AI has mastered language, but atoms remain out of reach. Liam Fedus, co-founder of ChatGPT and now of Periodic Labs, argues that AI's next leap requires literal hands - robots to conduct real-world chemistry and physics experiments.
Fedus spent years scaling large language models, but he sees a fundamental bottleneck. Scientific papers, the presumed training data for material science, are often wrong. Values for the same material can vary by orders of magnitude. Training a model on this data, Fedus says, only reproduces human error and confusion.
The solution is a closed loop. At Periodic Labs, AI directs robotic labs to synthesize and test materials, capturing ground-truth data to refine its own models. This moves AI from pattern recognition in text to active intervention in the physical world, aiming to design new semiconductors and aerospace alloys.
This shift is attracting a specific kind of talent. Fedus notes that many leading AI researchers, from Anthropic's Dario Amodei to himself, are former high-energy physicists. After the Higgs boson discovery, that field hit a hardware wall. AI offered a new frontier where first-principles thinking could yield immediate, measurable results.
These physicists treat neural networks as physical systems to be measured and optimized, bringing a hard-nosed experimental rigor to model training. They apply scaling laws to predict performance, treating software with the discipline of a lab experiment.
Liam Fedus, No Priors:
- Science ultimately isn't sitting in a room thinking really hard.
- You have to conduct experiments to interface with reality.
The ambition is a productivity spike for the physical world, akin to an agricultural revolution for materials. But the feedback loop will be slower, constrained by the stubborn pace of atoms, not bits.
