Joe Rogan Experience #2422 - Jensen Huang

Joe Rogan podcast. Check it out. >> The Joe Rogan Experience. >> TRAIN BY DAY. JOE ROGAN PODCAST BY. NIGHT. All day. >> Hello. Hey, Joe. >> Good to see you again. We were just. talking about Was that the first time we. ever spoke or did was the first time we. spoke at at SpaceX? >> SpaceX. >> SpaceX. The first time when you were. giving Elon that crazy AI chip, >> right? DJX Spark. >> Yeah. Oo, that was a big moment. That. was a huge. >> That felt crazy to be there. I was like.

watching these wizards of tech like. exchange information and and you're. giving him this crazy device, you know, and then the other time was uh I was. shooting arrows in my backyard and uh. randomly get this call from Trump and. he's hanging out with you. President. Trump called and I called you. >> Yeah. It's just. >> we were talking about you. [laughter]. >> It's just talking about he was talking. about the US UFC thing he was going to. do in his front yard. >> Yeah. And he pulls out. He's JJS, look. at this design. He's so proud of it. And.

I go, "You're going to have a fight in. the front lawn in the White House." He. goes, "Yeah, yeah, you're going to come. This is going to be awesome." And he's. showing me his design and how beautiful. it is. And he goes, and somehow your. name comes up. He goes, "Do you know. Joe?" And I said, "Yeah, I'm going to be. on his podcast." He Let's call him. [laughter]. >> He's like a kid. >> I know. Let's call him. It's so He's. like a 79y old kid. >> Oh, he's so incredible.

>> Yeah, he's an odd guy. Just very. different, you know, like the what you'd. expect from him. Very different than. what people think of him. And also just. very different as a president. A guy who. just calls you or texts you out of the. blue. Also, he makes when you te you. You have an Android, so it won't go. through with you, but with my iPhone, he. makes the text go big. >> Like, you know, USA is respected again. like [laughter]. all caps and it makes the te the the the. text enlarge is kind of ridiculous.

>> Well, the the 101 Trump President Trump. is very different. He he surprised me f. first of all he's an incredibly good. listener. Almost everything I've ever. said to him, he's remembered. >> Yeah. People don't they only want to. look at negative stories about him or. negative narratives about him. You know, you can catch anybody on a bad day. Like. there's a lot of things he does where I. don't think he should do. Like I don't. think he should say to a reporter rep. reporter, "Quiet piggy." Like that's. pretty ridiculous. Also objectively.

funny. I mean, it's unfortunate that it. happened to her. I wouldn't want that to. happen to her, but it was funny. Just. ridiculous that the president does that. I wish he didn't do that. But other than. that, like he's he's an interesting guy. Like he's a lot of different things. wrapped up into one person, you know? You know, part of part of his charm, well, part of his genius is Yes. He says. what's on his mind. >> Yes. >> And which is like an anti-olitician in a. lot of ways. >> So, you know, what's on his mind is.

really what's on his mind, >> which. I I do some people some people would. rather be lied to. >> Yeah. But but I I like the fact that. he's telling you what's on his mind. Um, almost every time he explains something, he says something, he starts with his, you could tell, his. love for America, what he wants to do. for America. And everything that he. thinks through is very practical and. very common sense. And, you know, it's. very logical and um.

I still remember the first time I I met. him and so this was I I'd never known. him, never met him before. and um uh. Secretary Lutnik called and we met right. before right at the beginning of the. administration. He said he told me what. was important to President Trump that. that um uh that United States. manufactures on shore and that was. really important to him because because. uh it's important to national security.

He wants to make sure that that the. important critical technology of our. nation is built in the United States and. that we re-industrialize. and get good at manufacturing again. because it's important for jobs. >> It just seems like common sense, right? >> Incredible common sense. And and that. was like literally the first. conversation I had with Secretary Letic. um and he was talking about how how um. that he started he started our. conversation with uh Jensen. This is. Secretary Lutnik and I I just want to.

let you know that you're a national. treasure. Uh Nvidia is a national. treasure and whenever you need access to. the president um the administration uh. you call us. We're always going to be. available to you. Literally, that was. the first sentence. >> That's pretty nice. >> And it was completely true. every single. time I called, if I needed something, I. want to get something off my chest, um, express some concern, uh, they're always.

available. Incredible. It's just. unfortunate we live in such a. politically polarized society that you. can't recognize good common sense things. if they're coming from a person that you. object to. And that, I think, is what's. going on here. I think most people. generally a as a country, you know, as a. a giant community, which we are, it just. only makes sense that we have. manufacturing in America that especially. critical technology like you're talking. about. Like it's kind of insane that we.

buy so much technology from other. countries. >> If United States doesn't grow, we will. have no prosperity. We can't invest in. anything domestically or otherwise. we. can't fix any of our problems. If we. don't have energy growth, we can't have. industrial growth. If we don't have. industrial growth, we can't have job. growth. These it's as simple as that, >> right? >> And the fact that the fact that he came. into office and the first thing that he. said was drill baby drill. His point is.

we need energy growth. Without energy. growth, we can have no industrial. growth. And that was it saved it saved. the AI industry. got I got to tell you. flat out if not for his progrowth energy. policy. we would not be able to build factories. for AI not be able to build chip. factories we won't sure surely won't be. able to build supercomputer factories. none of that stuff would be possible. without all of that. construction jobs would be challenged.

right electrical you know electrician. jobs all of these jobs that are now. flourishing would be challenged and so I. think he's got it right we need energy. growth We want to re-industrialize the. United States. We need to be back in. manufacturing. Every successful person. doesn't need to have a PhD. Every. successful person doesn't have to have. gone to Stanford or MIT. And I think I. think that that that you know that. sensibility is is um spot on. Now, when. we're talking about technology growth. and energy growth, there's a lot of.

people that go, "Oh, no. That's not what. we need. We need to, you know, simplify. our lives and get back." But the the. real issue is that we're in the middle. of a giant technology race. And whether. people are aware of it or not, whether. they like it or not, it's happening. And. it's a really important race because. whoever gets to. whatever the event horizon of artificial. intelligence is, whoever gets there. first has massive advantages in a huge. way. Do you agree with that? Well, first the.

part I I will say that we are in a. technology race and we are always in a. technology race. We've been in a. technology race with somebody forever. >> Right. >> Right. Since the industrial revolution, we've been in a technology. >> since the Manhattan project. >> Yeah. >> Or or you know, even going back to the. discovery of energy, right? The United. Kingdom was where the industrial. revolution was, if you will, invented. when they realized that they can turn. steam and such into into energy into. electricity.

All of that was invented largely in. Europe and the United States capitalized. on it. We were the ones that learned. from it. We industrialized it. We. diffused it faster than anybody in. Europe. They were all stuck in. discussions about. policy and. jobs and disruptions. Meanwhile, the. United States was forming. We just took. the technology and ran with it. And so I. I think we were always in in a bit of a.

technology race. World War II was a. technology race. Manhattan Project was a. technology race. We've been in the. technology race ever since during the. Cold War. I think we're still in a. technology race. It is probably the. single most important race. It is the. technology is uh it gives you. superpowers. you know whether it's information. superpowers or energy superpowers or. military superpowers is all founded in. technology and so technology leadership. is really important.

>> well the problem is if somebody else has. superior technology right that's that's. the issue it seems like with the AI race. people are very nervous about it like. you know Elon has famously said there. was like 80% chance it's awesome 20%. chance we're in trouble and people are. worried about that 20% % rightly so. I. mean that you know if you had 10 bullets. in a a a revolver and you know you you. took out eight of them and you still.

have tw two in there and you spin it, you're not going to feel real. comfortable when you pull that trigger. It's terrifying, >> right? >> And when we're working towards this. ultimate goal um of AI, it it just it's. impossible to imagine that it wouldn't. be of national security interest to get. there first. We should The question is what's there? That's the That was the part that. >> What is there? >> Yeah. I'm not sure. >> And I don't think anybody I don't think. anybody really knows. >> That's crazy though. If I ask you,

>> you're the head of Nvidia. If you don't. know what's there, who knows? >> Yeah. I I think it's probably going to. be much more gradual than we think. It. won't It won't be a moment. It won't be. It won't be as if um somebody arrived. and nobody else has. I don't think it's. going to be like that. I think it's. going to be things that just get better. and better and better and better just. like technology does. >> So, you are rosy about the future. You're you're very optimistic about. what's going to happen with AI. >> Obviously, will you make the best AI.

chips in the world? >> You probably better be. >> Uh h if history is a guide, um uh we. were always concerned about new. technology. Humanity has always been concerned about. new technology. There are always. somebody who's thinking there always a. lot of people who are quite concerned. were quite concerned and and and so if. if history is a guide, it is the case um. that all of this concern is channeled. into making the technology safer.

And so for example, in the last several. years, I would say AI technology has. increased probably in the last two years. alone, maybe a 100x. Let's just give it. a number, okay? It's like a car two. years ago was 100 times slower. So AI is. 100 times more capable today. Now, how. did we channel that technology? How do. we channel all of that power? We. directed it to um causing the AI to be.

able to think, meaning that it can take. a problem that we give it, break it down. step by step. It does research before it answers. And. so it grounds it on truth. It'll reflect on that answer. Ask. itself, is this the best, you know, answer that I can give you. Am I certain. about this answer? If it's not certain. about the answer or highly confident. about the answer, it'll go back and do. more research. It might actually even.

use a tool because that tool provides a. better solution than it could. hallucinate itself. As a result, we took. all of that computing capability and we. channeled it into having it produce a. safer result, safer answer, a more. truthful answer because as you know, one. of the greatest criticisms of AI in the. beginning was that it hallucinated, >> right? >> And so if you look at the reason why. people use AI so much today is because. the amount of hallucination has reduced.

You know, I use it almost I well I used. it the whole trip over here and so so I. think the. the uh the the capability most people. think about power. and they think about you know maybe as. an explosion power but the technology. power most of it is channeled to towards. safety. A car today is more powerful but. it's safer to drive. A lot of that power. goes towards better handling. You know, I'd rather have a Well, you have a 1000.

horsepower truck. I think 500 horsepower. is pretty good. No, I thousand's better. I think a th00and is better. >> I don't know if it's better, but it's. definitely faster. >> Yeah. No, I think it's better. You can. get out of trouble faster. Um, I enjoyed my 599 more than my 612. It. was I think it was a better better. horsepower is better. My 459 is better. than my 430. more horsepower is better. I I think. more horsepower is better. I think it's.

better handling. It's better control. In. the case of in the case of technology, it's also very similar in that way, you. know. And so if you if you look at what. we're going to do with the next thousand. times of performance in AI, a lot of it. is going to be channeled towards more. reflection, more research, thinking about the answer more deeply. So when you're defining safety, you're. defining a it as accuracy, >> functionality. >> Functionality. Okay. >> It it does what you expect it to do. And.

then you take all the the the technology. in the horsepower, you put guard rails. on it, just like our cars. We've got a. lot of technology in in a car today. A. lot of it is goes towards, for example, ABS. ABS is great. And so, uh, traction. control, that's fantastic. without a. without a computer in the car, how would. you do any of that, >> right? >> And that little computer, the computers. that you have doing your traction. control is more powerful than the. computer that went to Apollo 11. And so.

you want that technology, channel it towards safety, channel it. towards functionality. And so when. people talk about power, the advancement. of technology, often times I I I feel. what they're thinking and what we're. actually doing is very different. >> Well, what do you think they're. thinking? Well, they're thinking somehow. that this this uh this AI is being. powerful and their their mind probably. goes towards a sci-fi movie. The. definition of power, you know, often.

times the definition definition of power. is military power or physical power. But. in in the case of technology power when. we translate all of those operations. it's towards more refined thinking you. know more reflection more planning more. options. >> I think the big fears that people have. is one a big fear is military. applications that's a big fear. >> because people are very concerned that. you're going to have. >> AI systems that make decisions that. maybe an ethical person wouldn't make or.

a moral person wouldn't make based on. achieving an objective versus based on, you know, how it's going to look to. people. >> Well, I'm I'm happy that that uh our. military is going to use AI technology. for defense and I think that that um uh. Andural uh building military technology. I'm happy to hear that. I'm happy to see. um all these tech startups now. channeling their technology capabilities. towards defense and military.

applications. I think you needed to do. that. >> Yeah, we had Palmer Lucky on the. podcast. He was demonstrating some of. the stuff I put his helmet on. And we. show we he showed some videos how you. could see behind walls and stuff like. it's nuts. >> And he's he's actually the perfect guy. to go start that company. >> 100%. [laughter] Yeah. 100%. It's like. he was born for that. Yeah. He came in. here with a copper jacket on. He's a. freak. [clears throat] It's [laughter]. awesome. He's awesome. But it's also. it's a you know an unusual intellect. channeled into that very bizarre field.

is what you need, you And I think it's. it's uh I think I'm happy that we're. making it so more socially acceptable. You know, there was a time where when. somebody wanted to channel their. technology capability and their. intellect into defense technology, uh. somehow they're vilified. Um but uh we. need people like that. We need people. who enjoyed enjoy that part of uh. application of technology. >> Well, people are terrified of war, you. know. So it depends. >> Best way to avoid it has excessive.

military might. >> Do you think that's absolutely the best. way? Not not diplomacy, not working. stuff out. >> All of it. >> All of it. You have to have military. might in order to get people to sit down. with you. >> Right. Exactly. All of it. >> Otherwise, they just invade. >> That's right. [laughter] Why ask for. permission? >> Again, like you said, history. Go back. and look at history. Um, when you look. at the future of AI and and you just. said that no one really knows what's. happening, do you ever sit down and.

ponder scenarios? >> Like what do you what do you think is. like bestcase scenario for AI over the. next two decades? Um. the best case scenario is that AI. diffuses into everything that we do and. uh our. everything's more efficient but. the threat of war remains a threat of.

war. Uh, cyber security remains. a super difficult challenge. Somebody is going to try to. breach your security. You're going to. have thousands of millions of AI agents. protecting you from that threat. Your technology is going to get better. Their technology is going to get better. Just like cyber security. Right now, while we speak, we're being.

we're seeing cyber attacks all over the. planet on just about every front door. you can imagine. And. and yet you and I are sitting here. talking. And so the reason for that is. because we know that there's a whole. bunch of cyber security technology in. defense. And so we just have to keep. amping that up, keep stepping that up. This episode is brought to you by. Visible. When your phone plans as good. as visible, you've got to tell your.

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save beyond the season. It's a deal so. good you're going to want to tell your. people. Switch now at visible.com/rogan. Terms apply. Limited time offers subject. to change. See visible.com for planned. features and network management details. That's a big issue with people is the. the worry that technology is going to. get to a point where encryption is going. to be obsolete. Encryption is just it's. no longer going to protect data. It's no. longer going to protect systems. Do you. anticipate that ever being an issue or.

do you think there's it's as the defense. grows, the threat grows, the defense. grows, and it just keeps going on and on. and on and they'll always be able to. fight off any sort of intrusions? >> Not forever. some intrusion will get in. and then that we'll all learn from it. And you know the reason why cyber. security works is because of course the. technology of defense is advancing very. quickly. The technology offense is. advancing very quickly. However, the.

benefit of the cyber security defense is. that socially the community all of our. companies work together as one. Most. people don't realize this. There's a whole community of cyber. security experts. We exchange. ideas. We exchange best practices. We. exchange what we detect. The moment. something has been breached or maybe.

there's a loophole or whatever it is, it. is shared by everybody. The patches are. shared with everybody. >> That's interesting. >> Yeah. Most people don't realize this. >> No, I had no I had no idea. I've assumed. that it would just be competitive like. everything else. >> We work together. Interesting. Has that. always been the case? >> Uh, it surely has been the case for. about about 15 years. It might not have. been the case long ago, but this this. >> what do you think started off that. cooperation? >> Um, people recognizing it's a challenge. and no company can stand alone.

>> And the same thing is going to happen. with AI. I think we all have to decide. work working together uh to stay out of. harm's way is is our best chance for. defense. Then it's basically everybody. against the threat. >> And it also seems like you'd be way. better at detecting where these threats. are coming from and neutralizing them. >> Exactly. Because the moment you detect. it somewhere, >> you're going to find out right away. >> It'll be really hard to hide. >> That's right. >> Yeah. >> That's how it works. That's the reason.

why it's safe. That's why I'm sitting. here right now instead of, you know, locking everything down in video. [laughter]. >> It's not only am I watching my own back, I've got everybody watching my back. and. I'm watching everybody else's back. >> It's a bizarre world, isn't it? When you. think about that cyber threat, >> this idea about cyber security is. unknown to the people who are talking. about AI threats. They're I think when. they think about AI threats and AI cyber. security threats, they have to also. think about how we deal with it today. Now, there's no question that AI is a.

new technology. and it's a new type of software. In the. end, it's software just it's a new type. of software and so it's going to have. new capabilities but so will the defense. you know where you use the same AI. technology to go defend against it. So. you do you anticipate a time ever in the. future where it's going to be impossible. where there's not going to be any. secrets where the bottleneck between the.

technology that we have and the. information that we have. Information is. just all a bunch of ones and zeros. It's. out there on hard drives and the. technology has more and more access to. that information. Is it ever going to. get to a point in time where there's no. way to keep a secret? >> I don't think. >> because it seems like that's where. everything is kind of headed in a weird. way. >> I don't think so. I think the quantum. computers were supposed to will Yeah. quantum computers will make it possible. will make it so that the previous. quantum previous encryption technology. is obsolete. But that's the reason why.

the entire industry is working on. postquantum. encryption technology. >> What would that look like? >> New algorithms. >> But the crazy thing is when you hear. about the kind of computation that. quantum computing can do. >> Yeah. >> And the the power that it has. Yeah. >> Where you know you're looking at. >> all the supercomputers in the world. It. would take billions of years and it. takes them a few minutes to solve these. equations. Like how do you make. encryption for something that can do. that? I'm not sure, but there's. [laughter].

but I've got a bunch of scientists who. are working on that. >> Boy, I hope they [snorts] could figure. it out. >> Yeah, we got a bunch of scientists who. are expert in that. And. >> is the ultimate fear that it can't be. breached that quantum computing will. always be able to to decrypt all other. quantum computing encryption? >> I don't think that. >> it just gets to some point where it's. like, stop playing the stupid game. We. know everything. >> I don't think so. >> No, >> because I I'm you know, history is. guide. History is a guide before AI came. around. That's my worry. My worry is.

this is a totally, you know, it's like. history was one thing and then nuclear. weapons kind of changed all of our. thoughts on war and mutually assured. destruction came. everybody to stop using nuclear bombs. >> Yeah. >> My worry is that. >> the thing is Joe is that that AI is not. going to it's not like we're cavemen and. then all of a sudden one day AI shows. up. every single day we're getting. better and smarter because we have AI. and so we're stepping on our own AI's. shoulders. So when when that whatever.

that AI threat comes, it's a click. ahead. It's not a galaxy ahead, >> you know, it's just a click ahead. And. so so I think I think the the the idea. that somehow this AI. is going to pop out of nowhere and. somehow think in a way that we can't. even imagine thinking and do something. that we can't possibly imagine I think. is far-fetched. And the reason for that.

is because we're all have we all have. AIs and you know there's a whole bunch. of AIs being in development. we know. what they are and we're using it and and. so every single day we're getting we're. close to each other. >> But don't they do things that are very. surprising? >> Yeah. But so you you have an AI that. does something surprising. I'm going to. have an AI and my AI looks at your AI. and goes that's not that surprising. >> The fear for the lay person like myself. is that AI becomes sentient and makes. its own decisions. and then ultimately decides to just.

govern the world. do it its own way. They're like, "You guys, you had a good. run, but. >> we're taking over now.". >> Yeah, but my my AI is gonna take care of. me. I mean, [laughter]. so that's the this is the cyber security. argument. >> Yes. >> Do you have an AI and it's super smart, but my AI is super smart, too. And and. maybe your AI. Let let's pretend let's. let's pretend for a second that we.

understand what consciousness is and we. understand what sentience is and and. that in fact. >> and we really are just pretending. >> Okay, let's just pretend for a second. that we we believe that. I don't believe. actually I don't actually don't believe. that but nonetheless we let's pretend we. believe that. >> So your your your AI is conscious and my. AI is conscious and and let's say your. AI is you know wants to I don't know do. something surprising. My AI is so smart that it won't it might. be surprising to me, but it probably. won't be surprising to my AI. And so.

maybe my AI. thinks it's surprising as well, but it's. so smart the moment it sees it the first. time, it's not going to be a surprise. the second time, just like us. And so I. feel like I think the idea that that. only one person has [clears throat] AI. and that one person's AI is compares. everybody else's AI is Neanderthal. [snorts] is um probably unlikely. I. think it's much more like cyber. security.

>> Interesting. >> I think the fear is not that your AI is. going to battle with somebody else's AI. The fear is that AI is no longer going. to listen to you. That's the fear is. that human beings won't have control. over it after a certain point if it. achieves sensience and then has the. ability to be autonomous. >> that there's one AI. >> Well, they just combine. >> Yeah. Becomes one AI. >> that it's a life form. >> Yeah. >> But that's the there's arguments about. that, right? That we're dealing with. some sort of synthetic biology that it's.

not as simple as new technology that. you're creating a life form. >> If it's like life form, let's go along with that for a while. I. think if it's like life form, as you. know, all life forms don't agree. And so. I'm going to have to go with your life. form and my life form are going to agree. because my life form is going to want to. be the super life form. And and now that. now that we have disagreeing life forms, uh we're back back again to where we. are. Well, they would probably cooperate. with each other.

It would just the reason why we don't. cooperate with each other is we're. territorial primates. But AI wouldn't be a territorial. primate. It would realize the folly in. that sort of thinking and it would say, "Listen, there's plenty of energy for. everybody. We we don't need to dominate. We don't need We're not trying to. acquire resources and take over the. world. We're not looking to find a good. breeding partner. We're just existing as. a new super life form that these cute.

monkeys created for us.". Okay. Well, that would be a that would. be a um a superpower with no ego, >> right? And and if it has no ego, why would it have the ego to do any harm. to us? >> Well, I don't assume that it would do. harm to us, but the the fear would be. that we would no longer have control and. that we would no longer be the apex.

species on the planet. this thing that. we created would now be. [laughter]. >> Is that funny? >> No. >> I just think it's not gonna happen. >> I know you think it's not gonna happen, but. >> it could, right? And here's the other. thing is like. >> if we're racing towards could Yeah. >> And could could be the end of human. beings being in control of our own. destiny. >> I just think it's extremely unlikely. >> Yeah. >> That's what they said in the Terminator. movie [laughter]. >> and it hasn't happened. >> No, not yet. But you guys are working.

towards it. Um the the thing about. you're saying about conscience and. sensience that you don't think that AI. will achieve consciousness or that the. question is what's the definition? >> Yeah. What's the definition of. >> what is the definition to you? >> Um uh. consciousness. um. uh f I guess first of all uh you need to. know about your own existence.

Um, you have to have experience, not just. knowledge and intelligence. The concept of a machine. having an experience. I'm not well, first of all, I don't know. what defines experience, why we have. experiences, right? >> Yeah. and why this microphone doesn't.

uh and so it I think I know I well I. think I I I think I know what. consciousness is the sense of experience. the ability to know self versus. um. uh the ability to be able to reflect. know our own self the sense of ego I. think all of all of those human. experiences. uh probably is what consciousness is.

but why it exists versus. the concept of knowledge and. intelligence which is what AI is defined. by today [clears throat] it has. knowledge it has intelligence artificial. intelligence we don't call it artificial. consciousness. artificial intelligence the ability to. uh perceive believe, recognize, understand,

um, plan, uh, perform tasks. Those things are foundations of. intelligence. to know things, knowledge. I don't, it's clearly different than. consciousness. >> But consciousness is so loosely defined. How can we say that? I mean, doesn't a. dog have consciousness? Yeah. >> Dogs seem to be pretty conscious. >> That's right. >> Yeah. So, and that's a lower level. consciousness than a human being's. consciousness.

>> I'm not sure. Yeah. Right. Well, >> the question is what lower level. intelligence? It's lower level. intelligence, but I don't know that it's. lower level consciousness. >> That's a good point. Right. >> Because I believe my dogs feel as much. as I feel. >> Yeah. They feel a lot. Right. >> Yeah. They get attached to you. That's. right. They get depressed if you're not. there. >> That's right. Exactly. >> There's There's definitely that. >> Yeah. um the the concept of experience, >> right? >> Um but isn't AI interacting with. society? So, doesn't it acquire.

experience through that interaction? >> Um I don't think interactions is. experience. I think experience is uh. experience is a collection of feelings. I think. >> you're aware of that AI um I forget. which one where they gave it some false. information about one of the programmers. having an affair with his wife just to. see how it would respond to it and then. when they said they were going to shut. it down it threatened to blackmail him. and reveal his affair and it was like.

whoa like it's conniving like if that's. not learning from experience and being. aware that you're about to be shut down. which would imply at least some kind of. consciousness or you could kind defined. it as consciousness if you were very. loose with the term and if you imagine. that this is going to exponentially. become more powerful. Wouldn't that. ultimately lead to a different kind of. consciousness than we're defining from. biology? Well, first of all, let's just. break down what it probably did. It.

probably read somewhere. There's. probably text that that in these. consequences. certain people did that. I could imagine. a novel, >> right? >> Having those words related. >> Sure. >> And so inside. >> it realizes it strategy for survival is. >> it's just a bunch of numbers. >> that it's just a bunch of numbers that. that in the in the collection of numbers. that relates to a husband cheating on a.

wife. Um. has subsequently a bunch of numbers that. relates to blackmail and such things. However, whatever the revenge was, >> right? >> And so it has spewed it out. >> And so it's just like, you know, it it's. just as if I'm asking it to write me a. poem in Shakespeare. It just whatever. the words are in the world in in that. dimensionality, this dimensionality is. all these vectors and in in. multi-dimensional space. These words.

that were in the prompt that described. the affair um subsequently led to one. word after another led to um you know. some revenge and something but it's not. because it had consciousness or you know. it just spewed out those words generated. those words. >> I understand what you're saying that. patterns that human beings have. exhibited both in literature and in real. life. >> that's exactly right. >> but it at a certain point in time one.

would say, "Okay, well, it couldn't do. this two years ago and it couldn't do. this four years ago." Like when we're. looking towards the future, like at what. point in time when it can do everything. a person does, what point in time do we. decide that it's conscious? If it. absolutely mimics all human thinking and. behavior patterns, >> that doesn't make it conscious. >> It becomes in disccernible. It's it's. aware. It can communicate with you the. exact same way a person can. Like is con. is consciousness are we putting too much. weight on that concept because it seems.

like it's a version of a kind of. consciousness. >> It's a version of imitation. >> Imitation consciousness, right? But if. it perfectly imitates it, >> I still think it's a per it's an example. of imitation. >> So it's like a fake Rolex when they 3D. print them and make them. >> indestruable. The question is what's the. definition consciousness? >> Yeah. >> Yeah. >> That's the question. And I don't think. anybody's really clearly defined that. That's what get where it gets weird and. that that's where the real doomsday. people are worried that you are creating. a form of consciousness that you can't.

control. I believe it is possible to. create a machine. that imitates. human intelligence. and. has the ability to. understand information, understand. instructions, break the problem down, solve problems, and perform tasks. I. believe that completely.

I believe that that um we could have a. computer that has a vast amount of. knowledge. Some of it true, some of it. not true. Some of it generated by humans, some of. it generated synthetically. And more and. more of knowledge in the world will be. generated synthetically going forward. You know, until now the knowledge that. we've we have are knowledge that we.

generate and we propagate and we send to. each other and we amplify it and we add. to it and we modify it. We change it. In. the future, in a couple of years, maybe two or three. years, 90% of the world's knowledge will. likely be generated by AI. >> That's crazy. >> I know. But it's just fine. >> But it's just fine. >> I know. And the reason for that is this. Let me tell you why. >> Okay? >> It's because um what difference does it.

make to me that I am learning from a. textbook that was generated by a bunch. of people I didn't know or written by a. book that you know from somebody I don't. know uh to uh knowledge generated by AI. computers that are assimilating all of. this and reynthesizing things. To me, I. don't think there's a whole lot of. difference. We still have to we still. have to fact check it. We still have to. make sure that it's you know based on. fundamental first principles and we.

still have to do all of that just like. we do today. >> Is this taking into account the kind of. AI that exists currently? And do you. anticipate that just like we could have. never really believed that AI would be. at least a person like myself would. never believe AI would be as so. ubiquitous and so worth it. It's it's so. powerful today and so important today. We never thought that 10 years ago. Never thought that, >> right? >> You imagine like what are we looking at. 10 years from now?

>> I I think that if you reflect back 10. years from now, you would say the same. thing that we would have never believed. that. >> but. >> in a different direction, >> right? But if you if you go forward 9. years from now. and then ask yourself what's going to. happen 10 years from now, I think it'll. be quite gradual. Um, one of the things. that Elon said that makes me happy is he. he's he believes that we're going to get. to a point where it's not.

it's not necessary for people to work. and not meaning that you're going to. have no purpose in life, but you will. have in his words universal high income. because so much revenue is generated by. AI that it will take away this need for. people to do things that they don't. really enjoy doing just for money. And I. think a lot of people have a problem. with that because their entire identity. and who how they think of themselves and.

how they fit in the community is what. they do. Like this is Mike. He's an. amazing mechanic. Go to Mike and Mike. takes care of things. But there's going. to come a point in time where AI is. going to be able to do all those things. much better than than people do. And. people will just be able to receive. money. But then what does Mike do? Mike. is, you know, really loves being the. best mechanic around. You know, what. does the guy who, you know, codes, what does he do when AI can code. infinitely faster with zero errors? Like.

what what happens with all those people? And that is where it gets weird. It's. like because we've sort of wrapped our. identity as human beings around what we. do for a living. >> You know, when you meet someone, one of. the first things you meet somebody at a. party, hi Joe. What's your name? Mike. What do you do? Mike and you know Mike's. like, "Oh, I'm a lawyer." "Oh, what kind. of law?" And you have a conversation, you know, when Mike is like, "I get. money from the government. I play video. games.". >> Gets weird. >> Mhm. >> And I think um the concept sounds great.

until you take into account human. nature. And human nature is that we like. to have puzzles to solve and things to. do and and an identity that's wrapped. around our idea that we're very good at. this thing that we do for a living. >> Yeah. Yeah, I think um let's see, let me. start with the more mundane and I'll. work work backwards, okay? Work forward. Uh so one of the predictions from uh.

Jeff Hinton who who started the whole. deep learning phenomenon the deep. learning technology trend. and uh in incredible incredible. researcher uh professor at University of. Toronto. uh he invented discovered or invented. the the idea of of back propagation. which which uh allows the neural network. to learn. And um.

and as as as you know uh for for the. audience, software historically was humans. applying first principles and our. thinking to uh describe an algorithm. that is then codified just like a recipe. that's codified in software. It looks. just like a recipe. how to cook. something looks exactly the same just in. a slightly different language. We call. it Python or C or C++ or whatever it is.

In the case of deep learning, this. invention of artificial intelligence, we put a structure of a whole bunch of. neural networks and a whole bunch of. math units. and we make this large structure. It's. like a switchboard of little. u mathematical units and we connect it. all together. Um, and we give it the input that.

the software would eventually receive. and we just let it randomly guess what. the output is. And so we say, for. example, the input could be a picture of. a cat. And and um one of the outputs of the. switchboard is where the cat signal is. supposed to show up. And all of the. other signals, the other one's a dog, the other one's an elephant, the other. one's a tiger.

And all of the other signals are. supposed to be zero when I show it a. cat. And the one that is a cat should be. one. And I show at a cat through this big. huge network of switchboards and math. units and they're just doing multiply. and adds multiplies and ads. Okay? And and uh and this thing, this. switchboard is gigantic. The more information you're going to. give it, the more the bigger this.

switchboard has to be. And what Jeff. Hinton discovered was a invented was a. way for you to. guess that put the cat signal in put the. cat image in and that cat image you know. could be a million numbers because it's. you know a megapixel image for example. and it's just a whole a whole bunch of. numbers and somehow from those numbers. it has to light up the cat signal. Okay, that's the bottom line. And if it the.

first time you do it, it just comes up. with garbage. And so it says the right. answer is cat. And so you need to. increase this signal and decrease all of. the other and back propagates the. outcome through the entire network. And. then you show another. Now it's an image. of a dog and it guesses it takes a swing. at it and it comes up with a bunch of.

garbage and you say no no no the answer. is this is a dog I want you to produce. dog and all of the other switch all the. other outputs have to be zero and I want. to back propagate that and just do it. over and over and over again. It's just. like uh showing a a kid this is an. apple, this is a dog, this is a cat. And. you just keep showing it to them until. they eventually get it. Okay. Well, anyways, that big invention is deep. learning. That's the foundation of. artificial intelligence, a piece of.

software. that learns from examples. That's. basically we machine learning, a machine. that learns. Uh and so so one of the the. big. first. applications was image recognition and. one of the most important image. recognition applications is radiology. >> And so so uh uh he predicted uh about 5. years ago that in five years time the.

world won't need any radiologists. because AI would have swept the whole. field. Well, it turns out AI has swept the. whole field. That is completely true. Today, just about every radiologist is. using AI in some way. And what's ironic. though, what's what's interesting is. that the number of radiologist has. actually grown. And so the question is why? That's kind.

of interesting, right? >> It is. And so the prediction was in fact. that. 30 million radiologists will be wiped. out. But as it turns out, we needed more. And. the reason for that. [clears throat and cough]. is because the purpose of a radiologist. is to diagnose disease, not to study the image. This the image. studying is simply a task to in service. of diagnosing the disease. And so now.

the fact that you could study the images. more quickly and more precisely. without ever making a mistake and never. gets tired. You could study more images. You could. study it in. 3D form instead of 2D because you know. the AI doesn't care whether it studies. images in 3D or 2D. You could study it. in 4D. And so the now you could study. images in a way that radiologist. radiologists can't easily do and you.

could study a lot more of it. And so the. number of tests that people are able to. do increases and because they're able to. serve more patients, the hospital does. better. They have more clients, more. patients. As a result, they have better. economics. When they have better. economics, they hire more radiologists. because their purpose is not to study. the images. their purpose is to diagnose. disease. And so the question is the what. I'm leading up to is ultimately what is.

the purpose? What is the purpose of the. lawyer? And has the purpose changed? What is the purpose? You know, one of. the examples that I gave is is um that I. would give is for example uh if my car. became self-driving. will all chauffeers be out of jobs? The. answer probably is not because for some. per for some chauffeers they for some. people who are driving you they could be. protectors some people um they're part. of the experience part of the service so.

when you get there they you know they. could take care of things for you and so. for a lot of different reasons not all. chauffeers would lose their jobs some. chauffeers would lose their jobs and uh. many chauffeers would change their jobs. and the type of applications of. autonomous vehicles will probably. increase you know the usage of the. technology within find new homes and so. I I think you have to go back to what is. the purpose of a job you know like for. example if AI comes along I actually.

don't believe I'm going to lose my job. because my purpose isn't to I have to. look at a lot of documents I study a lot. of emails I look at a bunch of diagrams. you know um the question is what is the. job and and uh the purpose of somebody. probably hasn't changed a lawyer for. example help people that probably hasn't. changed studying legal documents. generating documents it's part of the. job not the job. >> but don't you think there's many jobs. that AI will replace.

>> if your job is automation. >> yeah if your job is the task. >> right so automation. >> yeah factor if your job is the task. >> that's a lot of people. >> it could be a lot of people but it'll. probably generate like for example. >> uh let's say we let's say I'm super. excited about the the the robots Elon's. working on. It's still a few years away. When it happens, when it happens, um.

there's a whole new industry of. technicians and people who have to. manufacture the robots, right? >> Mhm. >> And so that that job never existed. And. so you're going to have a whole industry. of people taking care of like for. example, you know, all the mechanics and. all the people who are building things. for cars, supercharging cars, uh that. didn't exist before cars and now we're. going to have robots. You're going to. have robot apparel. So a whole industry. of [laughter] Right. Isn't that right?

Because I want my robot to look. different than your robot. >> Oh god. >> And so [laughter] you're going to you're. going to have a whole, you know, apparel. industry for robots. You're going to. have mechanics for robots and you have. you know people who comes and maintain. your robots. >> automated though. >> No, >> you don't think so? You don't think. [clears throat] they'll be all done by. other robots. >> eventually? And then there'll be. something else. >> So you think ultimately people just. adapt except if you are the task. >> which is a large percentage of the. workforce. >> If your job is just to chop vegetables,

quezin art is going to replace you. >> Yeah. So people have to find meaning in. other things. Your job has to be more. than the task. >> What do you think about Elon's belief. that this universal basic income thing. will eventually become necessary? >> Many people think that. Andrew Yang. thinks that. >> he was one of the first people to sort. of sound that alarm during the the 2020. election.

Yeah, I I guess um. yeah, both ideas probably won't exist at. the same time and and um as in life, things will probably be in the middle. One idea, of course, is that there'll be. so much abundance of resource that. nobody needs a job and we'll all be. wealthy. On the other hand, um we're going to. need universal basic income. Both ideas. don't exist at the same time, >> right?

>> And so we're either going to be all. wealthy or we're going to be all. >> How could everybody be wealthy though? But. >> because scenario wealthy not because you. have a lot of dollars, wealthy because. there's a lot of abundance. Like for. example, today we are wealthy of. information. You know, this is some a concept several. thousand years ago only a few people. have. And so, uh, today we have wealth. of a whole bunch of things, resources. that that historic point. Yeah. And so, we're going to have wealth of resources, things that we think are valuable today.

that in the future are just not not that. valuable, you know, and so it because. it's automated. And so I think I think. the question. maybe maybe partly it's hard to answer. partly because. it's hard to talk about infinity and. it's hard to talk about a long time from. now and and the reason for that is. because. there's just too many scenarios to to. consider. But I think it I think in the.

next several years, call it 5 to 10. years, there are several things that I I. believe in hope. Um, and I say hope. because I'm not sure. One of the things. that I believe is that the technology. divide will be substantially collapsed. And of course the alternative. viewpoint is that AI is going to. increase the technology divide.

Now the reason why I believe AI is going. to reduce the technology divide. I is because we have proof. the evidence is that AI is the easiest. application in the world to use. Chat. GPT has grown to almost a billion users. frankly practically overnight. And if. you're not exactly sure how to use, everybody knows how to use chatpt. Just. say something to it. If you're not sure. how to use chatpt, you ask chatd how to. use it. No tool in history has ever had.

this capability. A quez an art, you. know, if you don't know how to use it, you're kind of screwed. You're going to. walk up to it and say, "How do you use a. quezin art?" You're going to have to. find somebody else. And so, but an AI. will just tell you exactly how to do it. Anybody could do this. It'll speak to. you in any language. And if it doesn't. know your language, you'll speak it in. that language and it'll probably figure. out that it doesn't completely. understand your language. Go learns it. instantly and comes back and talk to. you. And so I think the the technology.

divide has a real chance finally that. you don't have to speak Python or C++ or. forran. You can just speak human and. whatever form of human you like. And so. I think that that has a real chance of. closing the technology divine. Now, of. course, the counternarrative would say. that. AI is only going to be available for the. nations and the countries that have a.

vast amount of resources because AI. takes energy. and AI takes um a lot of GPUs and. factories to be able to produce the AI. No doubt at the scale that we would like. to do in the United States. But the fact. of the matter is your phone's going to. run AI just fine all by itself, you. know, in a few years. Today, it already. does it fairly decently. And so the the. the fact that every every country, every. nation, every every society will have.

the benefit of very good AI. It might. not be tomorrow's AI. It might be. yesterday's AI, but yesterday's AI is. freaking amazing. You know, in 10 years. time, 9year-old AI is going to be. amazing. You don't need, you know, 10. year old AI. You don't need frontier AI. like we need frontier AI because we want. to be the world leader. But for every. single country, everybody, I think the. ele the capability to elevate. everybody's knowledge and capability and. intelligence, uh, that day is coming.

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>> Almost everything is going to be energy. constrained. And so if you take a look. at um. one of the most important technology. advances in history is this idea called. Moore's law. Moore's law. was the started basically in my. generation. and my generation is the generation of. computers. I graduated in 1984 and that. was basically at the very beginning of. the PC revolution.

And the microprocessor and and um. every single year it approximately. doubled. and we describe it as every single year. we double the performance. But what it. really means is that every single year. the cost of computing halfed. And so the cost of computing in the. course of five years reduced by a factor. of 10. The amount of energy necessary to.

do computing to do any task reduced by a. factor of 10. Every single 10 years 100. a th00and 10,000. 100,000 so on and so forth. And so each. one of. the clicks of Moore's law, the amount of. energy necessary to do any computing. reduced. That's the reason why you have. a laptop today when back in 1984 sat on. the desk, you got to plug in, it wasn't. that fast and it consumed a lot of.

power. Today, you know, it is only a few. watts. And so Moore's law is the. fundamental technology, the fundamental. technology trend that made it possible. Well, what's going on in AI? The reason. why Nvidia is here is because in we. invented this new way of doing. computing. We call it accelerated. computing. We started it 33 years ago. Took us about 30 years to really made a. huge breakthrough. In that in that 30. years or so. we took computing you know probably a.

factor of well let me just say in last. 10 years the last 10 years we improved. the performance of computing by 100,000. times. Whoa. Imagine a car over the course of. 10 years that became a 100,000 times. faster or at the same speed 100,000. times cheaper or at the same speed. 100,000 times less energy. If your car. did that, it doesn't need energy at all.

What I mean what what I'm trying to say. is that in 10 years time the amount of. energy necessary for artificial. intelligence for most people will be. minuscule. utterly minuscule and so we'll have AI. running in all kinds of things and all. the time because it doesn't consume that. much energy and so if you're a nation. that uses AI for you know almost. everything in your social fabric of. course you're going to need these AI. factories but for a lot of countries I. think you're going You're going to have.

excellent AI and you're not going to. need as much energy. Everybody will be. able to come along is my point. >> So currently that that is a big. bottleneck, right? Is energy. >> Yeah, it is the bottleneck. >> The bottleneck is this. So was it Google. that is making nuclear power plants to. operate one of its AI factories? >> Oh, I haven't heard that. But I think in. the next six, seven years, I think. you're going to see a whole bunch of. small nuclear reactors. >> And by small, like how big are you. talking about? Hundreds of megawws.

Yeah. >> Okay. And that these will be local to. whatever specific company they have. >> That's right. Will all be power. generators. >> Whoa. >> You know, just like just like your you. know, somebody's farm. >> It probably is the smartest way to do. it, right? >> And it takes the burden off Yeah. takes. the burden off the grid. It takes and. you could build as much as you need. >> and you can contribute back to the grid. It's a really important point that I. think you just made about Moore's law. and the relationship to pricing because.

you know a laptop today like you can get. one of those little Mac MacBook Airs. They're incredible. They're so thin, unbelievably powerful. Battery life is. charge it. >> Yeah. Battery [laughter] life's crazy. And uh it's not that expensive. relatively speaking. Like something like. that. >> I remember. >> And that's just Moore's law, right? >> Then there's the Nvidia law. >> Oh, >> just right. the the the law I was. talking to you about, the computing that. we invented, >> right? >> The reason why we're here, this new this. new way of doing computing. >> is like Mo's law on energy drinks. I.

mean, it's [laughter]. it's like Mo's law. it's it's like Yeah. Moore's law and Joe. Rogan. >> Wow. That's interesting. >> Yeah. That's us. >> So, explain that. Um this this chip that. you brought to Elon, what what's the. significance of this? It's like why is. it so superior? And so. in 2012, Jeff Hinton's lab, this. gentleman I was talking talking about, um Ilas Suscober, Alex Kresevski, um.

they made a breakthrough in computer. vision in literally creating a. piece of software. called Alexnet. And its job was to recognize images. And. it recognized images. at a c at a level computer vision which. is fundamental to intelligence. If you. can't perceive, you can't it's hard to. have intelligence. And so computer.

vision is a fundamental pillar of not. the only but fundamental pillar of. And. so breaking. computer vision or breaking through in. computer vision is pretty foundational. to almost everything that everybody. wants to do in AI. And so in 2012, their lab in Toronto. uh made this made this breakthrough. called Alexnet. And Alexet was able to. recognize images. so much better than any human created.

computer vision algorithm in the 30. years prior. So all of these people, all. these scientists and we had many too. working on computer vision algorithms. and these two kids, Ilia and Alex under. the the uh. under under uh Jeff Hinton took a giant. leap above it and it was based on this. thing called Alexet this neural network.

And the way it ran, the way they they made it work was. literally buying two Nvidia graphics. cards. because Nvidia Nvidia's GPUs we've been. working on this new way of doing. computing and our GPUs application. and it's basically a supercomputing. application to back in 1984. in order to. process computer games and what you have.

in your racing simulator that is called. an image generator supercomputer. And so Nvidia started our first. application was computer graphics and we. applied this new way of doing computing. where we do things in parallel in. instead of sequentially. A CPU does. things sequentially. Step one, step two, step three. In our case, we break the. problem down and we give it to thousands. of processors.

And so our way of doing computation. is much more complicated. But if you're able to formulate the. problem in the way that we. created called CUDA, this is the. invention of our company. If you could. formulate it in that way, we could. process everything simultaneously. Now, in the case of computer graphics, it's easier to do because every single. pixel on your screen is not related to.

every other pixel. And so, I could. render multiple parts of the screen at. the same time. Not not completely true. because, you know, maybe maybe the way. lighting works or the way shadow works, there's a lot of dependency and and. such. But computer graphics with all the. dis with all the pixels, I should be. able to process everything. simultaneously. And so we we took. this embarrassingly parallel problem. called computer graphics and we applied. it to this new way of doing computing.

Nvidia's Nvidia's accelerated computing. We put it in all of our graphics cards. Kids were buying it to play games. We're. you probably don't know this, but we're. the largest gaming platform in the world. today. >> Oh, I know that. Oh, >> okay. >> I used to make my own computers. I used. to buy your graphics cards. >> Oh, that's super cool. >> Yeah. [laughter] set up SLI with two. graphics cards. >> Yeah, I love it. Okay, that's super. cool. >> Oh, yeah, man. I used to be a Quake. junkie. >> Oh, that's cool. >> Yeah. >> Okay, so SLI, I'll tell you the story in.

just a second and how it led to Elon. I'm still answering the question. And. so, anyways, these these two kids. trained this model using the technique I. described earlier on our GPUs because. our GPUs could process things in. parallel. It's essentially a supercomput. in a PC. The reason why you used it for. Quake is because it is the first. consumer supercomputer. Okay. And so. anyways, they made that breakthrough. We were.

working on computer vision at the time. It caught my attention. and so we went to learn about it. Simultaneously this deep learning. phenomenon was happening all over all. over the country. Universities after. another recognized the importance of. deep learning and all of this work was. happening at Stanford, at Harvard, at. Berkeley, just all over the place. New. York University, L Yan Lakun, Andrew. Yang at Stanford, so many different. places. And I see it cropping up.

everywhere. And so my curiosity asked, you know, what is so special about this form of. machine learning? And we've known about. machine learning for a very long time. We've known about AI for a very long. time. We've known about neural networks. for a very long time. What makes now the. moment? And so we realized that this. architecture for deep neural networks. back propagation the way deep neuronet. networks were created. We could probably.

scale this problem, scale the solution. to solve many problems. that is essentially. a universal function approximator. Okay? Meaning meaning you know back when. you're in in in school you have a you. have a you have a box inside of it is a. function you give it an input it gives. you an output and and the the reason why. I call it universal function. approximator. is that this computer instead of you.

describing the function a function could. be a new equation fals ma that's a. function you write the function in. software you give it input f mass. acceleration, it'll tell you the force. Okay? And. the way this computer works is really. interesting. You give it a universal function. It's. not fals, just a universal function. It's a big huge deep neural network.

and instead of describing the inside, you give it examples of input and output. and it figures out the inside. So you give it input and output and it. figures out the inside. A universal. function approximator. Today it could be. Newton's equation. Tomorrow it could be. Maxwell's equation. It could be Kulum's. law. It could be thermodynamics. equation. It could be you know. Shingers's equation for quantum physics. And so you could put any you could have.

this describe almost anything so long as. you have the input and the output. So. long as you have the input and the. output or it could learn the input and. output. >> And so we took a step back and we said, "Hang on a second. This isn't just for. computer vision. Deep learning could. solve any problem. All the problems that are interesting so. long as we have input and output. Now. what has input and output? Well, the world. The world has input and.

output. And so we could have a computer. that could learn almost anything. Machine learning, artificial. intelligence. And so we reasoned that. maybe this is the fundamental. breakthrough that we needed. There were. a couple of things that had to be. solved. For example, we had to believe. that you could actually scale this up to. giant systems. It was running in a they. had two graphics cards, two GTX 580s, [laughter]. which by the way is exactly your SLI.

configuration. Yeah. Okay. So, that GTX. 5880 SLI was the revolutionary computer. that put deep learning on the map. >> Wow. >> It was 2018 and you were using it to. play Quake. >> Wow. That's crazy. >> That was the moment. That was the big. bang of modern AI. We were lucky because. we were inventing this technology, this. computing approach. We were lucky that. they found it. Turns out they were gamers and it was.

lucky they found it. And it it was lucky. that we paid attention to that moment. It was a little bit like, you know, that. Star Trek, you know, first contact. The Vulcans had to have seen the warp. drive at that very moment. If they. didn't witness the warp drive, you know, they would have never come to Earth and. everything would have never happened. It's a little bit like if I hadn't paid. attention to that moment, that flash.

And that flash didn't last long. If I. hadn't paid attention to that flash or. our company didn't pay attention to it, who knows what would have happened, but. we saw that and we reasoned our way into. this is a this is a universal function. approximator. This is not just a. computer vision approximator. We could. use this for all kinds of things. if we. could solve two problems. The first. problem is that we have to prove to. oursel it could scale. The second. problem we had to.

wait for I guess contribute to and wait. for is. the world will never have enough data. on input and output where we could. supervise. the AI to learn everything. For example, if we have to supervise our children on. everything they learn, the amount of. information they could learn is limited. We needed the AI, we needed the computer. to have a method of learning without.

supervision. And that's where we had to wait a few. more years, but un unsupervised. AI learning is now here. And so the AI. could learn by itself. And and the. reason why the AI could learn by itself. is because we have many examples of. right answers. Like for example, if I want to learn uh if I want to teach. an AI how to predict the next word, I. could just grab it, grab a whole bunch. of text we already have, mask out the.

last word and make it try and try and. try again until it predicts the next. one. or I mask out random words inside. inside the text and I make it try and. try and try until it predicts it. You. know, like uh Mary uh Mary goes down to. the bank. Is it a river bank or a money. bank? Well, if you're going to go down. to the bank, it's probably a river bank. Okay. So, and it it it might not be. obvious even from that. It might need.

and. uh and uh and caught a fish. Okay. Now. you know it's must be the riverbank. And. so so you give you give these AIs a. whole bunch of these examples and you. mask out the words, it'll predict the. next one. Okay? And so unsupervised. learning came along. These two ideas, the fact that it's scalable and. unsupervised learning came along. We were convinced that we ought to put. everything into this and help create. this industry because we're going to.

solve a whole bunch of interesting. problems. And that was in 2012. By 2016, I had I had built this computer called. the DGX1. The one that you saw me give. to Elon is called DGX Spark. The DGX1. was $300,000. It cost Nvidia a few billion dollars to. make the first one. And instead of two chips SLI,

we connected eight chips with a. technology called MVLink, but it's. basically SLI supercharged. Okay. >> Okay. >> And so we connected eight of these chips. together instead of just two. And all of. them work together just like your Quake. rig did to solve this deep learning. problem to train this model. And so I. create we created this thing. I. announced it at GTC. and at one of our annual annual events.

and I described this deep learning. thing, computer vision thing and this. computer called DJX1. The audience was like completely silent. They had no idea what I was talking. about. [laughter]. And I was lucky because I I had known. Elon and uh uh I helped him build the. first computer for Model 3. uh uh the Model S. And uh and when he. wanted to start working on autonomous. vehicle, I helped him build the computer.

that went into the the Model S AV. system, his full full self-driving. system. We were basically the FSD. computer version one. And so. we we're already working together and um. when I announced this thing, nobody in. the world wanted it. I had no purchase. orders. Not not one. Nobody wanted to. buy it. Nobody wanted to be part of it. except for Elon. He goes, he was at the.

event and we were doing a fireside chat. about the future of self-driving cars. I think it's like 2016. Yeah, 20 maybe. at that time it was 2015. and he goes, "You know what? I have a company that could really use. this.". I said, "Wow, my first customer." And. so, so I was pretty excited about it. And he goes, "Uh, yeah. Uh, we have this. company. It's a nonprofit company.".

And all the blood drained out of my. face. Yeah. [laughter]. I just spent a few billion dollars. building this thing. Cost $300,000. and. you know the chances of a nonprofit. being able to pay for this thing is. approximately zero. And he goes, you. know, this is a it's an AI company and. uh it's a nonprofit and and uh we could. really use one of these supercomputers. And so I I picked it up. I built the. first one for ourselves. We're using it. inside the company. I boxed one up. I.

drove it up to San Francisco and I. delivered to Elon in 2016. A bunch of. researchers were were there. Peter Beiel was there, Ilia was there, and there was a bunch of people there. And uh I walk up to the second floor. where they were all kind of in a room. this smaller than your place here. And. and uh uh that place turned out to have. been open AI. >> 2016. >> Wow. >> Just a bunch of people sitting in a. room.

>> It's not really uh nonprofit anymore, though, is it? >> They're not They're not nonprofit. anymore. Yeah. >> Weird how that works. >> Yeah. Yeah. But anyhow, anyhow, Elon was. there. The Yeah, it was it was really a. great great moment. >> Oh, yeah. There you go. Yeah, that's it. [laughter]. >> Look at you, bro. Same jacket. >> Look at that. I haven't aged. >> Not not a lick of black hair, though. >> Uh the size of it is uh it's. significantly smaller. That was the. other day. SpaceX. >> Oh, yeah. There you go.

>> Yeah. Look at the difference. >> Exactly the same industrial design. He's. holding it in his hand. >> here. Here's the amazing thing. DJX1 was. one pedlops. Okay, that's a lot of. flops. And DJX Spark is one pedlops. Nine years later. >> Wow. >> The same the same amount of computing. horsepower. >> in a much smaller. >> shrunken down. Yeah. >> And instead of $300,000, it's now.

$4,000. And it's the size of a small. book. >> Incredible. >> Crazy. >> That's how technology moves. Anyways, that's the reason why I wanted to get. give him the first one. >> because I gave him the first one 2016. >> It's so fascinating. I mean you if you. wanted to make a story for a film I mean. that would be the story that like what. what better scenario if if if it really. does become a digital life form how. funny would it be that it is birthed out.

of the desire for computer graphics for. video games [laughter]. >> exactly. >> kind of cra it's kind of crazy. >> kind of crazy when you think about it. that way. >> because. it's just. >> perfect origin. Computer graphics was one of the hardest. computer supercomputer problems. generating reality. >> and also one of the most profitable to. solve because computer games are so. popular. >> When Nvidia started in 1993,

we were trying to create this new. computing approach. The question is. what's the killer app? And. the the problem we wanted to the the. company wanted to create a new type of. computing pro a computing architecture a. computing a a new type of computer that. can solve problems that normal computers. can't solve. Well, the applications that existed in the.

industry in 1993. are applications that normal computers. can solve because if the normal. computers can't solve them, why would. the application exist? And so, we had a mission statement for a. company that has no chance of success. [laughter]. But I didn't know that in 1993. It just. sounded like a good idea, >> right? And so if we created this thing that can.

solve problems, you know, it's like. you actually have to go create the. problem. And so that's what we did in 1993. There. was no quake. John Carmarmac hadn't been. reduced doom released Doom yet. You. probably remember that. >> Sure. Yeah. >> And and uh there were no applications. for it. And so I went to Japan because. the arcade industry had this at the time. of Sega, if you remember.

>> Sure. >> The arcade machines, they came out with. 3D arcade systems, virtual fighter, Daytona, Virtual Cop, all of those. arcade games were in 3D for the f very. first time. And the technology they were. using was from Martin Marietta, the. flight simulators. They took the guts. out of a flight simulator and put it. into an arcade machine. The system that. you have over here, it's got to be a.

million times more powerful than that. arcade machine. And that was a flight. simulator for NASA. Whoa. And so they. took the guts out of that. They were. they were using it for flight simulation. for jets and, you know, space shuttle. and and they took the guts out of that. and Sega uh had this brilliant computer. de developer. His name was Yuzuki. Yuzuki and Miiamoto. Sega and Nintendo.

These were the, you know, the incredible. pioneers, the visionaries, the. incredible artists, and they're both. very, very technical. They were the origins really of of the. gaming industry. and Y Suzuki. pioneered 3D graphics gaming and um. so I went we we created this company and. there were no apps. and we were spending all of our. afternoons you know we told our family.

we were going to work but it was just. the three of us you know who's going to. know and so we went to Curtis's my one. of one of the founders went to Curtis's. townhouse and uh Chris and I were. married we have kids I already had. Spencer at Madison. They were probably 2. years old. And um. and uh Chris's kids are about the same. age as ours. And we would go to work in. this townhouse. But you know, when. you're a startup and the mission.

statement is the way we described, you're not going to have too many. customers calling you. And so we had. really nothing to do. And so after. lunch, we would always have a great. lunch. After lunch, we would go to the. arcades and play the Sega V, you know, the Sega Virtual Fighter and Daytona and. all those games and analyze how they're. doing it, trying to figure out how they. they were doing that. And so we decided, um, let's just go to. Japan and let's.

convince Sega to move those applications. into the PC. and we would start the PC gaming the 3D. gaming industry partnering with Sega. That's how Nvidia started. >> Wow. >> And so so uh in exchange for them part. developing their games for our computers. in the PC, we would build a chip for. their game console. That was the. partnership. I build a chip for your.

game console. you port the Sega games to. us and um. and then they paid us a you know at the. time a quite a significant amount of. money to build that game console. and that was kind of the beginning of. Nvidia getting started and we thought we. were on our way and so so I started with. a business plan a mission statement that. was impossible we lucked into the Sega. partnership we started taking off.

started building our game console. And. about a couple years into it, we. discovered our first technology. didn't work. It was it it would have been a flaw. It. it was a flaw. And all of the technology. ideas that we had. the architecture concepts were were. sound, but the way we were doing. computer graphics was exactly backwards. you know, instead of. I won't bore you with the technology, but instead of inverse texture mapping,

we were doing forward texture mapping. Instead of triangles, we did curved. surfaces. So, other people did it flat, we did it round. Um, other technology, the technology that. ultimately won, the technology we use. today has has Zbuffers. It automatically. sorted. We had an architecture with no Zbuffers. The application had to sort it. And so. we chose a bunch of technology. approaches.

that. three major technology choices. All. three choices were wrong. Okay. So this. is how incredibly smart we were. And so. [laughter]. and so in 1995 19 early mid95. we realized we were going down the wrong. path. Meanwhile, the Silicon Valley was packed with 3D. graphics startups because it was the. most exciting technology of that time. And so 3D FX and rendition and Silicon.

Graphics was coming in. Intel was. already in there and you know gosh like. what added up eventually to a hundred. different startups we had to compete. against. Everybody had chosen the right. technology approach and we chose the. wrong one. And so we were the first. company to start. We found ourselves. essentially dead last with the wrong. answer. And so. the company was in trouble.

and um. ultimately we had to make several. decisions. The first decision is. well. if we change now. we will be the last company. And. even if we changed into the technology. that we believe to be right, we'd still. be dead. And so that argument,

you know, do we change and therefore be dead? Don't change and make this technology. work somehow or go do something. completely different. That question stirred the company. strategically and was a hard question. I. eventually, you know, advocated for we. don't know what the right strategy is, but we know what the wrong technology. is. So, let's stop doing it the wrong. way and let's give ourselves a chance to. go figure out what the strategy is. The.

second thing, the second problem we had. was our company was running out of money. and I had I was in a contract with Sega. and I owed them this game console. and if that contract would have been. cancelled, we'd be dead. We would have vaporized instantly. And so so uh uh I went to Japan and I. explained to uh the CEO of Sega, Erie.

Madri, really great man. He was the. former CEO of Honda USA. Went back to. Sega to run Sega. Went back to Japan to. run Sega. And I explained to him that I. was uh I guess I was what 30 33 years. old. you know, when I was 33 years old, I. still had acne. And I got this this, you. know, Chinese kid. I was super skinny.

And he he was already kind of elder. And uh I went to him and I said I said, "Listen, I've got some bad news for you." And and. first, the technology that we promised. you doesn't work. And second, we shouldn't finish your contract. because we'd waste all your money and.

you would have something that doesn't. work. And I recommend you find another. partner to build your game console. >> Whoa. >> And so I'm terribly sorry that we've set. you back in your product roadmap. And third, even though you're going to I'm asking. you to let me out of the contract, I still need the money. because if you didn't give me the money, we'd vaporize overnight.

And so. I explained it to him humbly, honestly. I gave him the background. explain to him why the technology. doesn't work, why we thought it was. going to work, why it doesn't work. And um and I asked him. to uh. convert the last $5 million that they.

were to complete the contract to give us. that money as an investment. instead. and he said, "But it's very likely your company will. go out of business, even with my. investment.". And it was completely true. Back then, 1995, $5 million was a lot of money. It's a lot of money today. $5 million. was a lot of money. And here's a pile of.

competitors doing it right. What are the. chances that giving Nvidia $5 million. that we would develop the right strategy. that he would get a return on that $5. million or even get it back? 0%. You do the math. It's 0%. If I were sitting there right there, I. wouldn't have done it. $5 million was a mountain of money to. Sega at the time. And so. I told him that that that um.

uh. if you invested that $5 million in us, it is most likely to be lost. But if you didn't invest that money, we'd be out of business and we would. have no chance. And I I told him that I. I don't even know exactly what I said in. the end, but I.

told him that I would understand if he. decided not to, but it would make the. world to me if he did. He went off and. thought about it for a couple days and. came back and said, "We'll do it.". >> Wow. strategy to how to correct what it was. doing wrong. Did you explain that to. him? >> Wait, oh man, wait until I tell you the. rest of it's scarier. Even scarier. >> Oh no. [laughter]. >> And so so um.

so what he what he decided was was uh. Jensen was a young man he liked. That's. it. >> Wow. to this day. >> That's nuts. >> I was. >> Boy, do you owe what the world owes that. guy. >> No doubt, >> right? >> Well, he's he c he's he celebrated today. in Japan. >> And if he would have kept that five. >> the the investment, I think it'd be.

worth probably about a trillion dollars. today. I know. But the moment we went public, they sold it. They go, "Wow, that's a. miracle." So, [laughter]. >> wow. >> They sold it. Yeah. They sold it at. Nvidia valuation about 300 million. That's our IPO valuation. 300 million. >> Wow. >> And so, so anyhow, I was incredibly grateful. Um,

and then now we had to figure out what. to do because we still were doing the. wrong strategy, wrong technology. So. unfortunately we had to lay off most of. the company. We shrunk the company all. back. All the people working on the game. console, you know, we had to shrunk it. all. Shrink it all back. And um. and then and then somebody told me that, but Jensen, we've never built it this way before. We've never built it the right way. before.

We've only know how to build it the. wrong way. And so nobody in the company knew how to. build this. supercomputing image generator 3D. graphics thing that Silicon Graphics. did. And so so uh I said, "Okay, how hard can it be? You got all these 30. companies, you know, 50 companies doing. it. How hard can it be?" And so luckily.

there was a textbook written by the. company Silicon Graphics. And so I went down to the store. I had. 200 bucks in my pocket. And I bought. three textbooks, the only three they. had, $60 a piece. I bought the three. textbooks. I brought it back and I gave. one to each one of the architects and I. said, "Read that and let's go save the. company.". >> [laughter]. >> And so. [gasps and sighs] so they they they read. this textbook, learned from the giant at.

the time, Silicon Graphics, about how to. do 3D graphics. But the thing that was. amazing and what makes Nvidia special. today is that. the people that are there are able to. start from first principles, learn best known art, but reimplement it. in a way that's never been done before. And so when we re-imagined. the technology of 3D graphics, we.

reimagined it in a way that manifest. today the modern 3D graphics. We really. invented modern 3D graphics, but we. learned from previous known arts and we. implement it fundamentally differently. >> What did you do that changed it? Well, you know, the ultimately. ultimately the um uh the simple the. simple answer is that the way silicon. graphics works uh the geometry engine is.

a bunch of software running on. processors. We took that and. eliminated all the generality, the general purposeness of it and we. reduced it down into the most essential. part of 3D graphics. and we hardcoded it into the chip. And. so instead of something general purpose, we hardcoded it very specifically into.

just the limited applications, limited functionality necessary for. video games. And that capability that super and and. because we reinvented a whole bunch of. stuff, it supercharged the capability of. that one little chip. And our one little. chip was generating images as fast as a. $1 million image generator. That was the. big breakthrough. We took a million. dollar thing and we put it into the.

graphics card that you now put into your. gaming PC. And that was [snorts] our big. invention. And then and of course the question is. is um. uh how do you compete against these 30. other companies doing what they were. doing? and and there we did we did several. things. One. uh instead of building a 3D graphics. chip for every 3D graphics application, we decided to build a 3D graphics chip.

for one application. We bet the farm on. video games. The needs of video games are very. different than needs for CAD, needs for. flight simulators. They're related, but. not the same. And so we narrowly focused. our problem statement so I could reject. all of the other complexities and we. shrunk it down into this one little. focus and then we supercharged it for. gamers. And then the second thing that. we did was we created a whole ecosystem.

of working with game developers and. getting their their games ported and. adapted to our silicon so that we could. get turn essentially what is a. technology business into a platform. business into a game platform business. So we, you know, GeForce is really today. it's also the most advanced 3D graphics. technology in the world, but a long time. ago GeForce is really the game console. inside your PC. It's, you know, it runs. Windows, it runs Excel, it runs.

PowerPoint, of course, those are easy. things, but its fundamental purpose was. simply to turn your PC into a game. console. So we we were the first. technology company to build all of this. incredible technology in service of one. audience gamers. Now of course in 1993. the gaming industry didn't exist. But by. the time that John Carmarmac came along. and the doom phenomenon happened and. then quake came out as you know.

that entire world oh that entire. community boom took off. Do you know. where the name Doom came from? >> It came from this se there's a scene in. the movie The Color of Money where Tom. Cruz who's this uh elite pool player. shows up at this pool hall and this. local hustler says what he got in the. case and he opens up this case. He has a. special pool queue. He goes in here and. he opens it up. He goes, "Doom. >> Doom." [laughter]. >> And that's where it came from. Yeah. Cuz.

Carmarmac said that's what they wanted. to do to the gaming industry. >> Doom. >> That when Doom came out, it would just. be everybody be like, "Oh, we're. fucked.". >> Oh, wow. >> This is Doom. >> That's awesome. >> Isn't that amazing? That's amazing. >> Cuz it's the perfect name for the game. >> Yeah. >> And the name came out of that scene in. that movie. >> That's right. Well, and then of course, uh, Tim Sweeney and. >> Epic Games and, uh, and the 3D gaming. genre took off. >> Yes. >> And so, if you just kind of in the. beginning was no gaming industry. We had.

no choice but to focus the company on. one thing. That one thing, >> it's a really incredible origin story. >> Oh, it's it's amazing. Like you must be. like look back. >> a disaster is what. >> a $5 [laughter] million that pivot with. that conversation with that gentleman if. he did not agree to that if he did not. like you what would the world look like. today that's crazy then then our entire. life hung on another gentleman. and so so now here we are we built so.

before GeForce it was Revo 128 revo 128. saved the company it revolutionized. computer graphics. The performance cost performance ratio. of 3D graphics for gaming was off the. charts amazing. And. we're getting ready to to ship it. Get. well, we're we're building it, but we're. so as you know, $5 million doesn't last.

long. And so every single month, every. single month, uh we were drawing down. You have to build it, prototype it. You. have to design it, prototype it, get the silicon back, which costs a lot of money. Test it with. software. because without the software testing the. chip, you don't know the chip works. And then you're going to find a bug.

probably. because every time you test something. you find bugs, which means you have to tape it out. again, which is more time, more money. And so we did the math. There was no. chance anybody was going to survive it. We didn't have that much time to tape. out a chip, send it to a foundry TSMC, get the silicon back, test it, send it. back out again. There was no no shot, no. hope. And so the math, the spreadsheet doesn't.

allow us to do that. And so I heard. about this company and this company. built this machine. And this machine is an emulator. You could take your design, all of the. software that describes the chip, and. you could put it into this machine. And. this machine will pretend it's our chip. So I don't have to send it to the fab, wait until the fab sends it back, test.

I could have this machine pretend it's. our chip and I could put all of the. software on top of this machine called. an emulator and test all of the software. on this pretend chip and I could fix it. all before I send it to the fab. >> Whoa. And if and and if I could do that. when I send it to the FAB, it should. work. Nobody knows, but it should work. And so. we came to the conclusion.

that let's take half of the money we had. left in the bank. At the time it was. about a million dollars. Take half of. that money and go buy this machine. So instead of keeping the money to stay. alive, I took half of the money to go. buy this machine. Well, I call this guy. up. This the company's called IOS. Call this company up and I say, "Hey, listen. I heard about this machine. I like to buy one.". And they go, "H,

that's terrific, but we're out of. business." I said, "What? You're out of. business?" He goes, "Yeah, we had no. customers.". [laughter]. I said, "Wait, hang on a sec. So, you. never made the machine?" They [snorts]. can say, "No, no, no. We made the. machine. We have one in inventory if you. want it, but we're out of business." So, I bought one out of inventory. Okay. After I bought it, they went out. of business. >> Wow.

>> I bought it out of inventory. And on this machine, we put Nvidia's. chip into it and we tested all of the. software on top. And at this point, we were on fumes. But we convinced ourselves that chip is. going to be great. And so I had to call some other. gentleman. So I called TSMC. And I told TSMC. that listen, TSMC is the world's largest. founder today. At the time they were.

just a few hundred million dollars. large, tiny little company. And I explained to them what we were. doing. And um I explained to him I told. him I had a lot of customers. I had one, you know, Diamond Multimedia, probably one of the companies you bought. the graphics card from back in the old. days. And I I said, you know, we have a. lot of customers, and the demand's.

really great, and. we're going to tape out a chip to you, and I like to go directly to production. because I know it works. >> [snorts]. >> And they said, "Nobody has ever done. that before. Nobody has ever taped out a chip that. worked the first time. And nobody starts out production without. looking at it.". But I knew that if I didn't start the.

production, I'd be out of business. anyways. And if I could start the. production, I might have a chance. And so. TSMC. decided to support me and uh this. gentleman is named Morris Chang. Morris. Chang is the father of the foundry. industry, the founder of TSMC. Really. great man. He decided to support our company. I.

explained to them everything. he decided to support us frankly. probably because they didn't have that. many other customers anyhow but they. were grateful and I was immensely. grateful and as we were starting the. production. Morris flew to United States and uh. he didn't so many words asked me so but. he asked me a whole lot of questions. that was trying to tease out do I have. any money.

but he didn't directly ask me that you. know and so the truth is that we didn't. have all the money but we had a strong. PO from the customer and if it didn't. work some wafers would have been lost. and I'm you know I I'm not exactly sure. what would have happened but we would. have come short it would have been it. would have been rough but they supported. us with all of that risk involved we. launched this chip turns out to.

been completely revolutionary. Knocked the ball out of the park. We. became the fastest growing technology. company in history to go from zero to $1. billion. >> So wild that you didn't test the chip. >> I know. We tested afterwards. Yeah, we. tested afterwards. >> Afterwards, but [laughter]. production already. But by the way, by. the way, that methodology that we. developed to save the company is used.

throughout the world today. >> That's amazing. >> Yeah, we changed we changed the whole. world's methodology of designing chips. The whole world's uh rhythm of designing. chips. Uh we changed everything. >> How well did you sleep those days? It. must have been so much stress, [laughter]. >> you know. Um, what is that feeling where where uh the. world just kind of feels like it's. flying? It you you have this what do you.

call that feeling? You can't you can't. stop the the feeling that everything's. moving super fast and you know and. you're laying in your laying in bed and. the world just feels like you know it. you and you're you you feel deeply. anxious. uh completely out of control. Um. I've felt that probably a couple of. times [laughter] in my life. It's during.

that time. >> Wow. >> Yeah. It it was incredible. >> What an incredible success story. >> But I I learned I learned a lot. I. learned I learned about I learned. several things. I learned I learned uh. how to develop strategies. Um I learned how to. uh uh and when I when I you know our. company learned how to develop. strategies. What are winning strategies? We learned how to create a market. We. created the modern 3D gaming market. We learned how and and so that exact.

same skill is how we created the modern. AI market. It's exactly the same. >> Wow. >> Yeah. Exactly the same skill. Exactly. the same blueprint. And. uh we learned how to uh deal with. crisis, how to stay calm, how to think. through things systematically. We learned how to remove all waste in. the company and work from first. principles and doing only the things.

that are essential. Everything else is. waste because we have no money for it. to live on fumes at all times. And the. feeling. no different than the feeling I had this. morning when I woke up that you're going. to be out of business soon. that you're. you know the phrase 30 days from going. out of business I've used for 33 years. because. >> you still feel that. >> Oh yeah. Oh yeah. Every morning. Every. morning. >> But but you guys are one of the biggest.

companies on planet earth. But the the. feeling doesn't change. >> Wow. >> The the sense of vulnerability, the. sense of uncertainty, the sense of. insecurity. Uh. it it doesn't leave you. >> That's crazy. We were, you know, we had. nothing. We had nothing. We were dealing. with giant. >> Oh, yeah. Oh, yeah. Every day, every. moment. >> Do you think that fuels you? Is that. part of the reason why the company's so. successful? That you have that hungry. mentality,

that you never rest, you're never. sitting on your laurels, you're always. on the edge. I have a greater drive from not wanting. to fail. than the drive of wanting to succeed. [laughter]. >> Isn't that like sex coaches would tell. you that's completely the wrong. psychology? >> The world has just heard me say that for.

out loud for the first time. >> But but it's true. >> Well, that's how fascinating. fear of. failure drives me more than the than the. the greed or whatever it is. >> Well, ultimately that's probably a more. healthy approach now that I'm thinking. about it because like the fear. >> I'm not ambitious for example, [laughter] you know, I just want to stay. alive, Joe. I want the company to. thrive, you know, I want us to make an. impact. >> That's interesting.

>> Yeah. >> Well, maybe that's why you're so humble. That's what maybe that's what keeps you. grounded, you know, because with the. kind of spectacular success the. company's achieved, it would be easy to. get a big head. >> No. >> Right. But isn't that interesting? It's. like the if you were the guy that your. main focus is just success. You probably. would go, "Well, made it. Nailed it. I'm. the [laughter] man. >> Drop the mic.". >> Instead, you wake up, you're like, "God, we can't [ __ ] this up.". >> No. Exactly. Every morning. Every.

morning. No. Every moment. Yeah. That's. crazy. >> Before I go to bed. >> Well, listen. If I was a major investor. in your company, that's what I'd want. running it. I'd want a guy who's. >> Yeah. >> That's what I work. That's why I work. seven days a week. Every moment I'm I'm. awake. >> You work every moment. >> Every moment I'm awake. >> Wow. >> I'm thinking about solving a problem. I'm thinking about. >> How long can you keep this up? >> I don't know. But so [laughter]. could be next week. Sounds exhausting.

>> It is exhausting. >> It sounds completely exhausting. >> Always in a state of anxiety. >> Wow. >> Always in a state of anxiety. >> Wow. Kudos to you for admitting that. I. think that's important for a lot of. people to hear because, you know, there's probably some young people out. there that are in a similar position to. where you were when you were starting. out that just feel like, oh, those. people that have made it, they're just. smarter than me and they had more. opportunities than me and it's just like.

it was handed to them or they're just in. the right place at the right time. And. >> Joe, I just described to you somebody. who didn't know what was going on, [laughter]. actually did it wrong. >> Yeah. Yeah. And the ultimate diving. catch like two or three times. >> Crazy. >> Yeah. >> The ultimate diving catch is the perfect. way to put it. >> You know, it's just like the edge of. your glove. [laughter]. >> It probably bounced off of somebody's. helmet and landed at the edge. [laughter].

>> God, that's incredible. That's. incredible. But it's also it's really. cool that you have this perspective that. you look at it that way because you know. a lot of people that have delusions of. grandeur or they have you know. >> and their rewriting of history. often times had them somehow extraord. extraordinarily smart and they were. geniuses and they knew all along and. they were they were spot-on. And the. business plan was exactly what they.

thought. And. >> yeah, >> they destroyed the competition and you. know and they emerged victorious. [laughter]. >> Meanwhile, you're like, I'm scared every. day. >> Exactly. [laughter]. Exactly. >> That's so funny. Oh my god, that's. amazing. >> It's so true, though. >> It's amazing. >> It's so true. >> It's amazing. Well, but I I think. there's nothing inconsistent. with being a leader and being. vulnerable. You know, I the company.

doesn't need me to be a genius right all. along, right? Absolutely certain about what I'm trying. to do and what I'm doing. The the. company doesn't need that. The company. wants me to succeed. You know, the thing. that and we started out today talking. about President Trump and I was about to. say something and listen, he is my. president. He is our president. We. should all and we're talking about just. because it's President Trump, we all. want him to be wrong. I think that.

United States, we all have to realize he. is our president, we want him to succeed. because. >> no matter who's president attitude. >> That's right. >> We want him to succeed. We need to help. him succeed because it helps everybody, all of us succeed. And. I'm lucky that I work in a company where. I have 40,000 people who wants me to. succeed. They want me to succeed and I can tell.

and they're all every single day to help. me overcome these challenges. trying to realize. realize what I describe to be our. strategy doing their best. And if it's. somehow. wrong or not perfectly right to tell me. so that we could pivot and the more. vulnerable we are as a leader the more. able other people are able to tell you. you know that Jensen that's not exactly. right or. >> right right. >> have you considered this information or.

and the more vulnerable we are. the more able we're actually able to. pivot if we put ourselves into this. superhuman capability then it's hard for. us to pivot strategy, >> right? >> Because we were supposed to be right all. along. >> And so if you're always right, how can. you possibly pivot? Because pivoting. requires you to be wrong. And so I've. got no trouble with being wrong. I just. have to make sure that I stay alert, that I reason about things from first. principles all the time. Always break.

things down to first principles. Understand why it's happening. Reassess continuously. The reassessing. continuously is kind of partly what. causes continuous anxiety, >> you know, because you're asking. yourself, were you wrong yesterday? Are. you still right? Is this the same? Has. that changed? Has that condition is that. worse than you thought? >> But God, that mindset is perfect for. your business, though, because this. business is ever changing. >> all the time. I've got competition.

coming from every direction. So much of. it is kind of up in the air. and you have to invent a future where. a 100 variables are included and there's. no way you could be right on all of. them. And so you have to be. >> you have to surf. >> Wow. That's a good way to put it. You. have to surf. Yeah. You're surfing waves. of technology and innovation. >> That's right. You can't predict the. waves. You got to deal with the ones you. have. >> Wow. And but skill matters and I've been.

doing this for 30 I'm the longest. running tech CEO in the world. >> Is that true? Congratulations. That's. amazing. >> And you know people ask me how is one. don't get fired. [laughter]. That'll stop a short heartbeat. And then two don't get bored. >> Yeah. >> Well, how do you maintain your. enthusiasm? Well, the honor truth is is not always.

enthusiasm. It's, you know, sometimes is. enthusiasm. Sometimes it's just good. oldfashioned fear and then sometimes, you know, a healthy dose of frustration, you know, it's whatever keeps you. moving. >> Yeah. Just all the emotions. I think, you know, >> CEOs, we have all the emotions, right? you know, and so probably probably. jacked up to the maximum because you're. you're kind of feeling it on behalf of. the whole company. I'm feeling it on. behalf of everybody at the same time.

And it kind of, you know, encapsulates. into into somebody. And so I have to be. mindful of the past. I have to be. mindful of the present. I've got to be. mindful of the future. And um you know, it can't it's not without emotion. It's not just it's it's not just a job. Let's just put it that way. >> It doesn't seem like it at all. I would. imagine one of the more difficult. aspects of your job currently now that. the company is massively successful is.

anticipating where technology is headed. and where the applications are going to. be. >> Yeah. >> So, how do you try to map that out? >> Yeah. there there um there there's a. whole bunch of ways and and it takes it. takes um. takes a whole bunch of things but let me. just start. uh you have to be surrounded by amazing. people and Nvidia is now you know if you. look at look at look at um the large.

tech companies in the world today. most of them have a business in. advertising or social media or you know. content distribution and at the core of. it is really fundamental computer. science and so the company's business is. not computers the company's business is. not technology technology drives the. company is the only company in the world. that's large whose only business is.

technology we only build techn we don't. advertise the only way that we make. money is to create amazing technology. and sell. And so. to be that to be NVIDIA today, you're. the number one thing is you're. surrounded by the finest computer. scientists in the world. And that's my. gift. My gift is that we've created a. company's culture, a condition by which the world's.

greatest computer scientists want to be. part of it because they get to do their. life's work and create the next thing. because that's what they want to do. because maybe they're not they don't. want to be in service of another. business. >> They want to be in service of the. technology itself. And we're the largest. form of its kind in the history of the. world. >> Wow. >> I know. It's pretty amazing. >> Wow. >> And so so one, you know, we have a we we. have got a great condition. We have a. great culture. We have great people. And.

then now now now the question is how do. you systematically. um. be able to see the future stay alert of. it. and uh reduce the reduce the the. likelihood of missing something or being. wrong. And so there's a lot of different ways. you could do that. For example, we have. great partnerships. We we have. fundamental research. We have a great. research lab, one of the largest.

industrial research labs in the world. today. And we partner with a whole bunch. of universities and other scientists. We. do a lot of open collaboration. and so I'm constantly working with. researchers outside the company. We have the benefit of having amazing. customers and so I have the benefit of. working with Elon and you know and. others in the industry and we have the. benefit of being the only pure pure play. technology company that can serve uh.

consumer internet. industrial manufacturing. um scientific computing healthcare. financial services all the industries. that we're in. They're all signals to. me. And so they all have mathematicians. and scientists and and so because I I. have the benefit now of a radar system. >> that is the most broad of any company in. the world working across every single. industry from agriculture to energy.

to video games. And so the ability for us to have this. vantage point, one doing fundamental research ourselves. and then two working with all the great. researchers, working with all the great. industries, the feedback system is. incredible. And then finally, you just have to have a culture of. staying super alert. There's no easy way. of being alert except for paying. attention.

I haven't found a single way of being. able to stay alert without paying. attention. And so, you know, I probably. read several thousand emails a day. >> How How do you have a time for that? >> I wake up early. This morning I was up. at 4:00. >> How much do you sleep? >> Uh, six, seven, six, seven hours. >> Yeah. >> And then you're up at 4 reading emails. for a few hours before you get going. >> That's right. Yeah. >> Wow.

Every day. >> Every single day. Not one day missed. [sighs] including. Thanksgiving, Christmas. >> Do you ever take a vacation? >> Uh, yeah. But they're um my definition. of a vacation is when I'm with my. family. And so if I'm with my family, I'm very happy. I don't care where we. are. >> And you don't work then or do you work a. little? >> No. No. I work a lot. [laughter]. >> Even like if you go on a trip somewhere, you're still working. >> Oh, sure. Oh, sure. >> Wow. Every day.

>> Every day. >> But my kids work every You make me tired. just saying this. >> My kids work every day. Both of my kids work at Nvidia. They. work every day. >> Wow. >> Yeah. I'm very lucky. >> Wow. >> Yeah. It's brutal now because, you know, it's just me working every day. Now we. have three people working every day and. they want to work with me every day and. so it's it's a lot of work. >> Well, you've obviously imparted that. ethic into them. >> They work incredibly hard. I mean, there's no unbelievable.

>> But my parents work incredibly hard. >> Yeah. I was I was born with the work. gene, >> the suffering gene. [laughter]. >> Well, listen, man. It has paid off. What. a crazy story. It was just It's really. an amazing origin story. It really I mean, it has to be kind of. surreal to be in the position that. you're in now when you look back at how. many times that it could have fallen. apart and humble beginnings. But Joe, this is great. It's a great country. You.

know, I'm an immigrant. My parents sent. my older brother and I here first. We're we're in Thailand. I was born in Taiwan, but my dad had a. job in Thailand. He was a chemical and. instrumentation engineer, incredible. engineer. And his job was to go start an oil. refinery. And so we moved to Thailand, lived in Bangkok. And um in 19.

I guess 1973 1974 time frame, you know how Thailand every so often. they would just have a coup. You know, the military would have an uprising and. all of a sudden one day there were tanks. and soldiers in the streets and my. parents thought, you know, it probably. isn't safe for the kids to be here. And. so they contacted my uncle. My uncle. lives in Tacoma, Washington. and um we had never met him and my. parents sent us to him.

>> How old were you? >> Uh I was about to turn nine and my older. brother uh almost turned 11 and so the. two of us came to United States and we. stayed in with our uncle for a little. bit while he looked for a school for us. and my parents didn't have very much. money and they never been to United. States. my father was. I'll tell you. that story in a second. And um.

and so my my uncle found a school that. would accept foreign students and. affordable enough for my parents. And that school turned out to have been. in Onita, Kentucky, Clark County, Kentucky, the epicenter of the opio. crisis today. cold country. Clark County, Kentucky is. was the poorest county in America when I.

showed up. It is the poorest county in. America today. And so we went to the school, it's a. great school, um, Onita Baptist. Institute. in a town of a few hundred. I think it. was 600 at the time that we showed up. No traffic light. And um I think it has 600 today. It's. quite an amazing feat actually. The ability to hold your population for.

[laughter]. when it's 600 people. It was quite a. magic quite a magical thing. however. they did it. And and so uh the school. had a mission of being an open school. for any children who would like to come. And what that basically means is that if. you're a trouble student, if you have a. troubled family, um if you're,

you know, whatever your background, you're welcome to come to Onita Baptist. Institute, including kids from. international who would like to stay. there. >> Did you speak English at the time? >> Uh, okay. Yeah. Yeah. Okay. Yeah. And so. we showed up. and uh. my first my first thought was gosh there.

are a lot of cigarette butts on the. ground. 100% of the kids smoked. [laughter]. So right away you know this is not a. normal school. >> Nineyear-olds? >> No, I was the youngest kid. >> Okay. 11 year olds. >> My roommate was 17 years old. Wow. >> Yeah. He just turned 17. And he was. jacked. and and um. I don't know where he is now. I know his.

name, but I don't know where he is now. But anyways, uh that night we got and. and the second thing I noticed when you. walk into the into your dorm room. is uh there are no drawers and no closet. doors. just like a prison. And. there are no locks. so that people could check check up on. you. And so I go into my room and he's 17 and.

uh you know get ready for for bed and he. had all this tape. all over his body and uh turned out he. was in a knife fight and he's been. stabbed all over his body and these were. just fresh wounds. >> Whoa. And the other kids were hurt much. worse. And uh so he was my roommate, the. toughest kid in school, and I was the. youngest kid in school. It was a it was.

a junior high, but they took me anyways because if I. walked about a mile across the Kentucky. River, the swing bridge, the other side. is a middle school that I could go to. and then I can go to that school and I. come back and then I stay in the dorm. And so basically Onita Baptist Institute. was my dorm when I went to this other. other school. My older brother went went.

to um went to the junior high. And so we. were there for a couple of years. Um. every kid had every kid had chores. My older brother's chore was to work in. the tobacco farm, you know. So tobac. they raised tobacco so that they could. raise some extra money for the school. Kind of like a penitentiary. >> Wow. And my job was just to clean the. dorm. And so I I was 9 years old. I was. cleaning toilets. And for a dorm of 100. 100 boys, I.

I clean more bathrooms than anybody. And. I just wish that everybody was a little. bit more careful, you know. [laughter]. But anyways, I was the youngest kid in. school. The my memories of it was really. good. Um, but it was a pretty tough It. was a tough town. >> Sounds like it. >> Yeah. Town kids, they all carried. Everybody had knives. >> Everybody had knives. Everybody smoked. Everybody had a Zippo lighter. I smoked.

for a week. >> Did you? >> Oh, yeah. Sure. >> How old were you? >> I was nine. Yeah. >> When you nine? You were nine, you tried. smoking. >> Yeah. I got myself a pack of cigarettes. Everybody else did. >> Did you get sick? No. I I got used to. it, you know, and I learned how to blow. blow smoke rings and, you know, [snorts]. you know, breathe out of my nose, you. know, take it in out of through my nose. I mean, there was a all the different. things that you learned. Yeah. >> At nine. >> Yeah. >> Wow. You just did it to fit in or it. looked cool.

>> Yeah. [clears throat] Because everybody. else did it, >> right? >> Yeah. And and then I did it for a couple. weeks, I guess. And I just rather have I. had a quarter, you know, I had a quarter. a month or something like that. I just rather buy popsicles and fried. sickles with it. I was nine, you know, [laughter]. >> right? >> I chose I chose the the better path. >> Wow. >> That was our school. And then my parents. came to United States two years later. and um we met him in Tacoma, Washington.

>> That's wild. It It was a really crazy. experience. What a strange formative. experience. >> Yeah. Tough kids. >> Thailand to one of the poorest places in. America or if not the poorest. as a 9-year-old. >> Yeah. It was my first experience with. your brother. >> Wow. >> Yeah. Yeah. No, I I remember and what. breaks my heart probably the only thing. that really breaks my heart of.

about that experience was. so. we didn't have enough money to make you. know international phone calls every. week and so my parents gave us this tape. deck this Iowa tape deck and a tape. and so every month we would sit in front. on that tape deck and that my older. brother Jeff and I, the two of us would just tell them what.

we did the whole month. >> Wow. >> And we would send that tape by mail. and my parents would take that tape and. record back on top of it and send it. back to us. >> Wow. >> Could you imagine if for two years. >> Wow. is that tape still existed. of these two kids just describing their. first experience with United States. Like I remember telling my parents.

that that uh I joined the swim team and. uh. my roommate was really buff and so every. day we spent a lot of time in the in the. gym and so uh uh every night 100. push-ups, 100 sit-ups every day in the. gym. So, I was nine years old. I was. getting I was pretty buff. and I'm pretty fit. And uh. and so I joined the soccer team. I.

joined the swim team because if you join. the team, they take you to meets and. then afterwards you get to go to a nice. restaurant. And that nice restaurant was. McDonald's. >> Wow. >> And and I recorded this thing. And I. said, "Mom and dad, we went to the most. amazing restaurant today. This whole place is lit up. It's like. the future.". And [snorts] the food comes in a box. [laughter].

and the food is incredible. The. hamburger is incredible. It was. McDonald's. [snorts] But anyhow, it it. wouldn't it be amazing? >> Oh my god. Two years recording. Yeah. Two years. Yeah. What a crazy connection. to your parents, too. Just sending a. tape and them sending you one back and. it's the only way you're communicating. for two years. >> Yeah. Wow. Yeah. No, I've My parents are. incredible actually. They're just. they're uh they grew up really poor and.

um when they came to United States, they. had almost no money. Uh probably one of. the most. impactful memories I have is is uh we. they came and we were we were staying in. a in a in a uh apartment complex. and they had they had just rent back in. the I guess people still do rent rent a. bunch of furniture. and.

we were messing around. and uh. we bumped into the coffee table and. crushed it. It's made out of particle. wood and we crushed it. And I just still remember my the look on. my mom's face, you know, because they. didn't have any money and she didn't. know how she was going to pay it back. And but anyhow, that's that kind of. tells you how hard it was for them to. come here. They they left everything.

behind and all they had was their. suitcase and the money they had in their. in their pocket and they came to United. States. >> How old were they pursued the American. dream? They were in their 40s. >> Wow. >> Yeah. Late late 30s. >> Pursued the American dream. This is this. is the American dream. I'm the first. generation of the American dream. >> Wow. >> Yeah. It's hard not to love this. country. >> That's. >> it's it's hard not to be romantic about. this country. >> That is a romantic story. That's an. amazing story. >> Yeah. And and my dad found his job.

literally in the newspaper, you know, the ads and he calls people. Got a job. >> What did he do? >> Uh he was a consulting engineer and a. and a consulting firm and they helped. people build oil refineries, paper mills. and fabs. And that's what he did. He was. an he he's really good at factory design. instrumentation engineer. And so he's. he's brilliant at that. And so he did.

that and my mom uh worked as a maid and. uh they found a way to raise us. >> Wow. That's an incredible story, Jensen. It. really is. Every all of it from your. childhood to the perils of Nvidia almost. falling. [laughter]. It's really incredible, man. >> It's a great story. Yeah. I I've lived a. great life. >> You really have. And it's a great story. for other people to hear, too. It really.

is. >> You don't You don't have to go to Ivy. League schools to succeed. This country creates opportunities. Has. opportunities for all of us. You do have. to strive. You have to claw your way here. >> Yeah. >> But if you put in the work, you can. succeed. Nobody works with. >> a lot of luck and a lot of. >> a lot and. >> good decision- making. >> and the good graces of others. >> Yes, that's really important. >> Yeah. You and I spoke about two two. people who are very dear to me. Um but.

the list goes on. the people the people. at NVIDIA who have have uh helped me um. uh many friends that are on the board uh. the decisions you know them giving me. the opportunity like when we were. inventing this new computing approach. I tanked our stock price because we. added this thing called CUDA to the chip. we had this big idea we added this thing. called CUDA to the chip but nobody paid. for it but our cost doubled and so we.

had this graphics chip company and we. invented GPUs, we invented programmable. shaders, we invented everything modern. computer graphics, we invented real-time tracing. That's. why it went from GTX to RTX. We invented all this stuff, but every. time we invented something, the market doesn't know how to. appreciate it, but the cost went way up. And in the case of CUDA that enabled AI,

the cost increased a lot. it and but I. really we really believed it you know. and so if you believe in that future and. you don't do anything about it you're. going to regret it for your life. and so we always you know I always tell. the team do you believe what do we. believe this or not and if you believe. it and so grounded on first principle is. not random you know hearsay and we. believe it we've got to we owe it to. ourselves to go pursue it if we're the. right people to go do it if it's really.

really hard to do. It's worth doing and. we believe it. Let's go pursue it. Well, we pursued it. We we launched the. product. Nobody knew. It was exactly. what like when I launched DGX1 and the. entire audience was like. complete silence. When I launched CUDA, the audience was complete silence. No. customer wanted it. Nobody asked for it. Nobody understood it. Nvidia was a. public company. >> What year was this? This is uh.

uh let's see 200. 2006. 20 years ago. 2005. >> Wow. >> Our stock price just went. our valuation went down to like two or. three billion dollars. >> from. >> from about 12 or something like that. I crushed it. >> [laughter].

>> in a very bad way. >> Yeah. >> What is it now though? >> H Yeah, it's higher. [laughter]. >> Very humble of you. [gasps]. >> It's higher. But it changed the world. >> Yeah, >> that invention changed the world. >> It's a It's an incredible story, Johnson. It really is. >> Thank you. >> I like your story. It's incredible. Ah, >> my story is not as incredible. My story. is more weird, you know. It's much more fertuitous and weird.

>> Okay. What are the three milestones that. most important milestones that led to. here? >> That's a good question. Um, >> what was step one? >> I think step one was seeing other people. do it. Step one was in the initial days. of podcasting, like in 2009 when I. started, podcasting had only been around. for a couple of years. Um, the first was. Adam Curry, my good friend, who was the.

podfather. He he invented podcasting. And then, you know, um, I remember Adam. Corolla had a show because he had a. radio show. His radio show got cancelled. and so he decided to just do the same. show but do it on the internet. And that. was pretty revolutionary. Nobody was. doing that. And then there was the. experience that I had had doing. different morning radio shows like Opie. and Anthony in particular because it was. fun and we would just get together with. a bunch of comedians, you know, I'd be. on the show with like three or four.

other guys that I knew and it was always. just looked forward to it. It was was. just such a good time and I said, "God, I miss doing that. It's so fun to do. that. I wish I could do something like. that." And then I saw Tom Green setup. Tom Green had a setup in his house and. he essentially turned his entire house. into a television studio and he did an. internet show from his living room. He. had servers in his house and cables. everywhere. Had to step over cables. I. was this is like 2007. I'm like Tom this. is nuts. Like this is. >> and I'm like you got to figure out a way. to make money from this. Like this.

everybody I wish everybody in the. internet could see your setup. It's. nuts. I just want to let you guys know. that [laughter]. >> it's not just this. >> Yeah. So that was the the beginning of. it is just seeing other people do it and. then saying all right let's just try it. and then so the beginning days we just. did it on a laptop had a laptop with a. webcam and just messed around had a. bunch of comedians come in we would just. talk and joke around and I did it like. once a week and then I started doing it. twice a week and then all a sudden I was.

doing it for a year and then I was doing. it for two years then it was like oh. it's starting to get a lot of viewers a. lot of listeners you know and then I. just kept doing It's all it is. I just. kept doing it because I enjoyed doing. it. Well, was there any setback? >> No. No, there was never really a setback. really. >> No, >> it must have been. Or you kind of. >> You're just You're just resilient. >> Or you're just tough. >> No. No. No. No. It wasn't tough or hard. It was just interesting. So, I just it. the the. >> You were never once punched in the face.

>> No, not in the show. No, not really. Not. Not doing the show. >> You never did something that that. big blowback. Nope. Not really. No, it all just kept. growing. >> It kept growing and the thing stayed the. same from the beginning to now. And the. thing is, I enjoy talking to people. I've always enjoyed talking to. interesting people. >> I could even tell just when we walked. in, the way you interacted with. everybody, not just me. >> Yeah, that's cool. >> People are cool. >> Yeah, that's cool. You know, I I it's a.

an amazing gift to be able to have so. many conversations with so many. interesting people because it changes. the way you see the world because you. see the world through so many different. people's eyes and you have so many. different people have different. perspectives and different opinions and. different philosophies and different. life stories. And you know, it's an. incredibly enriching and educating. experience having so many conversations.

with so many amazing people. And that's. all I started doing. And that's all I do. now. Even now, when I booked the show, I. do it on my phone. And I basically go. through this giant list of emails of all. the people that want to be on the show. or that request to be on the show. And. then I factor in another list that I. have of people that I would like to get. on the show that I'm interested in. And. I just map it out and that's it. And I. go, "Oh, I'd like to talk to him.". >> If it wasn't because of President Trump, I wouldn't have been bumped up on that.

list. [laughter]. >> No, I wanted to talk to you already. I I. just think, you know, what you're doing. is very fascinating. I mean, how would I. not want to talk to you? And then today, it proved to be absolutely the right. decision. >> Well, you know, listen, it's it's. strange to be an immigrant one day. going to Onita Baptist Institute. with with the students that were there. and then here. Nvidia's one of the most consequential. companies in the history of companies.

>> It is a crazy story. >> It has to be that journey is is a and. it's very humbling and. >> and um I'm very grateful. >> It's pretty amazing man. >> Surrounded by amazing people. You're. very fortunate and you've also you seem. very happy and you seem like you're 100%. on the right path in this life. You. know, >> you know, everybody says you must love. your job. Not every day. [laughter]. >> That's not that's part of the beauty of. everything is that there's ups and.

downs. It's never just like this giant. dopamine high. >> We leave we leave this impression here. Here's here's an impression I don't. think is healthy. We we um people who. are successful leave the impression. often that that. our job gives us great joy. I think. largely it does. that our jobs were passionate about our. work. Um and that passion relates to it's just.

so much fun. I think it largely is, but. it it it distracts from in fact a lot of. success comes from really really hard. work. >> Yes, >> there's long periods of suffering and. loneliness and uncertainty and fear and. embarrassment and humiliation. all of. the feelings that we most not love that.

creating something. from the ground up and and Elon will. tell you something similar very. difficult to invent invent something new. >> and people people don't believe you all. the time you're humiliated often. disbelieved most of the time and so so. people forget that part of success and. and I I don't think it's health. I think. it's it's good that we pass that forward. and let people know that that it's just.

part of the journey. >> Yes. >> Suffering is part of the journey. >> You will appreciate it so these horrible. feelings that you have when things are. not going so well. You will appreciate. it so much more when they do go well. >> Deeply grateful. >> Yeah. >> Yeah. Deep deep pride. Incredible pride. In incredible incredible gratefulness. and and and surely incredible memories. Absolutely. Jensen, thank you so much. for being here. This was really fun. I. really enjoyed it and your story is just.

absolutely incredible and very. inspirational and and I you know, I. think it really is the American dream. It is the American dream. >> It really is. Thank you so [music] much. Thank you. All right. Bye, everybody. [music].