The Exchanges

Q then build all the software around it. But you guys also build, you not only manufacture the chip, but you build the algorithms and the software that surrounds it. So that enables companies to get the most out of it. So just three questions on that one. Is that a right characterization of what you're doing? Um, and then, well, let's just start with that one. I'm not going to.

A Yeah, I, I think so. I mean, the, the core thesis that powers Nvidia is that a chip could never be enough. You know, um, just, just the same way that a chip couldn't be enough for my Apple phone, for example, you know, Apple makes awesome chips, but the experience of using my phone, uh, is a lot more than the chip. And, you know, the way that Apple's able to vertically integrate and optimize, um, their entire system in order to create an amazing consumer experience, I think is, is pretty incredible and super valuable. What Nvidia is doing is. Uh, not the same, but it's related in the sense that we understand that the value of the technology we create is only understood in composition, in context. It's really about, are we delivering acceleration, transformative acceleration to the most important computational workloads of our time?

AI assessment note: “Yeah, I, I think so. I mean, the, the core thesis that powers Nvidia”

Q Wait, how, so how do you use it for your own chip work?

A Um, at the moment, a lot of it has to do with improving communication between, uh, chip designers. So, uh, you have like a thousand people working on this project and there's a lot of interfaces that need to be described. And, um, you know, people have questions. They don't know, uh, exactly who to talk to. So basically we're making, uh, uh, knowledge bases about our own work that then people, uh, can use to answer questions. Um, and we found that that, um, It's kind of like having a, a more senior engineer, uh, that you can talk to all the time that, that helps you, uh, find the things you need to find in a, in a huge code base. Um, and so, so that's, that's the primary thing that we're doing right now is, is augmenting, uh, the engineers on the team with kind of, um, superpowers to understand our own code better and, and interact with it better. Uh, over time, I expect that Chip Nemo is going to, uh, do other things as well. Um, you know, improving the quality of our designs, you know, our hopper GPUs, for example, have a lot of circuits in them that were designed by AI that we built ourselves, uh, that have better, um, speed, um, and power and cost characteristics than, uh, we knew how to build with any other tool.

AI assessment note: “basically we're making, uh, uh, knowledge bases about our own work”

Q And is that the next place that we go with this, like the world models? Like we just saw Meta put something out where like it's, Uh, generative, or it's not even generative software, but it's AI software can kind of guess like what would happen if you black out like a certain frame in a video. Um, is that, is that where the next stage of this goes?

A I think that's gonna be really helpful. Um, you know, these, these sorts of world models, um, you know, I, I was really impressed with the, uh, open AI Sora project, uh, this week as well. I mean, really fantastic results. And I'm thinking about, you know, uh, how these things work together. So, you know, the Sora project, if you read their posts, they talk a lot about how building a world model is gonna help, uh, make artificial intelligence more useful because it, it, you know, it, it's gonna understand how things interact. In the real world. And then it's going to be able to use that to, to make better decisions in order to do things. Um, so, so I think that's great. Um, and I also think it's, uh, the other way around is also really important that, you know, having a world model then allows us to synthesize a world, which then, uh, allows virtual worlds to be richer, more interesting, more interactive. And I think that's hugely valuable.

AI assessment note: “I think that's gonna be really helpful. Um, you know, these, these sorts of world models”

Q few minutes left. I want to ask you a couple more questions. Uh, chat GPT when that comes out, I mean, obviously you had already been pretty impressed by GPT one and two. We're already at three, three and a half, right? By the time chat GPT comes out in November, 20, 22. And then this stuff explodes your reaction. Like what, what was it like sitting where you were?

A It was just extraordinary. I mean, the amount of change that chat GPT brought to the world, uh, incredible, uh, I didn't, I thought it was kind of cheeky of OpenAI to release it at the same time as the NeurIPS conference, because, um, you know, usually the AI world is entirely focused on like the cool papers that are coming out at the conference, but instead the entire world was focused on this chat bot, you know, that was doing things that, you know, no one had ever seen a chat bot do before. And, uh, you know, to me, that was a statement that we were entering a new era of AI where applied research, um, uh, starts to dominate, you know, so, uh, Chachapiti didn't come out with a fully fledged academic paper that described exactly what they did to make it so awesome. Um, but because the results were so strong, it kind of dominated the, the academic discussion. And, um, I felt like that, that was really interesting, um, in terms of, uh, a water, watermark, a watershed moment for, um, Uh, sort of the maturity of the AI industry. You know, that, that it was now possible, um, to create systems that would solve problems in ways that we'd never seen before. Um, uh, if we, we applied some really good engineering and applied research to it. Um, and so that, um, you know, definitely, definitely changed the world. And, and since then, you know, uh, my world has been just continuously on f…

AI assessment note: “It was just extraordinary. I mean, the amount of change that chat GPT brought”

Q Right. And so let just, let's walk through a little bit about like how this actually happens. So let's say I'm an organization. Uh, I come to NVIDIA, say I have a bunch of data, or maybe I even don't have data and I'm looking to build a large language model. Um, what do I do now?

A That's a great question. You know, the, the first thing that's on my mind is like, you know, what data center are you going to use to train this model in? Um, and, uh, you know, that's, that's a really important question, uh, because it turns out that, uh, the AI market is growing pretty fast because there's so many institutions that are training these huge models and you actually have to have a building to put these machines in and they're, they're not small, uh, and you need to hook it up to power, you know? So that, that would be one of my first questions is like, okay, are, are you ready? Uh, to stand this up? Or, um, are you going to be working with a CSP? Like for example, AWS, you know, um, and we love, we love to support our customers, um, through, through cloud providers as well.

AI assessment note: “the first thing that's on my mind is like, you know, what data center”

Q in, if they're gonna build a bot, and they had this whole bot platform that came out. But, overall, like, everyone's telling you, yeah, this is not worth building. I mean, it's maybe just one or two companies that are using it. So, why did you still think that, I mean, I guess it's hard to predict what happened next, but why did you believe that that was gonna happen?

A NVIDIA really thinks about these problems from first principles. You know, we know that, um, the way that computers are built is changing. We know that, um, because of, you know, Moore's law is, is slowing down. That requires more specialization. Um, we know that, uh, there's a lot of opportunities to really provide transformational, uh, speed ups to important workloads if we specialize the systems and the software for them. And, uh, we felt like, what is more important than this? You know, what's more important than intelligence? And does the world need more intelligence? Absolutely. The world needs enormous amounts of intelligence, like the problems that we face, um, as a planet, uh, I think we're, we're going to need a lot of intelligence to work through them. And so, uh, for us, it was, I think, just kind of an obvious thing to do. Um, we had a lot of conviction. We, we understood the technology. We also, Saw early indicators of success from a lot of different, um, directions. You know, a lot of different companies, a lot of research institutions that, um, were talking to us and saying, hey, we, um, we have these goals to, like, train this huge language model, like, on enormous amounts of text, but, you know, the, the current systems are just too slow. And, um, you know, there's this idea, you know, back 10 years ago, there was this idea that unsupervised learning was going…

AI assessment note: “NVIDIA really thinks about these problems from first principles.”