AI is a time machine, compressing years of lab work into days. Digital organisms simulate biology at every scale for drug discovery. AI-optimized sensor placement achieves the same results with 1% of traditional compute. Healthcare AI can predict disease 20 years early. But here's the reality check: zero generative AI systems have FDA approval for clinical use. Zero. You'll explore the gap between academic proof-of-concept and clinical deployment, the dual-use risk where the same models design both therapeutics and pathogens, and the central tension this entire series builds toward: we're accelerating discovery at unprecedented speed—but at what risk? How do we regulate systems that constantly learn and evolve? This finale leaves you with the right question to sit with. Topics Covered - Digital organisms: simulating biology at all scales - GNNs vs. transformers for biological discovery - Drug discovery: academic proof of concept vs. clinical reality - Sensor optimization (1% of traditional compute!) - Healthcare AI potential: predicting disease 20 years early - Healthcare AI reality: persistent failures in stress tests - Dual-use risk: same model designs therapeutics and pathogens - FDA's stance: zero approved generative AI, mandatory accountability - Interaction intelligence as a safety variable