AIDD's Ongoing & Next: Wet-labs, CXOs, AI drug developers

Citi sees AIDD developing across three coexisting models: pharma-owned federated networks that apply AI to internal data without transferring it outside the organisation; technology-owned workbenches that sell models, computing and workflow tools; and integrated drug-generation platforms that advance candidates and receive licensing payments, milestones and royalties. AI does not replace the wet lab; it makes the wet lab more scalable and more valuable, as rising model output makes experimental validation the key bottleneck and leading players scale computational and wet-lab capabilities simultaneously to create iterative "AI-to-experiment-to-AI" feedback loops, potentially producing a "Jevons effect" that widens the top of the drug-development funnel. A survey of 50 US pharma R&D technology decision-makers shows 98% at least testing AI and 72% scaling or having fully scaled deployment. Top picks are WuXi Biologics, with protein-generation service up over 40% yoy in 1H26, estimated nea