🔬 RL with Verifiable Rewards, but the Verifier is a Lab — Lila Sciences

Andy Beam (CTO) and Rafa Gómez-Bombarelli (Co-founder & CSO of Physical Sciences) of Lila Sciences join us to talk about building scientific superintelligence. Andy makes the case that the internet is a spent resource ("we have but one internet. It's the fossil fuel. We fracked"), and that the next internet-scale dataset comes from running the scientific method as reinforcement learning, with the wet lab as verifier. Science becomes an "infinite token generator" — the lab isn't the product, the model is. The counterintuitive result: one general model trained on ~10 trillion experimentally-verified reasoning tokens across biology, chemistry, and materials beats the domain-specific ones — "breadth gives us depth." Great blog post from Escalante Bio referenced in the episode: "Your Experiment has a Runtime" (https://blog.escalante.bio/your-exper...) Highlights: The lab as data center: instruments on "a PCI bus," humans "below the API line" A CAR-T candidate designed in six months by two or three people "Monster UTRs" hitting ~10x Moderna/Pfizer mRNA expression The "zero-FTE startup" business model "You can't have scientific superintelligence if you're just a good test taker" Rafa's "bittersweet lesson": "only the things that you can scale matter" Why there's still no AlphaFold for materials RL pathologies: collapsed chains of thought, a model that "swears" A vision-language model driving a Windows 95 instrument "The world's largest collection of voided warranties in biology" Links: Andy Beam:   / andrew-beam-01a6aa295   Andy Beam (Lila): https://www.lila.ai/team/andrew-beam Rafa Gómez-Bombarelli:   / rgbombarelli   Rafa Gómez-Bombarelli (Lila): https://www.lila.ai/team/rafael-gomez... Lila Sciences: https://www.lila.ai/ Lila Sciences (LinkedIn):   / lila-sciences   Chapters: 0:00 "We have but one internet" 0:46 Intro & guest backgrounds 5:36 The thesis: the bitter lesson & the infinite token generator 10:01 Inside the AI Science Factory: the "PCI bus" & the API line 14:34 Safety, security & scientific rigor 24:39 RL, reward hacking & chain-of-thought pathologies 28:16 Why Lila isn't a biotech: the model is the product 32:36 10 trillion tokens & why the general model wins 35:25 Not just TechBio: materials, quantum dots & MOFs 41:42 Scaling & the "bittersweet lesson" of materials 44:12 The in-vivo CAR-T proof point 49:13 The "zero-FTE startup" model 52:56 Clinical translation & loading the die 59:40 Ken Stanley & open-endedness 1:01:07 Lab video walkthrough & the lab as a data center 1:07:07 Orchestration, scaling & faster assays 1:14:54 Instrument onboarding & the 10T-token dataset 1:24:22 Lila & the Flagship ecosystem 1:31:33 What's harder: materials or biology? 1:35:53 Bottlenecks, MFU & closing thoughts