AI for Atoms: How Periodic Labs is Revolutionizing Materials Engineering with Co-Founder Liam Fedus

**Elad Gil** (0:05)
Today on No Priors, we're talking with Liam Fedus. Liam is one of the co-creators of ChatGPT, which I think almost everybody uses at this point. He was the VP of Post-training at OpenAI, and before that was at Google Brain, where he worked on a variety of really early AI innovations. Liam will be telling us a bit about Periodic Labs, his company which is focused on building an AI foundation lab for atoms. In other words, how do we impact the physical world, material sciences, chemistry, etc. using AI? Very exciting topic and excited to be talking with him today. Liam, thank you so much for joining us today on No Priors.

**Liam Fedus** (0:37)
Yeah, thank you so much for having me. It's great to see you.

**Elad Gil** (0:39)
Yeah. Maybe what we can do, I think you're doing incredibly interesting things in terms of alternative types of models specifically for material sciences, for the physical world. Effectively, what you're building is an AI foundation lab for atoms, which I think is fascinating.

**Liam Fedus** (0:52)
That's right.

**Elad Gil** (0:53)
But maybe we can start with a little bit more of your background. I think you were VP at OpenAI, you worked on one of the first trillion parameter models ever, etc. Could you tell us a little bit more about just what got you here?

**Liam Fedus** (1:04)
Yeah. Even further back, I was a physics major in undergrad, spent some time doing dark matter research. We had an apparatus that was directionally sensitive to dark matter's direction.

**Elad Gil** (1:22)
So it was very interesting. Why are there so many physicists in the air right now? So you look at Dario Amodi who runs Anthropic.

**Liam Fedus** (1:27)
Of course, yeah.

**Elad Gil** (1:29)
You look at Adam Brown at Google, you look at a variety of people and they all have these physics backgrounds.

**Liam Fedus** (1:33)
Yeah. My old manager, Joshua, also physics and now in Anthropic.

**Elad Gil** (1:37)
Yeah. Why do you think that is?

**Liam Fedus** (1:38)
I think it's a great way to think about the world. It's very principled, very hard-nosed scientists, very careful. And I don't know, I think it's such an incredible field. You have such high leverage in computer science, in AI. And so I think a lot of physicists were seeing that, particularly in like high-energy physics. After the discovery of the Higgs, I think a lot of high-energy physicists were sort of looking for what's next. Ultimately, it becomes bottlenecked on the new apparatus for pushing the next energy frontier. And I think a lot of physicists were looking at their skill set and looking at the progress elsewhere and saying like, hey, I think I could be a huge contributor elsewhere.

**Elad Gil** (2:22)
This has been fascinating to see like string theorists and people working on buckles and all sorts of effects like kind of moving into AI.

**Liam Fedus** (2:27)
Absolutely.

**Elad Gil** (2:29)
It almost feels like we're recreating them in a project or something, except now what we're seeking is different forms of intelligence.

**Liam Fedus** (2:34)
So yeah, that's right.

**Elad Gil** (2:35)
I'm just going to say sorry to interrupt. So you studied physics, you worked on dark matter.

**Liam Fedus** (2:39)
That's right. And then I was basically in grad school in physics, I was always gravitating towards the machine learning problems. I was looking at particle reconstruction, and it's thinking about effectively machine learning problems. But it felt if I really wanted to push frontier of machine learning, I should be in computer science. So I ended up at Google Brain, was overlapping with the first year residents there. Absolutely remarkable group of people, remarkable period for Google Brain. I mean, it's an era of when there's the creation of distributed training strategies, mixture of experts, the transformer.
It was a really rich period in that history. And it was a fun kind of like Cambrian era, where people were really pushing the frontier with just like a handful of GPUs, really small collaborations. The field was a much, much earlier. And I think there was a lot of diversity and entropy in the research. And it was very fun.

**Elad Gil** (3:33)
So it was kind of late 2010s or so, something like that.

**Liam Fedus** (3:36)
This was 2016, 2017 So Google brand at that point was really small and eventually was subsumed by DeepMind or combined with DeepMind. So it was at Google for many years, mostly just doing architecture work. So it was really pushing sparsity that allows for more efficient serving of models at scale and just really pushing the scale of what we could do. Towards late 2022, really became excited about the creation of products. The technology was getting very compelling. And so I ended up at OpenAI with some other Googlers as well.