Generative genomics, using generative AI models to create entirely new, high-fidelity genomic data, represents a major shift in how we understand and apply biology. Large-scale AI models trained on genomic and transcriptomic data enable researchers to predict biological behavior before running an experiment - accelerating discovery, improving clinical decision-making, and enabling more efficient development of new therapies. This session brings together innovators spanning the full AI-to-clinic pipeline to discuss how predictive biology is moving from theory to practice. The discussion will focus on how generative models improve biological predictability, optimizing trial design, and enabling faster, data-driven decision-making. Panelists will also explore emerging standards for validation, data integrity, and regulatory adoption to ensure clinical readiness.