Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Applications, AI in Life Sciences

Artificial intelligence is transforming drug discovery from a slow, trial-and-error process into a high-speed engineering discipline. By leveraging large language models and specialized foundation models, researchers can now design molecules with greater precision, potentially reducing the 10-to-15-year drug development timeline to under five years. Eric Abrams of Anthropic and Josh of Chai Discovery emphasize that AI’s impact extends beyond initial molecular design to clinical trial optimization and regulatory navigation. While traditional biotech relies on physical lab experiments, AI-native platforms are enabling "zero-shot" drug design and autonomous R&D workflows. This shift addresses the industry's critical bottleneck of target crowding, where innovation has historically stagnated around a limited set of targets. As AI tools become more accessible, the barrier to entry for developing therapeutics is lowering, fostering a new era of rapid, data-driven medical innovation.