Descriptions:
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-experiment-has-a-runtime/)
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: https://www.linkedin.com/in/andrew-beam-01a6aa295/
Andy Beam (Lila): https://www.lila.ai/team/andrew-beam
Rafa Gómez-Bombarelli: https://www.linkedin.com/in/rgbombarelli/
Rafa Gómez-Bombarelli (Lila): https://www.lila.ai/team/rafael-gomez-bombarelli
Lila Sciences: https://www.lila.ai/
Lila Sciences (LinkedIn): https://www.linkedin.com/company/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
