Working at fintech company Stripe taught how to manage immense complexity within a regulated, legacy system. This experience of 'wrangling complexity' is surprisingly similar to the challenges in biology, making it excellent preparation for a career in biotech.
Beyond accelerating timelines, AI's real value lies in its ability to design molecules for targets previously considered 'hard-to-drug.' These models operate on different principles than traditional lab methods and are indifferent to historical challenges, opening up entirely new therapeutic possibilities.
To demonstrate industrial-scale viability, Chai Discovery tested its antibody design model on 50 different targets. This focus on generalization, far beyond the typical 2-4 targets shown in academic research, is crucial for proving a model is not a statistical anomaly and is ready for real-world application.
Chai Discovery's partnership with Eli Lilly involves building a custom foundation model trained on Lilly's unique historical data. This signals a new collaboration model where AI firms act as specialized infrastructure builders, creating proprietary, data-moated AI for large pharmaceutical companies.