An industry commentary argues that AI is already embedded in pharma R&D — literature review, molecule generation and clinical-trial management — but models trained only on public data cannot create a moat, so companies are shifting toward building their own “dry-wet” design-build-test-learn loops to accumulate proprietary data. It notes that Anthropic was reported to have set up a biological wet lab in the San Francisco Bay Area, that Eli Lilly's Lilly TuneLab signed GenScript, Twist Bioscience and Ginkgo Bioworks to supply wet-lab data, and that in the Novo Nordisk breach the leaked proprietary biological and chemical training data amounted to only about 407MB. Citing estimates compiled by Southwest Securities, the piece says AI can cut timelines by 70–90% in discovery, 50–80% in pre-clinical and 50–60% in clinical stages, while stressing that AI remains a hypothesis-generating assistant rather than a decision system, and that success still hinges on whether a target truly causes the disease.