I mean, what would you do if you were starting a company today? Would you sort of structure your data in a certain way so that you could, you know, set yourself up for all of the AI capability improvements that are going to come over the next few years? Like, what would that look like?

Fundamentally, the way I think of AI is actually the way that electricity changed the factory floor back in the 19th century.

In the 19th century, if you actually go and see how the factory floor was designed, it was built around the main shaft that just ran across the factory and was powered by a steam engine.

So the whole idea is that you had just be connected to the rotor that was powered by the steam engine.

So everything had just be placed in a certain way.

And then once electricity became widespread, people just tried to switch from the steam engine to electricity.

And that didn't really give any productivity gains.

Do you agree with that? Like, do you think that's how people should think about it?

...and, and, you know, you, you think about Beam, w-where, you know, where do you start in terms of how you are thinking about this model, how you're going to design it, how you're going to go about training it? Where does that process start?

Yeah.

So this is actually like our first model.

We started building this model, uh, about a year ago.

So, uh, we, we kind of like, uh, we were a company of, uh, thirty people, uh, about a year ago, and we just like started with, uh, the mission of actually building, uh, a really strong open model, uh, a really strong Western open model.

We saw that like there was a, a gap in terms of, uh, strong open, open models coming out of the West.

Uh, so in the past one year, we've actually assembled a team of, uh, uh, two hundred like, uh, strong researchers, engineers, and, uh, a hundred of, uh, you know, kind of other people who were supported, uh, the, the company as a whole.

Where do you sit on that spectrum?

And Yannis and team did some incredible work to get that statement you wanted to share about

that.

The form factor for the model is a 500 billion parameter model.

It's 23 billion active.

If you actually think of what you want from a model that's an open model and you want to ensure that it has wide adoption, especially from enterprises, you need something that it is efficient, it is compact, and it actually really produces the most intelligence that you can get out of your flops.

In order to do that, you need to have a reasoner.

These are reasoning models.

Do you all think that way at all? It sounds like no.

Mm-hmm.

Um, at the same time, there are like, there've been accusations, I don't know if they're true or not, uh, but, you know, from like US labs that, uh, the Chinese labs are actually doing, uh, distillation at, like, an industrial scale, uh, from their models, and, uh, that's not, of course, something that, like any, uh, Western lab would ever do.

Uh, and you know, there are, like, definitely gains in terms of, uh, access to compute or like, you know, things that we, we can get access to, like the latest compute.

Um, at the same time, I've also heard that, like the Chinese labs, uh, you know, have found ways to bypass maybe the, the restrictions in terms of compute, like via, uh, getting computes from, like, other places.

Um, I think that, like, we have, uh, definitely the, uh, advance of, like, having, uh, you know, the talent here in the sense that, uh, you know, the frontier labs, the clo- closed frontier labs are based in the United States, and, like, many of these people, uh, have actually been, uh, to the frontier.

They've seen the frontier, and many of them have actually chosen to join us now and just, like, work on our models.

Yeah.

This is kind of the bedrock upon which research, uh, in the research institutions around the country, um, are building their solutions.