Scaling for physical AI means scaling the actual footprint required for physical world data.
These systems need to be tightly coupled with actual physical infrastructure to close the loop in their development.
Congrats to the @periodiclabs team on Neon!
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We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.
Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.
This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.
Read our blog posts below.
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