🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences
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 Escalante Bio’s question to Andy: if science is the token generator, what is the runtime of your data collection? His answer, in short, is that you cannot make the ribosome go faster. Why Lila bets on fast round-over-round iteration rather than big noisy multiplexed screens, and how Rafa’s team rebuilt a gas sorption measurement to run roughly 2,500x faster. * What is actually in 10 trillion scientific tokens. Not sequences. Experimentally verified reasoning traces, a kind of data that Andy argues exists on the internet in quantities that round to zero. * Breadth as a path to depth. Small molecule chemistry priors transferring to metal organic frameworks for carbon capture, and the claim that the general model beats domain-specific models sample for sample. * If you have the data, what do you need the model for? Sri Kosuri’s koan about the ML-for-drug-discovery business model, and Andy’s answer: the coding model got better because it also read Shakespeare and carnitas recipes. * The serendipity they want to automate. Emily Whitehead survived the first pediatric CAR-T cure only because the doctor treating her happened to know, from pediatric arthritis, which antibody would blunt her IL-6 response. Roll that dice again and you probably lose her. Breadth is how you stop depending on luck. * Move 37 for catalysts. Model suggestions for platinum-group-free electrocatalysts that went from boring, to what a 40-paper expert called stupid, to the best performers they have made. * Six months to in vivo CAR-T data in non-human primates, and the zero-FTE virtual startup commercial model that fell out of it. For context on why that number is startling, AbbVie paid $2.1B for Capstan on the strength of preclinical in vivo CAR-T data. * You cannot have scientific superintelligence if you are just a good test taker. Ken Stanley, who wrote Why Greatness Cannot Be Planned, runs open-endedness at Lila. RL at scale gives you a ruthlessly Vulcan problem solver. Machine creativity is a different thing, and it is the part nobody has solved. * The chain of thought is an unreliable narrator. The model reasons in latent space and only emits tokens. Sometimes it skips the experiment entirely and is still right. So how much do you trust the reasoning versus the verifier? * Reward hacking when the rollout is physical. Chains of thought that collapse into repetition, and a model that got annoyed and swore at the scientist who kept asking it to redo a plate map. What happens when a pathological loop has a wet lab inside it? * The bittersweet lesson. Rafa’s inversion of the bitter lesson: in AI, scaling is a roadmap. In materials, scaling is a filter, because only the things that scale end up mattering. * Not your typical Flagship company. Why a famously single-asset biotech incubator spun out a platform bet, and Andy’s line that if Lila called itself a biopharma it would have a top-three GPU cluster. * Bottlenecks they would remove by fiat. Sim-to-real for physics-based simulation, and the fact that RL training runs at roughly 5% mean FLOP utilization. Watch on YouTube: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe