RJ Honicky

RJ Honicky × Matt McPartlon × Neil Patil

Chai Discovery is commercializing a neutral modeling and product layer for pharma rather than developing its own drugs, with partnerships including Eli Lilly, Pfizer, Novartis, and Genentech.Chai-2 designed antibodies against 50 targets, finding binders for about half with an average binding hit rate of around 20%, while enabling GPCR agonists and multispecific formats traditional screening struggles to produce.The opportunity depends on improving developability and epitope prediction, but compute scarcity, validation latency, and talent shortages remain structural constraints as Chai scales its platform.

Brandon Anderson × RJ Honicky × Heather Kulik

Materials AI lacks an AlphaFold-like shortcut: variable bonding and sparse experimental ground truth make validation a central bottleneck.AI found a polymer-network design that made the material about four times tougher through electron rearrangement during molecular breakage.Active learning offers at least a hundred- to thousandfold speedup across seven direct-air-capture objectives, but reliable DFT replacement at two orders of magnitude greater speed and device-scale processing remain unresolved.

RJ Honicky × Brandon Anderson × Andrew White

FutureHouse’s Cosmos turns scientific discovery into a closed loop linking literature, data analysis, experiments and an evolving world model, shifting the bottleneck toward laboratory state, reagent logistics and experiment turnaround.Robin’s dry-AMD work showed verification can beat expert enthusiasm, while Ether0 exposed adversarial verifier failures; scaling discovery will depend on provenance, cheap filtering and robust tests before wet-lab spending.