ultimately, sciences driven against experiment in the real world. And so that's what we're doing

with periodic labs. We're taking these precursor technologies and we're saying, okay, if you care

about advancing science, we need to have experiment in the loop. The applications of building an AI

physicist, for lack of a better word, that can design the real world are so broad.

You can apply them to advanced manufacturing.

You can apply them material science, to chemistry.

Any process where there's R&D with the physical world required, it seems like we'll benefit

from breakthroughs that periodic is working on.

For example, if you could find a 200 Kelvin superconductor, even before we make any product with it.

To be able to see such quantum effects at such high temperatures,

I think it would be such an update to people's view or how they see the universe.

What if AI could move from talking about science to doing science?

Today's conversation features Anjane Mitae Mita, General Partner A16Z, with Liam Fedas

and Doge Chubuk, co-founders of periodic labs, a frontier research lab building experiment

in the loop AI for physics and chemistry. They unpack why real-world reward functions matter,

how mid-training and high-compute-RL fit together,

and why superconductivity and magnetism are the first B-lines towards an AI physicist.

They also get into noisy data sets and negative results,

what happens when ML researchers sit shoulder-to-shoulder with bench scientists,

and the near-term payoff, co-pilot tools for advanced industries from semiconductors to space and manufacturing.

Let's get into it.

So Liam, you were the co-creator of Chat, GPT.

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