A single spatial biology run can yield two to six terabytes of data, far more than a scientist can eyeball, and Kenny Workman argues that this is the raw material for teaching AI to actually do science. LatchBio, five years deep in pharma, treats experimental biology as a verifiable substrate: lay a chunk of a tumor over a sequencing surface and you get a giant matrix of numbers whose analysis has a right answer, which is exactly what you need to benchmark and improve a model. They adapted coding models into biology tools and built benchmarks like sequencing based spatial analysis, and found what everyone in post training now knows, that frontier models cannot yet be trusted with this and that measurement is what drives progress. The reason biology is hard is that it is messy and the field rarely agrees on the answer, so much of the work is designing tasks where reasoning, not memorized knowledge, is what gets rewarded. Workman walks through trajectory data from real scientists, tasks like finding the part of a tumor that matters, and why an ambiguous prompt quietly makes a benchmark uninformative. He also gets candid about biosecurity, where model refusals are their own evaluation problem and red team tasks have to be handled carefully, and frames the whole effort as a flywheel: better benchmarks, better tools, more of the program landscape indexed, repeat. Speaker info: - https://x.com/kenbwork - https://www.linkedin.com/in/kennyworkman - https://kenbw.com/ Timestamps: 0:00 - Terabytes of experimental data 1:41 - Decomposing a new paper into tasks 2:44 - A verifiable substrate for science 3:23 - Five years in pharma 4:13 - Coding models as biology tools 5:40 - Why frontier models can't be trusted yet 6:44 - Sequencing based spatial analysis 9:16 - Reasoning, not memorized knowledge 10:07 - Trajectory data from real scientists 12:14 - Why biology tasks are messy 15:36 - Biosecurity and refusals