Fedus bets AI will leap from code to atoms
- AI pioneers pivot from scaling digital models to accelerating material science for chips and batteries.
- Startups like Periodic Labs build closed-loop systems where AI directs physical lab experiments to generate proprietary data.
- The new AI bottleneck isn’t compute or algorithms - it's the stubborn physics of the real world.
AI’s next leap isn’t a better chatbot - it’s a new semiconductor. After years spent scaling GPT-4, co-founder Liam Fedus has launched Periodic Labs to apply AI to material discovery, targeting the physical bottlenecks in chips and batteries.
Fedus argues on No Priors that digital progress has outpaced our ability to manipulate atoms. “Science ultimately isn't sitting in a room thinking really hard,” he says. “You have to conduct experiments to interface with reality.” His system uses LLMs as an orchestration layer, directing robots to run physical tests and capture ground-truth data, a necessity because reported material properties in academic papers often vary by orders of magnitude.
This closed-loop approach aims to build a proprietary data moat, a sharp contrast to training models on the messy, often contradictory data scraped from the internet. The goal is to accelerate the slow feedback loops of physical R&D.
The materials push comes as others in the AI ecosystem attack different physical limits. On This Week in Startups, Nick Harris of Light Matter argued that copper wiring is now a ceiling for AI progress, forcing a shift to photonic chips that can link GPUs over a kilometer with light. He claims this photonic technology can triple model training speeds.
Liam Fedus, No Priors:
- For systems that are strongly governed by quantum mechanical effects, there is some generalization there.
- But if you produce a system that has modeled quantum mechanical objects really accurately, it's not really helping much on fluid dynamics.
The race reflects a broader trend: physicists like Fedus and Anthropic’s Dario Amodei are leading the AI charge. Fedus notes that after projects like the Large Hadron Collider, high-energy physics became bottlenecked by massive, slow hardware. AI offered a frontier where principled, first-principles thinking yields immediate, software-driven results.
The capital required is steep - Fedus says GPU compute is his biggest cost - but the potential payoff is foundational: redesigning the physical components that underpin everything from data centers to electric vehicles.