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special episode this week. We've preempted the weekly show and there's only three people we preempt the show
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for. President Trump, Jesus, and Jensen. And uh I'll let you pick which order we
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do that. Uh but what an amazing run you've had and a great event. Uh >> every industry is here. Every tech
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company is here. Every AI company is here. Incredible. Incredible. I'm going
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00:00:31
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00:00:50
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00:00:56
Arowallix. Build the future. >> And um one of the great announcements of the past year has been Grock. When you
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made the purchase of Grock, did you realize how insufferable Cha Chimath would become?
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>> I had I had an inkling that that that >> we're his friends. We have to deal with
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him every week. >> I know it. >> You had to deal with him for the six
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week close. >> I know. It's like two weeks. Two weeks. >> It's all coming back to me now. It's
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it's making me rather uncomfortable. The the thing is uh many of our strategies
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are are presented in in broad daylight at GTC years in advance of when we do it. Two and a half years ago, I
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introduced the operating system of the AI factory and it's called Dynamo. Dynamo as you know is a piece of
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instrument a machine that was created by seammens to turn essentially water into
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electricity and dynamo uh powered the factory of the last industrial revolution. So I thought it was the
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perfect name for the operating system of the next industrial revolution the factory of that and so inside Dynamo the
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fundamental technology is disagregated inference. Jason, I I know you're you're you're
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super technical. >> Absolutely. I know it. >> I'll let you take this one. Go ahead and
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define it for the audience. I don't want to step on you. >> Yeah. Thank you. I I I knew you wanted
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to jump in there for a second, but it's it's disagregating inference, which
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means the the pipeline, the processing pipeline of inference is extremely complicated. In fact, it is the most
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complicated computing problem today. Incredible scale, lots of mathematics of different shapes and sizes. And we came
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up came up with the idea that you would change you would you would disagregate parts of the processing such that some
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of it can run on some GPUs rest of it can run on different GPUs and that led to us realizing that maybe even
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disagregated computing could make sense that we could have different heterogeneous nature of computing that
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same sensibility led us to melanox >> you know today Nvidia's computing is
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spread across GPUs, CPUs, switches, scale up switches, scale out switches, networking processors, and now we're
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going to add Grock to that. And we're going to put the right workload on the
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right chips. You know, we just really evolved from a GPU company to an AI factory company.
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>> I mean, I think that was probably the biggest takeaway that I had. You're
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seeing this fundamental disagregation where we've gone from a GPU and now you
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have this complexion of all these different options that will eventually exist. The thing that you guys said on
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stage or you said on stage was I I would like the high value inference people to
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take a listen to this and 25% of your data center space you said should be allocated to this gro lpu GPU
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>> grock to about 25% of the ver rubins in the g in the data center. So can you
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tell us about how the industry looks at this idea of now basically creating this
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next generation form of disagregated pre-filled decode DSAG and how people do you think will react to it?
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>> Yeah and take a step back and at the time that we added this we went from
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large language model processing to agentic processing. Now when you're running an agent you're accessing
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working memory you're accessing long-term memory. You're using tools. You're really beating up on storage
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really hard. You have agents working with other agents. Some of the agents are very large models. Some of them are
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smaller models. Some of them are diffusion models. Some of them are auto reggressive models. And so there are all
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kinds of different types of models inside this data center. We created Vera Rubin to be able to run this
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extraordinarily diverse workload. My sense is and so we added what used to be a one rack company. we now added four
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more racks, >> right? >> So, Nvidia's TAM, if you will, increased
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from what whatever it was to probably something, call it, you know, 33% 50% higher. Now, part of that 33% or 50% a
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lot of it's going to be storage processors. It's called Blue Field. Some
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of it will be a lot of it, I'm hoping, will be Grock processors, and some of it
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will be CPUs. And they're all and a lot of it's going to be networking processors. And so all of this is going
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to be running basically the computer of the AI revolution called agents, right? >> The operating system of of um modern
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modern industry. >> What about embedded applications? So you know my daughter's teddy bear at home
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wants to talk to her. What goes in there? Is it a custom ASIC or does there end up becoming much more kind of a
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broader set of TAM with developing tools that are maybe different for different use cases at the edge and an embedded
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application? We think that there's three computers in the problem at the at the
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largest at the largest scale when you take take a step back. There's one computer that's really about training
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the AI model, developing creating the AI, another computer for evaluating it. Depending on the type of problem you're
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having, like for example, you look around, there's all kinds of robots and cars and things like that. You have to
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evaluate these robots inside a virtual gym that represents the physical world. So it has to be software that obeys the
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laws of physics. >> And that's a second computer. We call that omniverse. The third computer is
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the computer at the edge, the robotics computer. >> That robotics computer, one of them
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could be self-driving car. Another one's a robot. Another one could be a teddy
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bear. >> Little tiny one for a teddy bear. >> One of the most important ones is one
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that we're working on that basically turns the telecommunications base stations into part of the AI
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infrastructure. So now all of the it's a $2 trillion industry all of that in time
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will be transformed into an extension of the AI infrastructure and so radios radios will become a edge devices
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factories warehouses you name it and so so there are three these three basic computers
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all of them you know are going to be necessary >> Jensen last uh last year I think you
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were ahead of the the rest of the world in in in saying inference isn't going to
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a thousand last year. >> Yes. Is it going to hurt my feelings? Is it is it going to 1 millionx? Is it
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going to 1 billionx? Yeah. >> Right. And I think people at the time thought it was pretty hyperbolic because
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the world was still focused on pre-scaling on training. Here we are now. Inference has exploded. We're
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inference constrained. Um you announced an inference factory that I think is leading edge that's going to be 10x
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better in terms of throughput to the next factory. But yet if you if I listen to what the chatter is out there, it's
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that your inference factory is going to cost 40 or 50 billion and the alternatives the custom AS6 AMD others
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are going to cost 25 to 30 billion and you're going to lose share. So why don't
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you talk to us? What are you seeing? How do you think about share and does it make sense for all these folks to pay
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something that's a 2x premium to what others are marketing? The big takeaway,
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the big idea is that you should not equate the price of the factory and the price of the tokens, the
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cost of the tokens. It is very likely that the $50 billion factory, and in fact, I can prove it that the $50
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billion factory will generate for you the lowest cost tokens. And the reason for that is because we produce these
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tokens at extraordinary efficiency 10 times you know the difference between 50 billion now it turns out 20 billion
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is just land power and shell right >> and then on top of that you have storage
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anyways networking anyways you got CPUs anyways you got servers anyways you got cooling anyways the difference between
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that GPU being 1x price or halfx price >> is not between 50 billion and 30 billion
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Pick your favorite number, but let's say between 50 billion and 40 billion. >> That is not a large percentage when the
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$50 billion data center is actually 10 times the throughput. >> Right, Jess?
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>> That's the reason why I said that even for most chips, if you can't keep up
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with the state of the technology and the pace that we're running, even when the
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chips are free, it's not cheap enough. >> Yeah. >> Can I can I just ask a general strategy
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question? >> Yeah. I mean, you're running the most valuable company in the world. This
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thing is going to do 350 plus billion of revenue next year. 200 billion of free cash flow. It's compounding at these
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crazy rates. How do you decide what to do? Like, how do you actually get the information? I mean, it's famous now
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these sort of emails that are people are meant to send you, but how do you really
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decide to get an intuition of how to shape the market, where to really double down, where to maybe pull back, where to
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actually go into a green field? How how does that information get to you? How do
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you decide these things? >> In a final analysis, that's the job of the CEO. Yeah.
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>> And our job is to define the strategy, define the vision, define the strategy.
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We're informed, of course, by amazing computer scientists, amazing technologists, great people all over the
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company, but we have to shape that future. Well, part of it has to do with is this something that's insanely hard
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to do? If it's not hard to do, we should back away from it. And the reason for
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that if if it's easy to do obviously um lots of competitors a lot of competitors
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is this something that has never been done before that's insanely hard to do
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and that somehow taps into the special superpowers of our company and so I have to find this confluence of things to
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that meets the standard and in the end we also know that a lot of pain and suffering is going to go into it. Yeah,
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>> there no great things that are invented because it was just easy to do and just
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like first try here we are. >> And so if it's super hard to do, nobody's ever done it before, it's very
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likely that you're going to have a lot of pain and suffering and so you better
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enjoy it. >> So can you can you just look at maybe three or four of the more longtail
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things you announced >> and just talk about the long-term viability of whether it's the data
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centers in space or whether it's what you're trying to do with ADAS in autos
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or you know what you're trying to do on the biology side. just give us a sense
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of like how you see some of these curves inflecting upwards in some of these longer tail businesses.
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>> Excellent. U physical AI large category we believe and I just mentioned we have
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three computing systems all the software platforms on top of it. physical AI as a
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large category. It's technology industry's first opportunity to address a $50 trillion industry that
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has largely been, you know, void of technology until now. And so, we need to invent all of the technology necessary
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to do that. I felt that that was a 10-year journey. We started 10 years ago. We're seeing it inflecting now. It
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is a multi-billion dollar business for us. It's close to$10 billion a year now.
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And so it's a big business and it's growing exponentially. And so that's
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number one. I think in the case of digital biology, I think we are literally near the chat GPT moment of
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digital biology. We're about to understand how to represent genes, proteins, cells. We already know how to
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understand chemicals. And so the ability for us to represent and understand the dynamics of the building blocks of
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biology, that's a couple of two, three, five years from now. In five years time,
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I completely believe that the healthcare industry where digital biology is going
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to inflect. And so these are a couple of the really great ones and you could see
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they're all around us. >> Agriculture, >> agriculture >> reflecting now.
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>> No question. Yeah. >> Hson, I want to take you from the data center to the desktop. Uh the company
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was built in large part on hobbyists, video gamers, and and all those graphic cards in the beginning. And you
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mentioned in front of I think 10,000 people here just clawed open claw clawed code and what a revolution agents have
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become and specifically the hobbyists who are really where a lot of energy um we see you know a lot of
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the innovation breaks want desktops you announced one here uh I believe it's the
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Dell 6800 uh this is a very powerful workstation to run local models 750 gigs of RAM obviously the the Mac uh studio
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sold out everywhere in my company. We're moving to openclaw everything. Freeberg
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just got clawdelled. You got claw peld, I understand. And you're obsessed with
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these. >> What is this from the streets movement of creating open-source agents and using open source on the
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desktop mean to you? Great. Where is that going? >> Yeah. So great. First of all, let's take
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a step back. Um in the last two years, we saw basically three inflection points. The first one was generative
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chat GPT brought AI to the common everybody to our awareness. But the fact of the matter is the technology sat in
00:14:00
plain sight months before GPT. It wasn't until chat GPT put a user interface
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around it made it easy for us to use that generative AI took off. Now generative AI as you know generates
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tokens for internal consumption as well as external consumption. internal consumption is thinking which led to
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reasoning. 01 and 03 continue that wave of chat GBT grounded information made AI not only answer
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questions but answer questions in a more grounded way useful. We started seeing the revenues and the e the economic
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model of open AI start to inflect. Then the third one was only inside the industry that we saw clock code the
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first agentic system that was very useful really revolutionary stuff but but cloud code was only available for
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enterprises. Most people outside never saw anything about cloud code until open claw. Open claw basically put into the
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po popular consciousness what an AI agent can do. Mhm. >> That's the reason why open claw is so
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important from a cultural perspective. Now the second second reason why it's so
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important is that open claw is open but it formulates it structures a type of computing model
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that is basically reinventing computing all together. It has a memory system. It
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scratch is a short-term memory file system. It has it has it has scales. Yeah. Did you say skills or scales?
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>> Skills. >> Oh, skills. >> They do have scales theoretically. Yeah.
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Yeah. Skills. >> So, the first thing first thing it it, you know, it has resources. It it
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manages resources. It's it does scheduling. >> Yep. >> Right. And it cron jobs. It could it
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could spawn off agents. It could, you know, it could decompose a task and and cause and solve problems as does
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scheduling. It has IO subsystems. It could, you know, input. It has outputed connect to WhatsApp. And also it has a
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API that allows it to run multiple types of applications called skills. >> Yeah.
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>> These four elements fundamentally define a computer. >> Yeah. >> And therefore what do we have? We have a
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personal artificial intelligence >> computer for the very first time. >> Open source.
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>> It's open source. It runs literally everywhere. And so this is now the this
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is the op this is basically the blueprint the operating system of modern computing.
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>> Yeah. >> And it's going to run literally everywhere. Now of course one of the
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things that we had to help it do is whenever you have agentic software you have to make sure that and agentic
00:16:42
software has access to sensitive information. It could execute code. It could communicate externally. We have to
00:16:49
make sure that all of it has to be governed. all of it has to be secure and that we have policies that that gives
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these agents two of the three things but not all three things at the same time and so the governance part of it we
00:17:02
contributed to Peter Peter Steinberger was here and and so we've got a mountain
00:17:06
of great engineers working with him to help secure and keep that thing so that it could protect our privacy protect our
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security >> Jensen that paradigm shift makes some of the AI legislation that has passed
00:17:17
around the country to regulate AI and a lot of the proposed legislation effectively moot, doesn't it? Can you
00:17:23
just comment for a second on how quickly the paradigm shift kind of obiates a lot
00:17:27
of the models for regulatory oversight of AI, which is becoming a very hot topic in politics right now.
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>> Well, this is this is the part that that we just with policy makers, we need to
00:17:39
we need to always get in front of them and Brad, you do a great job doing this. We had to get in front of them and
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inform them about the state of the technology, what it is, what it is not. It is not a biological being. It is not
00:17:53
alien. It is not conscious. Um it is computer software. >> Yeah. Exactly. >> And and it is not something that um we
00:18:04
say things like we don't understand it at all. >> It is not true. We don't understand at
00:18:09
all. We understand a lot of things about this technology. and and so so I think one we have to make sure that we
00:18:14
continue to inform the policy makers and not affect not allow doomerism and extremism to affect how policy makers
00:18:24
think and understand about this technology. However, however, we still have to recognize the technology is
00:18:28
moving really fast and don't get policy ahead of the technology too quickly and
00:18:33
the risk that we we run as a nation. Our greatest source of national security concern with respect to AI is that other
00:18:41
countries adopt this technology while we are so angry at it or afraid of it or somehow paranoid of it that our
00:18:51
industries our society don't take advantage of AI and so I'm just mostly
00:18:55
worried about the diffusion of AI here in the United States. >> Can you just double click if you were in
00:18:59
the seat in the boardroom of Anthropic over that whole scuttlebutt with the department of war? It sort of builds on
00:19:06
this idea of people didn't know what to think. It's sort of added to this layer
00:19:11
of either resentment or fear or just general mistrust that people have sometimes at the software levels of AI.
00:19:19
What would do you think you would have told Daario and that team to do maybe differently to try to change some of
00:19:24
this outcome and some of this perception? >> The first thing that I I would I would
00:19:27
say about anthropic is first of all the technology is incredible. We are a large
00:19:31
consumer of anthropic technology. really admire their focus on security, really admires their focus on safety. Um the
00:19:38
the the the culture by which we they went about it, the the technology excellence by which they went about it
00:19:45
really fantastic. Um I I would say that that the the desire to warn people about
00:19:53
the capability, the technology is is also uh really terrific. We just have to make sure that we understand that the
00:19:59
world has a spectrum and that that warning is good, scaring is less good, >> right?
00:20:06
>> Um and because this technology is too important to us, >> right? >> And and I think that it is fine to uh
00:20:15
predict the future, but we need to be a little bit more circumspect. We need to have a little bit more humility that in
00:20:21
fact we can't completely predict the future and the abil and to say things that that are quite extreme, quite
00:20:29
catastrophic that there's no evidence of it happening um could be more damaging
00:20:35
than people think. And and of course we are technology leaders. >> There were there was a time when nobody
00:20:41
listened to us. Yeah. Um but now because technology is so important in the social
00:20:47
fabric, such an important industry, so important to national security, our words do matter and I think we have to
00:20:54
be much more circumspect. We have to be more moderate. We have to be more balanced. We have to be more for more
00:20:58
thoughtful. >> Well, I you know, I would nominate you. I think the industry's got to get
00:21:03
together. 17% popularity of AI in the United States. I mean, we see what happened to nuclear, right? We basically
00:21:11
shut down the entire nuclear industry and now we have a 100 fision reactors being built in China and zero in the
00:21:16
United States. Um we hear about moratoriums on data centers. So I think we have to be a lot more proactive about
00:21:21
that. But but I want to go back to this agentic explosion that you're seeing
00:21:25
inside your company, the efficiencies, the productivity gains inside your company. There's a lot of debate whether
00:21:31
or not we're seeing ROI, right? And you and I entering into this year, the big
00:21:36
question was are the revenues going to show up? are the revenues going to scale like intelligence and then we had this
00:21:42
kind of Oenheimer moment a five6 billion month by anthropic in February. Um do you think as you look ahead you
00:21:51
announced a trillion dollar you know visibility into a trillion dollars of just Blackwell and Vera Rubin over the
00:21:56
course of the next couple years when you see this happening at Anthropic and OpenAI do you think we're on that curve
00:22:02
now where we're going to see revenues scale in the way that intelligence is scaling
00:22:07
>> when you look around when you I'll answer this a couple different ways when
00:22:09
you look around this audience you will see that anthropic and open AAI is represented here but in fact everybody
00:22:16
99% of Everything that is here is all AI and it's not anthropic and open AI.
00:22:21
>> Right. Right. >> And the reason for that is because AI is very diverse.
00:22:26
>> I would say that the second most popular model as a category is open models.
00:22:32
>> Number one is yeah open open source open ways open source. >> Open AI is number one. Open source is
00:22:39
number two. Very distant third is anthropic. And that tells you something about the scale of all of the AI
00:22:45
companies that are here. And so so it's important to recognize recognize that um
00:22:51
let me let me come back and say a couple things. One when we went from generative
00:22:55
to reasoning the amount of computation we needed was about a hundred times. >> When we went from reasoning to agentic
00:23:03
the computation is probably another 100 times. Now we're looking at in just two
00:23:10
years computation went up by a fact 10,000x. Meanwhile, people pay for information, but people
00:23:21
mostly pay for work. >> Yes, >> talking to a chatbot and getting an answer is super great,
00:23:28
>> right? >> Helping me do some research, unbelievable, but getting work done,
00:23:32
I'll pay fordeed. >> And so that's where we are. >> Agentic systems get work done. They're
00:23:38
helping our software engineers get work done. And and so then you take that you got 10,000x more compute you get
00:23:46
probably at this point 100x more consumption now. >> Yes. >> Yeah. >> And we haven't even started scaling yet.
00:23:52
>> We are absolutely at a millionx >> which is I think a great place to talk
00:23:56
about the number of people have 20 30,000 at the company something. >> We have 43,000 employees. You know I
00:24:03
would say 38,000 are engineers. The conversation we've had on the pod a number of times is, "Oh
00:24:10
my god, look at the token usage in our companies. It is growing massively." And
00:24:15
some people are asking, "Hey, when I join a company, how many tokens do I get
00:24:19
cuz I want to be an effective employee?" And you postulated, I believe, during
00:24:23
your 2 and 1/2 hour keynote, pretty long keynote, well done, that you were spending,
00:24:30
>> if it was well done, it would be shorter. Yeah, he didn't have time to do
00:24:33
a time to write an hour. >> So you guys So you guys know So you guys know there is no practice
00:24:40
>> and so it's a gripping and ripping >> rip and rip. Yeah. >> So So I just want to let you know I was
00:24:44
writing the speech while I was giving the speech. Okay. So >> you never know.
00:24:49
>> But does that mean if we do back >> I apologize. >> Back of the envelope math 75,000 in
00:24:55
tokens for each engineer or something like that. So, are you spending in Nvidia a billion2 billion dollars on
00:25:00
tokens for your engineering team right now? >> We're trying to Let me give you a
00:25:03
thought experiment. Let's say you have a software engineer or AI researcher and
00:25:07
you pay them $500,000 a year. We do that all the time. >> Okay, this is happening all over the
00:25:13
time. Um, that $500,000 engineer at the end of the year, I'm going to ask them
00:25:18
how many token how much did you spend in tokens? If that person said $5,000, I will go ape something else.
00:25:24
>> Yes. >> Right. If that if that $500,000 engineer did not consume at least $250,000 worth
00:25:31
of tokens, I am going to be deeply alarmed. Okay? And this is no different than one of our chip designers who says,
00:25:40
"Guess what? I'm just going to use paper and pencil. I don't think I'm going to
00:25:44
need any CAD tools." >> This is a real paradigm shift to start thinking about these all-star employees.
00:25:50
It almost reminds me of of what we learned in the NBA when LeBron James started spending a million dollars a
00:25:55
year just on his health of his body like and maintaining it. That's right. >> Here he is at age 41 still playing. It
00:26:01
really is, hey, if these are incredible knowledge workers, why wouldn't we give
00:26:05
them >> superhuman abilities? >> That's exactly right. >> Where does that go? If we if we
00:26:10
extrapolate out two or three years from now, what is the efficiency of that allstar at an Nvidia and what they're
00:26:16
able to accomplish? What do they look like? Well, first of all, things that that that um wow, this is too hard. That
00:26:24
thought is gone. Uh this is going to take a long time. That thought is gone. Uh we're going to need a lot of people.
00:26:29
That thought is gone. This is no different than in this in the last industrial re revolution. Somebody goes,
00:26:36
"Boy, that building really looks heavy." Nobody says that. Nobody, wow, that
00:26:40
mountain looks too big. Nobody says that. Right. >> Everything that's too big, too heavy,
00:26:45
takes too long, >> those thought, those ideas are all gone. >> You're reduced to creativity.
00:26:50
>> That's right. >> What can you come up with? >> Exactly. Which means now the question is
00:26:53
how do you how do you work with these agents? Well, it's just a new way of doing computer programming. In the f in
00:26:59
the past, we code. In the future, we're going we're going to write ideas,
00:27:03
architectures, specifications. We're going to organize teams. We're going to give him, we're going to help
00:27:09
them define how to evaluate the definition of good versus bad. What's the what does it look like when
00:27:15
something is a great outcome, how to iterate with you, how to brainstorm. That's really what you're looking for.
00:27:21
And I'm I think that every engineer is going to have hundred hundred agents.
00:27:25
>> Back to the PR problem the industry has right now. You have executives uh like
00:27:31
David Freeberg with Oho who's looking at literally taking through the use of
00:27:36
technology your technology and AI the number of calories produced and making high quality cal calories what is the
00:27:44
factor you think you can bring the cost down Freeberg and what impact does this vision have for what you're doing
00:27:51
>> zero shot genomic modeling and it works >> and then you have that moment and you're
00:27:56
like holy >> honestly like and and that's after people are replacing entire enterprise
00:28:03
software stacks in a night. I did something in 90 minutes. I was telling the guys about replaced the whole
00:28:08
software stack and like a whole bunch of workload. 90 minutes on claude, ran this
00:28:12
agentic system, built the whole thing, deployed it, and we got we were on a Sunday night.
00:28:15
>> On a Sunday night, 10 p.m. I was done at 11:30. I went to bed. >> As the CEO, you replaced
00:28:20
>> Yeah. And everyone on my management team had to do a similar exercise over the
00:28:24
weekend. What we saw on Monday, I was like, it's over. But the technical stuff, the science stuff, we did
00:28:32
something in 30 minutes using auto research. And I'd love your view on auto research and what that tells us about
00:28:36
how far we still have to go in terms of efficiency. But using auto research and a chunk of data, something was published
00:28:43
internally that we said, "Oh my god." And that would normally be a PhD thesis
00:28:47
that would take seven years. It would be one of the most celebrated PhD thesis we've ever seen in this field and it
00:28:52
would be in the journal science. And it was done in 30 minutes on a desktop computer running on auto research with
00:28:58
all the data we just ingested. We got it on Friday. We're like, "Hey, let's try
00:29:00
it." try booted up, went to GitHub, downloaded auto research and ran it and
00:29:05
you see everyone's face just go like and then the potential of what this is unlocking for us is like the kind of
00:29:11
thing that would take seven years and it happened in 30 minutes and we're experiencing it in genomics and we're
00:29:17
like this is unbelievable. So I I think like the acceleration is widening the aperture for everyone in a way that like
00:29:25
you didn't imagine a few years ago. But just going back to the auto research
00:29:28
point, can you just comment on what you think about the fact that this thing got
00:29:32
published with 600 lines of code in a weekend and the capacity that it has to run locally and achieve what it can
00:29:38
achieve with all of these diverse data sets and what that tells us about the early stages we are in terms of
00:29:43
optimization on algorithms and hardware. The fundamental reason why open claw is
00:29:48
so incredible number one is its com its confluence its timing with the breakthroughs in large language model.
00:29:57
>> Yeah. >> Its timing was perfect. It was impeccable. Now in a lot of ways Peter
00:30:01
wouldn't have come up with it probably if not for the fact that claude and GPT
00:30:06
and chat GPT have reached a level that is really very good. >> Right. It is also a new capability that
00:30:13
allows these models to tool use the tools that we've created over time web browsers and Excel spreadsheets and you
00:30:22
know in the case of chip design synopsis and cadence and uh omniverse and blender
00:30:29
and autodesk and all of these tools are going to continue to be used. There's
00:30:33
some some people say that that the enterprise IT software industry is going to get destroyed. There's it's there's a
00:30:41
let me give you the alternative view. The enterprise software industry is limited by butts and seats. It's about
00:30:48
to get a hundred times more agents banging on those tools. They're going to be agents banging on SQL. They're going
00:30:53
to be agents bang on vector databases, agents bang on Blender, agents banging on Photoshop. And the reason for that is
00:30:59
because those tools are first of all do a very good job. Second, those tools are
00:31:05
the conduit between us in the final analysis. When the work is done, it has to be represented back to me in a way
00:31:12
that I can control, >> right? >> And I know how to control those tools.
00:31:17
And so I need everything to be put back in the synopsis. I want everything to put back in the cadence because that's
00:31:22
how I control it. That's how I've ground truth. >> Let me ask you a question about open
00:31:26
source. So we have these closed source models. They're excellent. >> We have these openweight models. Many of
00:31:31
the Chinese models are incredible. Absolutely incredible. Two days ago, you may not have seen this because you were
00:31:37
busy on stage, but there was a training run that happened in this crypto project
00:31:42
called Bit Tensor Subnet 3. They managed to train a 4 billion parameter llama model, totally distributed with a bunch
00:31:50
of people contributing excess compute, but they were able to do it statefully and manage a training run,
00:31:56
which I thought was like a pretty crazy technical accomplishment. Yeah, >> because it's like random people and each
00:32:02
person gets a little share. >> Our our modern version of folding at home. >> Exactly. So what what do you think about
00:32:08
the end state of open source? Do you see this decentralization of architecture as
00:32:13
well and decentralization of compute to support open weights and a totally open-
00:32:19
source approach to making sure AI is broadly available to everyone? I believe we fundamentally need
00:32:26
models as a first class product, proprietary product as well as models as open source. These two things are not A
00:32:35
or B. It's A and B. There's no question about it. And the reason for that is
00:32:39
because models is a technology, not a product. Model is a technology, not a service. For the vast majority of
00:32:46
consumers, the horizontal layer, the general intelligence, I would really really love not to go fine-tune my own,
00:32:54
right? I would really love to keep using chat GPT. I'd love to use cloud. I love
00:32:58
to use Gemini. I love to you use X. And they all have their own personalities as
00:33:02
you know, which is kind of depends on my mood and depends on what problem I'm
00:33:05
trying to solve. You know, I might, you know, do it on X or I might do it on on chat GBT. And so that that segment of
00:33:11
the of the industry is thriving. is going to be great. However, there all these industries their domain expertise
00:33:20
their specialization has to be channeled has to be captured in a way that they can control and that it can only come
00:33:27
from open models. The open model industry we're contributing tremendously to it is near the frontier
00:33:35
and quite frankly even if it reaches the frontier I think that products as a service
00:33:42
worldass products as a models as a product is going to continue to thrive. >> Every startup we're investing in now is
00:33:50
open source first and then going to the proprietary models. >> Yeah. The beautiful thing is because you
00:33:55
have a great router you connect it to by on on first day every single day you're
00:34:01
going to have access to the world's best model and and then it gives you time to
00:34:06
cost reduce and fine-tune and specialize and so you're going to have worldass
00:34:09
capabilities out to shoot every single time. Let >> J can I >> ask a question?
00:34:14
>> Nobody wants the US to win the global AI race more than you, right? But a year
00:34:20
ago, the Biden era diffusion rule really was an anti- American diffusion of AI around the world. So here we are a year
00:34:28
into the new administration. Give us a grade. Where is where are we in terms of global diffusion and the
00:34:35
rate at which we're spreading US AI technology around the world? Are we an A? Are we a B? or we see what what's
00:34:42
working, what's not working. >> Well, first of all, President Trump wants American industry to lead. He
00:34:50
wants American technology industry to lead. He wants American technology industry to win. He wants us to spread
00:34:56
American technology around the world. He wants United States to be the wealthiest
00:35:00
country in the world. He wants all of that. At the current moment, as we speak, Nvidia gave up a 95% market share in the
00:35:10
second largest market in the world, and we're at 0%. >> President Trump, That's right. President
00:35:16
Trump wants us to get back in there. And and uh the first thing is uh to get licens licensed for the companies that
00:35:25
we're going to be able to sell to. We've got many companies who have requested
00:35:30
for licenses. We've applied for licenses for them and we've got approved licenses
00:35:34
from sec secretary lutnik. Uh now uh we've we informed the Chinese companies
00:35:40
and many of them have given us purchase orders and so we're going to we're going
00:35:43
to we're in the process of cranking up our supply chain again to go ship. I think at the highest level Brad um I
00:35:50
think one of the things that we should acknowledge is this. Our national security
00:35:55
is diminished when we don't have access to miniature motors, rare earth minerals. It's diminished when we don't
00:36:05
control our telecommunications networks. It's diminished when we can't provide
00:36:10
for sustainable energy for our country. It is fundamentally diminished. Every single one of these industries is an
00:36:16
example of what I don't want the AI industry to be. >> Right? When we look forward in time and
00:36:23
we say what do we want? What is the what does it look like when American technology industry American AI industry
00:36:30
leads the world? We can all acknowledge that there is no way that AI models is one universally. It is we can all
00:36:40
acknowledge that that is an outcome that makes no sense. However, we can all imagine that the American tech stack
00:36:48
from chips to computing systems to the platforms are used broadly by the world where they build their own AI, they use
00:36:58
public AI, they use private AI whatever and they can build their applications in
00:37:02
their society. I would love that the American tech stack is 90% of the world. Yes, I would love that. The alternative
00:37:10
if it looks like solar, rare earth, magnets, motors, telecommunications, I consider that a very bad outcome for
00:37:20
national security. >> Agreed. >> Yeah. >> How much are you monitoring the
00:37:23
situation with the conflicts around the world right now? And how much does it worry you Jensen? So, China and Taiwan
00:37:30
and then helium availability coming out of the Middle East, I understand, can be
00:37:34
a supply chain risk to semiconductor manufacturing. How much do these situations worry you? How much are you
00:37:39
spending on them? >> Well, first of all, I think the in Middle East, I have we have 6,000
00:37:44
families there. >> Yeah, >> we have a lot of Iranians uh at NVIDIA and their families are still in Iran.
00:37:50
And so so we have we have a lot of families there. The first thing is is they're quite anxious. They're quite
00:37:55
concerned, quite scared. Um we're thinking about them all the time. Uh we're monitor and keeping an eye on them
00:38:00
all the time. They have 100% of our support. Uh I've been asked several times, are we still considering uh being
00:38:06
in Israel? We are 100% in Israel. We are 100% behind the families there. We are 100% in the Middle East. I was also
00:38:15
asked, you know, given what's happening in the Middle East, uh is that an area
00:38:20
where we believe that we can expand artificial intelligence to? Um I believe that there's a reason we went to war and
00:38:27
I believe at the end of the war, Middle East will be more stable than before. And so if we were there, if we're
00:38:34
considering it before, we should absolutely be considering it after. And so I'm 100% in on that. With respect to
00:38:40
with with with respect to to Taiwan, >> we have to do three things. One, we have
00:38:46
to make sure that we re-industrialize the United States as fast as we can. Yeah.
00:38:50
>> And whether it's the chip manufacturing plants, the the computer manufacturing
00:38:54
plants, or the AI factors. >> How are we doing on that? We're doing excellent with by by gaining the
00:39:01
strategic support by gaining the friendship of the supply chain of Taiwan. By gaining their friendship, by gaining
00:39:09
their support, we were able to build Arizona and Texas, California at incredible rates. They're they are
00:39:18
genuinely a strategic partner. Um we we we really they deserve our support. They
00:39:25
deserve our friendship. They deserve our uh generosity and they're doing everything they can to accelerate the
00:39:31
manufacturing process for us. And so, so I think that's number one. Number two,
00:39:36
we ought to diversify the manufacturing supply chain. And whether it's South
00:39:40
Korea, whether it's it's Japan, it's Europe, we got to we got to diversify
00:39:45
the supply chain, make it more resilient. And number three, let's be let's let's demonstrate restraint. And
00:39:53
while we're reducing uh increasing our diversity and resilience, let's not
00:40:00
press push um you know >> unnecessary we need to be patient. >> Is helium a problem?
00:40:07
>> A lot of reports, >> you know, I think helium could be a problem, but it's also the case that the
00:40:11
supply chain probably has a lot of buffer in it. >> These kind of things tend to have a lot
00:40:15
of buffer. Uh but but um you know >> Yeah, >> you've um made massive progress in
00:40:22
self-driving. You made a big announcement. You've added many more partners including BYD. There was just a
00:40:28
video of you driving around in a Mercedes and uh huge announcement uh with Uber that you're going to have a
00:40:36
number of cars on the road from many different manufacturers. your bet is I believe that there's going to be an
00:40:43
Android type open-source platform that you're going to play a major part in with
00:40:49
dozens of uh car providers and then maybe on the other side there could be an iOS with Tesla or Whimo. What's your
00:40:56
strategy thinking there and how that chessboard emerges because it feels like you have a a pretty deep stack and in
00:41:05
some ways you're competing and in other places you're collaborative. Yeah. Um,
00:41:10
it's taking a step back. We believe that everything that moves will be autonomous
00:41:16
completely or partly someday. Number one. Number two, we don't want to build self-driving cars,
00:41:22
but we want to enable every car company in the world to build self-driving cars.
00:41:26
And so, we built all three computers, the training computer, the simulation computer, the valuation evaluation
00:41:31
computer, as well as the car computer. We develop the world's safest driving
00:41:36
operating system. Uh we also created the world's first reasoning autonomous vehicle so that it could decompose
00:41:45
complicated scenarios into simpler scenarios that it knows how to navigate through just like us reasoning systems.
00:41:52
And so that reasoning system called Al Pomayo has enabled us to achieve incredible results.
00:41:59
We open this we ver we vertical optimization. We horizontally innovate and we let everybody decide. Do you want
00:42:08
to buy one computer from us? In the case of Elon and Tesla, they buy our training
00:42:12
computers. Um, do they want to buy our training computer and our simulation computers or do you want to let us uh
00:42:18
work with us to do all three and even put the car computer in your car. So, we, you know, our attitude is we want to
00:42:24
solve the problem. We're not the solution provider. And we're delighted however you work
00:42:31
with us. Let me build on this question because I think it's like it's so
00:42:34
fascinating. You actually do create this platform. A thousand flowers are blooming.
00:42:40
>> But it's also true that some of those flowers want to now go back down in the
00:42:43
stack and try to compete with you a little bit. Google has TPU, Amazon has inferentia and tranium. You know,
00:42:50
everybody's sort of spinning up their own version of I think I can out Nvidia
00:42:54
Nvidia >> even though they also tend to be huge customers. >> How do you navigate that? And what do
00:43:01
you think happens over time and >> where do those things play in the complexion of this kind of vision?
00:43:06
>> Yeah, really great. You know, first of all, um, we're the only AI company,
00:43:11
we're an AI company. We build foundation models. We're at the frontier in many
00:43:15
different domains. We build every single every single layer, every single stack.
00:43:19
Um, we're the only AI company in the world that works with every AI company in the world. They never show me what
00:43:25
they're building and I always show them exactly what I'm building. >> Right.
00:43:28
>> Yeah. And so so the confidence comes from this one. Uh we are delighted to
00:43:35
compete on what is the best technology and to the extent that to the extent that we can continue to run fast I
00:43:42
believe that buying from Nvidia still is one of the most economic things they could do and that's just incredible
00:43:48
confidence there. Number one. Number two, we're the only architecture that could be in every cloud and that gives
00:43:53
us some fundamental advantages. We're the only architecture you could take from a cloud and put into onrem in the
00:43:59
car in any region >> in space. >> That's right. In space. And so there's a
00:44:03
whole whole part of our market about 40% of our of our business most people don't
00:44:08
realize this 40% of our business unless you have the CUDA stack unless you can build an entire AI factory you have the
00:44:14
customers don't know what to do with you. They're not trying to build chips.
00:44:18
They're not trying to buy chips. They're trying to build AI infrastructure. And
00:44:22
so they want you to come in with the full stack. And we've got the whole stack. And so surprisingly, Nvidia is
00:44:28
gaining market share. If you look at where we are today, we're gaining share.
00:44:32
>> Do you think what happens is these guys try and they realize, oh my god, it's
00:44:35
too much. And then they come back. Is that why the share grows? >> Well, we're gaining share for several
00:44:39
reasons. One, um, our velocity has gone, we help people realize it's not about
00:44:45
building the chip, it's about building the system. >> And that system is really hard to build.
00:44:50
uh and and so their their their business with us is increasing. In the case of AWS, I think they just announced, I
00:44:56
think it was yesterday, that they're going to buy a a million chips uh in the
00:45:01
next couple years. I mean, that's a lot of chips from from AWS. And that's on
00:45:05
top of all the chips they've already bought. And so, we're delighted to do
00:45:08
that. But number one, we're gaining share this last couple years because we now have Anthropic coming to Nvidia.
00:45:16
Meta SL is coming to Nvidia. And the growth of open models is incredible. And that's all on Nvidia. And so we're
00:45:25
growing in share because of the number of models. We're also growing in share
00:45:29
because out all of these companies are outside of the cloud and they're growing
00:45:35
regionally in enterprise in industries at the edge and that entire segment of growth is you know really hard to do if
00:45:42
it's just building an as >> Brad >> related to that um and not to get in the
00:45:47
weeds on the numbers but analysts don't seem to believe right so if you look at
00:45:52
the consensus forecast you said compute could 1 millionx right and Yet they have
00:45:58
you growing next year at 30%, the year after that at 20%. And in 2029, which is supposed to be a monster year at 7%.
00:46:06
Right? So if you just if you take your TAM and you apply their growth numbers, it suggests that your share will
00:46:12
plummet. Do you see anything in your future order book that would make that correct?
00:46:18
>> Yeah. First of all, they just don't understand the scale and the breadth of
00:46:23
AI. >> Yes. >> Yeah. >> I think that's true. Most people think
00:46:26
that AI is in the top five hyperscalers, >> right? That's right. There's also an
00:46:31
orthodoxy around these law of large numbers where, >> you know, they have to go back to their
00:46:36
investment banking risk committee and show some model. >> They're not going to believe in their
00:46:41
minds that 5 trillion goes to 15 trillion. They're like go to it can go to seven or they can have a 10 trillion
00:46:48
company. >> It's all just CIA stuff that I think >> it's never happened before. So you can't
00:46:51
say it will >> and and because because you have to redefine what it is that you do. There
00:46:56
was somebody who made an observation recently that Nvidia Jensen how can you be larger than Intel
00:47:04
in servers and the reason for that is because the CPU market of the entire data center was about $25 billion a
00:47:11
year, >> right? >> We do $25 billion a year as you guys know in a very in the time that we were
00:47:16
sitting here. >> And so obviously obviously That was a joke. >> No, it's but it's
00:47:23
>> all in podcast. Don't worry. Everything on this show is roughly. Don't worry about it. It's all
00:47:28
in here. Anyway, that was not guidance. But anyhow, anyhow, it the the point is how big you can be
00:47:37
>> depends on what is it that you make, >> right? >> Nvidia is not making chips. Number one,
00:47:42
making chips does not help you solve the AI infrastructure problem anymore. It's
00:47:46
too complicated. Number three, most people think that AI is narrowly in the things that they talk about and hear and
00:47:53
see. >> It's AI is much open AI is incredible. They're going to be enormous. Anthropic
00:47:59
is incredible. They're going to be enormous. But AI is going to be much much bigger than that.
00:48:05
>> And we addressed that segment. >> Tell us about data centers in space for
00:48:08
a second. >> Yeah. >> Um >> we're already in space. How should the
00:48:12
layman think about what that business is versus when you hear about these big data center buildouts that's happening
00:48:19
in in on the ground? >> Well, we should definitely work on the ground first because we're already here
00:48:25
and number one. Number two, we should prepare to be out in space and obviously there's a lot of energy in space. Um the
00:48:31
challenge of course is that cooling you can't take advantage of conduction and convection and so you can only use
00:48:38
radiation and radiation requires very large surfaces and so now that's not an
00:48:43
impossible thing to solve and there's a lot of lot of space in space. Um but
00:48:48
nonetheless the expense is still quite there is is there uh we're going to go explore it.
00:48:53
We're already there. We're already radiation hardened. Uh we have we have
00:48:57
uh uh uh CUDA in satellites around the world. Um they're doing imaging, image
00:49:02
processing, AI imaging and um and that kind of stuff ought to be done in space instead of sending all the data back
00:49:08
here and do imaging down here. We ought to just do imaging out in space. And so there's a lot of things that we ought to
00:49:13
done do do in space. And in the meantime, uh we're going to explore what is the architecture of data centers look
00:49:18
like uh in space. And it'll take it'll take years. It's okay. We got I got
00:49:23
plenty of time. I wanted to um double click on healthcare. I know you've got a
00:49:26
big effort there. We're all of a certain age where we're thinking about lifespan,
00:49:30
health span. I mean, we all look great. I think >> some better than others.
00:49:34
>> I think some better than others. I don't know what your secret is, Jensen.
00:49:37
>> Pretty good these these >> I mean, what's what are you taking?
00:49:40
What's off the menu? You got to talk to me when we're backstage. I want to know
00:49:43
in the green room what you got going on. >> Squats and push-ups and sit-ups.
00:49:47
>> Perfect. Okay. Um but >> that works. what you know in terms of the buildout in healthcare
00:49:56
where is that going and what kind of progress are we making? I was just using Claude to do some analysis and saying
00:50:02
like where are all these billing codes? We spend twice as much money in the US. We get seem to get half as much. It
00:50:08
seemed like uh 15 to 25% of the dollar spent were on these first GP visits. And I think we all know like chat GBT and a
00:50:17
large language model does a better job more consistently today at a first visit. So what has to happen there to
00:50:25
kind of break through all that regulation and have AI have a true impact on the health care system?
00:50:29
>> There's several way several areas that we're involved in in um in healthcare.
00:50:34
One is uh AI uh physics uh and and that's or AI biology using AI to understand represent
00:50:45
predict biology behavior biological behavior and so that's one that's very
00:50:49
important in drug discovery. There's second which is AI agents and that's
00:50:54
where the assistance and helping diagnosis and things like that. Open evidence is a really good example.
00:50:59
Hypocratic is a really good example. Love working with those companies. Um I really think that this is an area uh
00:51:04
where agentic technology is going to revolutionize how we interact with doctors and how do we interact for
00:51:10
healthcare. The third part that we're in involved in is physical AI. The first
00:51:14
one is AI physics using AI to predict physics. The second one is physical AI. AI that understand the properties of the
00:51:21
laws of physics and that's used for a uh robotic surgery huge amounts of activities there. Every single
00:51:29
instrument whether it's ultrasound or you know CT or whatever instrument we interact with in a hospital in the
00:51:35
future will be agentic. >> Yeah. >> You know open claw in a safe version
00:51:39
will be inside every single instrument. And so in a lot of ways that instrument is going to be interacting with patients
00:51:45
and nurses and doctors in a very unique way. so much investment in AI weapons. It would be wonderful to see some
00:51:52
investment in AI EMTs and paramedics and saving lives, not just taking them, which I think is a great segue into
00:51:59
robotics. You've got dozens of partners. We have this very weird >> I I don't know I want to call a lost
00:52:05
decade or 20 years of Boston Dynamics. Google bought a bunch of companies. They then wound up selling them and spinning
00:52:12
them out where people just thought robotics is just not ready for prime time. And now here we have the world's
00:52:18
greatest entrepreneur at this time. Uh tied with you, uh Elon Musk doing well, that was a good save, I hope. Optimus,
00:52:25
uh pretty impressive. And then other companies in China. How how close is that to actually being in our lives
00:52:34
where we might see a chef, a robotic chef, a robotic nurse, a robotic housekeeper, you know, this humanoid
00:52:41
factor actually working in the real world, knowing what you know with those partners and the fidelity, especially in
00:52:48
China where they seem to be doing as good a job as we're doing here or maybe
00:52:51
better. >> Um, we invented the industry largely. America invented. We c you could argue
00:52:59
we got into it too soon. >> Yeah. >> And and we got exhausted. We got tired
00:53:04
um about five years before the enabling technology appeared. >> The brain.
00:53:09
>> Yeah. Yeah. And we we just got tired of it just a little too soon. Okay. That's
00:53:14
number one. But it's here now. Now the question is how much longer? From the
00:53:18
point of high functioning existence proof, high functioning exist existence proof to reasonable products
00:53:28
technology never takes more than a couple two three cycles. And so a couple two three cycles basically be somewhere
00:53:34
around 3 years to 5 years. That's it. 3 years to 5 years we're going to have
00:53:38
robots all over the place. Uh I think I think um uh China is is uh formidable and the reason for that is because their
00:53:46
micro electronics, their uh motors, their rare earth, their magnets, which is foundational to robotics,
00:53:53
>> they are the world's best. And so in a lot of ways, our robotics industry
00:53:57
relies deeply on their ecosystem and their supply chain. Um and uh and and they're, you know, obviously moving very
00:54:04
quickly. Uh we're going to, you know, our robotics industry will have to rely
00:54:08
a lot on it. the world's robotics industry will have to rely on a lot on it. And so so I think um you're gonna
00:54:14
see some fast fast movements here. >> Ultimately, one for one. Elon seems to
00:54:19
think we're going to have one robot for every human. 7 billion for 7 billion, 8
00:54:22
billion for 8 billion. >> Well, I'm hoping more. Yeah, I'm hoping more. Yeah. Uh well, first of all,
00:54:27
there's a whole bunch of robots that are going to be in factories working around
00:54:30
the clock. There's going to be a whole bunch of fac that that don't move. They move a little
00:54:36
bit. Uh almost everything will be robotic. What does the world look like? >> Sorry, let me I think like this is one
00:54:41
of the robotics for me is one of the pieces that I think unlocks uh economic mobility opportunities for every
00:54:47
individual. Everyone now like when everyone got a car, they could now go and do a lot of different jobs. When
00:54:53
everyone gets a robot, their robot can do a lot of work for them. They can stand up an Etsy store, a Shopify store.
00:54:59
They can create anything they want with their robot. They could do things that they independently cannot do. I think
00:55:05
the robot is going to end up being the greatest unlock for prosperity for more people on Earth than we've ever seen
00:55:11
with any technology before. >> Yeah, no doubt. I mean, just a simp the simple math at the moment is we're
00:55:17
millions of people short in labor today. Right. Yeah. >> Right. We're we're we're actually really
00:55:22
desperate in need of robotics and so that all of these companies could grow more if they had more labor. I mean,
00:55:29
we're we're number one. Some of the things that you mentioned are super fun.
00:55:33
I mean, because of robots, we'll have virtual presence. Uh, you know, I'll be
00:55:38
able to go into the robot of my house and virtually operate it. I'm on a business trip,
00:55:45
>> right? >> Walk around the house and walk the dog. >> Yeah. Walk the dog.
00:55:49
>> Break the leaves. >> Yeah. Exactly. Freak out the dog. >> Maybe not quite that, but just, you
00:55:53
know, just, you know, wander around and just see what's going on in the house.
00:55:56
You know, chat with the dog, chat with the kids. >> Yeah. >> Yeah. And that's time travel is also
00:56:01
we're going to be able to travel at the speed of light, you know, and so, you
00:56:04
know, clearly we're going to send our robots ahead of us. >> Yeah. >> Not going to send myself. I'm going to
00:56:09
send a robot, you know. >> Check it out. >> Yeah. Yeah. And then I'm going to upload
00:56:12
my AI. Yeah. >> Well, it's inevitable. It unlocks the moon and it unlocks Mars as um targets
00:56:17
for for colonization, which gives us >> infinite resources. Getting back from
00:56:21
the moon is effectively zero energy cost to move material back because you can use solar and accelerate. So you could
00:56:27
have factories that make everything the world needs on the moon and the robots are going to be the unlock for enabling
00:56:32
that. >> That's right. Distance no longer matters. >> Distance doesn't matter. Yeah.
00:56:35
>> The more the more revenue we get out of models and agents, the more we can
00:56:40
invest in building the infrastructure which then unlocks more capabilities on models and agents. Daario on Dwaresh's
00:56:46
podcast recently said by 2728 we'll have hundreds of billions of dollars of revenue out of the model companies and
00:56:53
the agent companies. and he forecasts a trillion dollars by 2030. Right? This is
00:56:58
non-infrastructure AI revenue. Um >> I think he I think he's he's being very
00:57:04
conservative. I believe Dario and Anthropic is going to do way better than that.
00:57:08
>> Wow. >> Way better than that. >> Wow. So from 30 billion to a trillion.
00:57:11
>> Yeah. and not and and the reason for that is the one part that he hasn't
00:57:15
considered is that I believe every single enterprise software company will also be a reseller
00:57:23
value added reseller of anthropic code anthropics tokens value added reseller open AAI that's right and they're going
00:57:31
to that that that part of their >> get this logarithmic expansion >> yes
00:57:36
>> their go to market is going to expand tremendously this year >> what do you think in that world is the
00:57:42
moat what's left over. I mean you have some modes that are frankly I think as
00:57:47
this scales almost insurmountable. The best one that nobody talks about is probably CUDA which is just like an
00:57:54
incredible strategic advantage. But in the future if a model can be used to create something incredible then the
00:58:01
next spin of a model can be used to maybe disrupt it. Sort of in your mind what do you think for these companies
00:58:06
that are building at that application layer? What's their moat? like how do they differentiate themselves?
00:58:12
>> Deep specialization. Deep specialization. I believe that um these models they're going to have
00:58:19
general general models that are connected into the software company's agentic system,
00:58:25
>> right? >> Many of those models are cloud models and proprietary models, but many of
00:58:31
those models are specialized sub aents that they've trained on their own. >> Right. So the call to arms for you for
00:58:39
entrepreneurs is look >> know your vertical. >> That's right. >> Know it as deep and as better than
00:58:44
everybody else. >> That's right. >> And then wait for these tools because
00:58:47
they're catching up to you and now you can imbue it with your knowledge. >> That's right. The sooner you connect
00:58:51
your agent, >> the sooner you connect your agent with customers, >> that flywheel is going to cause your
00:58:57
agent to get >> it very much is an inversion of what we do today because today we build a piece
00:59:02
of software and we say what generalizes >> and then let's try to sell it as broadly
00:59:06
as possible and then sell the customization around it >> and we in fact in fact exactly right we
00:59:11
we create a horizontal but notice there are all these gsis and all of these consultants who are specialists who then
00:59:20
take your horizontal platform and specializes it into >> and that's arguably a five or six time
00:59:26
bigger industry is the customization. >> It is absolutely the whole very much is
00:59:30
>> that's right. So I think that these platform companies have an opportunity
00:59:34
to become that specialist to become that vertical. >> Yeah. Domain expert.
00:59:38
>> You know, I just want to give you your flowers. I think it was 3 years ago you
00:59:41
said you're not going to lose your job to AI. You're going to lose your job to
00:59:44
somebody using AI. And here we are. The entire conversation has revolved around this concept of agents making people
00:59:52
superhuman and the business opportunity expanding and entrepreneurship expanding. You actually saw it pretty
00:59:58
clearly. Yeah. >> You changed your view. This is Doom Dmer. No, I'm not Doomer. I
01:00:04
do I do have Dmer. No, I you can hold space for I think two ideas. One is there are going to be a lot
01:00:09
>> that's viral Jake. >> Oh, no. There you can. >> But that's just because he doesn't hang
01:00:13
out with me enough. >> Well, we I mean we a little bit. We don't talk about it. He will show THE
01:00:19
TABLE. HE'LL FOLLOW YOU AROUND. >> I'm not asking for it. I'm just
01:00:22
>> follow you around. I'm not asking for it. >> You can come with me and Tucker. We ski
01:00:26
in Japan every January. Love it. and Tucker go road trip. There is going to be job displacement
01:00:33
and then the question becomes, >> you know, do those people have the fortitude, the resolve to then go
01:00:39
embrace these, >> you know, technologies. We're we're going to see 100% of driving go away by
01:00:44
humans. That's just it's that's a beautiful thing and the lives saved, but
01:00:49
we have to recognize that's 15 million people in the United States, 10 to 15
01:00:52
million who are employed in that way. And and so that is going to happen. Yes, >> I I think I think that jobs will change.
01:00:59
For example, um there are many chauffeers today uh who drives the car. I believe that though many of those
01:01:05
chauffeers will actually be in the car sitting behind the drive the steering wheel while the car is driving by
01:01:12
itself. And the reason for that is because remember what a chauffeur does in the end. These chauffeers, they're
01:01:18
helping you they're your assistants. They're helping you with your luggage.
01:01:21
They're helping you. I mean, they're helping you with a lot of things and and
01:01:24
so I wouldn't be surprised actually if the chauffeers of the future becomes your mobility assistant and they are
01:01:32
helping you do on a whole bunch of other stuff >> to the hotel. >> Yeah. And the car is driving by itself.
01:01:36
>> The autopilot in planes created a lot more pilots and didn't take any of the
01:01:41
pilots out of the cockpit even though the autopilot is flying the plane 90% of the time. By the way, while that car is
01:01:47
driving itself, that chauffeur is going to be doing a bunch of other work on his
01:01:50
phone and he's going to be >> arranging, for example, coordinating a bunch of things for you, getting, you
01:01:55
know, it's all the pie just grows in a way that >> one of the things that that that
01:02:01
yes, every job will be will be transformed. Um, some jobs will be eliminated. However, we also know that
01:02:07
many many jobs will be recre will be created. The one thing that I will say to young people who are coming out of
01:02:12
school who are concerned who are anxious about AI be the expert of using AI >> how much look we all want our employees
01:02:20
to be expert at using AI and it's not not >> not trivial not trivial and so knowing
01:02:28
how to specify not to overprescribe leaving enough room for the AI to innovate and create while we guide it to
01:02:37
the outcome we want. it. All of that requires artistry. >> You had you had this great advice to
01:02:43
when you were at Stanford, I think it was, which is I wish to you pain and suffering. Do you remember that?
01:02:47
>> Yeah. >> Fantastic. >> What's your advice to young people
01:02:50
around what they should be studying? So, if they're sort of about to leave high
01:02:54
school because now those are the kids that are at this like really native, they haven't made a decision about
01:02:59
college, what to study, if at all go to college. How do you guide those kids? What would you tell them? I I still
01:03:07
believe that deep science, deep math, um language skills, you know, as you know,
01:03:13
language is the programming language of AI, >> the ultimate programming language.
01:03:18
>> And so, as it turns out, it it could be that the English major could be the most
01:03:22
successful. Yeah. >> And and so so I think I think um I I would just advise whatever whatever
01:03:28
education you get, just make sure that you're deeply deeply expert in using AIS. One of the things that I wanted to
01:03:35
say with respect to jobs and I want everybody to hear it that in fact at the beginning of the deep learning
01:03:40
revolution, one of the the finest computer scientists in the world deeply deeply I deeply uh deeply uh um respect
01:03:49
uh predicted that computer vision will completely eliminate radiologists and and that the one the one field he
01:03:57
advises everybody to not go into is radiology. 10 years later, his prediction was at 100% right. Computer
01:04:06
vision has been integrated into all of the radiology technologies and radiology platforms in the world 100%. The
01:04:14
surprising outcome is the number of radiologists actually went up and the demand for radiologists is skyrocketed.
01:04:21
The reason for that is because everybody's job has a purpose and its task. The task
01:04:29
that you do is studying the scans, >> but your purpose is to diag helping the
01:04:36
doctors, helping the patient diagnose disease. >> And so what's surprising is because the
01:04:41
scans are now being done so quickly, >> they could do more scans, improving
01:04:46
healthcare. >> Yes. >> But doing more scans more quickly allows patients to
01:04:51
>> be onboarded a lot more quick, treated a lot more quickly. And as it turns out,
01:04:57
because hospitals enjoy making money, too. >> Yeah. >> Right. >> They're doing more scans,
01:05:04
>> they're treating more customers and patients, the revenues go up. Guess
01:05:07
what? >> And and a country that grows faster, productivity increases. A wealthier
01:05:14
country can put more teachers in the classroom, not less teachers in the classroom. That's right. You just give
01:05:19
every one of those teachers a personalized curriculum for every student in the room. It makes them all
01:05:23
bionic and leads to a lot more. Every single student will be assisted by AI, but every single student will need great
01:05:30
teachers. >> Yeah. Amazing. Uh Jensen, congratulations. I know your success and
01:05:35
really this is an incredibly positive, uplifting discussion. We really appreciate you taking the time for us.
01:05:40
He is the steward we need. >> You are you are the more vocal. I'm being very vocal about the positive side
01:05:47
of it. I think there's too much dumerism is >> but I also think it takes the humility
01:05:50
to have this level of success and be humble about we're making software guys.
01:05:55
Yeah. >> And I think that that's actually really healthy for people to hear. We have done
01:06:00
this before. We have invented categories and industries before. >> We don't need to go to this
01:06:06
>> scaremongering place. It does nothing. >> And we get to choose, right? We have
01:06:10
autonomy and and agency. We get to pick how to >> we sure do >> employ this. Okay, everybody. We'll see
01:06:15
you next time on the All-In interview. Okay. >> Well done, brother. >> Thanks, man.
01:06:20
>> Good job. >> Thank you, sir. That was awesome. >> Good. Good. Appreciate you.
01:06:24
>> You guys are awesome. >> Look at this. Look at this big crowd behind you guys,
01:06:27
>> man. I think they're here for you. >> I'm going all in.