Steeve Morin: Why Google Will Win the AI Arms Race & OpenAI Will Not | E1262

The thing with NVIDIA is that they spend a lot of energy making you care about stuff you shouldn't care about. And they were very successful. Like, who gives a shit about Kuda? OpenAI is amazing, but it's not their compute. Ultimately, if you don't own your compute, you're starting with, you know, something at your ankle. In five years, I would say 95% inference, 5% training. You have the products, the data, and the compute. Who has all three? Google has, like, you know, Android, Google Docs, whatever. They have everything. They can sprinkle everywhere. This is the sleeping giant in my mind.

Ready to go? Steve, dude, I am so grateful to you for joining me today. I've wanted to make this one happen for a while, but when we were discussing who'd be best for this topic, I was like, "We've got to have Steve on." So thank you for joining me today, man.

Man, well, well thank you. I feel, uh, humbled. I appreciate it. Thank you.

Dude, I want to start. Can you just give us a quick overview of ZML, and specifically your role in the infrastructure strategy today and where you sit?

At the very bottom of things, um, ZML is an ML framework that runs any models on any hardware. Uh, and it does so without compromise. So we sit ultimately at, at the, um, at the infrastructure layer. Uh, we enable anybody to run their model better, faster, more reliably, uh, but on any compute whatsoever. It doesn't really matter, it could be NVIDIA, it could be AMD, it could be TPU, and whatnot, uh, and we do all that without compromise. That's the key point because if there's a compromise, then it's not really, you know, agnostic, right?

Can I ask you then, if we think about sitting between any model and any provider, there in terms-

Right.

... of AMD, NVIDIA-

Right.

... do you think then we will be existing in a world where people are using multiple models simultaneously and that is-

Yeah.

... concurrently running?

Yes, um, you, you actually can see it. It's been happening for a while. Models now are, are not the right abstractions. At least if you look at closed source model, they're not really models. They're more like backends, right? Uh, and there are a lot of tricks that you feel like you're talking to one model, but ultimately you're talking to a constellation, an assembly of backends that produces, you know, a response. Uh, probably the number one, you know, I would say obvious thing, would be that if you ask a model to generate an image, then it will, you know, switch to a diffusion model, right, not an LLM. So, and there's many, many more tricks, the turbo models at OpenAI do that. There's a lot of tricks. So definitely, uh, models in the sense of, you know, getting, you know, weights and running them is something that is ultimately going away because, you know, in favor of, like, full-blown backends, right? You feel like you're talking to a model, but ultimately you're talking to an API. The thing is that API will be running locally, right? Or locally I mean in your own, you know, uh, cloud, you know, instances and so on.