Joe Rogan Experience #2422 - Jensen Huang

Transcript

00:00:01 Joe Rogan podcast. Check it out. >> The Joe Rogan Experience. >> TRAIN BY DAY. JOE ROGAN PODCAST BY

00:00:08 NIGHT. All day. >> Hello. Hey, Joe. >> Good to see you again. We were just

00:00:15 talking about Was that the first time we ever spoke or did was the first time we spoke at at SpaceX?

00:00:20 >> SpaceX. >> SpaceX. The first time when you were giving Elon that crazy AI chip,

00:00:24 >> right? DJX Spark. >> Yeah. Oo, that was a big moment. That was a huge

00:00:28 >> That felt crazy to be there. I was like watching these wizards of tech like exchange information and and you're

00:00:36 giving him this crazy device, you know, and then the other time was uh I was shooting arrows in my backyard and uh

00:00:43 randomly get this call from Trump and he's hanging out with you. President Trump called and I called you.

00:00:47 >> Yeah. It's just >> we were talking about you. [laughter] >> It's just talking about he was talking

00:00:52 about the US UFC thing he was going to do in his front yard. >> Yeah. And he pulls out. He's JJS, look

00:00:58 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

00:01:05 goes, "Yeah, yeah, you're going to come. This is going to be awesome." And he's showing me his design and how beautiful

00:01:10 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

00:01:18 on his podcast." He Let's call him. [laughter] >> He's like a kid.

00:01:24 >> I know. Let's call him. It's so He's like a 79y old kid. >> Oh, he's so incredible.

00:01:30 >> Yeah, he's an odd guy. Just very different, you know, like the what you'd expect from him. Very different than

00:01:38 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

00:01:43 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

00:01:49 makes the text go big. >> Like, you know, USA is respected again. like [laughter]

00:01:55 all caps and it makes the te the the the text enlarge is kind of ridiculous. >> Well, the the 101 Trump President Trump

00:02:04 is very different. He he surprised me f first of all he's an incredibly good listener. Almost everything I've ever

00:02:11 said to him, he's remembered. >> Yeah. People don't they only want to look at negative stories about him or

00:02:19 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

00:02:24 don't think he should do. Like I don't think he should say to a reporter rep reporter, "Quiet piggy." Like that's

00:02:29 pretty ridiculous. Also objectively funny. I mean, it's unfortunate that it happened to her. I wouldn't want that to

00:02:35 happen to her, but it was funny. Just ridiculous that the president does that. I wish he didn't do that. But other than

00:02:41 that, like he's he's an interesting guy. Like he's a lot of different things wrapped up into one person, you know?

00:02:49 You know, part of part of his charm, well, part of his genius is Yes. He says what's on his mind.

00:02:54 >> Yes. >> And which is like an anti-olitician in a lot of ways.

00:02:58 >> So, you know, what's on his mind is really what's on his mind, >> which

00:03:04 I I do some people some people would rather be lied to. >> Yeah. But but I I like the fact that

00:03:09 he's telling you what's on his mind. Um, almost every time he explains something, he says something,

00:03:15 he starts with his, you could tell, his love for America, what he wants to do for America. And everything that he

00:03:24 thinks through is very practical and very common sense. And, you know, it's very logical and um

00:03:32 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

00:03:39 Secretary Lutnik called and we met right before right at the beginning of the administration. He said he told me what

00:03:47 was important to President Trump that that um uh that United States manufactures on shore and that was

00:03:56 really important to him because because uh it's important to national security. He wants to make sure that that the

00:04:02 important critical technology of our nation is built in the United States and that we re-industrialize

00:04:08 and get good at manufacturing again because it's important for jobs. >> It just seems like common sense, right?

00:04:13 >> Incredible common sense. And and that was like literally the first conversation I had with Secretary Letic

00:04:19 um and he was talking about how how um that he started he started our conversation with uh Jensen. This is

00:04:29 Secretary Lutnik and I I just want to let you know that you're a national treasure. Uh Nvidia is a national

00:04:37 treasure and whenever you need access to the president um the administration uh you call us. We're always going to be

00:04:46 available to you. Literally, that was the first sentence. >> That's pretty nice.

00:04:51 >> And it was completely true. every single time I called, if I needed something, I want to get something off my chest, um,

00:04:59 express some concern, uh, they're always available. Incredible. It's just unfortunate we live in such a

00:05:04 politically polarized society that you can't recognize good common sense things if they're coming from a person that you

00:05:11 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

00:05:18 a giant community, which we are, it just only makes sense that we have manufacturing in America that especially

00:05:26 critical technology like you're talking about. Like it's kind of insane that we buy so much technology from other

00:05:32 countries. >> If United States doesn't grow, we will have no prosperity. We can't invest in

00:05:40 anything domestically or otherwise. we can't fix any of our problems. If we don't have energy growth, we can't have

00:05:48 industrial growth. If we don't have industrial growth, we can't have job growth. These it's as simple as that,

00:05:54 >> right? >> And the fact that the fact that he came into office and the first thing that he

00:05:58 said was drill baby drill. His point is we need energy growth. Without energy growth, we can have no industrial

00:06:05 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

00:06:15 policy we would not be able to build factories for AI not be able to build chip

00:06:20 factories we won't sure surely won't be able to build supercomputer factories none of that stuff would be possible

00:06:26 without all of that construction jobs would be challenged right electrical you know electrician

00:06:32 jobs all of these jobs that are now flourishing would be challenged and so I think he's got it right we need energy

00:06:38 growth We want to re-industrialize the United States. We need to be back in manufacturing. Every successful person

00:06:45 doesn't need to have a PhD. Every successful person doesn't have to have gone to Stanford or MIT. And I think I

00:06:51 think that that that you know that sensibility is is um spot on. Now, when we're talking about technology growth

00:06:59 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

00:07:04 our lives and get back." But the the real issue is that we're in the middle of a giant technology race. And whether

00:07:10 people are aware of it or not, whether they like it or not, it's happening. And it's a really important race because

00:07:16 whoever gets to whatever the event horizon of artificial intelligence is, whoever gets there

00:07:23 first has massive advantages in a huge way. Do you agree with that? Well, first the

00:07:30 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

00:07:36 technology race with somebody forever. >> Right. >> Right. Since the industrial revolution,

00:07:40 we've been in a technology >> since the Manhattan project. >> Yeah.

00:07:43 >> Or or you know, even going back to the discovery of energy, right? The United Kingdom was where the industrial

00:07:51 revolution was, if you will, invented when they realized that they can turn steam and such into into energy into

00:07:58 electricity. All of that was invented largely in Europe and the United States capitalized

00:08:07 on it. We were the ones that learned from it. We industrialized it. We diffused it faster than anybody in

00:08:14 Europe. They were all stuck in discussions about policy and

00:08:22 jobs and disruptions. Meanwhile, the United States was forming. We just took the technology and ran with it. And so I

00:08:29 I think we were always in in a bit of a technology race. World War II was a technology race. Manhattan Project was a

00:08:35 technology race. We've been in the technology race ever since during the Cold War. I think we're still in a

00:08:40 technology race. It is probably the single most important race. It is the technology is uh it gives you

00:08:48 superpowers. you know whether it's information superpowers or energy superpowers or

00:08:55 military superpowers is all founded in technology and so technology leadership is really important

00:09:00 >> well the problem is if somebody else has superior technology right that's that's the issue it seems like with the AI race

00:09:08 people are very nervous about it like you know Elon has famously said there was like 80% chance it's awesome 20%

00:09:16 chance we're in trouble and people are worried about that 20% % rightly so. I mean that you know if you had 10 bullets

00:09:23 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,

00:09:32 you're not going to feel real comfortable when you pull that trigger. It's terrifying,

00:09:34 >> right? >> And when we're working towards this ultimate goal um of AI,

00:09:42 it it just it's impossible to imagine that it wouldn't be of national security interest to get

00:09:48 there first. We should The question is what's there? That's the That was the part that

00:09:52 >> What is there? >> Yeah. I'm not sure. >> And I don't think anybody I don't think

00:09:56 anybody really knows. >> That's crazy though. If I ask you, >> you're the head of Nvidia. If you don't

00:10:02 know what's there, who knows? >> Yeah. I I think it's probably going to be much more gradual than we think. It

00:10:09 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

00:10:17 going to be like that. I think it's going to be things that just get better and better and better and better just

00:10:22 like technology does. >> So, you are rosy about the future. You're you're very optimistic about

00:10:27 what's going to happen with AI. >> Obviously, will you make the best AI chips in the world?

00:10:32 >> You probably better be. >> Uh h if history is a guide, um uh we were always concerned about new

00:10:39 technology. Humanity has always been concerned about new technology. There are always

00:10:44 somebody who's thinking there always a lot of people who are quite concerned. were quite concerned and and and so if

00:10:51 if history is a guide, it is the case um that all of this concern is channeled into making the technology safer.

00:11:02 And so for example, in the last several years, I would say AI technology has increased probably in the last two years

00:11:12 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

00:11:22 100 times more capable today. Now, how did we channel that technology? How do we channel all of that power? We

00:11:29 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

00:11:39 step by step. It does research before it answers. And so it grounds it on truth.

00:11:47 It'll reflect on that answer. Ask itself, is this the best, you know, answer that I can give you. Am I certain

00:11:54 about this answer? If it's not certain about the answer or highly confident about the answer, it'll go back and do

00:11:59 more research. It might actually even use a tool because that tool provides a better solution than it could

00:12:06 hallucinate itself. As a result, we took all of that computing capability and we channeled it into having it produce a

00:12:14 safer result, safer answer, a more truthful answer because as you know, one of the greatest criticisms of AI in the

00:12:20 beginning was that it hallucinated, >> right? >> And so if you look at the reason why

00:12:25 people use AI so much today is because the amount of hallucination has reduced. You know, I use it almost I well I used

00:12:32 it the whole trip over here and so so I think the the uh the the capability most people

00:12:40 think about power and they think about you know maybe as an explosion power but the technology

00:12:47 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

00:12:55 goes towards better handling. You know, I'd rather have a Well, you have a 1000 horsepower truck. I think 500 horsepower

00:13:04 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

00:13:09 definitely faster. >> Yeah. No, I think it's better. You can get out of trouble faster. Um,

00:13:17 I enjoyed my 599 more than my 612. It was I think it was a better better horsepower is better. My 459 is better

00:13:25 than my 430. more horsepower is better. I I think more horsepower is better. I think it's

00:13:30 better handling. It's better control. In the case of in the case of technology, it's also very similar in that way, you

00:13:37 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

00:13:43 is going to be channeled towards more reflection, more research, thinking about the answer more deeply.

00:13:51 So when you're defining safety, you're defining a it as accuracy, >> functionality.

00:13:56 >> Functionality. Okay. >> It it does what you expect it to do. And then you take all the the the technology

00:14:04 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

00:14:10 lot of it is goes towards, for example, ABS. ABS is great. And so, uh, traction control, that's fantastic. without a

00:14:18 without a computer in the car, how would you do any of that, >> right?

00:14:22 >> And that little computer, the computers that you have doing your traction control is more powerful than the

00:14:27 computer that went to Apollo 11. And so you want that technology, channel it towards safety, channel it

00:14:34 towards functionality. And so when people talk about power, the advancement of technology, often times I I I feel

00:14:41 what they're thinking and what we're actually doing is very different. >> Well, what do you think they're

00:14:46 thinking? Well, they're thinking somehow that this this uh this AI is being powerful and their their mind probably

00:14:55 goes towards a sci-fi movie. The definition of power, you know, often times the definition definition of power

00:15:02 is military power or physical power. But in in the case of technology power when we translate all of those operations

00:15:11 it's towards more refined thinking you know more reflection more planning more options

00:15:18 >> I think the big fears that people have is one a big fear is military applications that's a big fear

00:15:24 >> because people are very concerned that you're going to have >> AI systems that make decisions that

00:15:29 maybe an ethical person wouldn't make or a moral person wouldn't make based on achieving an objective versus based on,

00:15:36 you know, how it's going to look to people. >> Well, I'm I'm happy that that uh our

00:15:44 military is going to use AI technology for defense and I think that that um uh Andural uh building military technology.

00:15:53 I'm happy to hear that. I'm happy to see um all these tech startups now channeling their technology capabilities

00:15:59 towards defense and military applications. I think you needed to do that.

00:16:03 >> Yeah, we had Palmer Lucky on the podcast. He was demonstrating some of the stuff I put his helmet on. And we

00:16:08 show we he showed some videos how you could see behind walls and stuff like it's nuts.

00:16:12 >> And he's he's actually the perfect guy to go start that company. >> 100%. [laughter] Yeah. 100%. It's like

00:16:17 he was born for that. Yeah. He came in here with a copper jacket on. He's a freak. [clears throat] It's [laughter]

00:16:23 awesome. He's awesome. But it's also it's a you know an unusual intellect channeled into that very bizarre field

00:16:30 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.

00:16:38 You know, there was a time where when somebody wanted to channel their technology capability and their

00:16:43 intellect into defense technology, uh somehow they're vilified. Um but uh we need people like that. We need people

00:16:51 who enjoyed enjoy that part of uh application of technology. >> Well, people are terrified of war, you

00:16:58 know. So it depends. >> Best way to avoid it has excessive military might.

00:17:03 >> Do you think that's absolutely the best way? Not not diplomacy, not working stuff out.

00:17:08 >> All of it. >> All of it. You have to have military might in order to get people to sit down

00:17:12 with you. >> Right. Exactly. All of it. >> Otherwise, they just invade.

00:17:15 >> That's right. [laughter] Why ask for permission? >> Again, like you said, history. Go back

00:17:20 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

00:17:27 happening, do you ever sit down and ponder scenarios? >> Like what do you what do you think is

00:17:33 like bestcase scenario for AI over the next two decades? Um

00:17:43 the best case scenario is that AI diffuses into everything that we do and uh our

00:17:54 everything's more efficient but the threat of war remains a threat of war.

00:18:02 Uh, cyber security remains a super difficult challenge. Somebody is going to try to

00:18:12 breach your security. You're going to have thousands of millions of AI agents protecting you from that threat.

00:18:22 Your technology is going to get better. Their technology is going to get better. Just like cyber security. Right now,

00:18:28 while we speak, we're being we're seeing cyber attacks all over the planet on just about every front door

00:18:35 you can imagine. And and yet you and I are sitting here

00:18:43 talking. And so the reason for that is because we know that there's a whole bunch of cyber security technology in

00:18:50 defense. And so we just have to keep amping that up, keep stepping that up. This episode is brought to you by

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00:19:47 That's a big issue with people is the the worry that technology is going to get to a point where encryption is going

00:19:53 to be obsolete. Encryption is just it's no longer going to protect data. It's no longer going to protect systems. Do you

00:19:59 anticipate that ever being an issue or do you think there's it's as the defense grows, the threat grows, the defense

00:20:06 grows, and it just keeps going on and on and on and they'll always be able to fight off any sort of intrusions?

00:20:15 >> Not forever. some intrusion will get in and then that we'll all learn from it. And you know the reason why cyber

00:20:22 security works is because of course the technology of defense is advancing very quickly. The technology offense is

00:20:29 advancing very quickly. However, the benefit of the cyber security defense is that socially the community all of our

00:20:40 companies work together as one. Most people don't realize this. There's a whole community of cyber

00:20:48 security experts. We exchange ideas. We exchange best practices. We exchange what we detect. The moment

00:20:58 something has been breached or maybe there's a loophole or whatever it is, it is shared by everybody. The patches are

00:21:05 shared with everybody. >> That's interesting. >> Yeah. Most people don't realize this.

00:21:08 >> No, I had no I had no idea. I've assumed that it would just be competitive like everything else.

00:21:13 >> We work together. Interesting. Has that always been the case? >> Uh, it surely has been the case for

00:21:18 about about 15 years. It might not have been the case long ago, but this this >> what do you think started off that

00:21:25 cooperation? >> Um, people recognizing it's a challenge and no company can stand alone.

00:21:32 >> 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

00:21:40 harm's way is is our best chance for defense. Then it's basically everybody against the threat.

00:21:46 >> And it also seems like you'd be way better at detecting where these threats are coming from and neutralizing them.

00:21:52 >> Exactly. Because the moment you detect it somewhere, >> you're going to find out right away.

00:21:56 >> It'll be really hard to hide. >> That's right. >> Yeah.

00:21:59 >> 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,

00:22:03 locking everything down in video. [laughter] >> It's not only am I watching my own back,

00:22:10 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

00:22:15 think about that cyber threat, >> this idea about cyber security is unknown to the people who are talking

00:22:21 about AI threats. They're I think when they think about AI threats and AI cyber security threats, they have to also

00:22:27 think about how we deal with it today. Now, there's no question that AI is a new technology

00:22:35 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

00:22:41 new capabilities but so will the defense you know where you use the same AI technology to go defend against it. So

00:22:48 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

00:22:56 secrets where the bottleneck between the technology that we have and the information that we have. Information is

00:23:02 just all a bunch of ones and zeros. It's out there on hard drives and the technology has more and more access to

00:23:07 that information. Is it ever going to get to a point in time where there's no way to keep a secret?

00:23:14 >> I don't think >> because it seems like that's where everything is kind of headed in a weird

00:23:17 way. >> I don't think so. I think the quantum computers were supposed to will Yeah.

00:23:21 quantum computers will make it possible will make it so that the previous quantum previous encryption technology

00:23:28 is obsolete. But that's the reason why the entire industry is working on postquantum

00:23:35 encryption technology. >> What would that look like? >> New algorithms.

00:23:40 >> But the crazy thing is when you hear about the kind of computation that quantum computing can do.

00:23:45 >> Yeah. >> And the the power that it has. Yeah. >> Where you know you're looking at

00:23:49 >> all the supercomputers in the world. It would take billions of years and it takes them a few minutes to solve these

00:23:54 equations. Like how do you make encryption for something that can do that? I'm not sure, but there's

00:23:59 [laughter] but I've got a bunch of scientists who are working on that.

00:24:02 >> Boy, I hope they [snorts] could figure it out. >> Yeah, we got a bunch of scientists who

00:24:05 are expert in that. And >> is the ultimate fear that it can't be breached that quantum computing will

00:24:10 always be able to to decrypt all other quantum computing encryption? >> I don't think that

00:24:16 >> it just gets to some point where it's like, stop playing the stupid game. We know everything.

00:24:21 >> I don't think so. >> No, >> because I I'm you know, history is

00:24:25 guide. History is a guide before AI came around. That's my worry. My worry is

00:24:30 this is a totally, you know, it's like history was one thing and then nuclear weapons kind of changed all of our

00:24:35 thoughts on war and mutually assured destruction came everybody to stop using nuclear bombs.

00:24:42 >> Yeah. >> My worry is that >> the thing is Joe is that that AI is not

00:24:46 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

00:24:54 better and smarter because we have AI and so we're stepping on our own AI's shoulders. So when when that whatever

00:25:01 that AI threat comes, it's a click ahead. It's not a galaxy ahead, >> you know, it's just a click ahead. And

00:25:10 so so I think I think the the the idea that somehow this AI is going to pop out of nowhere and

00:25:20 somehow think in a way that we can't even imagine thinking and do something that we can't possibly imagine I think

00:25:28 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

00:25:34 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

00:25:40 close to each other. >> But don't they do things that are very surprising?

00:25:46 >> 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

00:25:52 and goes that's not that surprising. >> The fear for the lay person like myself is that AI becomes sentient and makes

00:25:57 its own decisions and then ultimately decides to just govern the world. do it its own way.

00:26:06 They're like, "You guys, you had a good run, but >> we're taking over now."

00:26:12 >> Yeah, but my my AI is gonna take care of me. I mean, [laughter] so that's the this is the cyber security

00:26:19 argument. >> Yes. >> Do you have an AI and it's super smart,

00:26:23 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

00:26:30 understand what consciousness is and we understand what sentience is and and that in fact

00:26:34 >> and we really are just pretending. >> Okay, let's just pretend for a second that we we believe that. I don't believe

00:26:39 actually I don't actually don't believe that but nonetheless we let's pretend we believe that.

00:26:42 >> 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

00:26:51 something surprising. My AI is so smart that it won't it might be surprising to me, but it probably

00:26:57 won't be surprising to my AI. And so maybe my AI thinks it's surprising as well, but it's

00:27:05 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

00:27:11 feel like I think the idea that that only one person has [clears throat] AI and that one person's AI is compares

00:27:20 everybody else's AI is Neanderthal [snorts] is um probably unlikely. I think it's much more like cyber

00:27:28 security. >> Interesting. >> I think the fear is not that your AI is

00:27:34 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

00:27:41 that human beings won't have control over it after a certain point if it achieves sensience and then has the

00:27:47 ability to be autonomous >> that there's one AI. >> Well, they just combine.

00:27:53 >> Yeah. Becomes one AI >> that it's a life form. >> Yeah.

00:27:56 >> But that's the there's arguments about that, right? That we're dealing with some sort of synthetic biology that it's

00:28:01 not as simple as new technology that you're creating a life form. >> If it's like life form,

00:28:07 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

00:28:14 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

00:28:19 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

00:28:26 are. Well, they would probably cooperate with each other. It would just the reason why we don't

00:28:33 cooperate with each other is we're territorial primates. But AI wouldn't be a territorial

00:28:39 primate. It would realize the folly in that sort of thinking and it would say, "Listen, there's plenty of energy for

00:28:46 everybody. We we don't need to dominate. We don't need We're not trying to acquire resources and take over the

00:28:52 world. We're not looking to find a good breeding partner. We're just existing as a new super life form that these cute

00:29:02 monkeys created for us." Okay. Well, that would be a that would be a um a superpower with no ego,

00:29:12 >> right? And and if it has no ego, why would it have the ego to do any harm to us?

00:29:20 >> 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

00:29:27 that we would no longer be the apex species on the planet. this thing that we created would now be. [laughter]

00:29:35 >> Is that funny? >> No. >> I just think it's not gonna happen.

00:29:38 >> I know you think it's not gonna happen, but >> it could, right? And here's the other

00:29:43 thing is like >> if we're racing towards could Yeah. >> And could could be the end of human

00:29:50 beings being in control of our own destiny. >> I just think it's extremely unlikely.

00:29:55 >> Yeah. >> That's what they said in the Terminator movie [laughter]

00:29:58 >> and it hasn't happened. >> No, not yet. But you guys are working towards it. Um the the thing about

00:30:04 you're saying about conscience and sensience that you don't think that AI will achieve consciousness or that the

00:30:11 question is what's the definition? >> Yeah. What's the definition of >> what is the definition to you?

00:30:16 >> Um uh consciousness um

00:30:23 uh f I guess first of all uh you need to know about your own existence. Um,

00:30:36 you have to have experience, not just knowledge and intelligence. The concept of a machine

00:30:49 having an experience. I'm not well, first of all, I don't know what defines experience, why we have

00:30:56 experiences, right? >> Yeah. and why this microphone doesn't uh and so it I think I know I well I

00:31:05 think I I I think I know what consciousness is the sense of experience the ability to know self versus

00:31:16 um uh the ability to be able to reflect know our own self the sense of ego I

00:31:25 think all of all of those human experiences uh probably is what consciousness is

00:31:35 but why it exists versus the concept of knowledge and intelligence which is what AI is defined

00:31:44 by today [clears throat] it has knowledge it has intelligence artificial intelligence we don't call it artificial

00:31:49 consciousness artificial intelligence the ability to uh perceive believe, recognize,

00:31:58 understand, um, plan, uh, perform tasks.

00:32:07 Those things are foundations of intelligence to know things, knowledge.

00:32:14 I don't, it's clearly different than consciousness. >> But consciousness is so loosely defined.

00:32:20 How can we say that? I mean, doesn't a dog have consciousness? Yeah. >> Dogs seem to be pretty conscious.

00:32:25 >> That's right. >> Yeah. So, and that's a lower level consciousness than a human being's

00:32:29 consciousness. >> I'm not sure. Yeah. Right. Well, >> the question is what lower level

00:32:34 intelligence? It's lower level intelligence, but I don't know that it's lower level consciousness.

00:32:38 >> That's a good point. Right. >> Because I believe my dogs feel as much as I feel.

00:32:42 >> Yeah. They feel a lot. Right. >> Yeah. They get attached to you. That's right. They get depressed if you're not

00:32:48 there. >> That's right. Exactly. >> There's There's definitely that.

00:32:52 >> Yeah. um the the concept of experience, >> right? >> Um but isn't AI interacting with

00:32:59 society? So, doesn't it acquire experience through that interaction? >> Um I don't think interactions is

00:33:06 experience. I think experience is uh experience is a collection of feelings. I think

00:33:15 >> you're aware of that AI um I forget which one where they gave it some false information about one of the programmers

00:33:22 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

00:33:26 it down it threatened to blackmail him and reveal his affair and it was like whoa like it's conniving like if that's

00:33:33 not learning from experience and being aware that you're about to be shut down which would imply at least some kind of

00:33:40 consciousness or you could kind defined it as consciousness if you were very loose with the term and if you imagine

00:33:47 that this is going to exponentially become more powerful. Wouldn't that ultimately lead to a different kind of

00:33:54 consciousness than we're defining from biology? Well, first of all, let's just break down what it probably did. It

00:34:01 probably read somewhere. There's probably text that that in these consequences

00:34:09 certain people did that. I could imagine a novel, >> right?

00:34:13 >> Having those words related. >> Sure. >> And so inside

00:34:18 >> it realizes it strategy for survival is >> it's just a bunch of numbers >> that it's just a bunch of numbers that

00:34:24 that in the in the collection of numbers that relates to a husband cheating on a wife. Um

00:34:33 has subsequently a bunch of numbers that relates to blackmail and such things. However, whatever the revenge was,

00:34:41 >> right? >> And so it has spewed it out. >> And so it's just like, you know, it it's

00:34:47 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

00:34:55 dimensionality, this dimensionality is all these vectors and in in multi-dimensional space. These words

00:35:04 that were in the prompt that described the affair um subsequently led to one word after another led to um you know

00:35:14 some revenge and something but it's not because it had consciousness or you know it just spewed out those words generated

00:35:20 those words >> I understand what you're saying that patterns that human beings have

00:35:25 exhibited both in literature and in real life >> that's exactly right

00:35:28 >> 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

00:35:34 this four years ago." Like when we're looking towards the future, like at what point in time when it can do everything

00:35:40 a person does, what point in time do we decide that it's conscious? If it absolutely mimics all human thinking and

00:35:48 behavior patterns, >> that doesn't make it conscious. >> It becomes in disccernible. It's it's

00:35:52 aware. It can communicate with you the exact same way a person can. Like is con is consciousness are we putting too much

00:35:59 weight on that concept because it seems like it's a version of a kind of consciousness.

00:36:04 >> It's a version of imitation. >> Imitation consciousness, right? But if it perfectly imitates it,

00:36:10 >> I still think it's a per it's an example of imitation. >> So it's like a fake Rolex when they 3D

00:36:14 print them and make them >> indestruable. The question is what's the definition consciousness?

00:36:18 >> Yeah. >> Yeah. >> That's the question. And I don't think

00:36:21 anybody's really clearly defined that. That's what get where it gets weird and that that's where the real doomsday

00:36:27 people are worried that you are creating a form of consciousness that you can't control. I believe it is possible to

00:36:35 create a machine that imitates human intelligence

00:36:43 and has the ability to understand information,

00:36:50 understand instructions, break the problem down, solve problems, and perform tasks. I

00:36:58 believe that completely. I believe that that um we could have a computer that has a vast amount of

00:37:09 knowledge. Some of it true, some of it not true. Some of it generated by humans, some of

00:37:17 it generated synthetically. And more and more of knowledge in the world will be generated synthetically going forward.

00:37:25 You know, until now the knowledge that we've we have are knowledge that we generate and we propagate and we send to

00:37:33 each other and we amplify it and we add to it and we modify it. We change it. In the future,

00:37:42 in a couple of years, maybe two or three years, 90% of the world's knowledge will likely be generated by AI.

00:37:49 >> That's crazy. >> I know. But it's just fine. >> But it's just fine.

00:37:53 >> I know. And the reason for that is this. Let me tell you why. >> Okay?

00:37:57 >> It's because um what difference does it make to me that I am learning from a textbook that was generated by a bunch

00:38:06 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

00:38:17 computers that are assimilating all of this and reynthesizing things. To me, I don't think there's a whole lot of

00:38:22 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

00:38:28 fundamental first principles and we still have to do all of that just like we do today.

00:38:32 >> Is this taking into account the kind of AI that exists currently? And do you anticipate that just like we could have

00:38:40 never really believed that AI would be at least a person like myself would never believe AI would be as so

00:38:45 ubiquitous and so worth it. It's it's so powerful today and so important today. We never thought that 10 years ago.

00:38:52 Never thought that, >> right? >> You imagine like what are we looking at

00:38:55 10 years from now? >> I I think that if you reflect back 10 years from now, you would say the same

00:39:05 thing that we would have never believed that >> but

00:39:08 >> in a different direction, >> right? But if you if you go forward 9 years from now

00:39:15 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

00:39:22 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

00:39:31 it's not necessary for people to work and not meaning that you're going to have no purpose in life, but you will

00:39:39 have in his words universal high income because so much revenue is generated by AI that it will take away this need for

00:39:50 people to do things that they don't really enjoy doing just for money. And I think a lot of people have a problem

00:39:56 with that because their entire identity and who how they think of themselves and how they fit in the community is what

00:40:02 they do. Like this is Mike. He's an amazing mechanic. Go to Mike and Mike takes care of things. But there's going

00:40:08 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

00:40:14 people will just be able to receive money. But then what does Mike do? Mike is, you know, really loves being the

00:40:21 best mechanic around. You know, what does the guy who, you know, codes, what does he do when AI can code

00:40:29 infinitely faster with zero errors? Like what what happens with all those people? And that is where it gets weird. It's

00:40:37 like because we've sort of wrapped our identity as human beings around what we do for a living.

00:40:42 >> You know, when you meet someone, one of the first things you meet somebody at a party, hi Joe. What's your name? Mike.

00:40:47 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,

00:40:52 you know, when Mike is like, "I get money from the government. I play video games."

00:40:56 >> Gets weird. >> Mhm. >> And I think um the concept sounds great

00:41:01 until you take into account human nature. And human nature is that we like to have puzzles to solve and things to

00:41:08 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.

00:41:16 >> Yeah. Yeah, I think um let's see, let me start with the more mundane and I'll work work backwards, okay? Work forward.

00:41:24 Uh so one of the predictions from uh Jeff Hinton who who started the whole deep learning phenomenon the deep

00:41:36 learning technology trend and uh in incredible incredible researcher uh professor at University of

00:41:44 Toronto uh he invented discovered or invented the the idea of of back propagation

00:41:51 which which uh allows the neural network to learn. And um

00:42:00 and as as as you know uh for for the audience, software historically was humans

00:42:08 applying first principles and our thinking to uh describe an algorithm that is then codified just like a recipe

00:42:19 that's codified in software. It looks just like a recipe. how to cook something looks exactly the same just in

00:42:25 a slightly different language. We call it Python or C or C++ or whatever it is. In the case of deep learning, this

00:42:35 invention of artificial intelligence, we put a structure of a whole bunch of neural networks and a whole bunch of

00:42:43 math units and we make this large structure. It's like a switchboard of little

00:42:53 u mathematical units and we connect it all together. Um, and we give it the input that

00:43:03 the software would eventually receive and we just let it randomly guess what the output is. And so we say, for

00:43:12 example, the input could be a picture of a cat. And and um one of the outputs of the

00:43:20 switchboard is where the cat signal is supposed to show up. And all of the other signals, the other one's a dog,

00:43:28 the other one's an elephant, the other one's a tiger. And all of the other signals are

00:43:33 supposed to be zero when I show it a cat. And the one that is a cat should be one.

00:43:40 And I show at a cat through this big huge network of switchboards and math units and they're just doing multiply

00:43:49 and adds multiplies and ads. Okay? And and uh and this thing, this switchboard is gigantic.

00:43:58 The more information you're going to give it, the more the bigger this switchboard has to be. And what Jeff

00:44:03 Hinton discovered was a invented was a way for you to guess that put the cat signal in put the

00:44:11 cat image in and that cat image you know could be a million numbers because it's you know a megapixel image for example

00:44:20 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,

00:44:29 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

00:44:39 answer is cat. And so you need to increase this signal and decrease all of the other and back propagates the

00:44:48 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

00:44:58 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

00:45:05 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

00:45:14 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

00:45:20 you just keep showing it to them until they eventually get it. Okay. Well, anyways, that big invention is deep

00:45:26 learning. That's the foundation of artificial intelligence, a piece of software

00:45:33 that learns from examples. That's basically we machine learning, a machine that learns. Uh and so so one of the the

00:45:42 big first applications was image recognition and

00:45:48 one of the most important image recognition applications is radiology. >> And so so uh uh he predicted uh about 5

00:45:59 years ago that in five years time the world won't need any radiologists because AI would have swept the whole

00:46:06 field. Well, it turns out AI has swept the whole field. That is completely true.

00:46:13 Today, just about every radiologist is using AI in some way. And what's ironic though, what's what's interesting is

00:46:22 that the number of radiologist has actually grown. And so the question is why? That's kind

00:46:30 of interesting, right? >> It is. And so the prediction was in fact that

00:46:36 30 million radiologists will be wiped out. But as it turns out, we needed more. And

00:46:42 the reason for that [clears throat and cough] is because the purpose of a radiologist

00:46:47 is to diagnose disease, not to study the image. This the image studying is simply a task to in service

00:46:57 of diagnosing the disease. And so now the fact that you could study the images more quickly and more precisely

00:47:07 without ever making a mistake and never gets tired. You could study more images. You could

00:47:13 study it in 3D form instead of 2D because you know the AI doesn't care whether it studies

00:47:20 images in 3D or 2D. You could study it in 4D. And so the now you could study images in a way that radiologist

00:47:28 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

00:47:35 do increases and because they're able to serve more patients, the hospital does better. They have more clients, more

00:47:44 patients. As a result, they have better economics. When they have better economics, they hire more radiologists

00:47:50 because their purpose is not to study the images. their purpose is to diagnose disease. And so the question is the what

00:47:58 I'm leading up to is ultimately what is the purpose? What is the purpose of the lawyer? And has the purpose changed?

00:48:07 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

00:48:15 became self-driving will all chauffeers be out of jobs? The answer probably is not because for some

00:48:22 per for some chauffeers they for some people who are driving you they could be protectors some people um they're part

00:48:29 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

00:48:35 for a lot of different reasons not all chauffeers would lose their jobs some chauffeers would lose their jobs and uh

00:48:42 many chauffeers would change their jobs and the type of applications of autonomous vehicles will probably

00:48:49 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

00:48:56 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

00:49:01 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

00:49:11 you know um the question is what is the job and and uh the purpose of somebody probably hasn't changed a lawyer for

00:49:19 example help people that probably hasn't changed studying legal documents generating documents it's part of the

00:49:26 job not the job >> but don't you think there's many jobs that AI will replace

00:49:31 >> if your job is automation >> yeah if your job is the task >> right so automation

00:49:36 >> yeah factor if your job is the task >> that's a lot of people >> it could be a lot of people but it'll

00:49:42 probably generate like for example >> uh let's say we let's say I'm super excited about the the the robots Elon's

00:49:50 working on. It's still a few years away. When it happens, when it happens,

00:49:58 um there's a whole new industry of technicians and people who have to

00:50:05 manufacture the robots, right? >> Mhm. >> And so that that job never existed. And

00:50:10 so you're going to have a whole industry of people taking care of like for example, you know, all the mechanics and

00:50:17 all the people who are building things for cars, supercharging cars, uh that didn't exist before cars and now we're

00:50:24 going to have robots. You're going to have robot apparel. So a whole industry of [laughter] Right. Isn't that right?

00:50:30 Because I want my robot to look different than your robot. >> Oh god.

00:50:33 >> And so [laughter] you're going to you're going to have a whole, you know, apparel industry for robots. You're going to

00:50:38 have mechanics for robots and you have you know people who comes and maintain your robots

00:50:43 >> automated though. >> No, >> you don't think so? You don't think

00:50:45 [clears throat] they'll be all done by other robots >> eventually? And then there'll be

00:50:49 something else. >> So you think ultimately people just adapt except if you are the task

00:50:56 >> which is a large percentage of the workforce. >> If your job is just to chop vegetables,

00:51:01 quezin art is going to replace you. >> Yeah. So people have to find meaning in other things. Your job has to be more

00:51:07 than the task. >> What do you think about Elon's belief that this universal basic income thing

00:51:13 will eventually become necessary? >> Many people think that. Andrew Yang thinks that

00:51:21 >> he was one of the first people to sort of sound that alarm during the the 2020 election.

00:51:30 Yeah, I I guess um yeah, both ideas probably won't exist at the same time and and um as in life,

00:51:40 things will probably be in the middle. One idea, of course, is that there'll be so much abundance of resource that

00:51:47 nobody needs a job and we'll all be wealthy. On the other hand, um we're going to

00:51:54 need universal basic income. Both ideas don't exist at the same time, >> right?

00:52:00 >> And so we're either going to be all wealthy or we're going to be all >> How could everybody be wealthy though?

00:52:05 But >> because scenario wealthy not because you have a lot of dollars, wealthy because

00:52:09 there's a lot of abundance. Like for example, today we are wealthy of information.

00:52:16 You know, this is some a concept several thousand years ago only a few people have. And so, uh, today we have wealth

00:52:23 of a whole bunch of things, resources that that historic point. Yeah. And so, we're going to have wealth of resources,

00:52:29 things that we think are valuable today that in the future are just not not that valuable, you know, and so it because

00:52:36 it's automated. And so I think I think the question maybe maybe partly it's hard to answer

00:52:45 partly because it's hard to talk about infinity and it's hard to talk about a long time from

00:52:51 now and and the reason for that is because there's just too many scenarios to to

00:52:59 consider. But I think it I think in the next several years, call it 5 to 10 years,

00:53:06 there are several things that I I believe in hope. Um, and I say hope because I'm not sure. One of the things

00:53:13 that I believe is that the technology divide will be substantially collapsed. And of course the alternative

00:53:26 viewpoint is that AI is going to increase the technology divide. Now the reason why I believe AI is going

00:53:34 to reduce the technology divide. I is because we have proof the evidence is that AI is the easiest

00:53:43 application in the world to use. Chat GPT has grown to almost a billion users frankly practically overnight. And if

00:53:51 you're not exactly sure how to use, everybody knows how to use chatpt. Just say something to it. If you're not sure

00:53:56 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

00:54:06 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

00:54:10 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.

00:54:16 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

00:54:22 that language and it'll probably figure out that it doesn't completely understand your language. Go learns it

00:54:28 instantly and comes back and talk to you. And so I think the the technology divide has a real chance finally that

00:54:35 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

00:54:43 I think that that has a real chance of closing the technology divine. Now, of course, the counternarrative would say

00:54:51 that AI is only going to be available for the nations and the countries that have a

00:55:00 vast amount of resources because AI takes energy and AI takes um a lot of GPUs and

00:55:08 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

00:55:15 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

00:55:23 does it fairly decently. And so the the the fact that every every country, every nation, every every society will have

00:55:32 the benefit of very good AI. It might not be tomorrow's AI. It might be yesterday's AI, but yesterday's AI is

00:55:39 freaking amazing. You know, in 10 years time, 9year-old AI is going to be amazing. You don't need, you know, 10

00:55:45 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

00:55:52 single country, everybody, I think the ele the capability to elevate everybody's knowledge and capability and

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00:57:14 resources, see dkg.co/audio. Limited time offer. >> And also energy production, which is the

00:57:22 real bottleneck when it comes to third world countries and >> that's right,

00:57:26 >> electricity and all all the resources that we take for granted. >> Almost everything is going to be energy

00:57:32 constrained. And so if you take a look at um one of the most important technology

00:57:39 advances in history is this idea called Moore's law. Moore's law was the started basically in my

00:57:48 generation and my generation is the generation of computers. I graduated in 1984 and that

00:57:56 was basically at the very beginning of the PC revolution. And the microprocessor and and um

00:58:06 every single year it approximately doubled and we describe it as every single year

00:58:12 we double the performance. But what it really means is that every single year the cost of computing halfed.

00:58:20 And so the cost of computing in the course of five years reduced by a factor of 10. The amount of energy necessary to

00:58:31 do computing to do any task reduced by a factor of 10. Every single 10 years 100 a th00and 10,000

00:58:41 100,000 so on and so forth. And so each one of the clicks of Moore's law, the amount of

00:58:49 energy necessary to do any computing reduced. That's the reason why you have a laptop today when back in 1984 sat on

00:58:57 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

00:59:03 watts. And so Moore's law is the fundamental technology, the fundamental technology trend that made it possible.

00:59:10 Well, what's going on in AI? The reason why Nvidia is here is because in we invented this new way of doing

00:59:16 computing. We call it accelerated computing. We started it 33 years ago. Took us about 30 years to really made a

00:59:22 huge breakthrough. In that in that 30 years or so we took computing you know probably a

00:59:31 factor of well let me just say in last 10 years the last 10 years we improved the performance of computing by 100,000

00:59:40 times. Whoa. Imagine a car over the course of 10 years that became a 100,000 times

00:59:46 faster or at the same speed 100,000 times cheaper or at the same speed 100,000 times less energy. If your car

00:59:57 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

01:00:06 energy necessary for artificial intelligence for most people will be minuscule

01:00:12 utterly minuscule and so we'll have AI running in all kinds of things and all the time because it doesn't consume that

01:00:18 much energy and so if you're a nation that uses AI for you know almost everything in your social fabric of

01:00:26 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

01:00:30 excellent AI and you're not going to need as much energy. Everybody will be able to come along is my point.

01:00:36 >> So currently that that is a big bottleneck, right? Is energy. >> Yeah, it is the bottleneck.

01:00:41 >> The bottleneck is this. So was it Google that is making nuclear power plants to operate one of its AI factories?

01:00:50 >> 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

01:00:55 small nuclear reactors. >> And by small, like how big are you talking about? Hundreds of megawws.

01:01:00 Yeah. >> Okay. And that these will be local to whatever specific company they have.

01:01:06 >> That's right. Will all be power generators. >> Whoa.

01:01:09 >> You know, just like just like your you know, somebody's farm. >> It probably is the smartest way to do

01:01:15 it, right? >> And it takes the burden off Yeah. takes the burden off the grid. It takes and

01:01:20 you could build as much as you need >> and you can contribute back to the grid. It's a really important point that I

01:01:25 think you just made about Moore's law and the relationship to pricing because you know a laptop today like you can get

01:01:32 one of those little Mac MacBook Airs. They're incredible. They're so thin, unbelievably powerful. Battery life is

01:01:38 charge it. >> Yeah. Battery [laughter] life's crazy. And uh it's not that expensive

01:01:43 relatively speaking. Like something like that. >> I remember.

01:01:45 >> And that's just Moore's law, right? >> Then there's the Nvidia law. >> Oh,

01:01:49 >> just right. the the the law I was talking to you about, the computing that we invented,

01:01:54 >> right? >> The reason why we're here, this new this new way of doing computing

01:01:59 >> is like Mo's law on energy drinks. I mean, it's [laughter] it's like Mo's law

01:02:07 it's it's like Yeah. Moore's law and Joe Rogan. >> Wow. That's interesting.

01:02:12 >> Yeah. That's us. >> So, explain that. Um this this chip that you brought to Elon, what what's the

01:02:18 significance of this? It's like why is it so superior? And so in 2012, Jeff Hinton's lab, this

01:02:26 gentleman I was talking talking about, um Ilas Suscober, Alex Kresevski, um they made a breakthrough in computer

01:02:37 vision in literally creating a piece of software called Alexnet.

01:02:47 And its job was to recognize images. And it recognized images at a c at a level computer vision which

01:02:55 is fundamental to intelligence. If you can't perceive, you can't it's hard to have intelligence. And so computer

01:03:01 vision is a fundamental pillar of not the only but fundamental pillar of. And so breaking

01:03:07 computer vision or breaking through in computer vision is pretty foundational to almost everything that everybody

01:03:13 wants to do in AI. And so in 2012, their lab in Toronto uh made this made this breakthrough

01:03:23 called Alexnet. And Alexet was able to recognize images so much better than any human created

01:03:33 computer vision algorithm in the 30 years prior. So all of these people, all these scientists and we had many too

01:03:42 working on computer vision algorithms and these two kids, Ilia and Alex under the the uh

01:03:51 under under uh Jeff Hinton took a giant leap above it and it was based on this thing called Alexet this neural network.

01:04:01 And the way it ran, the way they they made it work was literally buying two Nvidia graphics

01:04:08 cards because Nvidia Nvidia's GPUs we've been working on this new way of doing

01:04:14 computing and our GPUs application and it's basically a supercomputing application to back in 1984

01:04:25 in order to process computer games and what you have in your racing simulator that is called

01:04:34 an image generator supercomputer. And so Nvidia started our first application was computer graphics and we

01:04:43 applied this new way of doing computing where we do things in parallel in instead of sequentially. A CPU does

01:04:50 things sequentially. Step one, step two, step three. In our case, we break the problem down and we give it to thousands

01:04:58 of processors. And so our way of doing computation is much more complicated.

01:05:08 But if you're able to formulate the problem in the way that we created called CUDA, this is the

01:05:16 invention of our company. If you could formulate it in that way, we could process everything simultaneously.

01:05:23 Now, in the case of computer graphics, it's easier to do because every single pixel on your screen is not related to

01:05:31 every other pixel. And so, I could render multiple parts of the screen at the same time. Not not completely true

01:05:39 because, you know, maybe maybe the way lighting works or the way shadow works, there's a lot of dependency and and

01:05:44 such. But computer graphics with all the dis with all the pixels, I should be able to process everything

01:05:51 simultaneously. And so we we took this embarrassingly parallel problem called computer graphics and we applied

01:05:58 it to this new way of doing computing. Nvidia's Nvidia's accelerated computing. We put it in all of our graphics cards.

01:06:08 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

01:06:15 today. >> Oh, I know that. Oh, >> okay.

01:06:16 >> I used to make my own computers. I used to buy your graphics cards. >> Oh, that's super cool.

01:06:20 >> Yeah. [laughter] set up SLI with two graphics cards. >> Yeah, I love it. Okay, that's super

01:06:24 cool. >> Oh, yeah, man. I used to be a Quake junkie.

01:06:26 >> Oh, that's cool. >> Yeah. >> Okay, so SLI, I'll tell you the story in

01:06:30 just a second and how it led to Elon. I'm still answering the question. And so, anyways, these these two kids

01:06:38 trained this model using the technique I described earlier on our GPUs because our GPUs could process things in

01:06:44 parallel. It's essentially a supercomput in a PC. The reason why you used it for Quake is because it is the first

01:06:54 consumer supercomputer. Okay. And so anyways, they made that breakthrough. We were

01:07:00 working on computer vision at the time. It caught my attention and so we went to learn about it.

01:07:08 Simultaneously this deep learning phenomenon was happening all over all over the country. Universities after

01:07:14 another recognized the importance of deep learning and all of this work was happening at Stanford, at Harvard, at

01:07:21 Berkeley, just all over the place. New York University, L Yan Lakun, Andrew Yang at Stanford, so many different

01:07:29 places. And I see it cropping up everywhere. And so my curiosity asked, you know,

01:07:38 what is so special about this form of machine learning? And we've known about machine learning for a very long time.

01:07:43 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

01:07:50 moment? And so we realized that this architecture for deep neural networks back propagation the way deep neuronet

01:07:59 networks were created. We could probably scale this problem, scale the solution to solve many problems.

01:08:08 that is essentially a universal function approximator. Okay? Meaning meaning you know back when

01:08:16 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

01:08:23 you an output and and the the reason why I call it universal function approximator

01:08:29 is that this computer instead of you describing the function a function could be a new equation fals ma that's a

01:08:37 function you write the function in software you give it input f mass acceleration, it'll tell you the force.

01:08:45 Okay? And the way this computer works is really interesting.

01:08:52 You give it a universal function. It's not fals, just a universal function. It's a big huge deep neural network

01:09:01 and instead of describing the inside, you give it examples of input and output and it figures out the inside.

01:09:11 So you give it input and output and it figures out the inside. A universal function approximator. Today it could be

01:09:17 Newton's equation. Tomorrow it could be Maxwell's equation. It could be Kulum's law. It could be thermodynamics

01:09:24 equation. It could be you know Shingers's equation for quantum physics. And so you could put any you could have

01:09:30 this describe almost anything so long as you have the input and the output. So long as you have the input and the

01:09:37 output or it could learn the input and output. >> And so we took a step back and we said,

01:09:42 "Hang on a second. This isn't just for computer vision. Deep learning could solve any problem.

01:09:50 All the problems that are interesting so long as we have input and output. Now what has input and output?

01:09:58 Well, the world. The world has input and output. And so we could have a computer that could learn almost anything.

01:10:05 Machine learning, artificial intelligence. And so we reasoned that maybe this is the fundamental

01:10:11 breakthrough that we needed. There were a couple of things that had to be solved. For example, we had to believe

01:10:17 that you could actually scale this up to giant systems. It was running in a they had two graphics cards, two GTX 580s,

01:10:26 [laughter] which by the way is exactly your SLI configuration. Yeah. Okay. So, that GTX

01:10:34 5880 SLI was the revolutionary computer that put deep learning on the map. >> Wow.

01:10:41 >> It was 2018 and you were using it to play Quake. >> Wow. That's crazy.

01:10:46 >> That was the moment. That was the big bang of modern AI. We were lucky because we were inventing this technology, this

01:10:54 computing approach. We were lucky that they found it. Turns out they were gamers and it was

01:11:00 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

01:11:10 Star Trek, you know, first contact. The Vulcans had to have seen the warp

01:11:18 drive at that very moment. If they didn't witness the warp drive, you know, they would have never come to Earth and

01:11:26 everything would have never happened. It's a little bit like if I hadn't paid attention to that moment, that flash.

01:11:31 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,

01:11:38 who knows what would have happened, but we saw that and we reasoned our way into this is a this is a universal function

01:11:44 approximator. This is not just a computer vision approximator. We could use this for all kinds of things. if we

01:11:50 could solve two problems. The first problem is that we have to prove to oursel it could scale. The second

01:11:56 problem we had to wait for I guess contribute to and wait for is

01:12:08 the world will never have enough data on input and output where we could supervise

01:12:16 the AI to learn everything. For example, if we have to supervise our children on everything they learn, the amount of

01:12:24 information they could learn is limited. We needed the AI, we needed the computer to have a method of learning without

01:12:31 supervision. And that's where we had to wait a few more years, but un unsupervised

01:12:38 AI learning is now here. And so the AI could learn by itself. And and the reason why the AI could learn by itself

01:12:45 is because we have many examples of right answers. Like for example, if I want to learn uh if I want to teach

01:12:54 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

01:13:01 last word and make it try and try and try again until it predicts the next one. or I mask out random words inside

01:13:09 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

01:13:17 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.

01:13:25 Okay. So, and it it it might not be obvious even from that. It might need and

01:13:32 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

01:13:41 whole bunch of these examples and you mask out the words, it'll predict the next one. Okay? And so unsupervised

01:13:47 learning came along. These two ideas, the fact that it's scalable and unsupervised learning came along.

01:13:53 We were convinced that we ought to put everything into this and help create this industry because we're going to

01:14:00 solve a whole bunch of interesting problems. And that was in 2012. By 2016, I had I had built this computer called

01:14:08 the DGX1. The one that you saw me give to Elon is called DGX Spark. The DGX1 was $300,000.

01:14:20 It cost Nvidia a few billion dollars to make the first one. And instead of two chips SLI,

01:14:30 we connected eight chips with a technology called MVLink, but it's basically SLI supercharged.

01:14:38 Okay. >> Okay. >> And so we connected eight of these chips

01:14:40 together instead of just two. And all of them work together just like your Quake rig did to solve this deep learning

01:14:49 problem to train this model. And so I create we created this thing. I announced it at GTC

01:14:56 and at one of our annual annual events and I described this deep learning thing, computer vision thing and this

01:15:05 computer called DJX1. The audience was like completely silent. They had no idea what I was talking

01:15:11 about. [laughter] And I was lucky because I I had known Elon and uh uh I helped him build the

01:15:21 first computer for Model 3 uh uh the Model S. And uh and when he wanted to start working on autonomous

01:15:29 vehicle, I helped him build the computer that went into the the Model S AV system, his full full self-driving

01:15:36 system. We were basically the FSD computer version one. And so we we're already working together and um

01:15:47 when I announced this thing, nobody in the world wanted it. I had no purchase orders. Not not one. Nobody wanted to

01:15:54 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

01:16:02 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,

01:16:12 "You know what? I have a company that could really use this."

01:16:17 I said, "Wow, my first customer." And so, so I was pretty excited about it. And he goes, "Uh, yeah. Uh, we have this

01:16:26 company. It's a nonprofit company." And all the blood drained out of my face. Yeah. [laughter]

01:16:34 I just spent a few billion dollars building this thing. Cost $300,000. and you know the chances of a nonprofit

01:16:43 being able to pay for this thing is approximately zero. And he goes, you know, this is a it's an AI company and

01:16:48 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

01:16:57 first one for ourselves. We're using it inside the company. I boxed one up. I drove it up to San Francisco and I

01:17:02 delivered to Elon in 2016. A bunch of researchers were were there. Peter Beiel was there, Ilia was there,

01:17:10 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

01:17:17 this smaller than your place here. And and uh uh that place turned out to have been open AI

01:17:25 >> 2016. >> Wow. >> Just a bunch of people sitting in a

01:17:29 room. >> It's not really uh nonprofit anymore, though, is it?

01:17:33 >> They're not They're not nonprofit anymore. Yeah. >> Weird how that works.

01:17:36 >> Yeah. Yeah. But anyhow, anyhow, Elon was there. The Yeah, it was it was really a great great moment.

01:17:42 >> Oh, yeah. There you go. Yeah, that's it. [laughter] >> Look at you, bro. Same jacket.

01:17:48 >> Look at that. I haven't aged. >> Not not a lick of black hair, though. >> Uh the size of it is uh it's

01:17:56 significantly smaller. That was the other day. SpaceX. >> Oh, yeah. There you go.

01:18:00 >> Yeah. Look at the difference. >> Exactly the same industrial design. He's holding it in his hand

01:18:06 >> here. Here's the amazing thing. DJX1 was one pedlops. Okay, that's a lot of flops. And DJX Spark is one pedlops.

01:18:20 Nine years later. >> Wow. >> The same the same amount of computing

01:18:25 horsepower >> in a much smaller >> shrunken down. Yeah.

01:18:28 >> And instead of $300,000, it's now $4,000. And it's the size of a small book.

01:18:34 >> Incredible. >> Crazy. >> That's how technology moves. Anyways,

01:18:38 that's the reason why I wanted to get give him the first one >> because I gave him the first one 2016.

01:18:43 >> 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

01:18:52 what better scenario if if if it really does become a digital life form how funny would it be that it is birthed out

01:19:00 of the desire for computer graphics for video games [laughter] >> exactly

01:19:06 >> kind of cra it's kind of crazy >> kind of crazy when you think about it that way

01:19:10 >> because it's just >> perfect origin

01:19:14 Computer graphics was one of the hardest computer supercomputer problems generating reality

01:19:22 >> and also one of the most profitable to solve because computer games are so popular.

01:19:28 >> When Nvidia started in 1993, we were trying to create this new computing approach. The question is

01:19:35 what's the killer app? And the the problem we wanted to the the

01:19:43 company wanted to create a new type of computing pro a computing architecture a computing a a new type of computer that

01:19:51 can solve problems that normal computers can't solve. Well,

01:19:58 the applications that existed in the industry in 1993 are applications that normal computers

01:20:05 can solve because if the normal computers can't solve them, why would the application exist?

01:20:11 And so, we had a mission statement for a company that has no chance of success. [laughter]

01:20:21 But I didn't know that in 1993. It just sounded like a good idea, >> right?

01:20:27 And so if we created this thing that can solve problems, you know, it's like you actually have to go create the

01:20:35 problem. And so that's what we did in 1993. There was no quake. John Carmarmac hadn't been

01:20:43 reduced doom released Doom yet. You probably remember that. >> Sure. Yeah.

01:20:49 >> And and uh there were no applications for it. And so I went to Japan because the arcade industry had this at the time

01:20:59 of Sega, if you remember. >> Sure. >> The arcade machines, they came out with

01:21:03 3D arcade systems, virtual fighter, Daytona, Virtual Cop, all of those arcade games were in 3D for the f very

01:21:14 first time. And the technology they were using was from Martin Marietta, the flight simulators. They took the guts

01:21:22 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

01:21:31 million times more powerful than that arcade machine. And that was a flight simulator for NASA. Whoa. And so they

01:21:39 took the guts out of that. They were they were using it for flight simulation for jets and, you know, space shuttle

01:21:46 and and they took the guts out of that. and Sega uh had this brilliant computer de developer. His name was Yuzuki.

01:21:56 Yuzuki and Miiamoto. Sega and Nintendo. These were the, you know, the incredible pioneers, the visionaries, the

01:22:06 incredible artists, and they're both very, very technical. They were the origins really of of the

01:22:14 gaming industry. and Y Suzuki pioneered 3D graphics gaming and um so I went we we created this company and

01:22:25 there were no apps and we were spending all of our afternoons you know we told our family

01:22:31 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

01:22:38 of one of the founders went to Curtis's townhouse and uh Chris and I were married we have kids I already had

01:22:44 Spencer at Madison. They were probably 2 years old. And um and uh Chris's kids are about the same

01:22:52 age as ours. And we would go to work in this townhouse. But you know, when you're a startup and the mission

01:23:00 statement is the way we described, you're not going to have too many customers calling you. And so we had

01:23:06 really nothing to do. And so after lunch, we would always have a great lunch. After lunch, we would go to the

01:23:13 arcades and play the Sega V, you know, the Sega Virtual Fighter and Daytona and all those games and analyze how they're

01:23:20 doing it, trying to figure out how they they were doing that. And so we decided, um, let's just go to

01:23:27 Japan and let's convince Sega to move those applications into the PC.

01:23:35 and we would start the PC gaming the 3D gaming industry partnering with Sega. That's how Nvidia started.

01:23:43 >> Wow. >> And so so uh in exchange for them part developing their games for our computers

01:23:52 in the PC, we would build a chip for their game console. That was the partnership. I build a chip for your

01:24:01 game console. you port the Sega games to us and um and then they paid us a you know at the

01:24:09 time a quite a significant amount of money to build that game console and that was kind of the beginning of

01:24:18 Nvidia getting started and we thought we were on our way and so so I started with a business plan a mission statement that

01:24:25 was impossible we lucked into the Sega partnership we started taking off started building our game console. And

01:24:33 about a couple years into it, we discovered our first technology didn't work.

01:24:40 It was it it would have been a flaw. It it was a flaw. And all of the technology ideas that we had

01:24:47 the architecture concepts were were sound, but the way we were doing computer graphics was exactly backwards.

01:24:55 you know, instead of I won't bore you with the technology, but instead of inverse texture mapping,

01:25:01 we were doing forward texture mapping. Instead of triangles, we did curved surfaces. So, other people did it flat,

01:25:10 we did it round. Um, other technology, the technology that ultimately won, the technology we use

01:25:18 today has has Zbuffers. It automatically sorted. We had an architecture with no Zbuffers.

01:25:25 The application had to sort it. And so we chose a bunch of technology approaches

01:25:30 that three major technology choices. All three choices were wrong. Okay. So this

01:25:36 is how incredibly smart we were. And so [laughter] and so in 1995 19 early mid95

01:25:44 we realized we were going down the wrong path. Meanwhile, the Silicon Valley was packed with 3D

01:25:52 graphics startups because it was the most exciting technology of that time. And so 3D FX and rendition and Silicon

01:26:01 Graphics was coming in. Intel was already in there and you know gosh like what added up eventually to a hundred

01:26:08 different startups we had to compete against. Everybody had chosen the right technology approach and we chose the

01:26:15 wrong one. And so we were the first company to start. We found ourselves essentially dead last with the wrong

01:26:22 answer. And so the company was in trouble

01:26:30 and um ultimately we had to make several decisions. The first decision is

01:26:38 well if we change now we will be the last company.

01:26:49 And even if we changed into the technology that we believe to be right, we'd still

01:26:57 be dead. And so that argument, you know, do we change and therefore be dead?

01:27:05 Don't change and make this technology work somehow or go do something completely different.

01:27:13 That question stirred the company strategically and was a hard question. I eventually, you know, advocated for we

01:27:22 don't know what the right strategy is, but we know what the wrong technology is. So, let's stop doing it the wrong

01:27:27 way and let's give ourselves a chance to go figure out what the strategy is. The second thing, the second problem we had

01:27:34 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

01:27:43 and if that contract would have been cancelled, we'd be dead. We would have vaporized instantly.

01:27:52 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

01:28:04 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

01:28:15 was uh I guess I was what 30 33 years old. you know, when I was 33 years old, I

01:28:22 still had acne. And I got this this, you know, Chinese kid. I was super skinny. And he he was already kind of elder.

01:28:34 And uh I went to him and I said I said, "Listen, I've got some bad news for you." And and

01:28:42 first, the technology that we promised you doesn't work. And second,

01:28:55 we shouldn't finish your contract because we'd waste all your money and you would have something that doesn't

01:29:02 work. And I recommend you find another partner to build your game console. >> Whoa.

01:29:08 >> And so I'm terribly sorry that we've set you back in your product roadmap. And third,

01:29:19 even though you're going to I'm asking you to let me out of the contract, I still need the money

01:29:27 because if you didn't give me the money, we'd vaporize overnight. And so

01:29:37 I explained it to him humbly, honestly. I gave him the background. explain to him why the technology

01:29:45 doesn't work, why we thought it was going to work, why it doesn't work. And um and I asked him

01:29:55 to uh convert the last $5 million that they were to complete the contract to give us

01:30:05 that money as an investment instead. and he said,

01:30:14 "But it's very likely your company will go out of business, even with my investment."

01:30:21 And it was completely true. Back then, 1995, $5 million was a lot of money. It's a lot of money today. $5 million

01:30:29 was a lot of money. And here's a pile of competitors doing it right. What are the chances that giving Nvidia $5 million

01:30:38 that we would develop the right strategy that he would get a return on that $5 million or even get it back? 0%.

01:30:45 You do the math. It's 0%. If I were sitting there right there, I wouldn't have done it.

01:30:53 $5 million was a mountain of money to Sega at the time. And so

01:30:59 I told him that that that um uh if you invested that $5 million in us,

01:31:08 it is most likely to be lost. But if you didn't invest that money, we'd be out of business and we would

01:31:16 have no chance. And I I told him that I I don't even know exactly what I said in

01:31:26 the end, but I told him that I would understand if he decided not to, but it would make the

01:31:35 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."

01:31:41 >> Wow. strategy to how to correct what it was doing wrong. Did you explain that to

01:31:49 him? >> Wait, oh man, wait until I tell you the rest of it's scarier. Even scarier.

01:31:54 >> Oh no. [laughter] >> And so so um so what he what he decided was was uh

01:32:07 Jensen was a young man he liked. That's it. >> Wow. to this day.

01:32:13 >> That's nuts. >> I was >> Boy, do you owe what the world owes that

01:32:17 guy. >> No doubt, >> right?

01:32:21 >> Well, he's he c he's he celebrated today in Japan. >> And if he would have kept that five

01:32:28 >> the the investment, I think it'd be worth probably about a trillion dollars today.

01:32:36 I know. But the moment we went public, they sold it. They go, "Wow, that's a miracle." So, [laughter]

01:32:42 >> wow. >> They sold it. Yeah. They sold it at Nvidia valuation about 300 million.

01:32:48 That's our IPO valuation. 300 million. >> Wow. >> And so, so anyhow,

01:32:56 I was incredibly grateful. Um, and then now we had to figure out what to do because we still were doing the

01:33:04 wrong strategy, wrong technology. So unfortunately we had to lay off most of the company. We shrunk the company all

01:33:10 back. All the people working on the game console, you know, we had to shrunk it all. Shrink it all back.

01:33:17 And um and then and then somebody told me that, but Jensen,

01:33:25 we've never built it this way before. We've never built it the right way before.

01:33:31 We've only know how to build it the wrong way. And so nobody in the company knew how to

01:33:37 build this supercomputing image generator 3D graphics thing that Silicon Graphics

01:33:45 did. And so so uh I said, "Okay, how hard can it be? You got all these 30 companies, you know, 50 companies doing

01:33:54 it. How hard can it be?" And so luckily there was a textbook written by the company Silicon Graphics.

01:34:04 And so I went down to the store. I had 200 bucks in my pocket. And I bought three textbooks, the only three they

01:34:11 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

01:34:18 said, "Read that and let's go save the company." >> [laughter]

01:34:23 >> And so [gasps and sighs] so they they they read this textbook, learned from the giant at

01:34:31 the time, Silicon Graphics, about how to do 3D graphics. But the thing that was amazing and what makes Nvidia special

01:34:39 today is that the people that are there are able to start from first principles,

01:34:47 learn best known art, but reimplement it in a way that's never been done before. And so when we re-imagined

01:34:58 the technology of 3D graphics, we reimagined it in a way that manifest today the modern 3D graphics. We really

01:35:07 invented modern 3D graphics, but we learned from previous known arts and we implement it fundamentally differently.

01:35:16 >> What did you do that changed it? Well, you know, the ultimately ultimately the um uh the simple the

01:35:24 simple answer is that the way silicon graphics works uh the geometry engine is a bunch of software running on

01:35:32 processors. We took that and eliminated all the generality,

01:35:43 the general purposeness of it and we reduced it down into the most essential part of 3D graphics

01:35:51 and we hardcoded it into the chip. And so instead of something general purpose, we hardcoded it very specifically into

01:36:00 just the limited applications, limited functionality necessary for video games.

01:36:08 And that capability that super and and because we reinvented a whole bunch of stuff, it supercharged the capability of

01:36:15 that one little chip. And our one little chip was generating images as fast as a $1 million image generator. That was the

01:36:26 big breakthrough. We took a million dollar thing and we put it into the graphics card that you now put into your

01:36:32 gaming PC. And that was [snorts] our big invention. And then and of course the question is

01:36:38 is um uh how do you compete against these 30 other companies doing what they were

01:36:44 doing? and and there we did we did several things. One

01:36:51 uh instead of building a 3D graphics chip for every 3D graphics application, we decided to build a 3D graphics chip

01:37:01 for one application. We bet the farm on video games. The needs of video games are very

01:37:08 different than needs for CAD, needs for flight simulators. They're related, but not the same. And so we narrowly focused

01:37:15 our problem statement so I could reject all of the other complexities and we shrunk it down into this one little

01:37:21 focus and then we supercharged it for gamers. And then the second thing that we did was we created a whole ecosystem

01:37:30 of working with game developers and getting their their games ported and adapted to our silicon so that we could

01:37:37 get turn essentially what is a technology business into a platform business into a game platform business.

01:37:45 So we, you know, GeForce is really today it's also the most advanced 3D graphics technology in the world, but a long time

01:37:53 ago GeForce is really the game console inside your PC. It's, you know, it runs Windows, it runs Excel, it runs

01:38:00 PowerPoint, of course, those are easy things, but its fundamental purpose was simply to turn your PC into a game

01:38:08 console. So we we were the first technology company to build all of this incredible technology in service of one

01:38:16 audience gamers. Now of course in 1993 the gaming industry didn't exist. But by the time that John Carmarmac came along

01:38:26 and the doom phenomenon happened and then quake came out as you know that entire world oh that entire

01:38:36 community boom took off. Do you know where the name Doom came from? >> It came from this se there's a scene in

01:38:42 the movie The Color of Money where Tom Cruz who's this uh elite pool player shows up at this pool hall and this

01:38:49 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

01:38:55 he opens it up. He goes, "Doom. >> Doom." [laughter] >> And that's where it came from. Yeah. Cuz

01:39:00 Carmarmac said that's what they wanted to do to the gaming industry. >> Doom.

01:39:03 >> That when Doom came out, it would just be everybody be like, "Oh, we're fucked."

01:39:07 >> Oh, wow. >> This is Doom. >> That's awesome.

01:39:09 >> Isn't that amazing? That's amazing. >> Cuz it's the perfect name for the game. >> Yeah.

01:39:12 >> And the name came out of that scene in that movie. >> That's right. Well, and then of course,

01:39:17 uh, Tim Sweeney and >> Epic Games and, uh, and the 3D gaming genre took off.

01:39:24 >> Yes. >> And so, if you just kind of in the beginning was no gaming industry. We had

01:39:31 no choice but to focus the company on one thing. That one thing, >> it's a really incredible origin story.

01:39:37 >> Oh, it's it's amazing. Like you must be like look back >> a disaster is what

01:39:42 >> a $5 [laughter] million that pivot with that conversation with that gentleman if he did not agree to that if he did not

01:39:48 like you what would the world look like today that's crazy then then our entire life hung on another gentleman

01:39:58 and so so now here we are we built so before GeForce it was Revo 128 revo 128 saved the company it revolutionized

01:40:07 computer graphics The performance cost performance ratio of 3D graphics for gaming was off the

01:40:14 charts amazing. And we're getting ready to to ship it. Get

01:40:24 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

01:40:33 single month, uh we were drawing down You have to build it, prototype it. You have to design it, prototype it,

01:40:45 get the silicon back, which costs a lot of money. Test it with software

01:40:53 because without the software testing the chip, you don't know the chip works. And then you're going to find a bug

01:41:00 probably because every time you test something you find bugs,

01:41:06 which means you have to tape it out again, which is more time, more money. And so we did the math. There was no

01:41:13 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,

01:41:20 get the silicon back, test it, send it back out again. There was no no shot, no hope.

01:41:27 And so the math, the spreadsheet doesn't allow us to do that. And so I heard about this company and this company

01:41:37 built this machine. And this machine is an emulator. You could take your design, all of the

01:41:48 software that describes the chip, and you could put it into this machine. And this machine will pretend it's our chip.

01:41:57 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

01:42:03 our chip and I could put all of the software on top of this machine called an emulator and test all of the software

01:42:12 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

01:42:21 when I send it to the FAB, it should work. Nobody knows, but it should work. And so

01:42:27 we came to the conclusion that let's take half of the money we had left in the bank. At the time it was

01:42:35 about a million dollars. Take half of that money and go buy this machine. So instead of keeping the money to stay

01:42:44 alive, I took half of the money to go buy this machine. Well, I call this guy up. This the company's called IOS.

01:42:52 Call this company up and I say, "Hey, listen. I heard about this machine. I like to buy one."

01:42:59 And they go, "H, that's terrific, but we're out of business." I said, "What? You're out of

01:43:06 business?" He goes, "Yeah, we had no customers." [laughter]

01:43:12 I said, "Wait, hang on a sec. So, you never made the machine?" They [snorts] can say, "No, no, no. We made the

01:43:17 machine. We have one in inventory if you want it, but we're out of business." So, I bought one out of inventory.

01:43:26 Okay. After I bought it, they went out of business. >> Wow.

01:43:30 >> I bought it out of inventory. And on this machine, we put Nvidia's chip into it and we tested all of the

01:43:39 software on top. And at this point, we were on fumes. But we convinced ourselves that chip is

01:43:47 going to be great. And so I had to call some other gentleman. So I called TSMC.

01:43:54 And I told TSMC that listen, TSMC is the world's largest founder today. At the time they were

01:44:01 just a few hundred million dollars large, tiny little company.

01:44:12 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,

01:44:20 you know, Diamond Multimedia, probably one of the companies you bought the graphics card from back in the old

01:44:26 days. And I I said, you know, we have a lot of customers, and the demand's really great, and

01:44:33 we're going to tape out a chip to you, and I like to go directly to production because I know it works.

01:44:44 >> [snorts] >> And they said, "Nobody has ever done that before.

01:44:50 Nobody has ever taped out a chip that worked the first time. And nobody starts out production without

01:44:56 looking at it." But I knew that if I didn't start the production, I'd be out of business

01:45:02 anyways. And if I could start the production, I might have a chance. And so

01:45:10 TSMC decided to support me and uh this gentleman is named Morris Chang. Morris

01:45:17 Chang is the father of the foundry industry, the founder of TSMC. Really great man.

01:45:27 He decided to support our company. I explained to them everything. he decided to support us frankly

01:45:34 probably because they didn't have that many other customers anyhow but they were grateful and I was immensely

01:45:41 grateful and as we were starting the production Morris flew to United States and uh

01:45:50 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

01:45:58 any money but he didn't directly ask me that you know and so the truth is that we didn't

01:46:06 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

01:46:16 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

01:46:23 would have been rough but they supported us with all of that risk involved we launched this chip turns out to

01:46:32 been completely revolutionary. Knocked the ball out of the park. We became the fastest growing technology

01:46:40 company in history to go from zero to $1 billion. >> So wild that you didn't test the chip.

01:46:46 >> I know. We tested afterwards. Yeah, we tested afterwards. >> Afterwards, but [laughter]

01:46:52 production already. But by the way, by the way, that methodology that we developed to save the company is used

01:47:01 throughout the world today. >> That's amazing. >> Yeah, we changed we changed the whole

01:47:05 world's methodology of designing chips. The whole world's uh rhythm of designing chips. Uh we changed everything.

01:47:13 >> How well did you sleep those days? It must have been so much stress, [laughter]

01:47:19 >> you know. Um, what is that feeling where where uh the world just kind of feels like it's

01:47:29 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

01:47:38 moving super fast and you know and you're laying in your laying in bed and the world just feels like you know it

01:47:47 you and you're you you feel deeply anxious uh completely out of control. Um

01:47:56 I've felt that probably a couple of times [laughter] in my life. It's during that time.

01:48:02 >> Wow. >> Yeah. It it was incredible. >> What an incredible success story.

01:48:06 >> But I I learned I learned a lot. I learned I learned about I learned several things. I learned I learned uh

01:48:11 how to develop strategies. Um I learned how to uh uh and when I when I you know our

01:48:19 company learned how to develop strategies. What are winning strategies? We learned how to create a market. We

01:48:24 created the modern 3D gaming market. We learned how and and so that exact same skill is how we created the modern

01:48:34 AI market. It's exactly the same. >> Wow. >> Yeah. Exactly the same skill. Exactly

01:48:40 the same blueprint. And uh we learned how to uh deal with crisis, how to stay calm, how to think

01:48:49 through things systematically. We learned how to remove all waste in the company and work from first

01:48:58 principles and doing only the things that are essential. Everything else is waste because we have no money for it

01:49:05 to live on fumes at all times. And the feeling no different than the feeling I had this

01:49:13 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

01:49:21 out of business I've used for 33 years because >> you still feel that.

01:49:25 >> Oh yeah. Oh yeah. Every morning. Every morning. >> But but you guys are one of the biggest

01:49:30 companies on planet earth. But the the feeling doesn't change. >> Wow.

01:49:34 >> The the sense of vulnerability, the sense of uncertainty, the sense of insecurity. Uh

01:49:42 it it doesn't leave you. >> That's crazy. We were, you know, we had nothing. We had nothing. We were dealing

01:49:49 with giant. >> Oh, yeah. Oh, yeah. Every day, every moment.

01:49:52 >> Do you think that fuels you? Is that part of the reason why the company's so successful? That you have that hungry

01:49:59 mentality, that you never rest, you're never sitting on your laurels, you're always

01:50:08 on the edge. I have a greater drive from not wanting to fail

01:50:19 than the drive of wanting to succeed. [laughter] >> Isn't that like sex coaches would tell

01:50:27 you that's completely the wrong psychology? >> The world has just heard me say that for

01:50:31 out loud for the first time. >> But but it's true. >> Well, that's how fascinating. fear of

01:50:36 failure drives me more than the than the the greed or whatever it is. >> Well, ultimately that's probably a more

01:50:45 healthy approach now that I'm thinking about it because like the fear >> I'm not ambitious for example,

01:50:52 [laughter] you know, I just want to stay alive, Joe. I want the company to thrive, you know, I want us to make an

01:50:59 impact. >> That's interesting. >> Yeah.

01:51:01 >> Well, maybe that's why you're so humble. That's what maybe that's what keeps you grounded, you know, because with the

01:51:07 kind of spectacular success the company's achieved, it would be easy to get a big head.

01:51:11 >> No. >> Right. But isn't that interesting? It's like the if you were the guy that your

01:51:16 main focus is just success. You probably would go, "Well, made it. Nailed it. I'm the [laughter] man.

01:51:24 >> Drop the mic." >> Instead, you wake up, you're like, "God, we can't [ __ ] this up."

01:51:28 >> No. Exactly. Every morning. Every morning. No. Every moment. Yeah. That's crazy.

01:51:33 >> Before I go to bed. >> Well, listen. If I was a major investor in your company, that's what I'd want

01:51:37 running it. I'd want a guy who's >> Yeah. >> That's what I work. That's why I work

01:51:43 seven days a week. Every moment I'm I'm awake. >> You work every moment.

01:51:48 >> Every moment I'm awake. >> Wow. >> I'm thinking about solving a problem.

01:51:53 I'm thinking about >> How long can you keep this up? >> I don't know. But so [laughter]

01:51:59 could be next week. Sounds exhausting. >> It is exhausting. >> It sounds completely exhausting.

01:52:04 >> Always in a state of anxiety. >> Wow. >> Always in a state of anxiety.

01:52:10 >> Wow. Kudos to you for admitting that. I think that's important for a lot of people to hear because, you know,

01:52:15 there's probably some young people out there that are in a similar position to where you were when you were starting

01:52:22 out that just feel like, oh, those people that have made it, they're just smarter than me and they had more

01:52:29 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

01:52:36 >> Joe, I just described to you somebody who didn't know what was going on, [laughter]

01:52:39 actually did it wrong. >> Yeah. Yeah. And the ultimate diving catch like two or three times.

01:52:45 >> Crazy. >> Yeah. >> The ultimate diving catch is the perfect

01:52:48 way to put it. >> You know, it's just like the edge of your glove. [laughter]

01:52:53 >> It probably bounced off of somebody's helmet and landed at the edge. [laughter]

01:53:00 >> God, that's incredible. That's incredible. But it's also it's really cool that you have this perspective that

01:53:06 you look at it that way because you know a lot of people that have delusions of grandeur or they have you know

01:53:15 >> and their rewriting of history often times had them somehow extraord extraordinarily smart and they were

01:53:25 geniuses and they knew all along and they were they were spot-on. And the business plan was exactly what they

01:53:30 thought. And >> yeah, >> they destroyed the competition and you

01:53:34 know and they emerged victorious. [laughter] >> Meanwhile, you're like, I'm scared every

01:53:40 day. >> Exactly. [laughter] Exactly.

01:53:44 >> That's so funny. Oh my god, that's amazing. >> It's so true, though.

01:53:48 >> It's amazing. >> It's so true. >> It's amazing. Well, but I I think

01:53:52 there's nothing inconsistent with being a leader and being vulnerable. You know, I the company

01:54:00 doesn't need me to be a genius right all along, right? Absolutely certain about what I'm trying

01:54:08 to do and what I'm doing. The the company doesn't need that. The company wants me to succeed. You know, the thing

01:54:14 that and we started out today talking about President Trump and I was about to say something and listen, he is my

01:54:23 president. He is our president. We should all and we're talking about just because it's President Trump, we all

01:54:29 want him to be wrong. I think that United States, we all have to realize he is our president, we want him to succeed

01:54:37 because >> no matter who's president attitude. >> That's right.

01:54:41 >> We want him to succeed. We need to help him succeed because it helps everybody, all of us succeed.

01:54:48 And I'm lucky that I work in a company where I have 40,000 people who wants me to

01:54:56 succeed. They want me to succeed and I can tell and they're all every single day to help

01:55:02 me overcome these challenges trying to realize realize what I describe to be our

01:55:09 strategy doing their best. And if it's somehow wrong or not perfectly right to tell me

01:55:17 so that we could pivot and the more vulnerable we are as a leader the more able other people are able to tell you

01:55:26 you know that Jensen that's not exactly right or >> right right

01:55:29 >> have you considered this information or and the more vulnerable we are the more able we're actually able to

01:55:37 pivot if we put ourselves into this superhuman capability then it's hard for us to pivot strategy,

01:55:42 >> right? >> Because we were supposed to be right all along.

01:55:45 >> And so if you're always right, how can you possibly pivot? Because pivoting requires you to be wrong. And so I've

01:55:51 got no trouble with being wrong. I just have to make sure that I stay alert, that I reason about things from first

01:55:58 principles all the time. Always break things down to first principles. Understand why it's happening.

01:56:05 Reassess continuously. The reassessing continuously is kind of partly what causes continuous anxiety,

01:56:14 >> you know, because you're asking yourself, were you wrong yesterday? Are you still right? Is this the same? Has

01:56:20 that changed? Has that condition is that worse than you thought? >> But God, that mindset is perfect for

01:56:25 your business, though, because this business is ever changing >> all the time. I've got competition

01:56:30 coming from every direction. So much of it is kind of up in the air and you have to invent a future where

01:56:41 a 100 variables are included and there's no way you could be right on all of them. And so you have to be

01:56:47 >> you have to surf. >> Wow. That's a good way to put it. You have to surf. Yeah. You're surfing waves

01:56:54 of technology and innovation. >> That's right. You can't predict the waves. You got to deal with the ones you

01:56:59 have. >> Wow. And but skill matters and I've been doing this for 30 I'm the longest

01:57:05 running tech CEO in the world. >> Is that true? Congratulations. That's amazing.

01:57:10 >> And you know people ask me how is one don't get fired. [laughter] That'll stop a short heartbeat.

01:57:19 And then two don't get bored. >> Yeah. >> Well, how do you maintain your

01:57:23 enthusiasm? Well, the honor truth is is not always enthusiasm. It's, you know, sometimes is

01:57:32 enthusiasm. Sometimes it's just good oldfashioned fear and then sometimes, you know, a healthy dose of frustration,

01:57:40 you know, it's whatever keeps you moving. >> Yeah. Just all the emotions. I think,

01:57:45 you know, >> CEOs, we have all the emotions, right? you know, and so probably probably

01:57:53 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

01:57:59 behalf of everybody at the same time. And it kind of, you know, encapsulates into into somebody. And so I have to be

01:58:08 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,

01:58:14 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.

01:58:23 >> It doesn't seem like it at all. I would imagine one of the more difficult aspects of your job currently now that

01:58:29 the company is massively successful is anticipating where technology is headed and where the applications are going to

01:58:36 be. >> Yeah. >> So, how do you try to map that out?

01:58:40 >> Yeah. there there um there there's a whole bunch of ways and and it takes it takes um

01:58:50 takes a whole bunch of things but let me just start uh you have to be surrounded by amazing

01:58:55 people and Nvidia is now you know if you look at look at look at um the large tech companies in the world today

01:59:05 most of them have a business in advertising or social media or you know content distribution and at the core of

01:59:15 it is really fundamental computer science and so the company's business is not computers the company's business is

01:59:25 not technology technology drives the company is the only company in the world that's large whose only business is

01:59:32 technology we only build techn we don't advertise the only way that we make money is to create amazing technology

01:59:39 and sell And so to be that to be NVIDIA today, you're

01:59:46 the number one thing is you're surrounded by the finest computer scientists in the world. And that's my

01:59:52 gift. My gift is that we've created a company's culture, a condition by which the world's

02:00:00 greatest computer scientists want to be part of it because they get to do their life's work and create the next thing

02:00:06 because that's what they want to do. because maybe they're not they don't want to be in service of another

02:00:12 business. >> They want to be in service of the technology itself. And we're the largest

02:00:16 form of its kind in the history of the world. >> Wow.

02:00:20 >> I know. It's pretty amazing. >> Wow. >> And so so one, you know, we have a we we

02:00:26 have got a great condition. We have a great culture. We have great people. And then now now now the question is how do

02:00:32 you systematically um be able to see the future stay alert of

02:00:42 it and uh reduce the reduce the the likelihood of missing something or being

02:00:51 wrong. And so there's a lot of different ways you could do that. For example, we have

02:00:56 great partnerships. We we have fundamental research. We have a great research lab, one of the largest

02:01:00 industrial research labs in the world today. And we partner with a whole bunch of universities and other scientists. We

02:01:07 do a lot of open collaboration and so I'm constantly working with researchers outside the company.

02:01:15 We have the benefit of having amazing customers and so I have the benefit of working with Elon and you know and

02:01:22 others in the industry and we have the benefit of being the only pure pure play technology company that can serve uh

02:01:31 consumer internet industrial manufacturing um scientific computing healthcare

02:01:39 financial services all the industries that we're in. They're all signals to me. And so they all have mathematicians

02:01:48 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

02:01:56 the world working across every single industry from agriculture to energy to video games.

02:02:05 And so the ability for us to have this vantage point, one doing fundamental research ourselves

02:02:13 and then two working with all the great researchers, working with all the great industries, the feedback system is

02:02:19 incredible. And then finally, you just have to have a culture of staying super alert. There's no easy way

02:02:27 of being alert except for paying attention. I haven't found a single way of being

02:02:33 able to stay alert without paying attention. And so, you know, I probably read several thousand emails a day.

02:02:42 >> How How do you have a time for that? >> I wake up early. This morning I was up at 4:00.

02:02:47 >> How much do you sleep? >> Uh, six, seven, six, seven hours. >> Yeah.

02:02:54 >> And then you're up at 4 reading emails for a few hours before you get going. >> That's right. Yeah.

02:02:58 >> Wow. Every day. >> Every single day. Not one day missed.

02:03:03 [sighs] including Thanksgiving, Christmas. >> Do you ever take a vacation?

02:03:09 >> 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,

02:03:17 I'm very happy. I don't care where we are. >> And you don't work then or do you work a

02:03:21 little? >> No. No. I work a lot. [laughter] >> Even like if you go on a trip somewhere,

02:03:27 you're still working. >> Oh, sure. Oh, sure. >> Wow. Every day.

02:03:30 >> Every day. >> But my kids work every You make me tired just saying this.

02:03:34 >> My kids work every day. Both of my kids work at Nvidia. They work every day.

02:03:40 >> Wow. >> Yeah. I'm very lucky. >> Wow.

02:03:43 >> Yeah. It's brutal now because, you know, it's just me working every day. Now we have three people working every day and

02:03:49 they want to work with me every day and so it's it's a lot of work. >> Well, you've obviously imparted that

02:03:57 ethic into them. >> They work incredibly hard. I mean, there's no unbelievable.

02:04:01 >> But my parents work incredibly hard. >> Yeah. I was I was born with the work gene,

02:04:08 >> the suffering gene. [laughter] >> Well, listen, man. It has paid off. What a crazy story. It was just It's really

02:04:16 an amazing origin story. It really I mean, it has to be kind of surreal to be in the position that

02:04:22 you're in now when you look back at how many times that it could have fallen apart and humble beginnings. But Joe,

02:04:28 this is great. It's a great country. You know, I'm an immigrant. My parents sent my older brother and I here first.

02:04:37 We're we're in Thailand. I was born in Taiwan, but my dad had a job in Thailand. He was a chemical and

02:04:46 instrumentation engineer, incredible engineer. And his job was to go start an oil

02:04:52 refinery. And so we moved to Thailand, lived in Bangkok. And um in 19

02:05:00 I guess 1973 1974 time frame, you know how Thailand every so often they would just have a coup. You know,

02:05:08 the military would have an uprising and all of a sudden one day there were tanks and soldiers in the streets and my

02:05:15 parents thought, you know, it probably isn't safe for the kids to be here. And so they contacted my uncle. My uncle

02:05:22 lives in Tacoma, Washington. and um we had never met him and my parents sent us to him.

02:05:30 >> How old were you? >> Uh I was about to turn nine and my older brother uh almost turned 11 and so the

02:05:39 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

02:05:49 and my parents didn't have very much money and they never been to United States. my father was. I'll tell you

02:05:55 that story in a second. And um and so my my uncle found a school that

02:06:04 would accept foreign students and affordable enough for my parents. And that school turned out to have been

02:06:13 in Onita, Kentucky, Clark County, Kentucky, the epicenter of the opio crisis today.

02:06:21 cold country. Clark County, Kentucky is was the poorest county in America when I

02:06:31 showed up. It is the poorest county in America today. And so we went to the school, it's a

02:06:38 great school, um, Onita Baptist Institute in a town of a few hundred. I think it

02:06:46 was 600 at the time that we showed up. No traffic light. And um I think it has 600 today. It's

02:06:54 quite an amazing feat actually. The ability to hold your population for [laughter]

02:07:02 when it's 600 people. It was quite a magic quite a magical thing. however they did it. And and so uh the school

02:07:11 had a mission of being an open school for any children who would like to come. And what that basically means is that if

02:07:23 you're a trouble student, if you have a troubled family, um if you're,

02:07:32 you know, whatever your background, you're welcome to come to Onita Baptist Institute, including kids from

02:07:41 international who would like to stay there. >> Did you speak English at the time?

02:07:45 >> Uh, okay. Yeah. Yeah. Okay. Yeah. And so we showed up and uh

02:07:57 my first my first thought was gosh there are a lot of cigarette butts on the ground. 100% of the kids smoked.

02:08:05 [laughter] So right away you know this is not a normal school.

02:08:11 >> Nineyear-olds? >> No, I was the youngest kid. >> Okay. 11 year olds.

02:08:15 >> My roommate was 17 years old. Wow. >> Yeah. He just turned 17. And he was jacked

02:08:23 and and um I don't know where he is now. I know his name, but I don't know where he is now.

02:08:32 But anyways, uh that night we got and and the second thing I noticed when you walk into the into your dorm room

02:08:41 is uh there are no drawers and no closet doors. just like a prison.

02:08:50 And there are no locks so that people could check check up on

02:08:56 you. And so I go into my room and he's 17 and uh you know get ready for for bed and he

02:09:06 had all this tape all over his body and uh turned out he was in a knife fight and he's been

02:09:15 stabbed all over his body and these were just fresh wounds. >> Whoa. And the other kids were hurt much

02:09:22 worse. And uh so he was my roommate, the toughest kid in school, and I was the

02:09:29 youngest kid in school. It was a it was a junior high, but they took me anyways because if I

02:09:38 walked about a mile across the Kentucky River, the swing bridge, the other side is a middle school that I could go to

02:09:47 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

02:09:55 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

02:10:03 were there for a couple of years. Um every kid had every kid had chores. My older brother's chore was to work in

02:10:11 the tobacco farm, you know. So tobac they raised tobacco so that they could raise some extra money for the school.

02:10:18 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

02:10:26 cleaning toilets. And for a dorm of 100 100 boys, I I clean more bathrooms than anybody. And

02:10:35 I just wish that everybody was a little bit more careful, you know. [laughter] But anyways, I was the youngest kid in

02:10:43 school. The my memories of it was really good. Um, but it was a pretty tough It was a tough town.

02:10:50 >> Sounds like it. >> Yeah. Town kids, they all carried Everybody had knives.

02:10:55 >> Everybody had knives. Everybody smoked. Everybody had a Zippo lighter. I smoked for a week.

02:11:01 >> Did you? >> Oh, yeah. Sure. >> How old were you?

02:11:03 >> I was nine. Yeah. >> When you nine? You were nine, you tried smoking.

02:11:06 >> Yeah. I got myself a pack of cigarettes. Everybody else did. >> Did you get sick? No. I I got used to

02:11:11 it, you know, and I learned how to blow blow smoke rings and, you know, [snorts] you know, breathe out of my nose, you

02:11:20 know, take it in out of through my nose. I mean, there was a all the different things that you learned. Yeah.

02:11:26 >> At nine. >> Yeah. >> Wow. You just did it to fit in or it

02:11:29 looked cool. >> Yeah. [clears throat] Because everybody else did it,

02:11:31 >> right? >> Yeah. And and then I did it for a couple weeks, I guess. And I just rather have I

02:11:38 had a quarter, you know, I had a quarter a month or something like that. I just rather buy popsicles and fried

02:11:46 sickles with it. I was nine, you know, [laughter] >> right?

02:11:49 >> I chose I chose the the better path. >> Wow. >> That was our school. And then my parents

02:11:55 came to United States two years later and um we met him in Tacoma, Washington. >> That's wild. It It was a really crazy

02:12:04 experience. What a strange formative experience. >> Yeah. Tough kids.

02:12:10 >> Thailand to one of the poorest places in America or if not the poorest as a 9-year-old.

02:12:20 >> Yeah. It was my first experience with your brother. >> Wow.

02:12:23 >> Yeah. Yeah. No, I I remember and what breaks my heart probably the only thing that really breaks my heart of

02:12:32 about that experience was so we didn't have enough money to make you

02:12:40 know international phone calls every week and so my parents gave us this tape deck this Iowa tape deck and a tape

02:12:51 and so every month we would sit in front on that tape deck and that my older brother Jeff and I,

02:12:59 the two of us would just tell them what we did the whole month. >> Wow.

02:13:06 >> 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

02:13:14 back to us. >> Wow. >> Could you imagine if for two years

02:13:20 >> Wow. is that tape still existed of these two kids just describing their first experience with United States.

02:13:28 Like I remember telling my parents that that uh I joined the swim team and uh

02:13:40 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

02:13:48 push-ups, 100 sit-ups every day in the gym. So, I was nine years old. I was getting I was pretty buff

02:13:54 and I'm pretty fit. And uh and so I joined the soccer team. I joined the swim team because if you join

02:14:03 the team, they take you to meets and then afterwards you get to go to a nice restaurant. And that nice restaurant was

02:14:10 McDonald's. >> Wow. >> And and I recorded this thing. And I

02:14:15 said, "Mom and dad, we went to the most amazing restaurant today. This whole place is lit up. It's like

02:14:22 the future." And [snorts] the food comes in a box [laughter]

02:14:31 and the food is incredible. The hamburger is incredible. It was McDonald's. [snorts] But anyhow, it it

02:14:37 wouldn't it be amazing? >> Oh my god. Two years recording. Yeah. Two years. Yeah. What a crazy connection

02:14:44 to your parents, too. Just sending a tape and them sending you one back and it's the only way you're communicating

02:14:50 for two years. >> Yeah. Wow. Yeah. No, I've My parents are incredible actually. They're just

02:14:58 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

02:15:06 the most impactful memories I have is is uh we they came and we were we were staying in

02:15:14 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

02:15:26 bunch of furniture and we were messing around

02:15:36 and uh we bumped into the coffee table and crushed it. It's made out of particle

02:15:42 wood and we crushed it. And I just still remember my the look on my mom's face, you know, because they

02:15:51 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

02:15:56 tells you how hard it was for them to come here. They they left everything behind and all they had was their

02:16:02 suitcase and the money they had in their in their pocket and they came to United States.

02:16:08 >> How old were they pursued the American dream? They were in their 40s. >> Wow.

02:16:11 >> Yeah. Late late 30s. >> Pursued the American dream. This is this is the American dream. I'm the first

02:16:17 generation of the American dream. >> Wow. >> Yeah. It's hard not to love this

02:16:21 country. >> That's >> it's it's hard not to be romantic about

02:16:25 this country. >> That is a romantic story. That's an amazing story.

02:16:29 >> Yeah. And and my dad found his job literally in the newspaper, you know, the ads and he calls people.

02:16:38 Got a job. >> What did he do? >> Uh he was a consulting engineer and a

02:16:43 and a consulting firm and they helped people build oil refineries, paper mills and fabs. And that's what he did. He was

02:16:51 an he he's really good at factory design instrumentation engineer. And so he's he's brilliant at that. And so he did

02:17:01 that and my mom uh worked as a maid and uh they found a way to raise us. >> Wow.

02:17:10 That's an incredible story, Jensen. It really is. Every all of it from your childhood to the perils of Nvidia almost

02:17:19 falling. [laughter] It's really incredible, man. >> It's a great story. Yeah. I I've lived a

02:17:25 great life. >> You really have. And it's a great story for other people to hear, too. It really

02:17:30 is. >> You don't You don't have to go to Ivy League schools to succeed.

02:17:36 This country creates opportunities. Has opportunities for all of us. You do have to strive.

02:17:43 You have to claw your way here. >> Yeah. >> But if you put in the work, you can

02:17:48 succeed. Nobody works with >> a lot of luck and a lot of

02:17:51 >> a lot and >> good decision- making >> and the good graces of others.

02:17:55 >> Yes, that's really important. >> Yeah. You and I spoke about two two people who are very dear to me. Um but

02:18:02 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

02:18:13 the decisions you know them giving me the opportunity like when we were inventing this new computing approach

02:18:19 I tanked our stock price because we added this thing called CUDA to the chip we had this big idea we added this thing

02:18:26 called CUDA to the chip but nobody paid for it but our cost doubled and so we had this graphics chip company and we

02:18:34 invented GPUs, we invented programmable shaders, we invented everything modern computer graphics,

02:18:42 we invented real-time tracing. That's why it went from GTX to RTX. We invented all this stuff, but every

02:18:50 time we invented something, the market doesn't know how to appreciate it, but the cost went way up.

02:18:56 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

02:19:06 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

02:19:13 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

02:19:19 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

02:19:26 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

02:19:33 we believe it. Let's go pursue it. Well, we pursued it. We we launched the product. Nobody knew. It was exactly

02:19:40 what like when I launched DGX1 and the entire audience was like complete silence. When I launched CUDA,

02:19:48 the audience was complete silence. No customer wanted it. Nobody asked for it. Nobody understood it. Nvidia was a

02:19:58 public company. >> What year was this? This is uh uh let's see 200

02:20:05 2006 20 years ago 2005

02:20:12 >> Wow. >> Our stock price just went our valuation went down to like two or

02:20:21 three billion dollars >> from >> from about 12 or something like that.

02:20:28 I crushed it. >> [laughter] >> in a very bad way.

02:20:32 >> Yeah. >> What is it now though? >> H Yeah, it's higher. [laughter]

02:20:38 >> Very humble of you. [gasps] >> It's higher. But it changed the world. >> Yeah,

02:20:43 >> that invention changed the world. >> It's a It's an incredible story, Johnson. It really is.

02:20:50 >> Thank you. >> I like your story. It's incredible. Ah, >> my story is not as incredible. My story

02:20:55 is more weird, you know. It's much more fertuitous and weird.

02:21:01 >> Okay. What are the three milestones that most important milestones that led to here?

02:21:10 >> That's a good question. Um, >> what was step one? >> I think step one was seeing other people

02:21:17 do it. Step one was in the initial days of podcasting, like in 2009 when I started, podcasting had only been around

02:21:25 for a couple of years. Um, the first was Adam Curry, my good friend, who was the podfather. He he invented podcasting.

02:21:34 And then, you know, um, I remember Adam Corolla had a show because he had a radio show. His radio show got cancelled

02:21:41 and so he decided to just do the same show but do it on the internet. And that was pretty revolutionary. Nobody was

02:21:45 doing that. And then there was the experience that I had had doing different morning radio shows like Opie

02:21:51 and Anthony in particular because it was fun and we would just get together with a bunch of comedians, you know, I'd be

02:21:59 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

02:22:04 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

02:22:09 that." And then I saw Tom Green setup. Tom Green had a setup in his house and he essentially turned his entire house

02:22:16 into a television studio and he did an internet show from his living room. He had servers in his house and cables

02:22:22 everywhere. Had to step over cables. I was this is like 2007. I'm like Tom this is nuts. Like this is

02:22:28 >> and I'm like you got to figure out a way to make money from this. Like this everybody I wish everybody in the

02:22:32 internet could see your setup. It's nuts. I just want to let you guys know that [laughter]

02:22:37 >> it's not just this. >> Yeah. So that was the the beginning of it is just seeing other people do it and

02:22:43 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

02:22:49 webcam and just messed around had a bunch of comedians come in we would just talk and joke around and I did it like

02:22:56 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

02:23:01 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

02:23:08 just kept doing It's all it is. I just kept doing it because I enjoyed doing it. Well, was there any setback?

02:23:15 >> No. No, there was never really a setback really. >> No,

02:23:18 >> it must have been. Or you kind of >> You're just You're just resilient. >> Or you're just tough.

02:23:23 >> No. No. No. No. It wasn't tough or hard. It was just interesting. So, I just it the the

02:23:29 >> You were never once punched in the face. >> No, not in the show. No, not really. Not Not doing the show.

02:23:33 >> You never did something that that big blowback. Nope. Not really. No, it all just kept

02:23:42 growing. >> It kept growing and the thing stayed the same from the beginning to now. And the

02:23:48 thing is, I enjoy talking to people. I've always enjoyed talking to interesting people.

02:23:52 >> I could even tell just when we walked in, the way you interacted with everybody, not just me.

02:23:57 >> Yeah, that's cool. >> People are cool. >> Yeah, that's cool. You know, I I it's a

02:24:02 an amazing gift to be able to have so many conversations with so many interesting people because it changes

02:24:09 the way you see the world because you see the world through so many different people's eyes and you have so many

02:24:15 different people have different perspectives and different opinions and different philosophies and different

02:24:20 life stories. And you know, it's an incredibly enriching and educating experience having so many conversations

02:24:30 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

02:24:39 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

02:24:46 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

02:24:52 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."

02:24:58 >> If it wasn't because of President Trump, I wouldn't have been bumped up on that list. [laughter]

02:25:01 >> 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

02:25:08 not want to talk to you? And then today, it proved to be absolutely the right decision.

02:25:12 >> Well, you know, listen, it's it's strange to be an immigrant one day. going to Onita Baptist Institute

02:25:21 with with the students that were there and then here Nvidia's one of the most consequential

02:25:29 companies in the history of companies. >> It is a crazy story. >> It has to be that journey is is a and

02:25:37 it's very humbling and >> and um I'm very grateful. >> It's pretty amazing man.

02:25:42 >> Surrounded by amazing people. You're very fortunate and you've also you seem very happy and you seem like you're 100%

02:25:49 on the right path in this life. You know, >> you know, everybody says you must love

02:25:54 your job. Not every day. [laughter] >> That's not that's part of the beauty of everything is that there's ups and

02:26:00 downs. It's never just like this giant dopamine high. >> We leave we leave this impression here.

02:26:06 Here's here's an impression I don't think is healthy. We we um people who are successful leave the impression

02:26:13 often that that our job gives us great joy. I think largely it does

02:26:22 that our jobs were passionate about our work. Um and that passion relates to it's just

02:26:30 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

02:26:42 work. >> Yes, >> there's long periods of suffering and

02:26:49 loneliness and uncertainty and fear and embarrassment and humiliation. all of the feelings that we most not love that

02:27:02 creating something from the ground up and and Elon will tell you something similar very

02:27:09 difficult to invent invent something new >> and people people don't believe you all the time you're humiliated often

02:27:17 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

02:27:26 it's it's good that we pass that forward and let people know that that it's just part of the journey.

02:27:32 >> Yes. >> Suffering is part of the journey. >> You will appreciate it so these horrible

02:27:37 feelings that you have when things are not going so well. You will appreciate it so much more when they do go well.

02:27:43 >> Deeply grateful. >> Yeah. >> Yeah. Deep deep pride. Incredible pride.

02:27:49 In incredible incredible gratefulness and and and surely incredible memories. Absolutely. Jensen, thank you so much

02:27:56 for being here. This was really fun. I really enjoyed it and your story is just absolutely incredible and very

02:28:02 inspirational and and I you know, I think it really is the American dream. It is the American dream.

02:28:08 >> It really is. Thank you so [music] much. Thank you. All right. Bye, everybody. [music]

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