🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences

About this episode

Imagine a dark warehouse. Racks and racks of devices with wires, tubes, and electronics sticking out. The next AI data center? No. This is Lila Sciences‘ dream for the future of science. A dark warehouse full of AI-guided robotics and lab equipment, cranking out new experiments 24/7, building toward a scientific superintelligence.

Their automated lab is almost hypnotizing to watch. They have floating plates zipping around on Wall-E-esque tracks, used vision-language models to control Windows 95 boxes, and created the world’s largest collection of voided warranties. In the process they’ve built a massive library of scientific reasoning tokens. Over 10 trillion of them, all experimentally validated.

No warranties were voided in the making of this video

To say Lila is ambitious is an understatement. Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis.

In our latest episode we sat down with Lila’s very own Andy Beam (CTO) and Rafa Gómez-Bombarelli (CSO, physical sciences) and went on a journey through the possibilities of AI-run science, almost as wide-ranging as Lila’s goals.

Did we mention they do both materials science and biology? In the same AI science factory? Same time, same lab, same AI. Finally a guest who can settle a long-running debate we’ve had amongst ourselves: is biology or materials science harder?

Watch to find out!

We discuss:

* The internet is spent, science is next. Why Lila thinks the scientific method is the last untapped internet-scale dataset, and why they treat RL as a data generation mechanism with nature as the verifier.

* The lab as a data center. Instruments as nodes on a graph, a magnetically levitating “PCI bus” transport layer between them, orchestration as a slurm queue. Andy is not short on analogies.

* Why Lila insists it is not an automation company. They optimize for flexibility and generalizability over raw throughput, which means humans stay below the API line wherever automating does not pay.

* Your experiment has a runtime. We put <a target="_blank" href="http