Jensen Huang: prediction

Transcript context

…Yeah. There, there… Yeah, there’s a lot of low-hanging fruit here on Earth- … That we can utilize for the AI scaling. Quick pause. Quick 30-second thank you to our sponsors. Check them out in the description. It really is the best way to support this podcast. Go to lexfridman.com/sponsors. We got Perplexity for curiosity-driven knowledge exploration, Shopify for selling stuff online, LMNT for electrolytes, Fin for customer service AI agents, and Quo for a phone system, like calls, texts, contacts, for your business. Choose wisely, my friends. And now, back to my conversation with Jensen Huang. Do you think NVIDIA may be worth 10 trillion at some point? Let’s, let’s ask it this way. What does the future of the world look like where that’s true? I think that NVIDIA’s growth is extremely likely, and in my mind, inevitable. And let me explain why. We’re the largest computer company in history. That alone should beg the question, why? And the reason of course… Two reasons. First, two foundational technical reasons. The first reason is that computing went from being a retrieval-based, file retrieval system. Almost everything is a file… We pre-write something, we pre-record something. You know, we draw something, we put it on the web, we put it in a file. And we use a recommender system, some smart filter, to figure out what to retrieve for you. And so we were a pre-recording, human pre-recording, and file retrieving system. That’s what a computer is, largely. To now, AI computers are contextually aware, which means that it has to process and generate tokens in real time. So we went from a retrieval-based computing system to a generative-based computing system. We’re gonna need a lot more processing in this new world than in the old world. We need a lot of storage in the old world. We need a lot of computation in this new world. And so that’s the first part of it. We fundamentally changed computing and the way how computing is done. The only thing that would cause it to go back…… is if this way of computation, this way of computing generating information that’s contextually relevant, situationally aware, that is grounded on new insight before it generates information, this computation-intensive way of doing computing would only go back if it’s not effective. So if… For the last 10, 15 years while working on deep learning, if at any single moment I would have come to the conclusion that, “You know what? This is not gonna work out. I think this is a dead end.” Or, “It’s not gonna scale, it’s not gonna solve this modality, not gonna be used in this application. ” Then, of course, I would feel very differently about it, but I think the last five years has given me more confidence than the previous ten years. The second idea is computers, because it was a storage system, it was largely a warehouse. We’re now building factories. Warehouses don’t make much money. Factories directly correlates with the company’s revenues. And so, the computer did two things. Not only did it change the way it did it, its purpose in the world changed. It’s no longer a computer, it’s a factory. It’s a factory, it’s used for generation of revenues. We’re now seeing not only is this factory generating products, commodities that people want to consume, we’re seeing that the commodities are so interesting, so valuable to so many different audiences that the tokens are starting to segment, like iPhones. You have free tokens, you have premium tokens, and you have several tokens in the middle. interesting, so valuable to so many different audiences that the tokens are starting to segment, like iPhones. You have free tokens, you have premium tokens, and you have several tokens in the middle. And so intelligence, as it turns out, you know, it’s a scalable product. There’s extremely high intelligence products, tokens that you could… that are used for specialized things, people be willing to pay. You know, the idea that somebody’s willing to pay $1000 per million tokens is just around the corner. It’s not if, it’s only when. And so, so now we’re seeing that the commodity that this factory makes is actually valuable, and is revenue generating and profit generating. Now the question is how many of these factories does the world need? How many tokens does the world need? And how much is society willing to pay for these tokens? And what would happen to the world’s economy if the productivity were to improve so substantially? What would happen… Are we, are we gonna discover new drugs, new products, new services? And so when you take these things in combination, I am absolutely certain that the world’s GDP is going to accelerate in growth. I’m absolutely certain the percentage of that GDP that will be used for computation will be 100 times more than the past—mm-hmm—because it’s no longer a storage unit. It’s a product generation unit. And so when you look at it in that context and then you back into what is NVIDIA’s, what does NVIDIA sh—what does NVIDIA do and how much of that new economics, new industry would we have to benefit t—to address, I think we’re gonna be a lot, lot bigger. And then the rest of it, to me, is: is it possible for NVIDIA to be a, you know, $3 trillion revenue company in the near future? The answer is, of course, yes. And the reason for that is because it’s not limited by any physical limits. There’s nothing that I see that says, you know, gosh $3 trillion is not possible. And as it turns out, NVIDIA’s supply chain is—the burden is shared by 200 companies. And the fact that we scale out on the backs of, with the partnership of this ecosystem, the question is: do we have the energy to do so? And surely we will have the energy to do so. And so all of these things combined, that number is just a number, you know? And I still remember, NVIDIA was a… the first time we crossed a billion dollars, I was reminded of a CEO who told me, “You know, Jensen, it’s theoretically impossible for a fabless semiconductor company to exceed a billion dollars.” And I won’t bore you with why, but of course it’s illogical and there’s a lot of evidence we’re not. And then somebody told me, “You know, Jensen, you’ll never be more than $25 billion because of some other company.” Somebody told me that, “You’ll never be, you know, because…” And so those aren’t principled, first principled reason thinking. And the simple way to think about that is what is it that we make and how large is the opportunity that we can create?…

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