Biopharma’s ability to create new drugs has depended, from the start, on our collective knowledge of Earth’s genetic biodiversity. While many existing datasets have sought to describe the world, they have traditionally remained small and unrepresentative. This has put a hard ceiling on what AI can achieve in biopharma. Some companies, though, are going big, REALLY big - creating “digital twins” of the natural world at scale. This is an entirely new approach to data collection in biopharma. By applying new classes of foundational models, these companies are capturing a universe of hidden biological signals once invisible to biopharma. In turn, this has resulted in the design of genomes and proteins with unprecedented novelty, controllability, and performance. In this panel, the participants will explore the revolutionary impact, from the lab to new IP, of these new platforms in bridging the AI data gap and how biopharma is being transformed.