Jensen Huang: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis

special episode this week. We've special episode this week. We've preempted the weekly show and there's preempted the weekly show and there's preempted the weekly show and there's only three people we preempt the show only three people we preempt the show only three people we preempt the show for. President Trump, Jesus, and Jensen. for. President Trump, Jesus, and Jensen. for. President Trump, Jesus, and Jensen. And uh I'll let you pick which order we And uh I'll let you pick which order we And uh I'll let you pick which order we do that. Uh but what an amazing run do that. Uh but what an amazing run do that. Uh but what an amazing run you've had and a great event. Uh you've had and a great event. Uh you've had and a great event. Uh >> every industry is here. Every tech >> every industry is here. Every tech >> every industry is here. Every tech company is here. Every AI company is company is here. Every AI company is company is here. Every AI company is here. Incredible. Incredible. here. Incredible. Incredible. here. Incredible. Incredible. >> Extraordinary. And one of the great >> Extraordinary. And one of the great >> Extraordinary. And one of the great announcements of the past year has been announcements of the past year has been announcements of the past year has been Grock. When you made the purchase of Grock. When you made the purchase of Grock. When you made the purchase of Grock, did you realize how insufferable Grock, did you realize how insufferable Grock, did you realize how insufferable Cha Chimath would become? Cha Chimath would become? Cha Chimath would become? >> I had I had an inkling that that that >> I had I had an inkling that that that >> I had I had an inkling that that that >> we're his friends. We have to deal with >> we're his friends. We have to deal with >> we're his friends. We have to deal with him every week. him every week. him every week. >> I know it. >> I know it. >> I know it. >> You had to deal with him for the six >> You had to deal with him for the six >> You had to deal with him for the six week close. week close. week close. >> I know it's like two weeks. Two weeks. >> I know it's like two weeks. Two weeks. >> I know it's like two weeks. Two weeks. >> It's all coming back to me now. It's >> It's all coming back to me now. It's >> It's all coming back to me now. It's it's making me rather uncomfortable. The it's making me rather uncomfortable. The it's making me rather uncomfortable. The the thing is uh many of our strategies the thing is uh many of our strategies the thing is uh many of our strategies are are presented in in broad daylight are are presented in in broad daylight are are presented in in broad daylight at GTC years in advance of when we do at GTC years in advance of when we do at GTC years in advance of when we do it. Two and a half years ago, I it. Two and a half years ago, I it. Two and a half years ago, I introduced the operating system of the introduced the operating system of the introduced the operating system of the AI factory and it's called Dynamo. AI factory and it's called Dynamo. AI factory and it's called Dynamo. Dynamo, as you know, is a piece of Dynamo, as you know, is a piece of Dynamo, as you know, is a piece of instrument, a machine that was created instrument, a machine that was created instrument, a machine that was created by Seammens to turn essentially water by Seammens to turn essentially water by Seammens to turn essentially water into electricity. and Dynamo uh powered into electricity. and Dynamo uh powered into electricity. and Dynamo uh powered the factory of the last industrial the factory of the last industrial the factory of the last industrial revolution. So I thought it was the revolution. So I thought it was the revolution. So I thought it was the perfect name for the operating system of perfect name for the operating system of perfect name for the operating system of the next industrial revolution, the the next industrial revolution, the the next industrial revolution, the factory of that. And so inside Dynamo, factory of that. And so inside Dynamo, factory of that. And so inside Dynamo, the fundamental technology is the fundamental technology is the fundamental technology is disagregated inference. disagregated inference. disagregated inference. >> Jason, I I know you're you're super >> Jason, I I know you're you're super >> Jason, I I know you're you're super technical. technical. technical. >> Absolutely. >> Absolutely. >> Absolutely. >> I know it. >> I know it. >> I know it. >> I'll let you take this one. Go ahead and >> I'll let you take this one. Go ahead and >> I'll let you take this one. Go ahead and define it for the audience. I don't want define it for the audience. I don't want define it for the audience. I don't want to step on you. to step on you. to step on you. >> Yeah, thank you. I I I know you wanted >> Yeah, thank you. I I I know you wanted >> Yeah, thank you. I I I know you wanted to jump in there for a second, but it's to jump in there for a second, but it's to jump in there for a second, but it's it's this aggregating inference, which it's this aggregating inference, which it's this aggregating inference, which means the the pipeline, the processing means the the pipeline, the processing means the the pipeline, the processing pipeline of inference is extremely pipeline of inference is extremely pipeline of inference is extremely complicated. In fact, it is the most complicated. In fact, it is the most complicated. In fact, it is the most complicated computing problem today. complicated computing problem today. complicated computing problem today. Incredible scale, lots of mathematics of Incredible scale, lots of mathematics of Incredible scale, lots of mathematics of different shapes and sizes. And we came different shapes and sizes. And we came different shapes and sizes. And we came up came up with the idea that you would up came up with the idea that you would up came up with the idea that you would change you would you would disagregate change you would you would disagregate change you would you would disagregate parts of the processing such that some parts of the processing such that some parts of the processing such that some of it can run on some GPUs rest of it of it can run on some GPUs rest of it of it can run on some GPUs rest of it can run on different GPUs and that led can run on different GPUs and that led can run on different GPUs and that led to us realizing that maybe even to us realizing that maybe even to us realizing that maybe even disagregated computing could make sense disagregated computing could make sense disagregated computing could make sense that we could have different that we could have different that we could have different heterogeneous nature of computing that heterogeneous nature of computing that heterogeneous nature of computing that same sensibility led us to melanox same sensibility led us to melanox same sensibility led us to melanox >> you know today Nvidia's computing is >> you know today Nvidia's computing is >> you know today Nvidia's computing is spread across GPUs, CPUs, switches, spread across GPUs, CPUs, switches, spread across GPUs, CPUs, switches, scale up switches, scale out switches, scale up switches, scale out switches, scale up switches, scale out switches, networking processors, and now we're networking processors, and now we're networking processors, and now we're going to add Gro to that and we're going going to add Gro to that and we're going going to add Gro to that and we're going to put the right workload on the right to put the right workload on the right to put the right workload on the right chips. You know, we just really evolved chips. You know, we just really evolved chips. You know, we just really evolved from a GPU company to an AI factory from a GPU company to an AI factory from a GPU company to an AI factory company. company. company. >> I mean, I think that was probably the >> I mean, I think that was probably the >> I mean, I think that was probably the biggest takeaway that I had. You're biggest takeaway that I had. You're biggest takeaway that I had. You're seeing this fundamental disagregation seeing this fundamental disagregation seeing this fundamental disagregation where we've gone from a GPU and now you where we've gone from a GPU and now you where we've gone from a GPU and now you have this complexion of all these have this complexion of all these have this complexion of all these different options that will eventually different options that will eventually different options that will eventually exist. The thing that you guys said on exist. The thing that you guys said on exist. The thing that you guys said on stage or you said on stage was I I would stage or you said on stage was I I would stage or you said on stage was I I would like the high value inference people to like the high value inference people to like the high value inference people to take a listen to this and 25% of your take a listen to this and 25% of your take a listen to this and 25% of your data center space you said should be data center space you said should be data center space you said should be allocated to this gro lpu GPU combo allocated to this gro lpu GPU combo allocated to this gro lpu GPU combo >> grock to about 25% of the ver rubins in >> grock to about 25% of the ver rubins in >> grock to about 25% of the ver rubins in the in the data center. So can you tell the in the data center. So can you tell the in the data center. So can you tell us about how the industry looks at this us about how the industry looks at this us about how the industry looks at this idea of now basically creating this next idea of now basically creating this next idea of now basically creating this next generation form of disagregated prefill generation form of disagregated prefill generation form of disagregated prefill decode disag? decode disag? decode disag? >> Yeah and take a step back and at the >> Yeah and take a step back and at the >> Yeah and take a step back and at the time that we added this we went from time that we added this we went from time that we added this we went from large language model processing large language model processing large language model processing to agentic processing. Now when you're to agentic processing. Now when you're to agentic processing. Now when you're running an agent you're accessing running an agent you're accessing running an agent you're accessing working memory you're accessing working memory you're accessing working memory you're accessing long-term memory. You're using tools. long-term memory. You're using tools. long-term memory. You're using tools. You're really beating up on storage You're really beating up on storage You're really beating up on storage really hard. You have agents working really hard. You have agents working really hard. You have agents working with other agents. Some of the agents with other agents. Some of the agents with other agents. Some of the agents are very large models. Some of them are are very large models. Some of them are are very large models. Some of them are smaller models. Some of them are smaller models. Some of them are smaller models. Some of them are diffusion models. Some of them are auto diffusion models. Some of them are auto diffusion models. Some of them are auto reggressive models. And so there are all reggressive models. And so there are all reggressive models. And so there are all kinds of different types of models kinds of different types of models kinds of different types of models inside this data center. We created Vera inside this data center. We created Vera inside this data center. We created Vera Rubin to be able to run this Rubin to be able to run this Rubin to be able to run this extraordinarily diverse workload. My extraordinarily diverse workload. My extraordinarily diverse workload. My sense is and so we added what used to be sense is and so we added what used to be sense is and so we added what used to be a one rack company. we now added four a one rack company. we now added four a one rack company. we now added four more racks, more racks, more racks, >> right? >> right? >> right? >> So, Nvidia's TAM, if you will, increased >> So, Nvidia's TAM, if you will, increased >> So, Nvidia's TAM, if you will, increased from what whatever it was to probably from what whatever it was to probably from what whatever it was to probably something, call it, you know, 33% 50% something, call it, you know, 33% 50% something, call it, you know, 33% 50% higher. Now, part of that 33% or 50% a higher. Now, part of that 33% or 50% a higher. Now, part of that 33% or 50% a lot of it is going to be storage lot of it is going to be storage lot of it is going to be storage processors. It's called Blue Field. Some processors. It's called Blue Field. Some processors. It's called Blue Field. Some of it will be a lot of it, I'm hoping, of it will be a lot of it, I'm hoping, of it will be a lot of it, I'm hoping, will be Grock processors, and some of it will be Grock processors, and some of it will be Grock processors, and some of it will be CPUs. And they're all and a lot will be CPUs. And they're all and a lot will be CPUs. And they're all and a lot of it's going to be networking of it's going to be networking of it's going to be networking processors. And so all of this is going processors. And so all of this is going processors. And so all of this is going to be running basically the computer of to be running basically the computer of to be running basically the computer of the AI revolution called agents, right? the AI revolution called agents, right? the AI revolution called agents, right? >> The operating system of of um modern >> The operating system of of um modern >> The operating system of of um modern modern industry. modern industry. modern industry. >> What about embedded applications? So you >> What about embedded applications? So you >> What about embedded applications? So you know my daughter's teddy bear at home know my daughter's teddy bear at home know my daughter's teddy bear at home wants to talk to her. What goes in wants to talk to her. What goes in wants to talk to her. What goes in there? Is it a custom ASIC or does there there? Is it a custom ASIC or does there there? Is it a custom ASIC or does there end up becoming much more kind of a end up becoming much more kind of a end up becoming much more kind of a broader set of TAM with developing tools broader set of TAM with developing tools broader set of TAM with developing tools that are maybe different for different that are maybe different for different that are maybe different for different use cases at the edge and an embedded use cases at the edge and an embedded use cases at the edge and an embedded application? We think that there's three application? We think that there's three application? We think that there's three computers in the problem at the at the computers in the problem at the at the computers in the problem at the at the largest at the largest scale when you largest at the largest scale when you largest at the largest scale when you stick take a step back. There's one stick take a step back. There's one stick take a step back. There's one computer that's really about training computer that's really about training computer that's really about training the AI model, developing creating the the AI model, developing creating the the AI model, developing creating the AI, another computer for evaluating it. AI, another computer for evaluating it. AI, another computer for evaluating it. Depending on the type of problem you're Depending on the type of problem you're Depending on the type of problem you're having, like for example, you look having, like for example, you look having, like for example, you look around, there's all kinds of robots and around, there's all kinds of robots and around, there's all kinds of robots and cars and things like that. You have to cars and things like that. You have to cars and things like that. You have to evaluate these robots inside a virtual evaluate these robots inside a virtual evaluate these robots inside a virtual gym that represents the physical world. gym that represents the physical world. gym that represents the physical world. So it has to be software that obeys the So it has to be software that obeys the So it has to be software that obeys the laws of physics. And that's a second laws of physics. And that's a second laws of physics. And that's a second computer. We call that omniverse. The computer. We call that omniverse. The computer. We call that omniverse. The third computer is the computer at the third computer is the computer at the third computer is the computer at the edge, the robotics computer. edge, the robotics computer. edge, the robotics computer. >> That robotics computer, one of them >> That robotics computer, one of them >> That robotics computer, one of them could be self-driving car. Another one's could be self-driving car. Another one's could be self-driving car. Another one's a robot. Another one could be a teddy a robot. Another one could be a teddy a robot. Another one could be a teddy bear. Little tiny one for a teddy bear. bear. Little tiny one for a teddy bear. bear. Little tiny one for a teddy bear. One of the most important ones is one One of the most important ones is one One of the most important ones is one that we're working on that basically that we're working on that basically that we're working on that basically turns the telecommunications base turns the telecommunications base turns the telecommunications base stations into part of the AI stations into part of the AI stations into part of the AI infrastructure. So now all of the it's a infrastructure. So now all of the it's a infrastructure. So now all of the it's a $2 trillion industry. All of that in $2 trillion industry. All of that in $2 trillion industry. All of that in time will be transformed into an time will be transformed into an time will be transformed into an extension of the AI infrastructure. And extension of the AI infrastructure. And extension of the AI infrastructure. And so radios radios will become a edge so radios radios will become a edge so radios radios will become a edge devices, factories, warehouses, you name devices, factories, warehouses, you name devices, factories, warehouses, you name it. And so so there are three these it. And so so there are three these it. And so so there are three these three basic computers. three basic computers. three basic computers. All of them, you know, are going to be All of them, you know, are going to be All of them, you know, are going to be necessary. necessary. necessary. >> Jensen, last uh last year, I think you >> Jensen, last uh last year, I think you >> Jensen, last uh last year, I think you were ahead of the the rest of the world were ahead of the the rest of the world were ahead of the the rest of the world in in in saying inference isn't going to in in in saying inference isn't going to in in in saying inference isn't going to a thousand. a thousand. a thousand. >> Just last year, >> Just last year, >> Just last year, >> yes. >> yes. >> yes. Is it is it going to 1 millionx is going Is it is it going to 1 millionx is going Is it is it going to 1 millionx is going to 1 billionx? Yeah. to 1 billionx? Yeah. to 1 billionx? Yeah. >> Right. And I think people at the time >> Right. And I think people at the time >> Right. And I think people at the time thought it was pretty hyperbolic because thought it was pretty hyperbolic because thought it was pretty hyperbolic because the world was still focused on the world was still focused on the world was still focused on pre-scaling, on training. Here we are pre-scaling, on training. Here we are pre-scaling, on training. Here we are now. Inference has exploded. We're now. Inference has exploded. We're now. Inference has exploded. We're inference constrained. um you announced inference constrained. um you announced inference constrained. um you announced an inference factory that I think is an inference factory that I think is an inference factory that I think is leading edge that's going to be 10x leading edge that's going to be 10x leading edge that's going to be 10x better in terms of throughput to the better in terms of throughput to the better in terms of throughput to the next factory but yet if you if I listen next factory but yet if you if I listen next factory but yet if you if I listen to what the chatter is out there it's to what the chatter is out there it's to what the chatter is out there it's that your inference factory is going to that your inference factory is going to that your inference factory is going to cost 40 or 50 billion and the cost 40 or 50 billion and the cost 40 or 50 billion and the alternatives the custom AS6 AMD others alternatives the custom AS6 AMD others alternatives the custom AS6 AMD others are going to cost 25 to 30 billion and are going to cost 25 to 30 billion and are going to cost 25 to 30 billion and you're going to lose share so why don't you're going to lose share so why don't you're going to lose share so why don't you talk to us what are you seeing how you talk to us what are you seeing how you talk to us what are you seeing how do you think about share and does it do you think about share and does it do you think about share and does it make sense for all these folks to pay make sense for all these folks to pay make sense for all these folks to pay something that's a 2x X premium to what something that's a 2x X premium to what something that's a 2x X premium to what others are marketing. others are marketing. others are marketing. >> The big takeaway, the big idea is that >> The big takeaway, the big idea is that >> The big takeaway, the big idea is that you should not equate the price of the you should not equate the price of the you should not equate the price of the factory and the price of the tokens, the factory and the price of the tokens, the factory and the price of the tokens, the cost of the tokens. It is very likely cost of the tokens. It is very likely cost of the tokens. It is very likely that the $50 billion factory, and in that the $50 billion factory, and in that the $50 billion factory, and in fact, I can prove it that the $50 fact, I can prove it that the $50 fact, I can prove it that the $50 billion factory will generate for you billion factory will generate for you billion factory will generate for you the lowest cost tokens. And the reason the lowest cost tokens. And the reason the lowest cost tokens. And the reason for that is because we produce these for that is because we produce these for that is because we produce these tokens at extraordinary efficiency tokens at extraordinary efficiency tokens at extraordinary efficiency 10 times you know the difference between 10 times you know the difference between 10 times you know the difference between 50 billion. Now it turns out 20 billion 50 billion. Now it turns out 20 billion 50 billion. Now it turns out 20 billion is just land power and shell. Right. is just land power and shell. Right. is just land power and shell. Right. >> Right. >> Right. >> Right. >> And then on top of that you have storage >> And then on top of that you have storage >> And then on top of that you have storage anyways networking anyways you got CPUs anyways networking anyways you got CPUs anyways networking anyways you got CPUs anyways you got servers anyways you got anyways you got servers anyways you got anyways you got servers anyways you got cooling anyways. The difference between cooling anyways. The difference between cooling anyways. The difference between that GPU being 1x price or halfx price that GPU being 1x price or halfx price that GPU being 1x price or halfx price >> is not between 50 billion and 30 >> is not between 50 billion and 30 >> is not between 50 billion and 30 billion. Pick your favorite number, but billion. Pick your favorite number, but billion. Pick your favorite number, but let's say between 50 billion and 40 let's say between 50 billion and 40 let's say between 50 billion and 40 billion. billion. billion. >> That is not a large percentage when the >> That is not a large percentage when the >> That is not a large percentage when the $50 billion $50 billion $50 billion >> data center is actually 10 times the >> data center is actually 10 times the >> data center is actually 10 times the throughput, throughput, throughput, >> right? Jess, that's the reason why I >> right? Jess, that's the reason why I >> right? Jess, that's the reason why I said that even for most chips, said that even for most chips, said that even for most chips, >> if you can't keep up with the state of >> if you can't keep up with the state of >> if you can't keep up with the state of the technology and the pace that we're the technology and the pace that we're the technology and the pace that we're running, even when the chips are free, running, even when the chips are free, running, even when the chips are free, it's not cheap enough. it's not cheap enough. it's not cheap enough. >> Yeah. >> Yeah. >> Yeah. >> Can I can I just ask a general strategy >> Can I can I just ask a general strategy >> Can I can I just ask a general strategy question? question? question? >> Yeah. >> Yeah. >> Yeah. >> I mean, you're running the most valuable >> I mean, you're running the most valuable >> I mean, you're running the most valuable company in the world. This thing is company in the world. This thing is company in the world. This thing is going to do 350 plus billion of revenue going to do 350 plus billion of revenue going to do 350 plus billion of revenue next year, 200 billion of free cash next year, 200 billion of free cash next year, 200 billion of free cash flow. It's compounding at these crazy flow. It's compounding at these crazy flow. It's compounding at these crazy rates. How do you decide what to do? rates. How do you decide what to do? rates. How do you decide what to do? like how do you actually get the like how do you actually get the like how do you actually get the information? I mean it's famous now information? I mean it's famous now information? I mean it's famous now these sort of emails that are people are these sort of emails that are people are these sort of emails that are people are meant to send you but how do you really meant to send you but how do you really meant to send you but how do you really decide to get an intuition of how to decide to get an intuition of how to decide to get an intuition of how to shape the market where to really double shape the market where to really double shape the market where to really double down where to maybe pull back where to down where to maybe pull back where to down where to maybe pull back where to actually go into a green field how how actually go into a green field how how actually go into a green field how how does that information get to you how do does that information get to you how do does that information get to you how do you decide these things you decide these things you decide these things >> in a final analysis that's the job of >> in a final analysis that's the job of >> in a final analysis that's the job of the CEO yeah the CEO yeah the CEO yeah >> and our job is to define the strategy >> and our job is to define the strategy >> and our job is to define the strategy define the vision define the strategy define the vision define the strategy define the vision define the strategy we're informed of course by amazing we're informed of course by amazing we're informed of course by amazing computer scientists amazing computer scientists amazing computer scientists amazing technologists great people all over the technologists great people all over the technologists great people all over the company but we have to shape that future company but we have to shape that future company but we have to shape that future well Part of it has to do with is this well Part of it has to do with is this well Part of it has to do with is this something that's insanely hard to do? If something that's insanely hard to do? If something that's insanely hard to do? If it's not hard to do, we should back away it's not hard to do, we should back away it's not hard to do, we should back away from it. And the reason for that is if from it. And the reason for that is if from it. And the reason for that is if if it's easy to do, obviously, um, if it's easy to do, obviously, um, if it's easy to do, obviously, um, >> lots of competitors, >> lots of competitors, >> lots of competitors, >> a lot of competitors. Is this something >> a lot of competitors. Is this something >> a lot of competitors. Is this something that has never been done before that's that has never been done before that's that has never been done before that's insanely hard to do? And that somehow insanely hard to do? And that somehow insanely hard to do? And that somehow taps into taps into taps into >> the special superpowers of our company. >> the special superpowers of our company. >> the special superpowers of our company. >> And so I have to find this confluence of >> And so I have to find this confluence of >> And so I have to find this confluence of things to that meets the standard. And things to that meets the standard. And things to that meets the standard. And in the end, we also know that a lot of in the end, we also know that a lot of in the end, we also know that a lot of pain and suffering is going to go into pain and suffering is going to go into pain and suffering is going to go into it. Yeah. it. Yeah. it. Yeah. >> There no great things that are invented >> There no great things that are invented >> There no great things that are invented because it was just easy to do and just because it was just easy to do and just because it was just easy to do and just like first try, here we are. And so if like first try, here we are. And so if like first try, here we are. And so if it's super hard to do, nobody's ever it's super hard to do, nobody's ever it's super hard to do, nobody's ever done it before, it's very likely that done it before, it's very likely that done it before, it's very likely that you're going to have a lot of pain and you're going to have a lot of pain and you're going to have a lot of pain and suffering and so you better enjoy it. So suffering and so you better enjoy it. So suffering and so you better enjoy it. So can you can you just look at maybe three can you can you just look at maybe three can you can you just look at maybe three or four of the more longtail things you or four of the more longtail things you or four of the more longtail things you announced and just talk about the announced and just talk about the announced and just talk about the long-term viability of whether it's the long-term viability of whether it's the long-term viability of whether it's the data centers in space or whether it's data centers in space or whether it's data centers in space or whether it's what you're trying to do with ADAS in what you're trying to do with ADAS in what you're trying to do with ADAS in autos or you know what you're trying to autos or you know what you're trying to autos or you know what you're trying to do on the biology side just give us a do on the biology side just give us a do on the biology side just give us a sense of like how you see some of these sense of like how you see some of these sense of like how you see some of these curves inflecting upwards in some of curves inflecting upwards in some of curves inflecting upwards in some of these longer tail businesses. these longer tail businesses. these longer tail businesses. >> Excellent. uh physical AI large category >> Excellent. uh physical AI large category >> Excellent. uh physical AI large category we believe and I just mentioned we have we believe and I just mentioned we have we believe and I just mentioned we have three computing systems all the software three computing systems all the software three computing systems all the software platforms on top of it physical AI as a platforms on top of it physical AI as a platforms on top of it physical AI as a large category large category large category it's technology industry's first it's technology industry's first it's technology industry's first opportunity opportunity opportunity to address a $50 trillion industry that to address a $50 trillion industry that to address a $50 trillion industry that has largely been you know void of has largely been you know void of has largely been you know void of technology until now and so we need to technology until now and so we need to technology until now and so we need to invent all of the technology necessary invent all of the technology necessary invent all of the technology necessary to do that I felt that that was a to do that I felt that that was a to do that I felt that that was a 10-year journey 10-year journey 10-year journey We started 10 years ago. We're seeing it We started 10 years ago. We're seeing it We started 10 years ago. We're seeing it inflecting now. It is a multi-billion inflecting now. It is a multi-billion inflecting now. It is a multi-billion dollar business for us. It's close to10 dollar business for us. It's close to10 dollar business for us. It's close to10 billion a year now. And so it's a big billion a year now. And so it's a big billion a year now. And so it's a big business and it's growing exponentially. business and it's growing exponentially. business and it's growing exponentially. And so that's number one. I think in the And so that's number one. I think in the And so that's number one. I think in the case of digital biology, I think we are case of digital biology, I think we are case of digital biology, I think we are literally near the chat GPT moment of literally near the chat GPT moment of literally near the chat GPT moment of digital biology. We're about to digital biology. We're about to digital biology. We're about to understand how to represent genes, understand how to represent genes, understand how to represent genes, proteins, cells. We already know how to proteins, cells. We already know how to proteins, cells. We already know how to understand chemicals. And so the ability understand chemicals. And so the ability understand chemicals. And so the ability for us to represent and understand the for us to represent and understand the for us to represent and understand the dynamics of the building blocks of dynamics of the building blocks of dynamics of the building blocks of biology that's a couple of two three biology that's a couple of two three biology that's a couple of two three five years from now in 5 years time I five years from now in 5 years time I five years from now in 5 years time I completely believe that the healthcare completely believe that the healthcare completely believe that the healthcare industry where digital biology is going industry where digital biology is going industry where digital biology is going to inflect and so these are a couple of to inflect and so these are a couple of to inflect and so these are a couple of the really great ones and you could see the really great ones and you could see the really great ones and you could see they're all around us they're all around us they're all around us >> agriculture >> agriculture >> agriculture >> agriculture >> agriculture >> agriculture >> reflecting now >> reflecting now >> reflecting now >> no question yeah >> no question yeah >> no question yeah >> Jensen I want to take you from the data >> Jensen I want to take you from the data >> Jensen I want to take you from the data center to the desktop uh the company was center to the desktop uh the company was center to the desktop uh the company was built in large part on hobbyists, video built in large part on hobbyists, video built in large part on hobbyists, video gamers and and all those graphic cards gamers and and all those graphic cards gamers and and all those graphic cards in the beginning. And you mentioned in in the beginning. And you mentioned in in the beginning. And you mentioned in front of I think 10,000 people here just front of I think 10,000 people here just front of I think 10,000 people here just clawed open claw clawed code and what a clawed open claw clawed code and what a clawed open claw clawed code and what a revolution agents have become and revolution agents have become and revolution agents have become and specifically the hobbyists who are specifically the hobbyists who are specifically the hobbyists who are really where a lot of energy um we see really where a lot of energy um we see really where a lot of energy um we see you know a lot of the innovation breaks you know a lot of the innovation breaks you know a lot of the innovation breaks want desktops. You announced one here uh want desktops. You announced one here uh want desktops. You announced one here uh I believe it's the Dell 6800. Uh this is I believe it's the Dell 6800. Uh this is I believe it's the Dell 6800. Uh this is a very powerful workstation to run local a very powerful workstation to run local a very powerful workstation to run local models. 750 gigs of RAM. Obviously the models. 750 gigs of RAM. Obviously the models. 750 gigs of RAM. Obviously the the Mac uh studio sold out everywhere in the Mac uh studio sold out everywhere in the Mac uh studio sold out everywhere in my company. We're moving to OpenClaw my company. We're moving to OpenClaw my company. We're moving to OpenClaw everything. Freeberg just got clawed. everything. Freeberg just got clawed. everything. Freeberg just got clawed. You got claw pelled I understand. And You got claw pelled I understand. And You got claw pelled I understand. And you're obsessed with these. you're obsessed with these. you're obsessed with these. >> What is this from the streets movement >> What is this from the streets movement >> What is this from the streets movement of creating open-source agents and using of creating open-source agents and using of creating open-source agents and using open source on the desktop mean to you? open source on the desktop mean to you? open source on the desktop mean to you? Great. Where is that going? Great. Where is that going? Great. Where is that going? >> Yeah. So great. First of all, let's take >> Yeah. So great. First of all, let's take >> Yeah. So great. First of all, let's take a step back. Um in the last two years we a step back. Um in the last two years we a step back. Um in the last two years we saw basically three inflection points. saw basically three inflection points. saw basically three inflection points. The first one was generative chat GPT The first one was generative chat GPT The first one was generative chat GPT brought AI to the common brought AI to the common brought AI to the common everybody to our awareness. But the fact everybody to our awareness. But the fact everybody to our awareness. But the fact of the matter is the technology sat in of the matter is the technology sat in of the matter is the technology sat in plain sight months before GPT. It wasn't plain sight months before GPT. It wasn't plain sight months before GPT. It wasn't until chat GPT put a user interface until chat GPT put a user interface until chat GPT put a user interface around it made it easy for us to use around it made it easy for us to use around it made it easy for us to use that generative AI took off. Now that generative AI took off. Now that generative AI took off. Now generative AI as you know generates generative AI as you know generates generative AI as you know generates tokens for internal consumption as well tokens for internal consumption as well tokens for internal consumption as well as external consumption. Internal as external consumption. Internal as external consumption. Internal consumption is thinking which led to consumption is thinking which led to consumption is thinking which led to reasoning. 01 and 03 reasoning. 01 and 03 reasoning. 01 and 03 continue that wave of chat GPT grounded continue that wave of chat GPT grounded continue that wave of chat GPT grounded information made AI not only answer information made AI not only answer information made AI not only answer questions but answer questions in a more questions but answer questions in a more questions but answer questions in a more grounded way useful. grounded way useful. grounded way useful. We started seeing the revenues and the e We started seeing the revenues and the e We started seeing the revenues and the e the economic model of open AI start to the economic model of open AI start to the economic model of open AI start to inflect. Then the third one was only inflect. Then the third one was only inflect. Then the third one was only inside the industry that we saw clock inside the industry that we saw clock inside the industry that we saw clock code the first agentic system that was code the first agentic system that was code the first agentic system that was very useful really revolutionary stuff very useful really revolutionary stuff very useful really revolutionary stuff but but claw code was only available for but but claw code was only available for but but claw code was only available for enterprises. Most people outside never enterprises. Most people outside never enterprises. Most people outside never saw anything about cloud code until open saw anything about cloud code until open saw anything about cloud code until open claw. Open claw basically put into the claw. Open claw basically put into the claw. Open claw basically put into the po popular consciousness what an AI po popular consciousness what an AI po popular consciousness what an AI agent can do. Mhm. agent can do. Mhm. agent can do. Mhm. >> That's the reason why open claw is so >> That's the reason why open claw is so >> That's the reason why open claw is so important from a cultural perspective. important from a cultural perspective. important from a cultural perspective. Now the second second reason why it's so Now the second second reason why it's so Now the second second reason why it's so important is that open claw is open but important is that open claw is open but important is that open claw is open but it formulates it formulates it formulates it structures a type of computing model it structures a type of computing model it structures a type of computing model that is basically reinventing computing that is basically reinventing computing that is basically reinventing computing all together. It has a memory system. It all together. It has a memory system. It all together. It has a memory system. It scratch is a short-term memory file scratch is a short-term memory file scratch is a short-term memory file system. It has it has it has scales. Did system. It has it has it has scales. Did system. It has it has it has scales. Did you say skills or scales? you say skills or scales? you say skills or scales? >> Skills. >> Skills. >> Skills. >> Oh, skills. >> Oh, skills. >> Oh, skills. >> They do have scales theoretically. Yeah. >> They do have scales theoretically. Yeah. >> They do have scales theoretically. Yeah. >> Yeah. Skills. >> Yeah. Skills. >> Yeah. Skills. >> So, the first thing first thing it it, >> So, the first thing first thing it it, >> So, the first thing first thing it it, you know, it has resources. It it you know, it has resources. It it you know, it has resources. It it manages resources. It's it does manages resources. It's it does manages resources. It's it does scheduling. scheduling. scheduling. >> Yep. >> Yep. >> Yep. >> Right. And it cron jobs. It could it >> Right. And it cron jobs. It could it >> Right. And it cron jobs. It could it could spawn off agents. It could, you could spawn off agents. It could, you could spawn off agents. It could, you know, it could decompose a task and and know, it could decompose a task and and know, it could decompose a task and and cause and solve problems as does cause and solve problems as does cause and solve problems as does scheduling. It has IO subsystems. It scheduling. It has IO subsystems. It scheduling. It has IO subsystems. It could, you know, input. It has output. could, you know, input. It has output. could, you know, input. It has output. It connect to WhatsApp. And also it has It connect to WhatsApp. And also it has It connect to WhatsApp. And also it has a API that allows it to run multiple a API that allows it to run multiple a API that allows it to run multiple types of applications called skills. types of applications called skills. types of applications called skills. >> Yeah. >> Yeah. >> Yeah. >> These four elements fundamentally define >> These four elements fundamentally define >> These four elements fundamentally define a computer. a computer. a computer. >> Yeah. >> Yeah. >> Yeah. >> And therefore what do we have? We have a >> And therefore what do we have? We have a >> And therefore what do we have? We have a personal artificial intelligence personal artificial intelligence personal artificial intelligence computer for the very first time. computer for the very first time. computer for the very first time. >> Open source. >> Open source. >> Open source. >> It's open source. It runs literally >> It's open source. It runs literally >> It's open source. It runs literally everywhere. And so this is now the this everywhere. And so this is now the this everywhere. And so this is now the this is the op this is basically the is the op this is basically the is the op this is basically the blueprint the operating system of modern blueprint the operating system of modern blueprint the operating system of modern computing. computing. computing. >> Yeah. >> Yeah. >> Yeah. >> And it's going to run literally >> And it's going to run literally >> And it's going to run literally everywhere. Now of course one of the everywhere. Now of course one of the everywhere. Now of course one of the things that we had to help it do is things that we had to help it do is things that we had to help it do is whenever you have agentic software you whenever you have agentic software you whenever you have agentic software you have to make sure that and agentic have to make sure that and agentic have to make sure that and agentic software has access to sensitive software has access to sensitive software has access to sensitive information. It execute code. It could information. It execute code. It could information. It execute code. It could communicate externally. We have to make communicate externally. We have to make communicate externally. We have to make sure that all of it has to be governed. sure that all of it has to be governed. sure that all of it has to be governed. all of it has to be secure and that we all of it has to be secure and that we all of it has to be secure and that we have policies that that gives these have policies that that gives these have policies that that gives these agents two of the three things but not agents two of the three things but not agents two of the three things but not all three things at the same time all three things at the same time all three things at the same time >> and so the governance part of it we >> and so the governance part of it we >> and so the governance part of it we contributed to Peter Peter Steinberger contributed to Peter Peter Steinberger contributed to Peter Peter Steinberger was here and and so we've got a mountain was here and and so we've got a mountain was here and and so we've got a mountain of great engineers working with him to of great engineers working with him to of great engineers working with him to help secure and keep that thing so that help secure and keep that thing so that help secure and keep that thing so that it could protect our privacy protect our it could protect our privacy protect our it could protect our privacy protect our security security security >> Jensen that paradigm shift makes some of >> Jensen that paradigm shift makes some of >> Jensen that paradigm shift makes some of the AI legislation that has passed the AI legislation that has passed the AI legislation that has passed around the country to regulate AI and a around the country to regulate AI and a around the country to regulate AI and a lot of the proposed legislation lot of the proposed legislation lot of the proposed legislation effectively moot, doesn't it? Can you effectively moot, doesn't it? Can you effectively moot, doesn't it? Can you just comment for a second on how quickly just comment for a second on how quickly just comment for a second on how quickly the paradigm shift kind of obiates a lot the paradigm shift kind of obiates a lot the paradigm shift kind of obiates a lot of the models for regulatory oversight of the models for regulatory oversight of the models for regulatory oversight of AI, which is becoming a very hot of AI, which is becoming a very hot of AI, which is becoming a very hot topic in politics right now. topic in politics right now. topic in politics right now. >> Well, this is this is the part that that >> Well, this is this is the part that that >> Well, this is this is the part that that we just with policy makers, we need to we just with policy makers, we need to we just with policy makers, we need to we need to always get in front of them we need to always get in front of them we need to always get in front of them and Brad, you do a great job doing this. and Brad, you do a great job doing this. and Brad, you do a great job doing this. We had to get in front of them and We had to get in front of them and We had to get in front of them and inform them about the state of the inform them about the state of the inform them about the state of the technology, what it is, what it is not. technology, what it is, what it is not. technology, what it is, what it is not. It is not a biological being. It is not It is not a biological being. It is not It is not a biological being. It is not alien. It is not conscious. alien. It is not conscious. alien. It is not conscious. Um it is computer software. Um it is computer software. Um it is computer software. >> Yeah. Exactly. >> Yeah. Exactly. >> Yeah. Exactly. >> And and it is not something that um we >> And and it is not something that um we >> And and it is not something that um we say things like we don't understand it say things like we don't understand it say things like we don't understand it at all. at all. at all. >> It is not true. We don't understand at >> It is not true. We don't understand at >> It is not true. We don't understand at all. We understand a lot of things about all. We understand a lot of things about all. We understand a lot of things about this technology. and and so so I think this technology. and and so so I think this technology. and and so so I think one we have to make sure that we one we have to make sure that we one we have to make sure that we continue to inform the policy makers and continue to inform the policy makers and continue to inform the policy makers and not affect not allow dumerism and not affect not allow dumerism and not affect not allow dumerism and extremism to affect how policy makers extremism to affect how policy makers extremism to affect how policy makers think and understand about this think and understand about this think and understand about this technology. However, however, we still technology. However, however, we still technology. However, however, we still have to recognize is technology is have to recognize is technology is have to recognize is technology is moving really fast and don't get policy moving really fast and don't get policy moving really fast and don't get policy ahead of the technology too quickly. And ahead of the technology too quickly. And ahead of the technology too quickly. And the risk that we we run as a nation, our the risk that we we run as a nation, our the risk that we we run as a nation, our greatest source of national security greatest source of national security greatest source of national security concern with respect to AI is that other concern with respect to AI is that other concern with respect to AI is that other countries adopt this technology while we countries adopt this technology while we countries adopt this technology while we are so angry at it or afraid of it or are so angry at it or afraid of it or are so angry at it or afraid of it or somehow paranoid of it that our somehow paranoid of it that our somehow paranoid of it that our industries, our society don't take industries, our society don't take industries, our society don't take advantage of AI. So I'm just mostly advantage of AI. So I'm just mostly advantage of AI. So I'm just mostly worried about the diffusion of AI here worried about the diffusion of AI here worried about the diffusion of AI here in United States. in United States. in United States. >> Can you just double click if you were in >> Can you just double click if you were in >> Can you just double click if you were in the seat in the boardroom of anthropic the seat in the boardroom of anthropic the seat in the boardroom of anthropic over that whole scuttlebutt with the over that whole scuttlebutt with the over that whole scuttlebutt with the department of war? It sort of builds on department of war? It sort of builds on department of war? It sort of builds on this idea of people didn't know what to this idea of people didn't know what to this idea of people didn't know what to think. It's sort of added to this layer think. It's sort of added to this layer think. It's sort of added to this layer of either resentment or fear or just of either resentment or fear or just of either resentment or fear or just general mistrust that people have general mistrust that people have general mistrust that people have sometimes at the software levels of AI. sometimes at the software levels of AI. sometimes at the software levels of AI. What would do you think you would have What would do you think you would have What would do you think you would have told Daario and that team to do maybe told Daario and that team to do maybe told Daario and that team to do maybe differently to try to change some of differently to try to change some of differently to try to change some of this outcome and some of this this outcome and some of this this outcome and some of this perception? perception? perception? >> The first thing that I I would I would >> The first thing that I I would I would >> The first thing that I I would I would say about Anthropic is first of all the say about Anthropic is first of all the say about Anthropic is first of all the technology is incredible. We are a large technology is incredible. We are a large technology is incredible. We are a large consumer of anthropic technology. Really consumer of anthropic technology. Really consumer of anthropic technology. Really admire their focus on security. Really admire their focus on security. Really admire their focus on security. Really admires their focus on safety. Um the admires their focus on safety. Um the admires their focus on safety. Um the the the the culture by which we they the the the culture by which we they the the the culture by which we they went about it. The the technology went about it. The the technology went about it. The the technology excellence by which they went about it excellence by which they went about it excellence by which they went about it really fantastic. Um I I would say that really fantastic. Um I I would say that really fantastic. Um I I would say that that the the desire to warn people about that the the desire to warn people about that the the desire to warn people about the capability of the technology is is the capability of the technology is is the capability of the technology is is also uh really terrific. We just have to also uh really terrific. We just have to also uh really terrific. We just have to make sure that we understand that the make sure that we understand that the make sure that we understand that the world has a spectrum and that that world has a spectrum and that that world has a spectrum and that that warning is good, scaring is less good, warning is good, scaring is less good, warning is good, scaring is less good, >> right? >> right? >> right? >> Um and because this technology is too >> Um and because this technology is too >> Um and because this technology is too important to us, important to us, important to us, >> right? And and I think that it is fine >> right? And and I think that it is fine >> right? And and I think that it is fine to uh predict the future but we need to to uh predict the future but we need to to uh predict the future but we need to be a little bit more circumspect. We be a little bit more circumspect. We be a little bit more circumspect. We need to have a little bit more humility need to have a little bit more humility need to have a little bit more humility that in fact we can't completely predict that in fact we can't completely predict that in fact we can't completely predict the future and the abil and to say the future and the abil and to say the future and the abil and to say things that that are quite extreme quite things that that are quite extreme quite things that that are quite extreme quite catastrophic that there's no evidence of catastrophic that there's no evidence of catastrophic that there's no evidence of it happening um could be more damaging it happening um could be more damaging it happening um could be more damaging than people think. And and of course we than people think. And and of course we than people think. And and of course we are technology leaders. Uh there were are technology leaders. Uh there were are technology leaders. Uh there were there was a time when nobody listened to there was a time when nobody listened to there was a time when nobody listened to us. Yeah. us. Yeah. us. Yeah. >> Um but now because technology is so >> Um but now because technology is so >> Um but now because technology is so important in the social fabric such an important in the social fabric such an important in the social fabric such an important industry, so important to important industry, so important to important industry, so important to national security, our words do matter. national security, our words do matter. national security, our words do matter. And I think we have to be much more And I think we have to be much more And I think we have to be much more circumspect. We have to be more circumspect. We have to be more circumspect. We have to be more moderate. We have to be more balanced. moderate. We have to be more balanced. moderate. We have to be more balanced. We have to be more for more thoughtful. We have to be more for more thoughtful. We have to be more for more thoughtful. >> Well, I you know I would nominate you. I >> Well, I you know I would nominate you. I >> Well, I you know I would nominate you. I think the industry's got to get think the industry's got to get think the industry's got to get together. 17% popularity of AI in the together. 17% popularity of AI in the together. 17% popularity of AI in the United States. I mean, we see what United States. I mean, we see what United States. I mean, we see what happened to nuclear, right? We basically happened to nuclear, right? We basically happened to nuclear, right? We basically shut down the entire nuclear industry shut down the entire nuclear industry shut down the entire nuclear industry and now we have a 100 fision reactors and now we have a 100 fision reactors and now we have a 100 fision reactors being built in China and zero in the being built in China and zero in the being built in China and zero in the United States. Um, we hear about United States. Um, we hear about United States. Um, we hear about moratoriums on data centers. So, I think moratoriums on data centers. So, I think moratoriums on data centers. So, I think we have to be a lot more proactive about we have to be a lot more proactive about we have to be a lot more proactive about that. But, but I want to go back to this that. But, but I want to go back to this that. But, but I want to go back to this agentic explosion that you're seeing agentic explosion that you're seeing agentic explosion that you're seeing inside your company, the efficiencies, inside your company, the efficiencies, inside your company, the efficiencies, the productivity gains inside your the productivity gains inside your the productivity gains inside your company. There's a lot of debate whether company. There's a lot of debate whether company. There's a lot of debate whether or not we're seeing ROI, right? and you or not we're seeing ROI, right? and you or not we're seeing ROI, right? and you and I entering into into this year, the and I entering into into this year, the and I entering into into this year, the big question was, are the revenues going big question was, are the revenues going big question was, are the revenues going to show up? Are the revenues going to to show up? Are the revenues going to to show up? Are the revenues going to scale like intelligence? And then we had scale like intelligence? And then we had scale like intelligence? And then we had this kind of Oenheimer moment, a five6 this kind of Oenheimer moment, a five6 this kind of Oenheimer moment, a five6 billion month by Anthropic in February. billion month by Anthropic in February. billion month by Anthropic in February. Um, do you think as you look ahead, you Um, do you think as you look ahead, you Um, do you think as you look ahead, you announced a trillion dollar, you know, announced a trillion dollar, you know, announced a trillion dollar, you know, visibility into a trillion dollars of visibility into a trillion dollars of visibility into a trillion dollars of just Blackwell and Vera Rubin over the just Blackwell and Vera Rubin over the just Blackwell and Vera Rubin over the course of the next couple years. When course of the next couple years. When course of the next couple years. When you see this happening at Anthropic and you see this happening at Anthropic and you see this happening at Anthropic and Open AI, do you think we're on that Open AI, do you think we're on that Open AI, do you think we're on that curve now where we're going to see curve now where we're going to see curve now where we're going to see revenues scale in the way that revenues scale in the way that revenues scale in the way that intelligence is scaling? intelligence is scaling? intelligence is scaling? >> When you look around when you I'll >> When you look around when you I'll >> When you look around when you I'll answer this a couple different ways. answer this a couple different ways. answer this a couple different ways. When you look around this audience, you When you look around this audience, you When you look around this audience, you will see that anthropic and open AI is will see that anthropic and open AI is will see that anthropic and open AI is represented here. But in fact, everybody represented here. But in fact, everybody represented here. But in fact, everybody 99% of everything that is here is all AI 99% of everything that is here is all AI 99% of everything that is here is all AI and it's not anthropic and open AI. and it's not anthropic and open AI. and it's not anthropic and open AI. >> Right. Right. >> Right. Right. >> Right. Right. >> And the reason for that is because AI is >> And the reason for that is because AI is >> And the reason for that is because AI is very diverse. very diverse. very diverse. >> I would say that the second most popular >> I would say that the second most popular >> I would say that the second most popular model as a category is open models. model as a category is open models. model as a category is open models. >> Number one is yeah open open source open >> Number one is yeah open open source open >> Number one is yeah open open source open ways open source. ways open source. ways open source. >> Open AI is number one. Open source is >> Open AI is number one. Open source is >> Open AI is number one. Open source is number two. Very distant. Third is number two. Very distant. Third is number two. Very distant. Third is anthropic. And that tells you something anthropic. And that tells you something anthropic. And that tells you something about the scale of all of the AI about the scale of all of the AI about the scale of all of the AI companies that are here. And so, so it's companies that are here. And so, so it's companies that are here. And so, so it's important to recognize recognize that. important to recognize recognize that. important to recognize recognize that. Um, let me let me come back and say a Um, let me let me come back and say a Um, let me let me come back and say a couple things. One, when we went from couple things. One, when we went from couple things. One, when we went from generative to reasoning, the amount of generative to reasoning, the amount of generative to reasoning, the amount of computation we needed was about a computation we needed was about a computation we needed was about a hundred times. Right? hundred times. Right? hundred times. Right? >> When we went from reasoning to agentic, >> When we went from reasoning to agentic, >> When we went from reasoning to agentic, the computation is probably another 100 the computation is probably another 100 the computation is probably another 100 times. Now we're looking at in just two times. Now we're looking at in just two times. Now we're looking at in just two years, computation went up by a fact years, computation went up by a fact years, computation went up by a fact 10,000x. 10,000x. 10,000x. Meanwhile, Meanwhile, Meanwhile, people pay for information, but people people pay for information, but people people pay for information, but people mostly pay for work. mostly pay for work. mostly pay for work. >> Yes. >> Yes. >> Yes. >> Talking to a chatbot and getting an >> Talking to a chatbot and getting an >> Talking to a chatbot and getting an answer is super great, answer is super great, answer is super great, >> right? helping me do some research. >> right? helping me do some research. >> right? helping me do some research. Unbelievable. But getting work done, Unbelievable. But getting work done, Unbelievable. But getting work done, I'll pay fordeed. I'll pay fordeed. I'll pay fordeed. >> And so that's where we are. Agentic >> And so that's where we are. Agentic >> And so that's where we are. Agentic systems get work done. They're helping systems get work done. They're helping systems get work done. They're helping our software engineers get work done. our software engineers get work done. our software engineers get work done. And and so then you take that, And and so then you take that, And and so then you take that, >> you got 10,000x more compute. >> you got 10,000x more compute. >> you got 10,000x more compute. >> You get probably at this point 100x more >> You get probably at this point 100x more >> You get probably at this point 100x more consumption now. consumption now. consumption now. >> Yes. >> Yes. >> Yes. >> Yeah. >> Yeah. >> Yeah. >> And we haven't even started scaling yet. >> And we haven't even started scaling yet. >> And we haven't even started scaling yet. We are absolutely at a millionx We are absolutely at a millionx We are absolutely at a millionx >> which is I think a great place to talk >> which is I think a great place to talk >> which is I think a great place to talk about the number of about the number of about the number of have 20 30,000 at the company something have 20 30,000 at the company something have 20 30,000 at the company something like that. like that. like that. >> We have 43,000 employees. You know, I >> We have 43,000 employees. You know, I >> We have 43,000 employees. You know, I would say 38,000 would say 38,000 would say 38,000 are engineers. are engineers. are engineers. >> The conversation we've had on the pod a >> The conversation we've had on the pod a >> The conversation we've had on the pod a number of times is, "Oh my god, look at number of times is, "Oh my god, look at number of times is, "Oh my god, look at the token usage in our companies. It is the token usage in our companies. It is the token usage in our companies. It is growing massively." And some people are growing massively." And some people are growing massively." And some people are asking, "Hey, when I join a company, how asking, "Hey, when I join a company, how asking, "Hey, when I join a company, how many tokens do I get cuz I want to be an many tokens do I get cuz I want to be an many tokens do I get cuz I want to be an effective employee?" And you postulated, effective employee?" And you postulated, effective employee?" And you postulated, I believe, during your 2 and 1/2 hour I believe, during your 2 and 1/2 hour I believe, during your 2 and 1/2 hour keynote, pretty long keynote. Well done keynote, pretty long keynote. Well done keynote, pretty long keynote. Well done that you were spending that you were spending that you were spending >> if it was well done it would be shorter. >> if it was well done it would be shorter. >> if it was well done it would be shorter. I just want I just want I just want >> you didn't have time to do a time to >> you didn't have time to do a time to >> you didn't have time to do a time to write an hour 45. write an hour 45. write an hour 45. >> So you guys so you guys know so you guys >> So you guys so you guys know so you guys >> So you guys so you guys know so you guys know there is no practice know there is no practice know there is no practice >> and so it's a gripping and ripping >> and so it's a gripping and ripping >> and so it's a gripping and ripping >> rip and rip. Yeah. >> rip and rip. Yeah. >> rip and rip. Yeah. >> So so I just want to let you know I was >> So so I just want to let you know I was >> So so I just want to let you know I was writing the speech while I was giving writing the speech while I was giving writing the speech while I was giving the speech. Okay. So you never know the speech. Okay. So you never know the speech. Okay. So you never know >> but >> but >> but >> does that mean if we do back >> does that mean if we do back >> does that mean if we do back >> I apologize. back of the envelope math >> I apologize. back of the envelope math >> I apologize. back of the envelope math $75,000 in tokens for each engineer or $75,000 in tokens for each engineer or $75,000 in tokens for each engineer or something like that. So are you spending something like that. So are you spending something like that. So are you spending in Nvidia a billion2 billion on tokens in Nvidia a billion2 billion on tokens in Nvidia a billion2 billion on tokens for your engineering team right now? for your engineering team right now? for your engineering team right now? >> We're trying to Let me give you a >> We're trying to Let me give you a >> We're trying to Let me give you a thought experiment. Let's say you have a thought experiment. Let's say you have a thought experiment. Let's say you have a software engineer or AI researcher and software engineer or AI researcher and software engineer or AI researcher and you pay them $500,000 a year. We do that you pay them $500,000 a year. We do that you pay them $500,000 a year. We do that all the time. all the time. all the time. >> Okay, this is happening all over the >> Okay, this is happening all over the >> Okay, this is happening all over the time. um that $500,000 engineer at the time. um that $500,000 engineer at the time. um that $500,000 engineer at the end of the year I'm going to ask him how end of the year I'm going to ask him how end of the year I'm going to ask him how many tok how much did you spend in many tok how much did you spend in many tok how much did you spend in tokens and that person said $5,000 I tokens and that person said $5,000 I tokens and that person said $5,000 I will go ape something else will go ape something else will go ape something else >> yes >> yes >> yes >> right >> right >> right >> if that if that $500,000 engineer did >> if that if that $500,000 engineer did >> if that if that $500,000 engineer did not consume at least $250,000 worth of not consume at least $250,000 worth of not consume at least $250,000 worth of tokens I am going to be deeply alarmed tokens I am going to be deeply alarmed tokens I am going to be deeply alarmed okay and this is no different than one okay and this is no different than one okay and this is no different than one of our chip designers who says guess of our chip designers who says guess of our chip designers who says guess what I'm just going to use paper and what I'm just going to use paper and what I'm just going to use paper and pencil I don't think I'm going to need pencil I don't think I'm going to need pencil I don't think I'm going to need any CAD tools. any CAD tools. any CAD tools. >> This is a real paradigm shift to start >> This is a real paradigm shift to start >> This is a real paradigm shift to start thinking about these all-star employees. thinking about these all-star employees. thinking about these all-star employees. It almost reminds me of of what we It almost reminds me of of what we It almost reminds me of of what we learned in the NBA when LeBron James learned in the NBA when LeBron James learned in the NBA when LeBron James started spending a million dollars a started spending a million dollars a started spending a million dollars a year just on his health of his body like year just on his health of his body like year just on his health of his body like and maintaining it. and maintaining it. and maintaining it. >> That's right. >> That's right. >> That's right. >> Here he is at age 41 still playing. >> Here he is at age 41 still playing. >> Here he is at age 41 still playing. >> It really is, hey, if these are >> It really is, hey, if these are >> It really is, hey, if these are incredible knowledge workers, why incredible knowledge workers, why incredible knowledge workers, why wouldn't we give them wouldn't we give them wouldn't we give them >> superhuman abilities? >> superhuman abilities? >> superhuman abilities? >> That's exactly >> That's exactly >> That's exactly >> where does that go? If we if we >> where does that go? If we if we >> where does that go? If we if we extrapolate out two or three years from extrapolate out two or three years from extrapolate out two or three years from now, what is the efficiency of that now, what is the efficiency of that now, what is the efficiency of that allstar at an Nvidia and what they're allstar at an Nvidia and what they're allstar at an Nvidia and what they're able to accomplish? What do they look able to accomplish? What do they look able to accomplish? What do they look like? like? like? >> Well, first of all, things that that >> Well, first of all, things that that >> Well, first of all, things that that that um wow, this is too hard. That that um wow, this is too hard. That that um wow, this is too hard. That thought is gone. Uh this is going to thought is gone. Uh this is going to thought is gone. Uh this is going to take a long time. That thought is gone. take a long time. That thought is gone. take a long time. That thought is gone. Uh we're going to need a lot of people. Uh we're going to need a lot of people. Uh we're going to need a lot of people. That thought is gone. This is no That thought is gone. This is no That thought is gone. This is no different than in this in the last different than in this in the last different than in this in the last industrial re revolution. Somebody goes, industrial re revolution. Somebody goes, industrial re revolution. Somebody goes, "Boy, that building really looks heavy." "Boy, that building really looks heavy." "Boy, that building really looks heavy." Nobody says that. Nobody. Wow, that Nobody says that. Nobody. Wow, that Nobody says that. Nobody. Wow, that mountain looks too big. Nobody says mountain looks too big. Nobody says mountain looks too big. Nobody says that. Right. that. Right. that. Right. >> Everything that's too big, too heavy, >> Everything that's too big, too heavy, >> Everything that's too big, too heavy, takes too long, takes too long, takes too long, >> those thought, those ideas are all gone. >> those thought, those ideas are all gone. >> those thought, those ideas are all gone. >> You're reduced to creativity. That's >> You're reduced to creativity. That's >> You're reduced to creativity. That's right. What can you come up with? right. What can you come up with? right. What can you come up with? >> Exactly. Which means now the question is >> Exactly. Which means now the question is >> Exactly. Which means now the question is how do you how do you work with these how do you how do you work with these how do you how do you work with these agents? Well, it's just a new way of agents? Well, it's just a new way of agents? Well, it's just a new way of doing computer programming. In the f in doing computer programming. In the f in doing computer programming. In the f in the past, we code. In the future, we're the past, we code. In the future, we're the past, we code. In the future, we're going we're going to write ideas, going we're going to write ideas, going we're going to write ideas, architectures, specifications. architectures, specifications. architectures, specifications. We're going to organize teams. We're We're going to organize teams. We're We're going to organize teams. We're going to give them We're going to help going to give them We're going to help going to give them We're going to help them define how to evaluate the them define how to evaluate the them define how to evaluate the definition of good versus bad. What's definition of good versus bad. What's definition of good versus bad. What's the what does it look like when the what does it look like when the what does it look like when something is a great outcome? How to something is a great outcome? How to something is a great outcome? How to iterate with you, how to brainstorm. iterate with you, how to brainstorm. iterate with you, how to brainstorm. That's really what you're looking for. That's really what you're looking for. That's really what you're looking for. And I'm I think that every engineer is And I'm I think that every engineer is And I'm I think that every engineer is going to have hundred a hundred agents. going to have hundred a hundred agents. going to have hundred a hundred agents. >> Back to the PR problem the industry has >> Back to the PR problem the industry has >> Back to the PR problem the industry has right now. You have executives uh like right now. You have executives uh like right now. You have executives uh like David Freeberg with Oho who's looking at David Freeberg with Oho who's looking at David Freeberg with Oho who's looking at literally taking through the use of literally taking through the use of literally taking through the use of technology your technology and AI the technology your technology and AI the technology your technology and AI the number of calories produced and making number of calories produced and making number of calories produced and making high quality cal calories what is the high quality cal calories what is the high quality cal calories what is the factor you think you can bring the cost factor you think you can bring the cost factor you think you can bring the cost down for what impact does this vision down for what impact does this vision down for what impact does this vision have for what you're doing have for what you're doing have for what you're doing >> zero shot genomic modeling and it works >> zero shot genomic modeling and it works >> zero shot genomic modeling and it works >> and you have that moment and you're like >> and you have that moment and you're like >> and you have that moment and you're like holy holy holy >> honestly like and and that's after >> honestly like and and that's after >> honestly like and and that's after people are replacing entire enterprise people are replacing entire enterprise people are replacing entire enterprise software stacks in a night. I did software stacks in a night. I did software stacks in a night. I did something in 90 minutes I was telling something in 90 minutes I was telling something in 90 minutes I was telling the guys about replaced a whole software the guys about replaced a whole software the guys about replaced a whole software stack and like a whole bunch of workload stack and like a whole bunch of workload stack and like a whole bunch of workload 90 minutes on cloud ran this agentic 90 minutes on cloud ran this agentic 90 minutes on cloud ran this agentic system built the whole thing deployed it system built the whole thing deployed it system built the whole thing deployed it and we got we were on a Sunday night and we got we were on a Sunday night and we got we were on a Sunday night >> on a Sunday night 10 p.m. I was done at >> on a Sunday night 10 p.m. I was done at >> on a Sunday night 10 p.m. I was done at 11:30. I went to bed. 11:30. I went to bed. 11:30. I went to bed. >> As the CEO, you replaced >> As the CEO, you replaced >> As the CEO, you replaced >> Yeah. And everyone on my management team >> Yeah. And everyone on my management team >> Yeah. And everyone on my management team had to do a similar exercise over the had to do a similar exercise over the had to do a similar exercise over the weekend. What we saw on Monday, I was weekend. What we saw on Monday, I was weekend. What we saw on Monday, I was like, it's over. But the technical like, it's over. But the technical like, it's over. But the technical stuff, the science stuff, we did stuff, the science stuff, we did stuff, the science stuff, we did something in 30 minutes using auto something in 30 minutes using auto something in 30 minutes using auto research, and I'd love your view on auto research, and I'd love your view on auto research, and I'd love your view on auto research and what that tells us about research and what that tells us about research and what that tells us about how far we still have to go in terms of how far we still have to go in terms of how far we still have to go in terms of efficiency. But using auto research and efficiency. But using auto research and efficiency. But using auto research and a chunk of data, something was published a chunk of data, something was published a chunk of data, something was published internally that we said, "Oh my god." internally that we said, "Oh my god." internally that we said, "Oh my god." And that would normally be a PhD thesis And that would normally be a PhD thesis And that would normally be a PhD thesis that would take seven years. It would be that would take seven years. It would be that would take seven years. It would be one of the most celebrated PhD thesis one of the most celebrated PhD thesis one of the most celebrated PhD thesis we've ever seen in this field and it we've ever seen in this field and it we've ever seen in this field and it would be in the journal science and it would be in the journal science and it would be in the journal science and it was done in 30 minutes on a desktop was done in 30 minutes on a desktop was done in 30 minutes on a desktop computer running on auto research with computer running on auto research with computer running on auto research with all the data we just ingested. We got it all the data we just ingested. We got it all the data we just ingested. We got it on Friday and we're like, "Hey, let's on Friday and we're like, "Hey, let's on Friday and we're like, "Hey, let's try it." Try booted up, went to GitHub, try it." Try booted up, went to GitHub, try it." Try booted up, went to GitHub, downloaded Auto Research and ran it. And downloaded Auto Research and ran it. And downloaded Auto Research and ran it. And you see everyone's face just go like and you see everyone's face just go like and you see everyone's face just go like and then the potential of what this is then the potential of what this is then the potential of what this is unlocking for us is like the kind of unlocking for us is like the kind of unlocking for us is like the kind of thing that would take seven years and it thing that would take seven years and it thing that would take seven years and it happened in 30 minutes and we're happened in 30 minutes and we're happened in 30 minutes and we're experiencing it in genomics and we're experiencing it in genomics and we're experiencing it in genomics and we're like this is unbelievable. So I I think like this is unbelievable. So I I think like this is unbelievable. So I I think like the acceleration is widening the like the acceleration is widening the like the acceleration is widening the aperture for everyone in a way that like aperture for everyone in a way that like aperture for everyone in a way that like you didn't imagine a few years ago. But you didn't imagine a few years ago. But you didn't imagine a few years ago. But just going back to the auto research just going back to the auto research just going back to the auto research point, can you just comment on what you point, can you just comment on what you point, can you just comment on what you think about the fact that this thing got think about the fact that this thing got think about the fact that this thing got published with 600 lines of code in a published with 600 lines of code in a published with 600 lines of code in a weekend and the capacity that it has to weekend and the capacity that it has to weekend and the capacity that it has to run locally and achieve what it can run locally and achieve what it can run locally and achieve what it can achieve with all of these diverse data achieve with all of these diverse data achieve with all of these diverse data sets and what that tells us about the sets and what that tells us about the sets and what that tells us about the early stages we are in terms of early stages we are in terms of early stages we are in terms of optimization on algorithms and hardware. optimization on algorithms and hardware. optimization on algorithms and hardware. The fundamental reason why Open Claw is The fundamental reason why Open Claw is The fundamental reason why Open Claw is so incredible number one is it's com its so incredible number one is it's com its so incredible number one is it's com its confluence its timing with the confluence its timing with the confluence its timing with the breakthroughs in large language model. breakthroughs in large language model. breakthroughs in large language model. >> Yeah, >> Yeah, >> Yeah, >> its timing was perfect. It was >> its timing was perfect. It was >> its timing was perfect. It was impeccable. Now, in a lot of ways, Peter impeccable. Now, in a lot of ways, Peter impeccable. Now, in a lot of ways, Peter wouldn't have come up with it probably wouldn't have come up with it probably wouldn't have come up with it probably if not for the fact that Claude and GPT if not for the fact that Claude and GPT if not for the fact that Claude and GPT and chat GPT have reached a level that and chat GPT have reached a level that and chat GPT have reached a level that is really very good, is really very good, is really very good, >> right? It is also a new capability that >> right? It is also a new capability that >> right? It is also a new capability that allows these models to tool use the allows these models to tool use the allows these models to tool use the tools that we've created over time web tools that we've created over time web tools that we've created over time web browsers and Excel spreadsheets and you browsers and Excel spreadsheets and you browsers and Excel spreadsheets and you know in the case of chip design synopsis know in the case of chip design synopsis know in the case of chip design synopsis and cadence and uh omniverse and blender and cadence and uh omniverse and blender and cadence and uh omniverse and blender and autodesk and all of these tools are and autodesk and all of these tools are and autodesk and all of these tools are going to continue to be used. There's going to continue to be used. There's going to continue to be used. There's some some people say that that the some some people say that that the some some people say that that the enterprise IT software industry is going enterprise IT software industry is going enterprise IT software industry is going to get destroyed. There's it's there's a to get destroyed. There's it's there's a to get destroyed. There's it's there's a let me give you the alternative view. let me give you the alternative view. let me give you the alternative view. The enterprise software industry is The enterprise software industry is The enterprise software industry is limited by butts and seats. It's about limited by butts and seats. It's about limited by butts and seats. It's about to get a hundred times more agents to get a hundred times more agents to get a hundred times more agents banging on those tools. They're going to banging on those tools. They're going to banging on those tools. They're going to be agents banging on SQL. They're going be agents banging on SQL. They're going be agents banging on SQL. They're going to be agents bang on vector databases, to be agents bang on vector databases, to be agents bang on vector databases, agents bang on Blender, agents bang on agents bang on Blender, agents bang on agents bang on Blender, agents bang on Photoshop. And the reason for that is Photoshop. And the reason for that is Photoshop. And the reason for that is because those tools are first of all do because those tools are first of all do because those tools are first of all do a very good job. Second, those tools are a very good job. Second, those tools are a very good job. Second, those tools are the conduit between us in the final the conduit between us in the final the conduit between us in the final analysis. When the work is done, it has analysis. When the work is done, it has analysis. When the work is done, it has to be represented back to me in a way to be represented back to me in a way to be represented back to me in a way that I can control. that I can control. that I can control. >> Right? >> Right? >> Right? >> And I know how to control those tools. >> And I know how to control those tools. >> And I know how to control those tools. And so I need everything to be put back And so I need everything to be put back And so I need everything to be put back into synopsis. I want everything to put into synopsis. I want everything to put into synopsis. I want everything to put back into cadence because that's how I back into cadence because that's how I back into cadence because that's how I control it. That's how I've ground control it. That's how I've ground control it. That's how I've ground truth. truth. truth. >> Let me ask you a question about open >> Let me ask you a question about open >> Let me ask you a question about open source. So we have these closed source source. So we have these closed source source. So we have these closed source models. They're excellent. models. They're excellent. models. They're excellent. >> We have these openweight models. Many of >> We have these openweight models. Many of >> We have these openweight models. Many of the Chinese models are incredible. the Chinese models are incredible. the Chinese models are incredible. Absolutely incredible. Two days ago, you Absolutely incredible. Two days ago, you Absolutely incredible. Two days ago, you may not have seen this because you were may not have seen this because you were may not have seen this because you were busy on stage, but there was a training busy on stage, but there was a training busy on stage, but there was a training run that happened in this crypto project run that happened in this crypto project run that happened in this crypto project called Bit Tensor Subnet 3. They managed called Bit Tensor Subnet 3. They managed called Bit Tensor Subnet 3. They managed to train a 4 billion parameter llama to train a 4 billion parameter llama to train a 4 billion parameter llama model totally distributed with a bunch model totally distributed with a bunch model totally distributed with a bunch of people contributing of people contributing of people contributing excess compute, but they were able to do excess compute, but they were able to do excess compute, but they were able to do it statefully and manage a training run, it statefully and manage a training run, it statefully and manage a training run, which I thought was like a pretty crazy which I thought was like a pretty crazy which I thought was like a pretty crazy technical accomplishment. technical accomplishment. technical accomplishment. >> Yeah. Because it's like random people >> Yeah. Because it's like random people >> Yeah. Because it's like random people and each person gets a little share. and each person gets a little share. and each person gets a little share. >> Our our modern version of folding at >> Our our modern version of folding at >> Our our modern version of folding at home. home. home. >> Exactly. So what what do you think about >> Exactly. So what what do you think about >> Exactly. So what what do you think about the end state of open source? Do you see the end state of open source? Do you see the end state of open source? Do you see this decentralization of architecture as this decentralization of architecture as this decentralization of architecture as well and decentralization of compute to well and decentralization of compute to well and decentralization of compute to support open weights and a totally open- support open weights and a totally open- support open weights and a totally open- source approach to making sure AI is source approach to making sure AI is source approach to making sure AI is broadly available to every? broadly available to every? broadly available to every? >> I believe we fundamentally need >> I believe we fundamentally need >> I believe we fundamentally need models as a firstass product proprietary models as a firstass product proprietary models as a firstass product proprietary product as well as models as open product as well as models as open product as well as models as open source. These two things are not A or B. source. These two things are not A or B. source. These two things are not A or B. It's A and B. There's no question about It's A and B. There's no question about It's A and B. There's no question about it. And the reason for that is because it. And the reason for that is because it. And the reason for that is because models is a technology, not a product. models is a technology, not a product. models is a technology, not a product. Model is a technology, not a service. Model is a technology, not a service. Model is a technology, not a service. For the vast majority of consumers, the For the vast majority of consumers, the For the vast majority of consumers, the horizontal layer, the general horizontal layer, the general horizontal layer, the general intelligence, I would really, really intelligence, I would really, really intelligence, I would really, really love not to go fine-tune my own. I would love not to go fine-tune my own. I would love not to go fine-tune my own. I would really love to keep using chat GPT. I'd really love to keep using chat GPT. I'd really love to keep using chat GPT. I'd love to use Claude. I love to use love to use Claude. I love to use love to use Claude. I love to use Gemini. I love to you use X. And they Gemini. I love to you use X. And they Gemini. I love to you use X. And they all have their own personalities as you all have their own personalities as you all have their own personalities as you know, which is kind of depends on my know, which is kind of depends on my know, which is kind of depends on my mood and depends on what problem I'm mood and depends on what problem I'm mood and depends on what problem I'm trying to solve. you know I might you trying to solve. you know I might you trying to solve. you know I might you know do it on X or I might do it on on know do it on X or I might do it on on know do it on X or I might do it on on chat GBT and so that that segment of the chat GBT and so that that segment of the chat GBT and so that that segment of the of the industry is thriving it's going of the industry is thriving it's going of the industry is thriving it's going to be great however there all these to be great however there all these to be great however there all these industries their domain expertise their industries their domain expertise their industries their domain expertise their specialization has to be channeled has specialization has to be channeled has specialization has to be channeled has to be captured in a way that they can to be captured in a way that they can to be captured in a way that they can control and that it can only come from control and that it can only come from control and that it can only come from open models the open model industry open models the open model industry open models the open model industry we're contributing tremendously to it is we're contributing tremendously to it is we're contributing tremendously to it is near the frontier near the frontier near the frontier and quite Quite frankly, even if it and quite Quite frankly, even if it and quite Quite frankly, even if it reaches the frontier, reaches the frontier, reaches the frontier, I think that products as a service, I think that products as a service, I think that products as a service, worldclass products as as a models as a worldclass products as as a models as a worldclass products as as a models as a product is going to continue to thrive. product is going to continue to thrive. product is going to continue to thrive. >> Every startup we're investing in now is >> Every startup we're investing in now is >> Every startup we're investing in now is open- source first and then going to the open- source first and then going to the open- source first and then going to the proprietary models. proprietary models. proprietary models. >> Yeah. And the beautiful thing is because >> Yeah. And the beautiful thing is because >> Yeah. And the beautiful thing is because you have a great router you connect it you have a great router you connect it you have a great router you connect it to by on on first day every single day to by on on first day every single day to by on on first day every single day you're going to have access to the you're going to have access to the you're going to have access to the world's best model and and then it gives world's best model and and then it gives world's best model and and then it gives you time to cost reduce and fine-tune you time to cost reduce and fine-tune you time to cost reduce and fine-tune and specialize and so you're going to and specialize and so you're going to and specialize and so you're going to have worldclass capabilities out to have worldclass capabilities out to have worldclass capabilities out to shoot every single time. Let J can I shoot every single time. Let J can I shoot every single time. Let J can I question? question? question? >> Nobody wants the US to win the global AI >> Nobody wants the US to win the global AI >> Nobody wants the US to win the global AI race more than you, right? But a year race more than you, right? But a year race more than you, right? But a year ago, the Biden era diffusion rule really ago, the Biden era diffusion rule really ago, the Biden era diffusion rule really was an anti- American diffusion of AI was an anti- American diffusion of AI was an anti- American diffusion of AI around the world. So here we are a year around the world. So here we are a year around the world. So here we are a year into the new administration. into the new administration. into the new administration. Give us a grade. Where is where are we Give us a grade. Where is where are we Give us a grade. Where is where are we in terms of global diffusion and the in terms of global diffusion and the in terms of global diffusion and the rate at which we're spreading US AI rate at which we're spreading US AI rate at which we're spreading US AI technology around the world? Are we an technology around the world? Are we an technology around the world? Are we an A? Are we a B? or we see what what's A? Are we a B? or we see what what's A? Are we a B? or we see what what's working, what's not working. working, what's not working. working, what's not working. >> Well, first of all, President Trump >> Well, first of all, President Trump >> Well, first of all, President Trump wants American industry to lead. He wants American industry to lead. He wants American industry to lead. He wants American technology industry to wants American technology industry to wants American technology industry to lead. He wants American technology lead. He wants American technology lead. He wants American technology industry to win. He wants us to spread industry to win. He wants us to spread industry to win. He wants us to spread American technology around the world. He American technology around the world. He American technology around the world. He wants the United States to be the wants the United States to be the wants the United States to be the wealthiest country in the world. He wealthiest country in the world. He wealthiest country in the world. He wants all of that. At the current wants all of that. At the current wants all of that. At the current moment, as we speak, moment, as we speak, moment, as we speak, Nvidia gave up a 95% market share in the Nvidia gave up a 95% market share in the Nvidia gave up a 95% market share in the second largest market in the world, and second largest market in the world, and second largest market in the world, and we're at 0%. we're at 0%. we're at 0%. >> President Trump, That's right. President >> President Trump, That's right. President >> President Trump, That's right. President Trump wants us to get back in there. And Trump wants us to get back in there. And Trump wants us to get back in there. And and uh the first thing is uh to get and uh the first thing is uh to get and uh the first thing is uh to get license licensed for the companies that license licensed for the companies that license licensed for the companies that we're going to be able to sell to. We've we're going to be able to sell to. We've we're going to be able to sell to. We've got many companies who have requested got many companies who have requested got many companies who have requested for licenses. We've applied for licenses for licenses. We've applied for licenses for licenses. We've applied for licenses for them and we've got approved licenses for them and we've got approved licenses for them and we've got approved licenses from sec secretary lutnik. Uh now uh from sec secretary lutnik. Uh now uh from sec secretary lutnik. Uh now uh we've we informed the Chinese companies we've we informed the Chinese companies we've we informed the Chinese companies and many of them have given us purchase and many of them have given us purchase and many of them have given us purchase orders and so we're going to we're going orders and so we're going to we're going orders and so we're going to we're going to we're in the process of cranking up to we're in the process of cranking up to we're in the process of cranking up our supply chain again to go ship. I our supply chain again to go ship. I our supply chain again to go ship. I think at the highest level Brad um I think at the highest level Brad um I think at the highest level Brad um I think one of the things that we should think one of the things that we should think one of the things that we should acknowledge is this. Our national acknowledge is this. Our national acknowledge is this. Our national security security security is diminished when we don't have access is diminished when we don't have access is diminished when we don't have access to miniature motors, rare earth to miniature motors, rare earth to miniature motors, rare earth minerals. It's diminished when we don't minerals. It's diminished when we don't minerals. It's diminished when we don't control our telecommunications networks. control our telecommunications networks. control our telecommunications networks. It's diminished when we can't provide It's diminished when we can't provide It's diminished when we can't provide for sustainable energy for our country. for sustainable energy for our country. for sustainable energy for our country. It is fundamentally diminished. Every It is fundamentally diminished. Every It is fundamentally diminished. Every single one of these industries is an single one of these industries is an single one of these industries is an example of what I don't want the AI example of what I don't want the AI example of what I don't want the AI industry to be. industry to be. industry to be. >> Right? When we look forward in time and >> Right? When we look forward in time and >> Right? When we look forward in time and we say what do we want? What is the what we say what do we want? What is the what we say what do we want? What is the what does it look like when American does it look like when American does it look like when American technology industry American AI industry technology industry American AI industry technology industry American AI industry leads the world? We can all acknowledge leads the world? We can all acknowledge leads the world? We can all acknowledge that there is no way that AI models is that there is no way that AI models is that there is no way that AI models is one universally. It is we can all one universally. It is we can all one universally. It is we can all acknowledge that that is an outcome that acknowledge that that is an outcome that acknowledge that that is an outcome that makes no sense. However, we can all makes no sense. However, we can all makes no sense. However, we can all imagine that the American tech stack imagine that the American tech stack imagine that the American tech stack from chips to computing systems to the from chips to computing systems to the from chips to computing systems to the platforms are used broadly by the world platforms are used broadly by the world platforms are used broadly by the world where they build their own AI, they use where they build their own AI, they use where they build their own AI, they use public AI, they use private AI whatever public AI, they use private AI whatever public AI, they use private AI whatever and they can build their applications in and they can build their applications in and they can build their applications in their society. I would love that the their society. I would love that the their society. I would love that the American tech stack is 90% of the world. American tech stack is 90% of the world. American tech stack is 90% of the world. Yes, I would love that. The alternative Yes, I would love that. The alternative Yes, I would love that. The alternative if it looks like solar, rare earth, if it looks like solar, rare earth, if it looks like solar, rare earth, magnets, motors, telecommunications, I magnets, motors, telecommunications, I magnets, motors, telecommunications, I consider that a very bad outcome for consider that a very bad outcome for consider that a very bad outcome for national security. national security. national security. >> Great. >> Great. >> Great. >> Yeah. >> Yeah. >> Yeah. >> How much are you monitoring the >> How much are you monitoring the >> How much are you monitoring the situation with the conflicts around the situation with the conflicts around the situation with the conflicts around the world right now? And how much does it world right now? And how much does it world right now? And how much does it worry you Jensen? So, China and Taiwan worry you Jensen? So, China and Taiwan worry you Jensen? So, China and Taiwan and then helium availability coming out and then helium availability coming out and then helium availability coming out of the Middle East, I understand, can be of the Middle East, I understand, can be of the Middle East, I understand, can be a supply chain risk to semiconductor a supply chain risk to semiconductor a supply chain risk to semiconductor manufacturing. How much do these manufacturing. How much do these manufacturing. How much do these situations worry you? How much are you situations worry you? How much are you situations worry you? How much are you spending on them? spending on them? spending on them? >> Well, first of all, I think the in >> Well, first of all, I think the in >> Well, first of all, I think the in Middle East, I have we have 6,000 Middle East, I have we have 6,000 Middle East, I have we have 6,000 families there. families there. families there. >> Yeah. >> Yeah. >> Yeah. >> Uh we have a lot of Iranians uh at >> Uh we have a lot of Iranians uh at >> Uh we have a lot of Iranians uh at NVIDIA and their families are still in NVIDIA and their families are still in NVIDIA and their families are still in Iran. And so so we have we have a lot of Iran. And so so we have we have a lot of Iran. And so so we have we have a lot of families there. The first thing is is families there. The first thing is is families there. The first thing is is they're quite anxious. They're quite they're quite anxious. They're quite they're quite anxious. They're quite concerned, quite scared. Um we're concerned, quite scared. Um we're concerned, quite scared. Um we're thinking about them all the time. Uh thinking about them all the time. Uh thinking about them all the time. Uh we're monitoring and keeping an eye on we're monitoring and keeping an eye on we're monitoring and keeping an eye on them all the time. They have 100% of our them all the time. They have 100% of our them all the time. They have 100% of our support. Uh I've been asked several support. Uh I've been asked several support. Uh I've been asked several times, are we still considering uh being times, are we still considering uh being times, are we still considering uh being in Israel? We are 100% in Israel. We are in Israel? We are 100% in Israel. We are in Israel? We are 100% in Israel. We are 100% behind the families there. We are 100% behind the families there. We are 100% behind the families there. We are 100% in the Middle East. I was also 100% in the Middle East. I was also 100% in the Middle East. I was also asked, you know, given what's happening asked, you know, given what's happening asked, you know, given what's happening in the Middle East, uh is that an area in the Middle East, uh is that an area in the Middle East, uh is that an area where we believe that we can expand where we believe that we can expand where we believe that we can expand artificial intelligence to? Um I believe artificial intelligence to? Um I believe artificial intelligence to? Um I believe that there's a reason we went to war and that there's a reason we went to war and that there's a reason we went to war and I believe at the end of the war, Middle I believe at the end of the war, Middle I believe at the end of the war, Middle East will be more stable than before. East will be more stable than before. East will be more stable than before. And so if we were there, if we're And so if we were there, if we're And so if we were there, if we're considering it before, we should considering it before, we should considering it before, we should absolutely be considering it after. And absolutely be considering it after. And absolutely be considering it after. And so I'm 100% in on that. With respect to so I'm 100% in on that. With respect to so I'm 100% in on that. With respect to with with with respect to to Taiwan, with with with respect to to Taiwan, with with with respect to to Taiwan, >> we have to do three things. One, we have >> we have to do three things. One, we have >> we have to do three things. One, we have to make sure that we re-industrialize to make sure that we re-industrialize to make sure that we re-industrialize the United States as fast as we can. the United States as fast as we can. the United States as fast as we can. >> And whether it's the chip manufacturing >> And whether it's the chip manufacturing >> And whether it's the chip manufacturing plants, the the computer manufacturing plants, the the computer manufacturing plants, the the computer manufacturing plants, or the AI factories, plants, or the AI factories, plants, or the AI factories, >> how are we doing on that? We're doing >> how are we doing on that? We're doing >> how are we doing on that? We're doing excellent with by by gaining the excellent with by by gaining the excellent with by by gaining the strategic support by gaining the strategic support by gaining the strategic support by gaining the friendship of the supply chain of friendship of the supply chain of friendship of the supply chain of Taiwan. Taiwan. Taiwan. By gaining their friendship by gaining By gaining their friendship by gaining By gaining their friendship by gaining their support, we were able to build their support, we were able to build their support, we were able to build Arizona and Texas, California at Arizona and Texas, California at Arizona and Texas, California at incredible rates. They're they are incredible rates. They're they are incredible rates. They're they are genuinely a strategic partner. Um we we genuinely a strategic partner. Um we we genuinely a strategic partner. Um we we we really they deserve our support. They we really they deserve our support. They we really they deserve our support. They deserve our friendship. They deserve our deserve our friendship. They deserve our deserve our friendship. They deserve our generosity and they're doing everything generosity and they're doing everything generosity and they're doing everything they can to accelerate the manufacturing they can to accelerate the manufacturing they can to accelerate the manufacturing process for us. And so, so I think process for us. And so, so I think process for us. And so, so I think that's number one. Number two, we ought that's number one. Number two, we ought that's number one. Number two, we ought to diversify the manufacturing supply to diversify the manufacturing supply to diversify the manufacturing supply chain. And whether it's South Korea, chain. And whether it's South Korea, chain. And whether it's South Korea, whether it's it's Japan, it's Europe, we whether it's it's Japan, it's Europe, we whether it's it's Japan, it's Europe, we got to we got to diversify the supply got to we got to diversify the supply got to we got to diversify the supply chain, make it more resilient. And chain, make it more resilient. And chain, make it more resilient. And number three, let's be let's let's number three, let's be let's let's number three, let's be let's let's demonstrate restraint. And while we're demonstrate restraint. And while we're demonstrate restraint. And while we're reducing uh increasing our diversity and reducing uh increasing our diversity and reducing uh increasing our diversity and resilience, let's not resilience, let's not resilience, let's not press push um press push um press push um >> unnecessary. We need to be patient. >> unnecessary. We need to be patient. >> unnecessary. We need to be patient. >> Is helium a problem? A lot of reports, >> Is helium a problem? A lot of reports, >> Is helium a problem? A lot of reports, >> you know, I think helium could be a >> you know, I think helium could be a >> you know, I think helium could be a problem, but it's also the case that the problem, but it's also the case that the problem, but it's also the case that the supply chain probably has a lot of supply chain probably has a lot of supply chain probably has a lot of buffer in it. buffer in it. buffer in it. >> These kind of things tend to have a lot >> These kind of things tend to have a lot >> These kind of things tend to have a lot of buffer. Uh but but um you know yeah of buffer. Uh but but um you know yeah of buffer. Uh but but um you know yeah >> you've um made massive progress in >> you've um made massive progress in >> you've um made massive progress in self-driving. You made a big self-driving. You made a big self-driving. You made a big announcement. You've added many more announcement. You've added many more announcement. You've added many more partners including BYD. There was just a partners including BYD. There was just a partners including BYD. There was just a video of you driving around in a video of you driving around in a video of you driving around in a Mercedes and uh huge announcement uh Mercedes and uh huge announcement uh Mercedes and uh huge announcement uh with Uber that you're going to have a with Uber that you're going to have a with Uber that you're going to have a number of cars on the road from many number of cars on the road from many number of cars on the road from many different manufacturers. your bet is I different manufacturers. your bet is I different manufacturers. your bet is I believe that there's going to be an believe that there's going to be an believe that there's going to be an Android Android Android type open-source platform that you're type open-source platform that you're type open-source platform that you're going to play a major part in with going to play a major part in with going to play a major part in with dozens of uh car providers and then dozens of uh car providers and then dozens of uh car providers and then maybe on the other side there could be maybe on the other side there could be maybe on the other side there could be an iOS with Tesla or Whimo. What's your an iOS with Tesla or Whimo. What's your an iOS with Tesla or Whimo. What's your strategy thinking there and how that strategy thinking there and how that strategy thinking there and how that chessboard emerges because it feels like chessboard emerges because it feels like chessboard emerges because it feels like you have a a pretty deep stack and in you have a a pretty deep stack and in you have a a pretty deep stack and in some ways you're competing and in other some ways you're competing and in other some ways you're competing and in other places you're collaborative. Yeah. Um, places you're collaborative. Yeah. Um, places you're collaborative. Yeah. Um, it's taking a step back. We believe that it's taking a step back. We believe that it's taking a step back. We believe that everything that moves will be autonomous everything that moves will be autonomous everything that moves will be autonomous completely or partly completely or partly completely or partly someday. Number one. Number two, we someday. Number one. Number two, we someday. Number one. Number two, we don't want to build self-driving cars, don't want to build self-driving cars, don't want to build self-driving cars, but we want to enable every car company but we want to enable every car company but we want to enable every car company in the world to build self-driving cars. in the world to build self-driving cars. in the world to build self-driving cars. And so, we built all three computers, And so, we built all three computers, And so, we built all three computers, the training computer, the simulation the training computer, the simulation the training computer, the simulation computer, the valu evaluation computer, computer, the valu evaluation computer, computer, the valu evaluation computer, as well as the car computer. We develop as well as the car computer. We develop as well as the car computer. We develop the world's safest driving operating the world's safest driving operating the world's safest driving operating system. Uh we also created the world's system. Uh we also created the world's system. Uh we also created the world's first reasoning autonomous vehicle so first reasoning autonomous vehicle so first reasoning autonomous vehicle so that it could decompose complicated that it could decompose complicated that it could decompose complicated scenarios into simpler scenarios that it scenarios into simpler scenarios that it scenarios into simpler scenarios that it knows how to navigate through just like knows how to navigate through just like knows how to navigate through just like us reasoning systems. And so that us reasoning systems. And so that us reasoning systems. And so that reasoning system called Alpommyo has reasoning system called Alpommyo has reasoning system called Alpommyo has enabled us to achieve incredible enabled us to achieve incredible enabled us to achieve incredible results. results. results. We We We open this we ver we vertical open this we ver we vertical open this we ver we vertical optimization. We horizontally innovate optimization. We horizontally innovate optimization. We horizontally innovate and we let everybody decide. Do you want and we let everybody decide. Do you want and we let everybody decide. Do you want to buy one computer from us? In the case to buy one computer from us? In the case to buy one computer from us? In the case of Elon and Tesla, they buy our training of Elon and Tesla, they buy our training of Elon and Tesla, they buy our training computers. Um, do they want to buy our computers. Um, do they want to buy our computers. Um, do they want to buy our training computer and our simulation training computer and our simulation training computer and our simulation computers or do you want to let us uh computers or do you want to let us uh computers or do you want to let us uh work with us to do all three and even work with us to do all three and even work with us to do all three and even put the car computer in your car. So, we put the car computer in your car. So, we put the car computer in your car. So, we you know, our attitude is we want to you know, our attitude is we want to you know, our attitude is we want to solve the problem. solve the problem. solve the problem. We're not the solution provider We're not the solution provider We're not the solution provider and we're delighted however you work and we're delighted however you work and we're delighted however you work with us. Let me build on this question with us. Let me build on this question with us. Let me build on this question because I think it's like it's so because I think it's like it's so because I think it's like it's so fascinating. You actually do create this fascinating. You actually do create this fascinating. You actually do create this platform. A thousand flowers are platform. A thousand flowers are platform. A thousand flowers are blooming. blooming. blooming. >> But it's also true that some of those >> But it's also true that some of those >> But it's also true that some of those flowers want to now go back down in the flowers want to now go back down in the flowers want to now go back down in the stack and try to compete with you a stack and try to compete with you a stack and try to compete with you a little bit. Google has TPU, Amazon has little bit. Google has TPU, Amazon has little bit. Google has TPU, Amazon has inferentia and tranium. You know, inferentia and tranium. You know, inferentia and tranium. You know, everybody's sort of spinning up their everybody's sort of spinning up their everybody's sort of spinning up their own version of I think I can out Nvidia own version of I think I can out Nvidia own version of I think I can out Nvidia Nvidia Nvidia Nvidia >> even though they also tend to be huge >> even though they also tend to be huge >> even though they also tend to be huge customers. customers. customers. >> How do you navigate that? And yeah, what >> How do you navigate that? And yeah, what >> How do you navigate that? And yeah, what do you think happens over time and do you think happens over time and do you think happens over time and >> where do those things play in the >> where do those things play in the >> where do those things play in the complexion of this kind of vision? complexion of this kind of vision? complexion of this kind of vision? >> Yeah, really great. You know, first of >> Yeah, really great. You know, first of >> Yeah, really great. You know, first of all, um, we're the only AI company, all, um, we're the only AI company, all, um, we're the only AI company, we're an AI company. We build foundation we're an AI company. We build foundation we're an AI company. We build foundation models. We're at the frontier in many models. We're at the frontier in many models. We're at the frontier in many different domains. We build every single different domains. We build every single different domains. We build every single every single layer, every single stack. every single layer, every single stack. every single layer, every single stack. Um, we're the only AI company in the Um, we're the only AI company in the Um, we're the only AI company in the world that works with every AI company world that works with every AI company world that works with every AI company in the world. They never show me what in the world. They never show me what in the world. They never show me what they're building and I always show them they're building and I always show them they're building and I always show them exactly what I'm building. exactly what I'm building. exactly what I'm building. >> Right. >> Right. >> Right. >> Yeah. And so so the confidence comes >> Yeah. And so so the confidence comes >> Yeah. And so so the confidence comes from this one. Uh we are delighted to from this one. Uh we are delighted to from this one. Uh we are delighted to compete on what is the best technology compete on what is the best technology compete on what is the best technology and to the extent that to the extent and to the extent that to the extent and to the extent that to the extent that we can continue to run fast I that we can continue to run fast I that we can continue to run fast I believe that buying from Nvidia still is believe that buying from Nvidia still is believe that buying from Nvidia still is one of the most economic things they one of the most economic things they one of the most economic things they could do and that's just incredible could do and that's just incredible could do and that's just incredible confidence there. Number one. Number two confidence there. Number one. Number two confidence there. Number one. Number two we're the only architecture that could we're the only architecture that could we're the only architecture that could be in every cloud and that gives us some be in every cloud and that gives us some be in every cloud and that gives us some fundamental advantages. where the only fundamental advantages. where the only fundamental advantages. where the only architecture you could take from a cloud architecture you could take from a cloud architecture you could take from a cloud and put into onrem in the car in any and put into onrem in the car in any and put into onrem in the car in any region region region >> in space. >> in space. >> in space. >> That's right. In space. And so there's a >> That's right. In space. And so there's a >> That's right. In space. And so there's a whole whole part of our market about 40% whole whole part of our market about 40% whole whole part of our market about 40% of our of our business most people don't of our of our business most people don't of our of our business most people don't realize this 40% of our business unless realize this 40% of our business unless realize this 40% of our business unless you have the CUDA stack unless you can you have the CUDA stack unless you can you have the CUDA stack unless you can build an entire AI factory you have the build an entire AI factory you have the build an entire AI factory you have the customers don't know what to do with customers don't know what to do with customers don't know what to do with you. They're not trying to build chips. you. They're not trying to build chips. you. They're not trying to build chips. They're not trying to buy chips. They're They're not trying to buy chips. They're They're not trying to buy chips. They're trying to build AI infrastructure. And trying to build AI infrastructure. And trying to build AI infrastructure. And so they want you to come in with the so they want you to come in with the so they want you to come in with the full stack. And we've got the whole full stack. And we've got the whole full stack. And we've got the whole stack. And so surprisingly, Nvidia is stack. And so surprisingly, Nvidia is stack. And so surprisingly, Nvidia is gaining market share. If you look at gaining market share. If you look at gaining market share. If you look at where we are today, we're gaining share. where we are today, we're gaining share. where we are today, we're gaining share. >> Do you think what happens is these guys >> Do you think what happens is these guys >> Do you think what happens is these guys try and they realize, oh my god, it's try and they realize, oh my god, it's try and they realize, oh my god, it's too much. And then they come back. Is too much. And then they come back. Is too much. And then they come back. Is that why the share grows? that why the share grows? that why the share grows? >> Well, we're gaining share for several >> Well, we're gaining share for several >> Well, we're gaining share for several reasons. One, um, our velocity has gone. reasons. One, um, our velocity has gone. reasons. One, um, our velocity has gone. We help people realize it's not about We help people realize it's not about We help people realize it's not about building the chip, it's about building building the chip, it's about building building the chip, it's about building the system. the system. the system. >> And that system is really hard to build. >> And that system is really hard to build. >> And that system is really hard to build. uh and and so their their their business uh and and so their their their business uh and and so their their their business with us is increasing. In the case of with us is increasing. In the case of with us is increasing. In the case of AWS, I think they just announced, I AWS, I think they just announced, I AWS, I think they just announced, I think it was yesterday, that they're think it was yesterday, that they're think it was yesterday, that they're going to buy a a million chips uh in the going to buy a a million chips uh in the going to buy a a million chips uh in the next couple years. I mean, that's a lot next couple years. I mean, that's a lot next couple years. I mean, that's a lot of chips from from AWS. And that's on of chips from from AWS. And that's on of chips from from AWS. And that's on top of all the chips they've already top of all the chips they've already top of all the chips they've already bought. And so, we're delighted to do bought. And so, we're delighted to do bought. And so, we're delighted to do that. But number one, we're gaining that. But number one, we're gaining that. But number one, we're gaining share this last couple years because we share this last couple years because we share this last couple years because we now have Anthropic coming to Nvidia. now have Anthropic coming to Nvidia. now have Anthropic coming to Nvidia. Meta SL is coming to Nvidia. And the Meta SL is coming to Nvidia. And the Meta SL is coming to Nvidia. And the growth of open models is incredible. And growth of open models is incredible. And growth of open models is incredible. And that's all on Nvidia. And so we're that's all on Nvidia. And so we're that's all on Nvidia. And so we're growing in share because of the number growing in share because of the number growing in share because of the number of models. We're also growing in share of models. We're also growing in share of models. We're also growing in share because out all of these companies are because out all of these companies are because out all of these companies are outside of the cloud and they're growing outside of the cloud and they're growing outside of the cloud and they're growing regionally in enterprise in industries regionally in enterprise in industries regionally in enterprise in industries at the edge and that entire segment of at the edge and that entire segment of at the edge and that entire segment of growth is you know really hard to do if growth is you know really hard to do if growth is you know really hard to do if it's just building an as it's just building an as it's just building an as >> Brad >> Brad >> Brad >> related to that um and not to get in the >> related to that um and not to get in the >> related to that um and not to get in the weeds on the numbers but analysts don't weeds on the numbers but analysts don't weeds on the numbers but analysts don't seem to believe right so if you look at seem to believe right so if you look at seem to believe right so if you look at the consensus forecast you said compute the consensus forecast you said compute the consensus forecast you said compute could 1 millionx right and Yet they have could 1 millionx right and Yet they have could 1 millionx right and Yet they have you growing next year at 30%, the year you growing next year at 30%, the year you growing next year at 30%, the year after that at 20%. And in 2029, which is after that at 20%. And in 2029, which is after that at 20%. And in 2029, which is supposed to be a monster year at 7%. supposed to be a monster year at 7%. supposed to be a monster year at 7%. Right? So if you just if you take your Right? So if you just if you take your Right? So if you just if you take your TAM and you apply their growth numbers, TAM and you apply their growth numbers, TAM and you apply their growth numbers, it suggests that your share will it suggests that your share will it suggests that your share will plummet. Do you see anything in your plummet. Do you see anything in your plummet. Do you see anything in your future order book that would make that future order book that would make that future order book that would make that correct? correct? correct? >> Yeah. First of all, they just don't >> Yeah. First of all, they just don't >> Yeah. First of all, they just don't understand the scale and the breadth of understand the scale and the breadth of understand the scale and the breadth of AI. AI. AI. >> Yes. >> Yes. >> Yes. >> Yeah. >> Yeah. >> Yeah. >> Yeah. I think that's true. Most people >> Yeah. I think that's true. Most people >> Yeah. I think that's true. Most people think that AI is in the top five think that AI is in the top five think that AI is in the top five hyperscalers, hyperscalers, hyperscalers, >> right? That's right. There's also an >> right? That's right. There's also an >> right? That's right. There's also an orthodoxy around these law of large orthodoxy around these law of large orthodoxy around these law of large numbers where, numbers where, numbers where, >> you know, they have to go back to their >> you know, they have to go back to their >> you know, they have to go back to their investment banking risk committee and investment banking risk committee and investment banking risk committee and show some model. show some model. show some model. >> They're not going to believe in their >> They're not going to believe in their >> They're not going to believe in their minds that 5 trillion goes to 15 minds that 5 trillion goes to 15 minds that 5 trillion goes to 15 trillion. They're like go to it can go trillion. They're like go to it can go trillion. They're like go to it can go to seven or they can have a 10 trillion to seven or they can have a 10 trillion to seven or they can have a 10 trillion company. company. company. >> It's all just CIA stuff that I think >> It's all just CIA stuff that I think >> It's all just CIA stuff that I think >> it's never happened before. So you can't >> it's never happened before. So you can't >> it's never happened before. So you can't say it will say it will say it will >> and and because because you have to >> and and because because you have to >> and and because because you have to redefine what it is that you do. There redefine what it is that you do. There redefine what it is that you do. There was somebody who made an observation was somebody who made an observation was somebody who made an observation recently that Nvidia recently that Nvidia recently that Nvidia Jensen, how can you be larger than Intel Jensen, how can you be larger than Intel Jensen, how can you be larger than Intel in servers and the reason for that is in servers and the reason for that is in servers and the reason for that is because the CPU market of the entire because the CPU market of the entire because the CPU market of the entire data center was about $25 billion a data center was about $25 billion a data center was about $25 billion a year, year, year, >> right? >> right? >> right? >> We do $25 billion a year as you guys >> We do $25 billion a year as you guys >> We do $25 billion a year as you guys know in a very in the time that we were know in a very in the time that we were know in a very in the time that we were sitting here. sitting here. sitting here. >> And so obviously obviously >> And so obviously obviously >> And so obviously obviously That was a joke. That was a joke. That was a joke. >> No, it's but it's >> No, it's but it's >> No, it's but it's >> all in podcast. >> all in podcast. >> all in podcast. >> Don't worry. Everything on this show is >> Don't worry. Everything on this show is >> Don't worry. Everything on this show is rough. Don't worry about it. It's all in rough. Don't worry about it. It's all in rough. Don't worry about it. It's all in here. Anyways, that was not guidance. here. Anyways, that was not guidance. here. Anyways, that was not guidance. But anyhow, anyhow, it the the point is But anyhow, anyhow, it the the point is But anyhow, anyhow, it the the point is how big you can be how big you can be how big you can be >> depends on what is it that you make, >> depends on what is it that you make, >> depends on what is it that you make, >> right? >> right? >> right? >> Nvidia is not making chips. Number one, >> Nvidia is not making chips. Number one, >> Nvidia is not making chips. Number one, making chips does not help you solve the making chips does not help you solve the making chips does not help you solve the AI infrastructure problem anymore. It's AI infrastructure problem anymore. It's AI infrastructure problem anymore. It's too complicated. Number three, most too complicated. Number three, most too complicated. Number three, most people think that AI is narrowly in the people think that AI is narrowly in the people think that AI is narrowly in the things that they talk about and hear and things that they talk about and hear and things that they talk about and hear and see. see. see. >> It's AI is much open AI is incredible. >> It's AI is much open AI is incredible. >> It's AI is much open AI is incredible. They're going to be enormous. Anthropic They're going to be enormous. Anthropic They're going to be enormous. Anthropic is incredible. They're going to be is incredible. They're going to be is incredible. They're going to be enormous. But AI is going to be much enormous. But AI is going to be much enormous. But AI is going to be much much bigger than that. much bigger than that. much bigger than that. >> Tell us >> Tell us >> Tell us >> and we addressed that segment. >> and we addressed that segment. >> and we addressed that segment. >> Tell us about data centers in space for >> Tell us about data centers in space for >> Tell us about data centers in space for a second. a second. a second. >> Yeah. >> Yeah. >> Yeah. >> Um >> Um >> Um >> we're already in space. How should the >> we're already in space. How should the >> we're already in space. How should the layman think about what that business is layman think about what that business is layman think about what that business is versus when you hear about these big versus when you hear about these big versus when you hear about these big data center buildouts that's happening data center buildouts that's happening data center buildouts that's happening in in on the ground? in in on the ground? in in on the ground? >> Well, we should definitely work on the >> Well, we should definitely work on the >> Well, we should definitely work on the ground first because we're already here ground first because we're already here ground first because we're already here and number one. Number two, we should and number one. Number two, we should and number one. Number two, we should prepare to be out in space and obviously prepare to be out in space and obviously prepare to be out in space and obviously there's a lot of energy in space. Um the there's a lot of energy in space. Um the there's a lot of energy in space. Um the challenge of course is that cooling challenge of course is that cooling challenge of course is that cooling you can't take advantage of conduction you can't take advantage of conduction you can't take advantage of conduction and convection and so you can only use and convection and so you can only use and convection and so you can only use radiation and radiation requires very radiation and radiation requires very radiation and radiation requires very large surfaces and so now that's not an large surfaces and so now that's not an large surfaces and so now that's not an impossible thing to solve and there's a impossible thing to solve and there's a impossible thing to solve and there's a lot of lot of space in space. Um but lot of lot of space in space. Um but lot of lot of space in space. Um but nonetheless nonetheless nonetheless the expense is still quite there is is the expense is still quite there is is the expense is still quite there is is there uh we're going to go explore it. there uh we're going to go explore it. there uh we're going to go explore it. We're already there. We're already We're already there. We're already We're already there. We're already radiation hardened. Uh we have we have radiation hardened. Uh we have we have radiation hardened. Uh we have we have uh uh uh CUDA in satellites around the uh uh uh CUDA in satellites around the uh uh uh CUDA in satellites around the world. Um they're doing imaging, image world. Um they're doing imaging, image world. Um they're doing imaging, image processing, AI imaging and um and that processing, AI imaging and um and that processing, AI imaging and um and that kind of stuff ought to be done in space kind of stuff ought to be done in space kind of stuff ought to be done in space instead of sending all the data back instead of sending all the data back instead of sending all the data back here and do imaging down here. We ought here and do imaging down here. We ought here and do imaging down here. We ought to just do imaging out in space. And so to just do imaging out in space. And so to just do imaging out in space. And so there's a lot of things that we ought to there's a lot of things that we ought to there's a lot of things that we ought to done do do in space. And in the done do do in space. And in the done do do in space. And in the meantime, uh we're going to explore what meantime, uh we're going to explore what meantime, uh we're going to explore what is the architecture of data centers look is the architecture of data centers look is the architecture of data centers look like uh in space. And it'll take it'll like uh in space. And it'll take it'll like uh in space. And it'll take it'll take years. It's okay. We got I got take years. It's okay. We got I got take years. It's okay. We got I got plenty of time. I wanted to um double plenty of time. I wanted to um double plenty of time. I wanted to um double click on healthcare. I know you've got a click on healthcare. I know you've got a click on healthcare. I know you've got a big effort there. We're all of a certain big effort there. We're all of a certain big effort there. We're all of a certain age where we're thinking about lifespan, age where we're thinking about lifespan, age where we're thinking about lifespan, health span. I mean, we all look great. health span. I mean, we all look great. health span. I mean, we all look great. I think I think I think >> some better than others. >> some better than others. >> some better than others. >> I think some better than others. I don't >> I think some better than others. I don't >> I think some better than others. I don't know what your secret is, Jensen. know what your secret is, Jensen. know what your secret is, Jensen. >> Pretty good these these >> Pretty good these these >> Pretty good these these >> I mean what's what are you taking what's >> I mean what's what are you taking what's >> I mean what's what are you taking what's off the menu? You got to talk to me when off the menu? You got to talk to me when off the menu? You got to talk to me when we're backstage. I want to know in the we're backstage. I want to know in the we're backstage. I want to know in the green room what you got going on. green room what you got going on. green room what you got going on. >> Squat squats and push-ups and sits. >> Squat squats and push-ups and sits. >> Squat squats and push-ups and sits. >> Perfect. Okay. Um but >> Perfect. Okay. Um but >> Perfect. Okay. Um but >> that works. what you know in terms of >> that works. what you know in terms of >> that works. what you know in terms of the buildout in healthcare the buildout in healthcare the buildout in healthcare where is that going and what kind of where is that going and what kind of where is that going and what kind of progress are we making? I was just using progress are we making? I was just using progress are we making? I was just using Claude to do some analysis and saying Claude to do some analysis and saying Claude to do some analysis and saying like where are all these billing codes? like where are all these billing codes? like where are all these billing codes? We spend twice as much money in the US. We spend twice as much money in the US. We spend twice as much money in the US. We get seem to get half as much. It We get seem to get half as much. It We get seem to get half as much. It seemed like uh 15 to 25% of the dollar seemed like uh 15 to 25% of the dollar seemed like uh 15 to 25% of the dollar spent were on these first GP visits. And spent were on these first GP visits. And spent were on these first GP visits. And I think we all know like chat GBT and a I think we all know like chat GBT and a I think we all know like chat GBT and a large language model does a better job large language model does a better job large language model does a better job more consistently today at a first more consistently today at a first more consistently today at a first visit. So what has to happen there to visit. So what has to happen there to visit. So what has to happen there to kind of break through all that kind of break through all that kind of break through all that regulation and have AI have a true regulation and have AI have a true regulation and have AI have a true impact on the health care system? impact on the health care system? impact on the health care system? >> There's several several areas that we're >> There's several several areas that we're >> There's several several areas that we're involved in in um in healthcare. One is involved in in um in healthcare. One is involved in in um in healthcare. One is uh AI uh AI uh AI uh physics uh and and that's or AI uh physics uh and and that's or AI uh physics uh and and that's or AI biology using AI to understand represent biology using AI to understand represent biology using AI to understand represent predict biology behavior biological predict biology behavior biological predict biology behavior biological behavior and so that's one that's very behavior and so that's one that's very behavior and so that's one that's very important in drug discovery. There's important in drug discovery. There's important in drug discovery. There's second which is AI agents and that's second which is AI agents and that's second which is AI agents and that's where the assistance and helping where the assistance and helping where the assistance and helping diagnosis and things like that. Open diagnosis and things like that. Open diagnosis and things like that. Open evidence is a really good example. evidence is a really good example. evidence is a really good example. Hypocratic is a really good example. Hypocratic is a really good example. Hypocratic is a really good example. Love working with those companies. Um I Love working with those companies. Um I Love working with those companies. Um I really think that this is an area uh really think that this is an area uh really think that this is an area uh where agentic technology is going to where agentic technology is going to where agentic technology is going to revolutionize how we interact with revolutionize how we interact with revolutionize how we interact with doctors and how do we interact for doctors and how do we interact for doctors and how do we interact for healthcare. The third part that we're in healthcare. The third part that we're in healthcare. The third part that we're in involved in is physical AI. The first involved in is physical AI. The first involved in is physical AI. The first one is AI physics using AI to predict one is AI physics using AI to predict one is AI physics using AI to predict physics. The second one is physical AI. physics. The second one is physical AI. physics. The second one is physical AI. AI that understand the properties of the AI that understand the properties of the AI that understand the properties of the laws of physics and that's used for a uh laws of physics and that's used for a uh laws of physics and that's used for a uh robotic surgery huge amounts of robotic surgery huge amounts of robotic surgery huge amounts of activities there. Every single activities there. Every single activities there. Every single instrument whether it's ultrasound or instrument whether it's ultrasound or instrument whether it's ultrasound or you know CT or whatever instrument we you know CT or whatever instrument we you know CT or whatever instrument we interact with in a hospital in the interact with in a hospital in the interact with in a hospital in the future will be agentic. future will be agentic. future will be agentic. >> Yeah. >> Yeah. >> Yeah. >> You know open claw in a safe version >> You know open claw in a safe version >> You know open claw in a safe version will be inside every single instrument. will be inside every single instrument. will be inside every single instrument. And so in a lot of ways that instrument And so in a lot of ways that instrument And so in a lot of ways that instrument is going to be interacting with patients is going to be interacting with patients is going to be interacting with patients and nurses and doctors in a very unique and nurses and doctors in a very unique and nurses and doctors in a very unique way. so much investment in AI weapons. way. so much investment in AI weapons. way. so much investment in AI weapons. It would be wonderful to see some It would be wonderful to see some It would be wonderful to see some investment in AI EMTs and paramedics and investment in AI EMTs and paramedics and investment in AI EMTs and paramedics and saving lives, not just taking them, saving lives, not just taking them, saving lives, not just taking them, which I think is a great segue into which I think is a great segue into which I think is a great segue into robotics. You've got dozens of partners. robotics. You've got dozens of partners. robotics. You've got dozens of partners. We have this very weird We have this very weird We have this very weird >> I I don't know want to call a lost >> I I don't know want to call a lost >> I I don't know want to call a lost decade or 20 years of Boston Dynamics. decade or 20 years of Boston Dynamics. decade or 20 years of Boston Dynamics. Google bought a bunch of companies. They Google bought a bunch of companies. They Google bought a bunch of companies. They then wound up selling them and spinning then wound up selling them and spinning then wound up selling them and spinning them out where people just thought them out where people just thought them out where people just thought robotics is just not ready for prime robotics is just not ready for prime robotics is just not ready for prime time. And now here we have the world's time. And now here we have the world's time. And now here we have the world's greatest entrepreneur at this time. Uh greatest entrepreneur at this time. Uh greatest entrepreneur at this time. Uh tied with you, uh Elon Musk doing well, tied with you, uh Elon Musk doing well, tied with you, uh Elon Musk doing well, that was a good save, I hope. Optimus, that was a good save, I hope. Optimus, that was a good save, I hope. Optimus, uh pretty impressive. And then other uh pretty impressive. And then other uh pretty impressive. And then other companies in China. How how close is companies in China. How how close is companies in China. How how close is that to actually being in our lives that to actually being in our lives that to actually being in our lives where we might see a chef, a robotic where we might see a chef, a robotic where we might see a chef, a robotic chef, a robotic nurse, a robotic chef, a robotic nurse, a robotic chef, a robotic nurse, a robotic housekeeper, you know, this humanoid housekeeper, you know, this humanoid housekeeper, you know, this humanoid factor actually working in the real factor actually working in the real factor actually working in the real world knowing what you know with those world knowing what you know with those world knowing what you know with those partners and the fidelity, especially in partners and the fidelity, especially in partners and the fidelity, especially in China where they seem to be doing as China where they seem to be doing as China where they seem to be doing as good a job as we're doing here or maybe good a job as we're doing here or maybe good a job as we're doing here or maybe better. better. better. >> Um, >> Um, >> Um, we invented the industry largely. we invented the industry largely. we invented the industry largely. America invented. We c you could argue America invented. We c you could argue America invented. We c you could argue we got into it too soon. we got into it too soon. we got into it too soon. >> Yeah. >> Yeah. >> Yeah. >> And and we got exhausted. We got tired >> And and we got exhausted. We got tired >> And and we got exhausted. We got tired um about five years before the enabling um about five years before the enabling um about five years before the enabling technology appeared. technology appeared. technology appeared. >> The brain. >> The brain. >> The brain. >> Yeah. Yeah. And we we just got tired of >> Yeah. Yeah. And we we just got tired of >> Yeah. Yeah. And we we just got tired of it just a little too soon. Okay. That's it just a little too soon. Okay. That's it just a little too soon. Okay. That's number one. But it's here now. Now the number one. But it's here now. Now the number one. But it's here now. Now the question is how much longer? From the question is how much longer? From the question is how much longer? From the point of high functioning existence point of high functioning existence point of high functioning existence proof, high functioning exist existence proof, high functioning exist existence proof, high functioning exist existence proof to reasonable products proof to reasonable products proof to reasonable products technology never takes more than a technology never takes more than a technology never takes more than a couple two three cycles. And so a couple couple two three cycles. And so a couple couple two three cycles. And so a couple two three cycles basically be somewhere two three cycles basically be somewhere two three cycles basically be somewhere around three years to 5 years. That's around three years to 5 years. That's around three years to 5 years. That's it. 3 years to 5 years we're going to it. 3 years to 5 years we're going to it. 3 years to 5 years we're going to have robots all over the place. Uh I have robots all over the place. Uh I have robots all over the place. Uh I think I think um uh China is is uh think I think um uh China is is uh think I think um uh China is is uh formidable and the reason for that is formidable and the reason for that is formidable and the reason for that is because their micro electronics, their because their micro electronics, their because their micro electronics, their uh motors, their rare earth, their uh motors, their rare earth, their uh motors, their rare earth, their magnets, which is foundational to magnets, which is foundational to magnets, which is foundational to robotics, they are the world's best. And robotics, they are the world's best. And robotics, they are the world's best. And so in a lot of ways, our robotics so in a lot of ways, our robotics so in a lot of ways, our robotics industry relies deeply on their industry relies deeply on their industry relies deeply on their ecosystem and their supply chain. Um and ecosystem and their supply chain. Um and ecosystem and their supply chain. Um and uh and and they're, you know, obviously uh and and they're, you know, obviously uh and and they're, you know, obviously moving very quickly. Uh we're going to, moving very quickly. Uh we're going to, moving very quickly. Uh we're going to, you know, our robotics industry will you know, our robotics industry will you know, our robotics industry will have to rely a lot on it. the world's have to rely a lot on it. the world's have to rely a lot on it. the world's robotics industry will have to rely on a robotics industry will have to rely on a robotics industry will have to rely on a lot on it. And so so I think um you're lot on it. And so so I think um you're lot on it. And so so I think um you're gonna see some fast fast movements here gonna see some fast fast movements here gonna see some fast fast movements here >> ultimately one for one. Elon seems to >> ultimately one for one. Elon seems to >> ultimately one for one. Elon seems to think we're going to have one robot for think we're going to have one robot for think we're going to have one robot for every human. 7 billion for 7 billion, 8 every human. 7 billion for 7 billion, 8 every human. 7 billion for 7 billion, 8 billion for 8 billion. billion for 8 billion. billion for 8 billion. >> Well, I'm hoping more. Yeah, I'm hoping >> Well, I'm hoping more. Yeah, I'm hoping >> Well, I'm hoping more. Yeah, I'm hoping more. Yeah. Uh well, first of all, more. Yeah. Uh well, first of all, more. Yeah. Uh well, first of all, there's a whole bunch of robots that are there's a whole bunch of robots that are there's a whole bunch of robots that are going to be in factories working around going to be in factories working around going to be in factories working around the clock. There's going to be a whole the clock. There's going to be a whole the clock. There's going to be a whole bunch of fac that that don't move. They bunch of fac that that don't move. They bunch of fac that that don't move. They move a little bit. Uh almost everything move a little bit. Uh almost everything move a little bit. Uh almost everything will be robotic. What does the world will be robotic. What does the world will be robotic. What does the world look like? look like? look like? >> Sorry, let me I think like this is one >> Sorry, let me I think like this is one >> Sorry, let me I think like this is one of the robotics for me is one of the of the robotics for me is one of the of the robotics for me is one of the pieces that I think unlocks uh economic pieces that I think unlocks uh economic pieces that I think unlocks uh economic mobility opportunities for every mobility opportunities for every mobility opportunities for every individual. Everyone now like when individual. Everyone now like when individual. Everyone now like when everyone got a car, they could now go everyone got a car, they could now go everyone got a car, they could now go and do a lot of different jobs. When and do a lot of different jobs. When and do a lot of different jobs. When everyone gets a robot, their robot can everyone gets a robot, their robot can everyone gets a robot, their robot can do a lot of work for them. They can do a lot of work for them. They can do a lot of work for them. They can stand up an Etsy store, a Shopify store. stand up an Etsy store, a Shopify store. stand up an Etsy store, a Shopify store. They can create anything they want with They can create anything they want with They can create anything they want with their robot. They could do things that their robot. They could do things that their robot. They could do things that they independently cannot do. I think they independently cannot do. I think they independently cannot do. I think the robot is going to end up being the the robot is going to end up being the the robot is going to end up being the greatest unlock for prosperity for more greatest unlock for prosperity for more greatest unlock for prosperity for more people on Earth than we've ever seen people on Earth than we've ever seen people on Earth than we've ever seen with any technology before. with any technology before. with any technology before. >> Yeah, no doubt. I mean, just a simp the >> Yeah, no doubt. I mean, just a simp the >> Yeah, no doubt. I mean, just a simp the simple math at the moment is we're simple math at the moment is we're simple math at the moment is we're millions of people short in labor today. millions of people short in labor today. millions of people short in labor today. Right. Yeah. Right. Yeah. Right. Yeah. >> Right. We're we're we're actually really >> Right. We're we're we're actually really >> Right. We're we're we're actually really desperate in need of robotics and so desperate in need of robotics and so desperate in need of robotics and so that all of these companies could grow that all of these companies could grow that all of these companies could grow more if they had more labor. I mean, more if they had more labor. I mean, more if they had more labor. I mean, we're we're number one. Some of the we're we're number one. Some of the we're we're number one. Some of the things that you mentioned are super fun. things that you mentioned are super fun. things that you mentioned are super fun. I mean, because of robots, we'll have I mean, because of robots, we'll have I mean, because of robots, we'll have virtual presence. Uh, you know, I'll be virtual presence. Uh, you know, I'll be virtual presence. Uh, you know, I'll be able to go into the robot of my house able to go into the robot of my house able to go into the robot of my house and virtually operate it. I'm on a and virtually operate it. I'm on a and virtually operate it. I'm on a business trip, business trip, business trip, >> right? >> right? >> right? >> Walk around the house. >> Walk around the house. >> Walk around the house. >> Yeah. Walk the dog. >> Yeah. Walk the dog. >> Yeah. Walk the dog. >> Yeah. Walk the dog. >> Yeah. Walk the dog. >> Yeah. Walk the dog. >> Break the leaves. >> Break the leaves. >> Break the leaves. >> Yeah. Exactly. Freak out the dog. >> Yeah. Exactly. Freak out the dog. >> Yeah. Exactly. Freak out the dog. >> Maybe not quite that, but just, you >> Maybe not quite that, but just, you >> Maybe not quite that, but just, you know, just, you know, wander around and know, just, you know, wander around and know, just, you know, wander around and just see what's going on in the house. just see what's going on in the house. just see what's going on in the house. You know, chat with the dogs, chat with You know, chat with the dogs, chat with You know, chat with the dogs, chat with the kids. Yeah. the kids. Yeah. the kids. Yeah. >> Yeah. >> Yeah. >> Yeah. And time travel is also we're going to And time travel is also we're going to And time travel is also we're going to be able to travel at the speed of light, be able to travel at the speed of light, be able to travel at the speed of light, you know, and so, you know, clearly you know, and so, you know, clearly you know, and so, you know, clearly we're going to send our robots ahead of we're going to send our robots ahead of we're going to send our robots ahead of us. us. us. >> Yeah. >> Yeah. >> Yeah. >> Not going to send myself. I'm going to >> Not going to send myself. I'm going to >> Not going to send myself. I'm going to send a robot, you know. send a robot, you know. send a robot, you know. >> Check it out. >> Check it out. >> Check it out. >> Yeah. Yeah. And then I'm going to upload >> Yeah. Yeah. And then I'm going to upload >> Yeah. Yeah. And then I'm going to upload my AI. my AI. my AI. >> Well, it's inevitable. It unlocks the >> Well, it's inevitable. It unlocks the >> Well, it's inevitable. It unlocks the moon and it unlocks Mars as um targets moon and it unlocks Mars as um targets moon and it unlocks Mars as um targets for for colonization, which gives us for for colonization, which gives us for for colonization, which gives us >> infinite resources. Getting back from >> infinite resources. Getting back from >> infinite resources. Getting back from the moon is effectively zero energy cost the moon is effectively zero energy cost the moon is effectively zero energy cost to move material back because you can to move material back because you can to move material back because you can use solar and accelerate. So you could use solar and accelerate. So you could use solar and accelerate. So you could have factories that make everything the have factories that make everything the have factories that make everything the world needs on the moon and the robots world needs on the moon and the robots world needs on the moon and the robots are going to be the unlock for enabling. are going to be the unlock for enabling. are going to be the unlock for enabling. >> That's right. Distance no longer >> That's right. Distance no longer >> That's right. Distance no longer matters. matters. matters. >> Distance doesn't matter. Yeah. >> Distance doesn't matter. Yeah. >> Distance doesn't matter. Yeah. >> The more the more revenue we get out of >> The more the more revenue we get out of >> The more the more revenue we get out of models and agents, the more we can models and agents, the more we can models and agents, the more we can invest in building the infrastructure invest in building the infrastructure invest in building the infrastructure which then unlocks more capabilities on which then unlocks more capabilities on which then unlocks more capabilities on models and agents. Dario on Dwaresh's models and agents. Dario on Dwaresh's models and agents. Dario on Dwaresh's podcast recently said by 2728 we'll have podcast recently said by 2728 we'll have podcast recently said by 2728 we'll have hundreds of billions of dollars of hundreds of billions of dollars of hundreds of billions of dollars of revenue out of the model companies and revenue out of the model companies and revenue out of the model companies and the agent companies. and he forecasts a the agent companies. and he forecasts a the agent companies. and he forecasts a trillion dollars by 2030, right? This is trillion dollars by 2030, right? This is trillion dollars by 2030, right? This is non-infrastructure AI revenue. Um, non-infrastructure AI revenue. Um, non-infrastructure AI revenue. Um, >> I think he I think he's he's being very >> I think he I think he's he's being very >> I think he I think he's he's being very conservative. I believe Dario and conservative. I believe Dario and conservative. I believe Dario and Anthropic is going to do way better than Anthropic is going to do way better than Anthropic is going to do way better than that. that. that. >> Wow. >> Wow. >> Wow. >> Way better than that. >> Way better than that. >> Way better than that. >> Wow. So, from 30 billion to a trillion. >> Wow. So, from 30 billion to a trillion. >> Wow. So, from 30 billion to a trillion. >> Yeah. and not and and the reason for >> Yeah. and not and and the reason for >> Yeah. and not and and the reason for that is the one part that he hasn't that is the one part that he hasn't that is the one part that he hasn't considered is that I believe every considered is that I believe every considered is that I believe every single enterprise software company will single enterprise software company will single enterprise software company will also be a reseller also be a reseller also be a reseller value added reseller of anthropic code value added reseller of anthropic code value added reseller of anthropic code anthropics tokens value added reseller anthropics tokens value added reseller anthropics tokens value added reseller open AI that's right and they're going open AI that's right and they're going open AI that's right and they're going to that that that part of their to that that that part of their to that that that part of their >> get this logarithmic expansion >> get this logarithmic expansion >> get this logarithmic expansion >> yes >> yes >> yes >> their go to market is going to expand >> their go to market is going to expand >> their go to market is going to expand tremendously this year tremendously this year tremendously this year >> what do you think in that world is the >> what do you think in that world is the >> what do you think in that world is the moat what's left over. I mean you have moat what's left over. I mean you have moat what's left over. I mean you have some moes that are frankly I think as some moes that are frankly I think as some moes that are frankly I think as this scales almost insurmountable the this scales almost insurmountable the this scales almost insurmountable the best one that nobody talks about is best one that nobody talks about is best one that nobody talks about is probably CUDA which is just like an probably CUDA which is just like an probably CUDA which is just like an incredible strategic advantage. But in incredible strategic advantage. But in incredible strategic advantage. But in the future if a model can be used to the future if a model can be used to the future if a model can be used to create something incredible then the create something incredible then the create something incredible then the next spin of a model can be used to next spin of a model can be used to next spin of a model can be used to maybe disrupt it. Sort of in your mind maybe disrupt it. Sort of in your mind maybe disrupt it. Sort of in your mind what do you think for these companies what do you think for these companies what do you think for these companies that are building at that application that are building at that application that are building at that application layer? What's their moat? like how do layer? What's their moat? like how do layer? What's their moat? like how do they differentiate themselves? they differentiate themselves? they differentiate themselves? >> Deep specialization. >> Deep specialization. >> Deep specialization. Deep specialization. I believe that um Deep specialization. I believe that um Deep specialization. I believe that um these models they're going to have these models they're going to have these models they're going to have general general models that are general general models that are general general models that are connected into the software company's connected into the software company's connected into the software company's agentic system, agentic system, agentic system, >> right? >> right? >> right? >> Many of those models are cloud models >> Many of those models are cloud models >> Many of those models are cloud models and proprietary models, but many of and proprietary models, but many of and proprietary models, but many of those models are specialized those models are specialized those models are specialized sub aents that they've trained on their sub aents that they've trained on their sub aents that they've trained on their own. own. own. >> Right. All right. So, the call to arms >> Right. All right. So, the call to arms >> Right. All right. So, the call to arms for you for entrepreneurs is look, for you for entrepreneurs is look, for you for entrepreneurs is look, >> know your vertical. >> know your vertical. >> know your vertical. >> That's right. >> That's right. >> That's right. >> Know it as deep and as better than >> Know it as deep and as better than >> Know it as deep and as better than everybody else. everybody else. everybody else. >> That's right. >> That's right. >> That's right. >> And then wait for these tools because >> And then wait for these tools because >> And then wait for these tools because they're catching up to you and now you they're catching up to you and now you they're catching up to you and now you can imbue it with your knowledge. can imbue it with your knowledge. can imbue it with your knowledge. >> That's right. The sooner you connect >> That's right. The sooner you connect >> That's right. The sooner you connect your agent, your agent, your agent, >> the sooner you connect your agent with >> the sooner you connect your agent with >> the sooner you connect your agent with customers, customers, customers, >> that flywheel is going to cause your >> that flywheel is going to cause your >> that flywheel is going to cause your agent to get agent to get agent to get >> it very much is an inversion of what we >> it very much is an inversion of what we >> it very much is an inversion of what we do today because today we build a piece do today because today we build a piece do today because today we build a piece of software and we say what generalizes of software and we say what generalizes of software and we say what generalizes >> and then let's try to sell it as broadly >> and then let's try to sell it as broadly >> and then let's try to sell it as broadly as possible and then sell the as possible and then sell the as possible and then sell the customization around it customization around it customization around it >> and we in fact in fact exactly right we >> and we in fact in fact exactly right we >> and we in fact in fact exactly right we we create a horizontal but notice there we create a horizontal but notice there we create a horizontal but notice there are all these gsis and all of these are all these gsis and all of these are all these gsis and all of these consultants who are specialists Yes. consultants who are specialists Yes. consultants who are specialists Yes. >> Who then take your horizontal platform >> Who then take your horizontal platform >> Who then take your horizontal platform and specializes it into and specializes it into and specializes it into >> Exactly. >> Exactly. >> Exactly. >> And that's arguably a five or six time >> And that's arguably a five or six time >> And that's arguably a five or six time bigger industry is the customization. bigger industry is the customization. bigger industry is the customization. >> It is absolutely the whole very much is >> It is absolutely the whole very much is >> It is absolutely the whole very much is >> that's right. So I think that these >> that's right. So I think that these >> that's right. So I think that these platform companies have an opportunity platform companies have an opportunity platform companies have an opportunity to become that specialist to become that to become that specialist to become that to become that specialist to become that vertical. vertical. vertical. >> Yeah. Domain expert. >> Yeah. Domain expert. >> Yeah. Domain expert. >> You know, I just want to give you your >> You know, I just want to give you your >> You know, I just want to give you your flowers. I think it was three years ago flowers. I think it was three years ago flowers. I think it was three years ago you said you're not going to lose your you said you're not going to lose your you said you're not going to lose your job to AI. You're going to lose your job job to AI. You're going to lose your job job to AI. You're going to lose your job to somebody using AI. And here we are. to somebody using AI. And here we are. to somebody using AI. And here we are. The entire conversation has revolved The entire conversation has revolved The entire conversation has revolved around this concept of agents making around this concept of agents making around this concept of agents making people superhuman and the business people superhuman and the business people superhuman and the business opportunity expanding and opportunity expanding and opportunity expanding and entrepreneurship expanding. You actually entrepreneurship expanding. You actually entrepreneurship expanding. You actually saw it pretty clearly. Yeah. saw it pretty clearly. Yeah. saw it pretty clearly. Yeah. >> Have you changed your view? >> Have you changed your view? >> Have you changed your view? >> I do. I'm not I'm not doomer. I do I do >> I do. I'm not I'm not doomer. I do I do >> I do. I'm not I'm not doomer. I do I do have doomer. have doomer. have doomer. >> No I you can hold space for I think two >> No I you can hold space for I think two >> No I you can hold space for I think two ideas. One is there are going to be a ideas. One is there are going to be a ideas. One is there are going to be a lot lot lot >> that's spiral Jake we call it. >> that's spiral Jake we call it. >> that's spiral Jake we call it. >> No there you can >> No there you can >> No there you can >> but that's just because he doesn't hang >> but that's just because he doesn't hang >> but that's just because he doesn't hang out with me enough. Well, we I mean we out with me enough. Well, we I mean we out with me enough. Well, we I mean we a little bit. Be careful. a little bit. Be careful. a little bit. Be careful. >> We don't talk about it. >> We don't talk about it. >> We don't talk about it. >> He will show your breakfast. HE'LL >> He will show your breakfast. HE'LL >> He will show your breakfast. HE'LL FOLLOW YOU AROUND. FOLLOW YOU AROUND. FOLLOW YOU AROUND. >> I'm not asking for it. He'll follow you >> I'm not asking for it. He'll follow you >> I'm not asking for it. He'll follow you around. I'm not asking for it. around. I'm not asking for it. around. I'm not asking for it. >> You can come with me and Tucker. We ski >> You can come with me and Tucker. We ski >> You can come with me and Tucker. We ski in Japan every January. Love it. Me and in Japan every January. Love it. Me and in Japan every January. Love it. Me and Tucker go road trip. Um there is going Tucker go road trip. Um there is going Tucker go road trip. Um there is going to be job displacement. And then the to be job displacement. And then the to be job displacement. And then the question becomes, question becomes, question becomes, >> you know, do those people have the >> you know, do those people have the >> you know, do those people have the fortitude, the resolve to then go fortitude, the resolve to then go fortitude, the resolve to then go embrace these, embrace these, embrace these, >> you know, technologies? We're we're >> you know, technologies? We're we're >> you know, technologies? We're we're going to see 100% of driving go away by going to see 100% of driving go away by going to see 100% of driving go away by humans. That's just it's that's a humans. That's just it's that's a humans. That's just it's that's a beautiful thing in the lives saved, but beautiful thing in the lives saved, but beautiful thing in the lives saved, but we have to recognize that's 15 million we have to recognize that's 15 million we have to recognize that's 15 million people in the United States, 10 to 15 people in the United States, 10 to 15 people in the United States, 10 to 15 million who are employed in that way. million who are employed in that way. million who are employed in that way. And and so that is going to happen. Yes, And and so that is going to happen. Yes, And and so that is going to happen. Yes, >> I I think I think that jobs will change. >> I I think I think that jobs will change. >> I I think I think that jobs will change. For example, um there are many For example, um there are many For example, um there are many chauffeers today uh who drives the car. chauffeers today uh who drives the car. chauffeers today uh who drives the car. I believe that though many of those I believe that though many of those I believe that though many of those chauffeers will actually be in the car chauffeers will actually be in the car chauffeers will actually be in the car sitting behind the drive the steering sitting behind the drive the steering sitting behind the drive the steering wheel while the car is driving by wheel while the car is driving by wheel while the car is driving by itself. And the reason for that is itself. And the reason for that is itself. And the reason for that is because remember what a chauffeur does because remember what a chauffeur does because remember what a chauffeur does in the end. These chauffeers they're in the end. These chauffeers they're in the end. These chauffeers they're helping you they're your assistants. helping you they're your assistants. helping you they're your assistants. They're helping you with your luggage. They're helping you with your luggage. They're helping you with your luggage. They're helping you. I mean, they're They're helping you. I mean, they're They're helping you. I mean, they're helping you with a lot of things and and helping you with a lot of things and and helping you with a lot of things and and so I wouldn't be surprised actually if so I wouldn't be surprised actually if so I wouldn't be surprised actually if the chauffeers of the future becomes the chauffeers of the future becomes the chauffeers of the future becomes your mobility assistant and they are your mobility assistant and they are your mobility assistant and they are helping you do on a whole bunch of other helping you do on a whole bunch of other helping you do on a whole bunch of other stuff to the hotel. stuff to the hotel. stuff to the hotel. >> Yeah. And the car is driving by itself. >> Yeah. And the car is driving by itself. >> Yeah. And the car is driving by itself. >> The autopilot in planes created a lot >> The autopilot in planes created a lot >> The autopilot in planes created a lot more pilots and didn't take any of the more pilots and didn't take any of the more pilots and didn't take any of the pilots out of the cockpit even though pilots out of the cockpit even though pilots out of the cockpit even though the autopilot is flying the plane 90% of the autopilot is flying the plane 90% of the autopilot is flying the plane 90% of the time. And by the way, while that car the time. And by the way, while that car the time. And by the way, while that car is driving itself, that chauffeur is is driving itself, that chauffeur is is driving itself, that chauffeur is going to be doing a bunch of other work going to be doing a bunch of other work going to be doing a bunch of other work on his phone and he's going to be on his phone and he's going to be on his phone and he's going to be >> arranging, for example, coordinating a >> arranging, for example, coordinating a >> arranging, for example, coordinating a bunch of things for you, getting, you bunch of things for you, getting, you bunch of things for you, getting, you know, know, know, >> it's all the pie just grows in a way >> it's all the pie just grows in a way >> it's all the pie just grows in a way that that that >> one of the things that that that >> one of the things that that that >> one of the things that that that yes, every job will be will be yes, every job will be will be yes, every job will be will be transformed. Um, some jobs will be transformed. Um, some jobs will be transformed. Um, some jobs will be eliminated. However, we also know that eliminated. However, we also know that eliminated. However, we also know that many many jobs will be recre will be many many jobs will be recre will be many many jobs will be recre will be created. The one thing that I will say created. The one thing that I will say created. The one thing that I will say to young people who are coming out of to young people who are coming out of to young people who are coming out of school who are concerned who are anxious school who are concerned who are anxious school who are concerned who are anxious about AI be the expert of using AI about AI be the expert of using AI about AI be the expert of using AI >> how much look we all want our employees >> how much look we all want our employees >> how much look we all want our employees to be expert at using AI and it's not to be expert at using AI and it's not to be expert at using AI and it's not not not not >> not trivial not trivial and so knowing >> not trivial not trivial and so knowing >> not trivial not trivial and so knowing how to specify not to overprescribe how to specify not to overprescribe how to specify not to overprescribe leaving enough room for the AI to leaving enough room for the AI to leaving enough room for the AI to innovate and create while we guide it to innovate and create while we guide it to innovate and create while we guide it to the outcome we want. it. All of that the outcome we want. it. All of that the outcome we want. it. All of that requires artistry. requires artistry. requires artistry. >> You had you had this great advice to >> You had you had this great advice to >> You had you had this great advice to when you were at Stanford, I think it when you were at Stanford, I think it when you were at Stanford, I think it was, which is I wish to you pain and was, which is I wish to you pain and was, which is I wish to you pain and suffering. Do you remember that? suffering. Do you remember that? suffering. Do you remember that? >> Yeah. >> Yeah. >> Yeah. >> Fantastic. >> Fantastic. >> Fantastic. >> What's your advice to young people >> What's your advice to young people >> What's your advice to young people around what they should be studying? So, around what they should be studying? So, around what they should be studying? So, if they're sort of about to leave high if they're sort of about to leave high if they're sort of about to leave high school because now those are the kids school because now those are the kids school because now those are the kids that are at this like really native, that are at this like really native, that are at this like really native, they haven't made a decision about they haven't made a decision about they haven't made a decision about college, what to study, if at all go to college, what to study, if at all go to college, what to study, if at all go to college. How do you guide those kids? college. How do you guide those kids? college. How do you guide those kids? What would you tell them? I I still What would you tell them? I I still What would you tell them? I I still believe that deep science, deep math, um believe that deep science, deep math, um believe that deep science, deep math, um language skills, you know, as you know, language skills, you know, as you know, language skills, you know, as you know, language is the programming language of language is the programming language of language is the programming language of AI, the ultimate program. AI, the ultimate program. AI, the ultimate program. >> And so, as it turns out, it it could be >> And so, as it turns out, it it could be >> And so, as it turns out, it it could be that the English major could be the most that the English major could be the most that the English major could be the most successful. Yeah. successful. Yeah. successful. Yeah. >> And and so so I think I think um I I >> And and so so I think I think um I I >> And and so so I think I think um I I would just advise whatever whatever would just advise whatever whatever would just advise whatever whatever education you get, just make sure that education you get, just make sure that education you get, just make sure that you're deeply deeply expert in using you're deeply deeply expert in using you're deeply deeply expert in using AIs. One of the things that I wanted to AIs. One of the things that I wanted to AIs. One of the things that I wanted to say with respect to jobs and I want say with respect to jobs and I want say with respect to jobs and I want everybody to hear it that in fact at the everybody to hear it that in fact at the everybody to hear it that in fact at the beginning of the deep learning beginning of the deep learning beginning of the deep learning revolution, one of the the finest revolution, one of the the finest revolution, one of the the finest computer scientists in the world deeply computer scientists in the world deeply computer scientists in the world deeply deeply I deeply uh deeply uh um respect deeply I deeply uh deeply uh um respect deeply I deeply uh deeply uh um respect uh predicted that computer vision will uh predicted that computer vision will uh predicted that computer vision will completely eliminate radiologists completely eliminate radiologists completely eliminate radiologists and and that the one at the one field he and and that the one at the one field he and and that the one at the one field he advises everybody to not go into is advises everybody to not go into is advises everybody to not go into is radiology. 10 years later, his radiology. 10 years later, his radiology. 10 years later, his prediction was at 100% right. Computer prediction was at 100% right. Computer prediction was at 100% right. Computer vision has been integrated into all of vision has been integrated into all of vision has been integrated into all of the radiology technologies and radiology the radiology technologies and radiology the radiology technologies and radiology platforms in the world 100%. The platforms in the world 100%. The platforms in the world 100%. The surprising outcome is the number of surprising outcome is the number of surprising outcome is the number of radiologists actually went up and the radiologists actually went up and the radiologists actually went up and the demand for radiologists is skyrocketed. demand for radiologists is skyrocketed. demand for radiologists is skyrocketed. The reason for that is because The reason for that is because The reason for that is because everybody's job everybody's job everybody's job has a purpose and its task. The task has a purpose and its task. The task has a purpose and its task. The task that you do is studying the scans, that you do is studying the scans, that you do is studying the scans, >> but your purpose is to diag helping the >> but your purpose is to diag helping the >> but your purpose is to diag helping the doctors, helping the patient diagnose doctors, helping the patient diagnose doctors, helping the patient diagnose disease. disease. disease. >> And so what's surprising is because the >> And so what's surprising is because the >> And so what's surprising is because the scans are now being done so quickly, scans are now being done so quickly, scans are now being done so quickly, >> they could do more scans, improving >> they could do more scans, improving >> they could do more scans, improving healthcare. healthcare. healthcare. >> Yes. >> Yes. >> Yes. >> But doing more scans more quickly allows >> But doing more scans more quickly allows >> But doing more scans more quickly allows patients to patients to patients to >> be >> be >> be onboarded a lot more quick, treated a onboarded a lot more quick, treated a onboarded a lot more quick, treated a lot more quickly. And as it turns out, lot more quickly. And as it turns out, lot more quickly. And as it turns out, because hospitals enjoy making money, because hospitals enjoy making money, because hospitals enjoy making money, too. too. too. >> Yeah. >> Yeah. >> Yeah. >> Right. >> Right. >> Right. >> They're doing more scans. >> They're doing more scans. >> They're doing more scans. >> They're treating more customers. >> They're treating more customers. >> They're treating more customers. The revenues go up. And guess what? The revenues go up. And guess what? The revenues go up. And guess what? Perfect. And and a country that grows Perfect. And and a country that grows Perfect. And and a country that grows faster, productivity increases. A faster, productivity increases. A faster, productivity increases. A wealthier country can put more teachers wealthier country can put more teachers wealthier country can put more teachers in the classroom, not less teachers in in the classroom, not less teachers in in the classroom, not less teachers in the classroom. That's right. You just the classroom. That's right. You just the classroom. That's right. You just give every one of those teachers a give every one of those teachers a give every one of those teachers a personalized curriculum for every personalized curriculum for every personalized curriculum for every student in the room. It makes them all student in the room. It makes them all student in the room. It makes them all bionic and leads to a lot more. Every bionic and leads to a lot more. Every bionic and leads to a lot more. Every single student will be assisted by AI, single student will be assisted by AI, single student will be assisted by AI, but every single student will need great but every single student will need great but every single student will need great teachers. teachers. teachers. >> Yeah. Yeah. Amazing. Uh Jensen, >> Yeah. Yeah. Amazing. Uh Jensen, >> Yeah. Yeah. Amazing. Uh Jensen, congratulations. I know your success and congratulations. I know your success and congratulations. I know your success and really this is an incredibly positive, really this is an incredibly positive, really this is an incredibly positive, uplifting discussion. We really uplifting discussion. We really uplifting discussion. We really appreciate you taking the time for us. appreciate you taking the time for us. appreciate you taking the time for us. He is the steward we need. He is the steward we need. He is the steward we need. >> You are you are you need to be more >> You are you are you need to be more >> You are you are you need to be more vocal. I'm being very vocal about the vocal. I'm being very vocal about the vocal. I'm being very vocal about the positive side of it. I think there's too positive side of it. I think there's too positive side of it. I think there's too much dumerism is much dumerism is much dumerism is >> but I also think it takes the humility >> but I also think it takes the humility >> but I also think it takes the humility to have this level of success and be to have this level of success and be to have this level of success and be humble about we're making software guys. humble about we're making software guys. humble about we're making software guys. Yeah. Yeah. Yeah. >> And I think that that's actually really >> And I think that that's actually really >> And I think that that's actually really healthy for people to hear. We have done healthy for people to hear. We have done healthy for people to hear. We have done this before. We have invented categories this before. We have invented categories this before. We have invented categories and industries before. and industries before. and industries before. >> We don't need to go to this >> We don't need to go to this >> We don't need to go to this >> scaremongering place. It does nothing. >> scaremongering place. It does nothing. >> scaremongering place. It does nothing. >> And we get to choose, right? We have >> And we get to choose, right? We have >> And we get to choose, right? We have autonomy and and agency. We get to pick autonomy and and agency. We get to pick autonomy and and agency. We get to pick how to how to how to >> we do this. Okay, everybody. We'll see >> we do this. Okay, everybody. We'll see >> we do this. Okay, everybody. We'll see you next time on the All-In interview. you next time on the All-In interview. you next time on the All-In interview. Okay. Okay. Okay. >> Well done, brother. >> Well done, brother. >> Well done, brother. >> Thanks, man. >> Thanks, man. >> Thanks, man. >> Good job. >> Good job. >> Good job. >> Thank you, sir. That was awesome. >> Thank you, sir. That was awesome. >> Thank you, sir. That was awesome. >> Good. Good. >> Good. Good. >> Good. Good. >> Appreciate you. You guys are awesome. >> Appreciate you. You guys are awesome. >> Appreciate you. You guys are awesome. >> Look at this. Look at this big crowd >> Look at this. Look at this big crowd >> Look at this. Look at this big crowd behind you guys, behind you guys, behind you guys, >> man. I think they're here for you.