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

AI is fundamentally transforming drug discovery by shifting the industry from traditional, labor-intensive lab work to an engineering-driven, computer-aided design model. By leveraging large language models and specialized foundation models, researchers can now target thousands of potential disease markers, moving beyond the current "target crowding" that limits innovation. This technological leap promises to compress the traditional 10-15 year drug development timeline to under five years by automating preclinical design and optimizing clinical trial efficiency. While incumbents rely on massive capital, the democratization of these AI tools allows smaller, agile teams to manage early-stage drug portfolios. As models gain the ability to interface directly with lab instruments, the industry is entering a new era where autonomous, high-throughput experimentation will accelerate the development of complex therapeutics, ultimately shifting the value proposition from traditional pharmaceutical manufacturing to high-leverage, AI-native R&D platforms.