised scientific workflow. 7. hard targets are still hard. large, flexible, conformationally heterogeneous proteins remain difficult, so GPCRs, IDRs, dynamic complexes, etc. should remain relative white space even as easier protein design gets commoditized.