This prodcast discuss the current state and future potential of AI in pharmaceutical research and development (R&D), particularly in addressing the "Eroom's Law" phenomenon, where drug development costs exponentially increase over time. While AI is showing promising results in accelerating early discovery phases—such as identifying targets and designing molecules more quickly with fewer compounds synthesized—these program-level efficiencies have not yet translated into a significant reduction in overall R&D costs or clinical trial timelines across the industry. The sources highlight that no AI-discovered drug has yet received regulatory approval, and structural bottlenecks, including fragmented data, complex late-stage trials, regulatory inertia, and organizational challenges, are hindering AI's full impact. Despite substantial investments and a rise in AI-driven partnerships, the overall productivity of drug development remains largely stagnant or worsening, with the cost per new drug continuing to be exceptionally high, prompting a call for foundational shifts in data infrastructure, trial design, regulatory frameworks, and organizational culture to leverage AI's transformative power. Produced by Dr. Jake Chen.