We meet Daniel McKinnon (@danielmckinn0n), CEO of Gamow Labs, who is building AI systems to reanalyze unresolved rare-disease genomes—and using a rescued laboratory to generate new biological evidence. We spend much of the conversation at the VUS boundary: a biology study can add ACMG points to a patient's case while also teaching the machines what that region of the genome does. We trace the personal origin of the work through his son Owen: McKinnon's own reanalysis found what a prenatal sequencing lab had missed. We then follow Owen's missed diagnosis to the workflow level: a disease-causing deletion fell outside a clinical lab's search cutoff, while an AI agent kept looking. The conversation is worth our time because it gets concrete about the AI layer: a harness, tools, and trace analysis are used to catch failures as basic as changing a gene's name. We also visit the lab to examine the proposed hill climb from $50,000 per functional study toward a 50-cent target, with a robot arm moving plates through high-throughput experiments. That target is not an achieved cost; it is the engineering question on the table. We learned a ton. Enjoy! TIMESTAMPS 0:00 Can a rescued lab teach AI? 3:09 Why AI genetics needs biological experiments 6:10 A failed drug program unlocked Gamow's lab 7:51 Owen's missed diagnosis became a life's mission 10:21 AI agents can search beyond human cutoffs 13:14 Sequencing got cheap; interpretation stayed the bottleneck 15:20 Why Gamow chose diagnostics over screening 18:41 Reanalysis in a week for ten dollars a genome 20:28 What Gamow builds on top of frontier models 23:10 Stronger models can break existing tools 25:45 Can robots make genetic experiments affordable? 27:08 Can biological experiments teach genetic AI?