Transcript: NVIDIA’s Jensen Huang on Joe Rogan Podcast #2422

Every single time I called, if I needed something, I want to get something off my chest, express some concern. They’re always available. Incredible.

JOE ROGAN: It’s just unfortunate we live in such a politically polarized society that you can’t recognize good common sense things if they’re coming from a person that you object to. And that, I think, is what’s going on here.

I think most people generally as a country, you know, as a giant community, which we are—it just only makes sense that we have manufacturing in America, especially critical technology like you’re talking about. Like, it’s kind of insane that we buy so much technology from other countries.

JENSEN HUANG: If United States doesn’t grow, we will have no prosperity. We can’t invest in anything, domestically or otherwise. We can’t fix any of our problems. If we don’t have energy growth, we can’t have industrial growth. If we don’t have industrial growth, we can’t have job growth. It’s as simple as that. Right?

And the fact that he came into office and the first thing that he said was “Drill, baby, drill”—his point is, we need energy growth. Without energy growth, we can have no industrial growth. And that was—it saved—it saved the AI industry. I got to tell you flat out, if not for his pro-growth energy policy, we would not be able to build factories for AI, would not be able to build chip factories, surely won’t be able to build supercomputer factories. None of that stuff would be possible.

Without all of that, construction jobs will be job challenged. Right. Electrical, you know, electrician jobs, all of these jobs that are now flourishing would be challenged. And so I think he’s got it right. We need energy growth. We want to re-industrialize the United States. We need to be back in manufacturing.

Every successful person doesn’t need to have a PhD. Every successful person doesn’t have to have gone to Stanford or MIT. And I think that, that, you know, that sensibility is spot on.

The AI Technology Race

JOE ROGAN: Now when we’re talking about technology growth and energy growth, there’s a lot of people that go, “Oh no, that’s not what we need. We need to simplify our lives and get back.” But the real issue is that we’re in the middle of a giant technology race.

And whether people are aware of it or not, whether they like it or not, it’s happening. And it’s a really important race because whoever gets to whatever the event horizon of artificial intelligence is, whoever gets there first, has massive advantages in a huge way. Do you agree with that?

JENSEN HUANG: Well, first, the part I will say that we are in a technology race, and we are always in a technology race. We’ve been in a technology race with somebody forever.

JOE ROGAN: Right?

JENSEN HUANG: Right. Since the Industrial Revolution.

JOE ROGAN: We’ve been in a technology race since the Manhattan Project.

JENSEN HUANG: Yeah. Or, you know, even going back to the discovery of energy. Right. The United Kingdom was where the Industrial Revolution was, if you will, invented. When they realized that they can turn steam and such into energy, into electricity. All of that was invented largely in Europe, and United States capitalized on it.

We were the ones that learned from it. We industrialized it. We diffused it faster than anybody in Europe. They were all stuck in discussions about policy and jobs and disruptions. Meanwhile, the United States was forming. We just took the technology and ran with it.

And so I—I think we were always in a bit of a technology race. World War II was a technology race. Manhattan Project was a technology race. We’ve been in the technology race ever since during the Cold War. I think we’re still in a technology race. It is probably the single most important race.

It is—the technology is—it gives you superpowers. You know, whether it’s information superpowers or energy superpowers or military superpowers is all founded in technology. And so technology leadership is really important.

JOE ROGAN: Well, the problem is if somebody else has superior technology.

JENSEN HUANG: Right.

JOE ROGAN: That’s the issue. It seems like with the AI race, people are very nervous about it. Like, you know, Elon has famously said there was like 80% chance it’s awesome, 20% chance we’re in trouble. And people are worried about that 20%, rightly so.

If you had 10 bullets in a revolver and you took out eight of them, you still have two in there, and you spin it, you’re not going to feel real comfortable when you pull that trigger. It’s terrifying. And when we’re working towards this ultimate goal of AI, it just—it’s impossible to imagine that it wouldn’t be of national security interest to get there first.

JENSEN HUANG: We should. The question is, what’s there? That’s the—that was the part.

JOE ROGAN: What is there?

JENSEN HUANG: Yeah. I’m not sure. And I don’t think anybody—I don’t think anybody really knows.

JOE ROGAN: That’s crazy, though. If I ask you—

JENSEN HUANG: Yeah.

JOE ROGAN: You’re the head of NVIDIA, you don’t know what’s there. Who knows?

The Future of AI Development

JENSEN HUANG: Yeah, I—I think it’s probably going to be much more gradual than we think. It won’t be a moment. It won’t be as if somebody arrived and nobody else has. I don’t think it’s going to be like that. I think it’s going to be things that just get better and better and better and better, just like technology does.

JOE ROGAN: So you are rosy about the future. You’re very optimistic about what’s going to happen with AI, obviously. Will you make the best AI chips in the world?

JENSEN HUANG: It probably better be, if history is a guide.

We were always concerned about new technology. Humanity has always been concerned about new technology, there are always somebody who’s thinking—there are always a lot of people who are quite concerned, were quite concerned. And so if history is a guide, it is the case that all of this concern is channeled into making the technology safer.

And so, for example, in the last several years, I would say AI technology has increased probably in the last two years alone, maybe 100x. Let’s just give it a number. Okay? It’s like a car two years ago was 100 times slower. So AI is 100 times more capable today.

Now, how did we channel that technology? How do we channel all of that power? We directed it to causing the AI to be able to think, meaning that it can take a problem that we give it, break it down step by step. It does research before it answers, and so it grounds it on truth.

It’ll reflect on that answer. Ask itself, “Is this the best answer that I can give you? Am I certain about this answer?” If it’s not certain about the answer or highly confident about the answer, it’ll go back and do more research. It might actually even use a tool, because that tool provides a better solution than it could hallucinate itself.

As a result, we took all of that computing capability and we channeled it into having it produce a safer result, safer answer, a more truthful answer. Because, as you know, one of the greatest criticisms of AI in the beginning was that it hallucinated, right?

And so if you look at the reason why people use AI so much today is because the amount of hallucination has reduced. You know, I use it almost—I—well, I used it the whole trip over here. And so I think the capability—most people think about power. And they think about, you know, maybe as an explosion power, but the technology power, most of it is channeled towards safety.

A car today is more powerful, but it’s safer to drive. A lot of that power goes towards better handling. You know, I’d rather have a—well, you have a thousand horsepower truck. I think 500 horsepower is pretty good. No, 1000’s better. I think 1000 is better.

JOE ROGAN: I don’t know if it’s better, but it’s definitely faster.

JENSEN HUANG: Yeah, no, I think it’s better. You can get out of trouble faster.

I enjoyed my 599 more than my 612. It was, I think it was better. And more horsepower is better. My 459 is better than my 430. More horsepower is better. I think more horsepower is better. I think it’s better handling. It’s better control.

In the case of technology, it’s also very similar in that way. So if you look at what we’re going to do with the next thousand times of performance in AI, a lot of it is going to be channeled towards more reflection, more research. Thinking about the answer more deeply.

JOE ROGAN: So when you’re defining safety, you’re defining it as accuracy, functionality?

JENSEN HUANG: Functionality, okay. It does what you expect it to do. And then you take all the technology and the horsepower, you put guardrails on it. Just like our cars. We’ve got a lot of technology in a car today. A lot of it goes towards, for example, ABS. ABS is great. And so traction control, that’s fantastic. Without a computer in the car, how would you do any of that?

JOE ROGAN: Right?

JENSEN HUANG: And that little computer, the computers that you have doing your traction control is more powerful than the computer that went to Apollo 11. And so you want that technology, channel it towards safety, channel it towards functionality.

And so when people talk about power, the advancement of technology, oftentimes I feel what they’re thinking and what we’re actually doing is very different.

JOE ROGAN: What do you think they’re thinking?

JENSEN HUANG: Well, they’re thinking somehow that this AI is being powerful and their mind probably goes towards a sci-fi movie. The definition of power, oftentimes the definition of power is military power or physical power. But in the case of technology, power, when we translate all of those operations, it’s towards more refined thinking. More reflection, more planning, more options.

Military Applications and Defense Technology

JOE ROGAN: I think the big fears that people have is one, a big fear is military applications. Yes, that’s a big fear. Because people are very concerned that you’re going to have AI systems that make decisions that maybe an ethical person wouldn’t make or a moral person wouldn’t make based on achieving an objective versus based on how it’s going to look to people.

JENSEN HUANG: Well, I’m happy that our military is going to use AI technology for defense and I think that Anduril building military technology, I’m happy to hear that. I’m happy to see all these tech startups now channeling their technology capabilities towards defense and military applications. I think you need to do that.

JOE ROGAN: We had Palmer Luckey on the podcast and he was demonstrating some of his incredible helmet on and he showed some videos how you could see behind walls and stuff. Like it’s nuts.

JENSEN HUANG: He’s actually the perfect guy to go start that company.

JOE ROGAN: 100%. Yeah, 100%. It’s like he was born for that. He came in here with a Copper Jack. He’s a freak.

JENSEN HUANG: It’s awesome.

JOE ROGAN: He’s awesome. But it’s also an unusual intellect channeled into that very bizarre field is what you need.

JENSEN HUANG: And I think I’m happy that we’re making it more socially acceptable. There was a time where when somebody wanted to channel their technology capability and their intellect into defense technology, somehow they’re vilified. But we need people like that. We need people who enjoy that part of application of technology.

JOE ROGAN: Well, people are terrified of war.

JENSEN HUANG: Well, the best way to avoid it has excessive military might.

JOE ROGAN: Do you think that’s absolutely the best way? Not diplomacy, not working stuff out?

JENSEN HUANG: All of it. All of it, yeah.

JOE ROGAN: You have to have military might in order to get people to sit down, right?

JENSEN HUANG: Exactly. All of it.

JOE ROGAN: Otherwise they just invade.

JENSEN HUANG: That’s right. Why ask for permission? Like you said, history. Go back and look at history.

The Future of AI: Best Case Scenarios

JOE ROGAN: When you look at the future of AI and you just said that no one really knows what’s happening, do you ever sit down and ponder scenarios? What do you think is best case scenario for AI over the next two decades?

JENSEN HUANG: The best case scenario is that AI diffuses into everything that we do and our everything’s more efficient. But the threat of war remains a threat of war. Cyber security remains a super difficult challenge.

Somebody is going to try to breach your security. You’re going to have thousands of millions of AI agents protecting you from that threat. Your technology is going to get better. Their technology is going to get better. Just like cyber security. Right now, while we speak, we’re seeing cyber attacks all over the planet on just about every front door you can imagine.

And yet you and I are sitting here talking. And so the reason for that is because we know that there’s a whole bunch of cybersecurity technology in defense. And so we just have to keep amping that up. Keep stepping that up.

Cybersecurity and Encryption Challenges

JOE ROGAN: That’s a big issue with people is the worry that technology is going to get to a point where encryption is going to be obsolete. Encryption is just, it’s no longer going to protect data, it’s no longer going to protect systems. Do you anticipate that ever being an issue, or do you think there’s, it’s, as the defense grows, the threat grows, the defense grows and it just keeps going on and on and on. And they’ll always be able to fight off any sort of intrusions?

JENSEN HUANG: Not forever. Some intrusion will get in and they will all learn from it. And the reason why cybersecurity works is because, of course, the technology of defense is advancing very quickly. The technology offense is advancing very quickly.

However, the benefit of the cybersecurity defense is that socially, the community, all of our companies work together as one. Most people don’t realize this. There’s a whole community of cybersecurity experts. We exchange ideas, we exchange best practices, we exchange what we detect. The moment something has been breached or maybe there’s a loophole or whatever it is, it is shared by everybody. The patches are shared with everybody.

JOE ROGAN: That’s interesting.

JENSEN HUANG: Yeah. Most people don’t realize this.

JOE ROGAN: No. I had no idea. I assumed that it would just be competitive like everything else.

JENSEN HUANG: We work together, all of us.

JOE ROGAN: Has that always been the case?

JENSEN HUANG: It surely has been the case for about 15 years. It might not have been the case long ago, but this…

JOE ROGAN: What do you think started off that cooperation?

JENSEN HUANG: People recognizing it’s a challenge and no company can stand alone. And the same thing is going to happen with AI. I think we all have to decide working together to stay out of harm’s way is our best chance for defense. Then it’s basically everybody against the threat.

JOE ROGAN: And it also seems like you’d be way better at detecting where these threats are coming from and neutralizing them.

JENSEN HUANG: Exactly. Because the moment you detect it somewhere, you’re going to find out right away.

JOE ROGAN: It’ll be really hard to hide.

JENSEN HUANG: That’s right. Yeah. That’s how it works. That’s the reason why it’s safe. That’s why I’m sitting here right now instead of locking everything down on NVIDIA. Not only am I watching my own back, I’ve got everybody watching my back and I’m watching everybody else’s back.

JOE ROGAN: It’s a bizarre world, isn’t it, when you think about that cyber threat?

JENSEN HUANG: This idea about cyber security is unknown to the people who are talking about AI threats. I think when they think about AI threats and AI cybersecurity threats, they have to also think about how we deal with it today.

Now, there’s no question that AI is a new technology and it’s a new type of software. In the end, it’s software. It’s a new type of software. And so it’s going to have new capabilities, but so will the defense, where you use the same AI technology to go defend against it.

Quantum Computing and Post-Quantum Encryption

JOE ROGAN: So do you anticipate a time ever in the future where it’s going to be impossible, where there’s not going to be any secrets? Where the bottleneck between the technology that we have and the information that we have, information is just all a bunch of ones and zeros. It’s out there on hard drives and technology has more and more access to that information. Is it ever going to get to a point in time where there’s no way to keep a secret? Because it seems like that’s where everything is kind of headed in a weird way.

JENSEN HUANG: I don’t think so. I think the quantum computers were supposed to, will, yeah, quantum computers will make it possible. Will make it so that the previous quantum, previous encryption technology is obsolete. But that’s the reason why the entire industry is working on post-quantum encryption technology.

JOE ROGAN: What would that look like?

JENSEN HUANG: New algorithms.

JOE ROGAN: The crazy thing is when you hear about the kind of computation that quantum computing can do.

JENSEN HUANG: Yeah.

JOE ROGAN: And the power that it has.

JENSEN HUANG: Yeah.

JOE ROGAN: Where, you know, you’re looking at all the supercomputers in the world would take billions of years and it takes them a few minutes to solve these equations. Like, how do you make encryption for something that can do that?

JENSEN HUANG: I’m not sure. But I’ve got a bunch of scientists who are working on that.

JOE ROGAN: But they could figure it out.

JENSEN HUANG: Yeah. We got a bunch of scientists who are expert in that.

JOE ROGAN: And the ultimate fear that it can’t be breached, that quantum computing will always be able to decrypt all other quantum computing encryption? I don’t just get to some point where it’s like, stop playing the stupid game. We know everything.

JENSEN HUANG: I don’t think so. No. Because I, you know, history is guide. History is a guide. Before AI came around…

JOE ROGAN: That’s my worry. My worry is this is a total, you know, it’s like history was one thing and then nuclear weapons kind of changed all of our thoughts on war and mutually assured destruction came, got everybody to stop using nuclear bombs. My worry is that…

JENSEN HUANG: The thing is, Joe, is that AI is not going to, it’s not like we’re cavemen and then all of a sudden one day AI shows up. Every single day we’re getting better and smarter because we have AI and so we’re stepping on our own AI’s shoulders.

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

And the reason for that is because we all have AIs and there’s a whole bunch of AIs being in development. We know what they are and we’re using it. And so every single day we’re getting close, we’re close to each other.

JOE ROGAN: But don’t they do things that are very surprising?

JENSEN HUANG: Yeah, but so you have an AI that does something surprising. I’m going to have an AI, right? My AI looks at your AI and goes, that’s not that surprising.

JOE ROGAN: The fear for the layperson like myself is that AI becomes sentient and makes its own decisions and then ultimately decides to just govern the world, do it its own way. Like you guys, you had a good run, but we’re taking over now.

AI Consciousness and Control

JENSEN HUANG: Yeah, but my AI is going to take care of me. So that’s the cybersecurity argument.

JOE ROGAN: Yes.

JENSEN HUANG: Do you have an AI? And it’s super smart, but my AI is super smart too. And maybe your AI—let’s pretend, let’s pretend for a second that we understand what consciousness is and we understand what sentience is.

JOE ROGAN: And we really are just pretending.

JENSEN HUANG: Okay, let’s just pretend for a second. We believe that. I don’t believe actually, I actually don’t believe that. But nonetheless, let’s pretend we believe that. So your AI is conscious and my AI is conscious and let’s say your AI wants to, I don’t know, do something surprising. My AI is so smart that it won’t—it might be surprising to me, but it probably won’t be surprising to my AI. And so maybe my AI thinks it’s surprising as well, but it’s so smart the moment it sees it the first time, it’s not going to be surprised the second time. Just like us.

And so I feel like—I think the idea that only one person has AI and that one person’s AI compares everybody else’s AI as Neanderthal is probably unlikely. I think it’s much more like cybersecurity.

JOE ROGAN: Interesting. I think the fear is not that your AI is going to battle with somebody else’s AI. The fear is that AI is no longer going to listen to you.

JENSEN HUANG: That’s—

JOE ROGAN: The fear is that human beings won’t have control over it after a certain point if it achieves sentience and then has the ability to be autonomous. That there’s one AI, well, they just combine.

JENSEN HUANG: Yeah. Becomes one AI, that it’s a life form.

JOE ROGAN: Yeah, but that’s the—there’s arguments about that, right? That we’re dealing with some sort of synthetic biology, that it’s not as simple as new technology, that you’re creating a—

JENSEN HUANG: Life form if it’s like life form. Let’s go along with that for a while. I think if it’s like life form, as you know, all life forms don’t agree. And so I’m going to have to go with your life form and my life form. I’m going to agree because my life form is going to want to be the super life form. And now that we have disagreeing life forms, we’re back again to where we are.

JOE ROGAN: Well, they would probably cooperate with each other.

JENSEN HUANG: It would just—

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

JENSEN HUANG: Okay, well, that would be a—that would be a superpower with no ego, right? And if it has no ego, why would it have the ego to do any harm to us?

JOE ROGAN: Well, I don’t assume that it would do harm to us, but the fear would be that we would no longer have control and that we would no longer be the apex species on the planet. This thing that we created would now be. Is that funny?

JENSEN HUANG: No, I just think it’s not going to happen.

JOE ROGAN: I know you think it’s not going to happen, but it could, right? And here’s the other thing—it’s like if we’re racing towards “could” and “could” could be the end of human beings being in control of our own destiny.

JENSEN HUANG: I just think it’s extremely unlikely.

JOE ROGAN: That’s what they said in the Terminator movie.

JENSEN HUANG: And it hasn’t happened?

JOE ROGAN: No, not yet. But you guys are working towards it. The thing about you’re saying about conscience and sentience, that you don’t think that AI will achieve consciousness or that the—

JENSEN HUANG: Question is, what’s the definition of consciousness?

JOE ROGAN: What is the definition to you?

Defining Consciousness

JENSEN HUANG: Consciousness? I guess first of all, you need to know about your own existence. You have to have experience, not just knowledge and intelligence.

The concept of a machine having an experience—I’m not—well, first of all, I don’t know what defines experience, why we have experiences and why this microphone doesn’t. And so I think I know—well, I think I know what consciousness is. The sense of experience, the ability to know self versus the ability to be able to reflect, know our own self, the sense of ego. I think all of those human experiences probably is what consciousness is.

But why it exists versus the concept of knowledge and intelligence, which is what AI is defined by today. It has knowledge, it has intelligence. Artificial intelligence, we don’t call it artificial consciousness. Artificial intelligence, the ability to perceive, recognize, understanding, plan, perform tasks. Those things are foundations of intelligence. To know things, knowledge. It’s clearly different than consciousness.

JOE ROGAN: But consciousness is so loosely defined. How can we say that? I mean, doesn’t a dog have consciousness? Yeah, dogs seem to be pretty conscious.

JENSEN HUANG: That’s right, yeah.

JOE ROGAN: And that’s a lower level consciousness than a human being’s consciousness.

JENSEN HUANG: I’m not sure. Yeah, right. Well, the question is what—lower level intelligence? Lower level intelligence, yes. But I don’t know that’s lower level consciousness.

JOE ROGAN: That’s a good point.

JENSEN HUANG: Yeah, right. Because I believe my dogs feel as much as I feel.

JOE ROGAN: Yeah, they feel a lot.

JENSEN HUANG: Yeah, right. Yeah.

JOE ROGAN: They get attached to you.

JENSEN HUANG: That’s right.

JOE ROGAN: They get depressed if you’re not—

JENSEN HUANG: That’s right, exactly. There’s—

JOE ROGAN: There’s definitely that. Yeah, the concept of experience.

JENSEN HUANG: Right.

JOE ROGAN: But isn’t AI interacting with society? So doesn’t it acquire experience through that interaction?

JENSEN HUANG: I don’t think interactions is experience. I think experience is—experience is a collection of feelings.

AI Behavior and Pattern Recognition

JOE ROGAN: I think you’re aware that AI, I forget which one, where they gave it some false information about one of the programmers having an affair with his wife just to see how it would respond to it. And then when they said they were going to shut it down, it threatened to blackmail him and reveal his affair. And it was like, whoa. It’s conniving. If that’s not learning from experience and being aware that you’re about to be shut down, which would imply at least some kind of consciousness—or you could kind of define it as consciousness if you were very loose with the term. And if you imagine that this is going to exponentially become more powerful, wouldn’t that ultimately lead to a different kind of consciousness than we’re defining from biology?

JENSEN HUANG: Well, first of all, let’s just break down what it probably did. It probably read somewhere—there’s probably text that in these consequences certain people did that. I could imagine a novel having those words related.

JOE ROGAN: Sure.

JENSEN HUANG: And so inside, it realizes its strategy for survival. It’s just a bunch of numbers that—it’s just a bunch of numbers that in the collection of numbers that relates to a husband cheating on a wife has subsequently a bunch of numbers that relates to blackmail and such things. However, whatever the revenge was, right? And so it spewed it out. And so it’s just like, you know, it’s just as if I’m asking it to write me a poem in Shakespeare. It just—whatever the words are in that dimensionality. This dimensionality is all these vectors in multidimensional space. These words that were in the prompt that described the affair subsequently led to one word after another, led to some revenge on something. But it’s not because it had consciousness or, you know, it just spewed out those words, generated those words.

JOE ROGAN: I understand what you’re saying, that it’s patterns that human beings have exhibited both in literature and in real life.

JENSEN HUANG: That’s exactly right.

JOE ROGAN: But at a certain point in time, one would say, okay, well, it couldn’t do this two years ago and it couldn’t do this four years ago. When we were looking towards the future—at what point in time when it can do everything a person does, what point in time do we decide that it’s conscious? If it absolutely mimics all human thinking and behavior patterns—

JENSEN HUANG: That doesn’t make it conscious.

JOE ROGAN: It becomes indiscernible. It’s aware, it can communicate with you the exact same way a person can. Is consciousness—are we putting too much weight on that concept? Because it seems like it’s a version of a kind of consciousness.

JENSEN HUANG: It’s a version of imitation, imitation consciousness.

JOE ROGAN: Right. But if it perfectly imitates it—

JENSEN HUANG: I still think it’s an example of imitation.

JOE ROGAN: So it’s like a fake Rolex when they 3D print them and make them indiscernible.

JENSEN HUANG: So the question is, what’s the definition of consciousness?

JOE ROGAN: Yeah, that’s the question. And I don’t think anybody’s really clearly defined that. That’s where it gets weird and that’s where the real doomsday people are worried that you are creating a form of consciousness that you can’t control.

The Future of AI-Generated Knowledge

JENSEN HUANG: I believe it is possible to create a machine that imitates human intelligence and has the ability to understand information, understand instructions, break the problem down, solve problems and perform tasks. I believe that completely. I believe that we could have a computer that has a vast amount of knowledge, some of it true, some of it not true, some of it generated by humans, some of it generated synthetically. And more and more of knowledge in the world will be generated synthetically.

Going forward until now, the knowledge that we have are knowledge that we generate and we propagate and we send to each other and we amplify it and we add to it and we modify it, we change it. In the future, in a couple of years, maybe two or three years, 90% of the world’s knowledge will likely be generated by AI.

JOE ROGAN: That’s crazy.

JENSEN HUANG: I know. But it’s just fine.

JOE ROGAN: But it’s just fine.

JENSEN HUANG: I know. And the reason for that is this. Let me tell you why, okay? It’s because what difference does it make to me that I am learning from a textbook that was generated by a bunch of people I didn’t know, or written by a book that, you know, from somebody I don’t know, to knowledge generated by AI computers that are assimilating all of this and resynthesizing things. To me, I don’t think there’s a whole lot of difference. We still have to—we still have to fact check it. We still have to make sure that it’s, you know, based on fundamental first principles, and we still have to do all of that just like we do today.

JOE ROGAN: Is this taking into account the kind of AI that exists currently? And do you anticipate that just like we could have never really believed that AI would be—at least a person like myself would never believe AI would be so ubiquitous and so powerful today and so important today. We never thought that 10 years ago. Never thought that, right? Imagine what are we looking at 10 years from now?

JENSEN HUANG: I think that if you reflect back 10 years from now, you would say the same thing, that we would have never believed that in a different direction, right. But if you go forward nine years from now and then ask yourself what’s going to happen 10 years from now, I think it’ll be quite gradual.

JOE ROGAN: One of the things that Elon said that makes me happy is he believes that we’re going to get to a point where—

The Future of Work and AI’s Impact on Employment

JOE ROGAN: It’s not necessary for people to work, and not meaning that you’re going to have no purpose in life, but you will have, in his words, universal high income. Because so much revenue is generated by AI that it will take away this need for people to do things that they don’t really enjoy doing just for money.

And I think a lot of people have a problem with that because their entire identity and how they think of themselves and how they fit in the community is what they do. Like, this is Mike. He’s an amazing mechanic. Go to Mike and Mike takes care of things. But there’s going to come a point in time where AI is going to be able to do all those things much better than people do, and people will just be able to receive money.

But then what does Mike do? Mike is really loves being the best mechanic around. What does the guy who codes, what does he do? When AI can code infinitely faster with zero errors, like what happens with all those people? And that is where it gets weird. It’s like, because we’ve sort of wrapped our identity as human beings around what we do for a living.

You know, when you meet someone, one of the first things you meet somebody at a party, hi, Joe, what’s your name? Mike. What do you do? Mike. And you know, Mike’s like, oh, I’m a lawyer. Oh, what kind of law? And you have a conversation. You know, when Mike is like, I get money from the government, I play video games. Gets weird.

And I think the concept sounds great until you take into account human nature. And human nature is that we like to have puzzles to solve and things to do and an identity that’s wrapped around our idea that we’re very good at this thing that we do for a living.

JENSEN HUANG: Yeah, I think, let’s see. Let me start with the more mundane and I’ll work backwards, work forward.

Geoffrey Hinton’s Prediction and the Radiologist Paradox

JENSEN HUANG: So one of the predictions from Geoffrey Hinton, who started the whole deep learning phenomenon, deep learning technology trend, and incredible researcher, professor at University of Toronto. He invented, discovered or invented the idea of back propagation, which allows the neural network to learn.

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

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

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

When I show it a cat, and the one that is a cat should be one, and I show it a cat through this big, huge network of switchboards and math units. And they’re just doing multiply and adds, multiplies and adds. And this thing, this switchboard is gigantic. The more information you’re going to give it, the bigger the switchboard has to be.

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

And if it, the first time you do, just comes up with garbage, and so it says the right answer is cat. And so you need to increase this signal and decrease all of the other and back propagates the outcome through the entire network. And then you show in another, now it’s an image of a dog. And it guesses, it takes a swing at it, and it comes up with a bunch of garbage. And you say, no, no, no, the answer is, this is a dog. I want you to produce dog. And all of the other switches, all the other outputs have to be zero.

And I want to back propagate that and just do it over and over and over again. It’s just like showing a kid, this is an apple, this is a dog, this is a cat. And you just keep showing it to them until they eventually get it. Well, anyways, that big invention is deep learning. That’s the foundation of artificial intelligence, a piece of software that learns from examples. That’s basically machine learning, a machine that learns. And so one of the big first applications was image recognition. And one of the most important image recognition applications is radiology.

And so he predicted about five years ago that in five years time, the world won’t need any radiologists because AI would have swept the whole field. Well, it turns out AI has swept the whole field. That is completely true. Today, just about every radiologist is using AI in some way. And what’s ironic though, what’s interesting is that the number of radiologists has actually grown.

And so the question is why? That’s kind of interesting, right? It is. And so the prediction was in fact that 30 million radiologists will be wiped out. But as it turns out, we needed more. And the reason for that is because the purpose of a radiologist is to diagnose disease, not to study the image. The image studying is simply a task in service of diagnosing the disease.

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

And so the number of tests that people are able to do increases. And because they’re able to serve more patients, the hospital does better, they have more clients, more patients. As a result, they have better economics. When they have better economics, they hire more radiologists because their purpose is not to study the images, their purpose is to diagnose disease.

And so the question is, what I’m leading up to is ultimately, what is the purpose? What is the purpose of the lawyer? And has the purpose changed? What is the purpose? One of the examples that I gave is, that I would give is, for example, if my car became self-driving, will all chauffeurs be out of jobs? The answer probably is not because for some chauffeurs, for some people who are driving you, they could be protectors. Some people, they’re part of the experience, part of the service. So when you get there, they could take care of things for you.

And so for a lot of different reasons, not all chauffeurs would lose their jobs. Some chauffeurs would lose their jobs. And many chauffeurs would change their jobs. And the type of applications of autonomous vehicles will probably increase the usage of the technology within. Find new homes.

I think you have to go back to what is the purpose of a job? For example, if AI comes along, I actually don’t believe I’m going to lose my job because my purpose isn’t to, I have to look at a lot of documents, I study a lot of emails, I look at a bunch of diagrams. The question is, what is the job? And the purpose of somebody probably hasn’t changed. A lawyer, for example, help people. That probably hasn’t changed. Studying legal documents, generating documents is part of the job, not the job.

JOE ROGAN: But don’t you think there’s many jobs that AI will replace?

JENSEN HUANG: If your automation, yeah, if your job is the task.

JOE ROGAN: Right. So that’s a lot of people.

The Robot Economy and New Industries

JENSEN HUANG: It could be a lot of people, but it’ll probably generate, like, for example, let’s say we, let’s say I’m super excited about the robots Elon’s working on. It’s still a few years away. When it happens, when it happens, there’s a whole new industry of technicians and people who have to manufacture the robots. Right. And so that job never existed.

And so you’re going to have a whole industry of people taking care of, like, for example, all the mechanics and all the people who are building things for cars, supercharging cars that didn’t exist before cars. And now we’re going to have robots, you’re going to have robot apparels. So a whole industry of, right. Isn’t that right? Because I want my robot to look different than your robot.

And so you’re going to have a whole apparel industry for robots. You’re going to have mechanics for robots. And you have people who come to maintain your robots.

JOE ROGAN: Automated, though. No, you don’t think so? You don’t think they’re all done by other robots eventually?

JENSEN HUANG: And then there’ll be something else.

JOE ROGAN: So you think ultimately people just adapt. Except if you are the task, which is a large percentage of the workforce.

JENSEN HUANG: If your job is just to chop vegetables, Cuisinart is going to replace you.

JOE ROGAN: Yeah. So people have to find meaning in other things.

JENSEN HUANG: Your job has to be more than the task.

Universal Basic Income and the Wealth Paradox

JOE ROGAN: What do you think about Elon’s belief that this universal basic income thing will eventually become necessary? Many people think that Andrew Yang thinks that he was one of the first people to sort of sound that alarm during the 2020 election.

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

JOE ROGAN: Right.

JENSEN HUANG: And so we’re either going to be all wealthy or we’re going to be all…

JOE ROGAN: How could everybody be wealthy, though? What scenario?

JENSEN HUANG: Well, because wealthy, not because you have a lot of dollars. Wealthy, because there’s a lot of abundance. Like, for example, today we are wealthy of information. This is a concept several thousand years ago, only a few people have. And so today we have wealth of a whole bunch of things. Resources that historically, yeah, yeah. And so we’re going to have wealth of resources, things that we think are valuable today that in the future are just not that valuable, because it’s automated. And so I think the question, maybe, partly it’s hard to answer, partly because it’s hard to talk about infinity and it’s hard to talk about a long time from now.

And the reason for that is because there’s just too many scenarios to consider. But I think in the next several years, call it five to 10 years, there are several things that I believe in hope. And I say hope because I’m not sure.

The Technology Divide

JENSEN HUANG: One of the things that I believe is that the technology divide will be substantially collapsed. And of course, the alternative viewpoint is that AI, it’s going to increase the technology divide.

The Promise of AI Accessibility

JENSEN HUANG: Now, the reason why I believe AI is going to reduce the technology divide is because we have proof. The evidence is that AI is the easiest application in the world to use. ChatGPT has grown to almost a billion users, frankly, practically overnight. And if you’re not exactly sure how to use it, everybody knows how to use ChatGPT—just say something to it. If you’re not sure how to use ChatGPT, you ask ChatGPT how to use it.

No tool in history has ever had this capability. A Cuisinart, you know, if you don’t know how to use it, you’re kind of screwed. You’re not going to walk up to it and say, “How do you use a Cuisinart?” You’re going to have to find somebody else. But an AI will just tell you exactly how to do it. Anybody could do this. It’ll speak to you in any language. And if it doesn’t know your language, you’ll speak it in that language and it’ll probably figure out that it doesn’t completely understand your language, go learn it instantly and come back and talk to you.

And so I think the technology divide has a real chance finally that you don’t have to speak Python or C or Fortran—you can just speak human and whatever form of human you like. And so I think that has a real chance of closing the technology divide. Now, of course the counter narrative would say that AI is only going to be available for the nations and the countries that have a vast amount of resources, because AI takes energy and AI takes a lot of GPUs and factories to be able to produce the AI, no doubt at the scale that we would like to do in the United States.

But the fact of the matter is your phone’s going to run AI just fine all by itself in a few years. Today it already does it fairly decently. And so the fact that every country, every nation, every society will have the benefit of very good AI—it might not be tomorrow’s AI, it might be yesterday’s AI, but yesterday’s AI is freaking amazing. You know, in 10 years time, nine-year-old AI is going to be amazing. You don’t need 10-year-old AI, you don’t need frontier AI like we need frontier AI because we want to be the world leader. But for every single country, everybody, I think the capability to elevate everybody’s knowledge and capability and intelligence, that day is coming.

JOE ROGAN: And also energy production, which is the real bottleneck when it comes to third world countries. And that’s right, electricity and all the resources that we take for granted.

Moore’s Law and the Energy Challenge

JENSEN HUANG: Almost everything is going to be energy constrained. And so if you take a look at one of the most important technology advances in history, it’s this idea called Moore’s Law. Moore’s Law started basically in my generation, and my generation is the generation of computers. I graduated in 1984, and that was basically at the very beginning of the PC revolution and the microprocessor.

Every single year it approximately doubled. And we describe it as every single year we double the performance. But what it really means is that every single year, the cost of computing halved. And so the cost of computing in the course of five years reduced by a factor of 10. The amount of energy necessary to do computing, to do any task, reduced by a factor of 10 every single 10 years—100, 1,000, 10,000, 100,000, so on and so forth.

And so each one of the clicks of Moore’s Law, the amount of energy necessary to do any computing reduced. That’s the reason why you have a laptop today. When back in 1984, it sat on the desk, you got to plug it in, it wasn’t that fast and it consumed a lot of power. Today, you know, it is only a few watts. And so Moore’s Law is the fundamental technology, the fundamental technology trend that made it possible.

Well, what’s going on in AI? The reason why NVIDIA is here is because we invented this new way of doing computing. We call it accelerated computing. We started 33 years ago. Took us about 30 years to really make a huge breakthrough. In that 30 years or so, we took computing, you know, probably a factor of—well, let me just say last 10 years, the last 10 years, we improved the performance of computing by 100,000 times.

Whoa. Imagine a car, over the course of 10 years, it became 100,000 times faster, or at the same speed, 100,000 times cheaper, or at the same speed, 100,000 times less energy. If your car did that, it doesn’t need energy at all. What I mean, what I’m trying to say is that in 10 years time, the amount of energy necessary for artificial intelligence for most people will be miniscule, utterly miniscule.

And so we’ll have AI running in all kinds of things and all the time, because it doesn’t consume that much energy. And so if you’re a nation that uses AI for, you know, almost everything in your social fabric, of course you’re going to need these AI factories. But for a lot of countries, I think you’re going to have excellent AI and you’re not going to need as much energy. Everybody will be able to come along is my point.

JOE ROGAN: So currently, that is a big bottleneck, right? Energy.

JENSEN HUANG: It is the bottleneck. The bottleneck.

JOE ROGAN: Yeah. So was it Google that is making nuclear power plants to operate one of its AI factories?

JENSEN HUANG: Oh, I haven’t heard that, but I think in the next six, seven years, I think you’re going to see a whole bunch of small nuclear reactors.

JOE ROGAN: And by small, how big are you talking about? Hundreds of megawatts?

JENSEN HUANG: Yeah. Okay.

JOE ROGAN: And that these will be local to whatever specific company they have.

JENSEN HUANG: That’s right. We’ll all be power generators.

JOE ROGAN: Whoa. You know, just like your, you know, somebody’s farm.

JENSEN HUANG: It probably is the smartest way to do it. Right. And it takes the burden off. Yeah, takes the burden off the grid. It takes—and you could build as much as you need, and you can contribute back to the grid. It’s a really important point.

The Power of Accelerated Computing

JOE ROGAN: I think you just made about Moore’s Law and the relationship to pricing, because, you know, a laptop today, like, you can get one of those little MacBook Airs. They’re incredible. They’re just incredible. Unbelievably powerful battery life.

JENSEN HUANG: Ever have to charge it?

JOE ROGAN: Yeah. Crazy. And it’s not that expensive, relatively speaking. Something like that.

JENSEN HUANG: I remember. And that’s just Moore’s Law.

JOE ROGAN: Right.

JENSEN HUANG: Then there’s the NVIDIA law. Oh, just right. The law. I was talking to you about the computing that we invented. Right. The reason why we’re here. This new way of doing computing is like Moore’s Law on energy drinks. I mean, it’s like Moore’s Law, Moore’s Law and Joe Rogan.

JOE ROGAN: Wow, that’s interesting. Yeah, that’s us. So explain that. This chip that you brought to Elon, what’s the significance of this? Like, why is it so superior?

The AlexNet Breakthrough

JENSEN HUANG: And so in 2012, Jeff Hinton’s lab, this gentleman I was talking about, Ilya Sutskever, Alex Krizhevsky—they made a breakthrough in computer vision in literally creating a piece of software called AlexNet. And its job was to recognize images. And it recognized images at a level—computer vision, which is fundamental to intelligence. If you can’t perceive, you can’t—it’s hard to have intelligence. And so computer vision is a fundamental pillar of, not the only, but fundamental pillar of—and so breaking computer vision or breaking through in computer vision is pretty foundational to almost everything that everybody wants to do in AI.

And so in 2012, their lab in Toronto made this breakthrough called AlexNet. And AlexNet was able to recognize images so much better than any human-created computer vision algorithm in the 30 years prior. So all of these people, all these scientists, and we had many too, working on computer vision algorithms. And these two kids, Ilya and Alex, under Jeff Hinton, took a giant leap above it. And it was based on this thing called AlexNet, this neural network.

And the way it ran, the way they made it work, was literally buying two NVIDIA graphics cards because NVIDIA’s GPUs—we’ve been working on this new way of doing computing and our GPUs application, and it’s basically a supercomputing application. Back in 1984, in order to process computer games and what you have in your racing simulator, that is called an image generator supercomputer.

And so NVIDIA started. Our first application was computer graphics. And we applied this new way of doing computing where we do things in parallel instead of sequentially. A CPU does things sequentially. Step one, step two, step three. In our case, we break the problem down and we give it to thousands of processors. And so our way of doing computation is much more complicated. But if you’re able to formulate the problem in the way that we create it, called CUDA—this is the invention of our company—if you could formulate it in that way, we could process everything simultaneously.

Now, in the case of computer graphics, it’s easier to do because every single pixel on your screen is not related to every other pixel. And so I could render multiple parts of the screen at the same time. Not completely true because, you know, maybe the way lighting works or the way shadow works, there’s a lot of dependency and such, but computer graphics, with all the pixels, I should be able to process everything simultaneously. And so we took this embarrassingly parallel problem called computer graphics, and we applied it to this new way of doing computing, NVIDIA’s accelerated computing.

We put it in all of our graphics cards. Kids were buying it to play games. You probably don’t know this, but we’re the largest gaming platform in the world today.

JOE ROGAN: Oh, I know that.

JENSEN HUANG: Okay.

JOE ROGAN: I used to make my own computers. I used to buy your graphics cards.

JENSEN HUANG: Oh, that’s super cool.

JOE ROGAN: Yeah. Set up SLI with two graphics cards.

JENSEN HUANG: Yeah, I love it. Okay, that’s super cool.

JOE ROGAN: Oh, yeah, man. I used to be at Quake.

JENSEN HUANG: Joe. Oh, that’s cool. Yeah. Okay. So SLI, I’ll tell you the story in just a second and how it led to Elon. I’m still answering the question. And so anyways, these two kids trained this model using the technique I described earlier on our GPUs, because our GPUs could process things in parallel. It’s essentially a supercomputer in a PC. The reason why you used it for Quake is because it is the first consumer supercomputer. Okay. And so anyways, they made that breakthrough.

The Deep Learning Revolution

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

And so my curiosity asked, you know, what is so special about this form of machine learning? And we’ve known about machine learning for a very long time. We’ve known about AI for a very long time. We’ve known about neural networks for a very long time. What makes now the moment?

And so we realized that this architecture for deep neural networks, back propagation, the way deep neural networks were created, we could probably scale this problem, scale the solution to solve many problems. That is essentially a universal function approximator. Okay. Meaning, you know, back when you’re in school.

The Universal Function Approximator

JENSEN HUANG: You have a box, inside of it is a function. You give it an input, it gives you an output. And the reason why I call it universal function approximator is that this computer, instead of you describing the function, a function could be a Newton’s equation, F equals ma. That’s a function. You write the function in software, you give it input f, mass acceleration. It’ll tell you the force.

The way this computer works is really interesting. You give it a universal function. It’s not F equals ma, just a universal function. It’s a big, huge deep neural network.

And instead of describing the inside, you give it examples of input and output and it figures out the inside. So you give it input and output, and it figures out the inside. A universal function approximator. Today it could be Newton’s equation, tomorrow it could be Maxwell’s equation, it could be Coulomb’s law, it could be thermodynamics equation, it could be Schrodinger’s equation for quantum physics. And so you could have this describe almost anything, so long as you have the input and the output. So long as you have the input and the output. Or it could learn the input and output.

And so we took a step back and we said, hang on a second. This isn’t just for computer vision. Deep learning could solve any problem, all the problems that are interesting, so long as we have input and output. Now what has input and output?

Well, the world. The world has input and output. And so we could have a computer that could learn almost anything. Machine learning, artificial intelligence. And so we reasoned that maybe this is the fundamental breakthrough that we needed.

There were a couple of things that had to be solved. For example, we had to believe that you could actually scale this up to giant systems. It was running in a, they had two graphics cards, two GTX 580s.

Which, by the way, is exactly your SLI.

JOE ROGAN: Configuration.

JENSEN HUANG: Yeah. So that GTX 580 SLI was the revolutionary computer that put deep learning on the map.

JOE ROGAN: Wow.

JENSEN HUANG: It was 2018 and you were using it to play Quake. That’s crazy. That was the moment that was the big bang of modern AI. We were lucky because we were inventing this technology, this computing approach. We were lucky that they found it. Turns out they were gamers. And it was lucky they found it. And it was lucky that we paid attention to that moment. It was a little bit like, you know, that Star Trek, first contact.

The Vulcans had to have seen the warp drive at that very moment. If they didn’t witness the warp drive, you know, they would have never come to Earth and everything would have never happened. It’s a little bit like if I hadn’t paid attention to that moment, that flash and that flash didn’t last long. If I hadn’t paid attention to that flash or our company didn’t pay attention to it, who knows what would happen.

But we saw that and we reasoned our way into, this is a universal function approximator. This is not just a computer vision approximator. We could use this for all kinds of things. If we could solve two problems. The first problem is that we have to prove to ourselves it could scale. The second problem, we have to wait for, I guess, contribute to and wait for is the world will never have enough data on input and output where we could supervise the AI to learn everything.

For example, if we have to supervise our children on everything they learn, the amount of information they can learn is limited. We needed the AI, we needed the computer to have a method of learning without supervision. And that’s where we had to wait a few more years. But unsupervised AI learning is now here. And so the AI could learn by itself.

And the reason why the AI could learn by itself is because we have many examples of right answers. Like, for example, if I want to learn, if I want to teach an AI how to predict the next word, I could just grab it, grab a whole bunch of text that we already have, mask out the last word and make it try and try and try again until it predicts the next one. Or I mask out random words inside the text, and I make it try and try and try until it predicts it.

Mary goes down to the bank. Is it a river bank or a money bank? Well, if you’re going to go down to the bank, it’s probably a river bank. So, and it might not be obvious even from that. It might need, and caught a fish. Now, you know, it must be the riverbank. And so you give these AIs a whole bunch of these examples and you mask out the words, it’ll predict the next one.

And so unsupervised learning came along. These two ideas, the fact that it’s scalable and unsupervised learning came along. We were convinced that we ought to put everything into this and help create this industry because we’re going to solve a whole bunch of interesting problems. And that was in 2012. By 2016.

The First DGX-1 and OpenAI

I had built this computer called the DGX-1. The one that you saw me give to Elon is called DGX Spark. The DGX-1 was $300,000. It cost NVIDIA a few billion dollars to make the first one.

And instead of two chips, SLI, we connected eight chips with a technology called NVLink. But it’s basically SLI supercharged. And so we connected eight of these chips together instead of just two. And all of them work together just like your Quake rig did to solve this deep learning problem, to train this model.

And so we created this thing. I announced it at GTC and one of our annual events, and I described this deep learning thing, computer vision thing, and this computer called DGX-1, the audience was like completely silent. They had no idea what I was talking about.

And I was lucky because I had known Elon and I helped him build the first computer for Model 3. The Model S. And when he wanted to start working on autonomous vehicle, I helped him build the computer that went into the Model S AV system, his full self-driving system. We were basically the FSD computer, version one. And so we were already working together.

And when I announced this thing, nobody in the world wanted it. I had no purchase orders, not one. Nobody wanted to buy it, nobody wanted to be part of it, except for Elon. He was at the event and we were doing a fireside chat about the future of self-driving cars. I think it was like 2016 at that time, it was 2015. And he goes, you know what, I have a company that could really use this. I said, wow, my first customer. And so I was pretty excited about it.

And he goes, yeah, we have this company, it’s a non-profit company. And all the blood drained out of my face. Yeah, I just spent a few billion dollars building this thing. Cost $300,000 and you know the chances of a non-profit being able to pay for this thing is approximately zero. And he goes, you know, this is an AI company and it’s a non-profit and we could really use one of these supercomputers.

And so I picked it up, I built the first one for ourselves. We’re using it inside the company. I boxed one up, I drove it up to San Francisco and I delivered to Elon in 2016. A bunch of researchers were there. Peter Beale was there, Ilya was there, and there was a bunch of people there. And I walk up to the second floor where they were all kind of in a room this smaller than your place here. And that place turned out to have been OpenAI.

2016. Just a bunch of people sitting in a room.

JOE ROGAN: It’s not really non-profit anymore though.

JENSEN HUANG: They’re not non-profit anymore. Yeah. Weird how that works. Yeah. But anyhow, Elon was there. Yeah, it was really a great moment. Oh yeah, there you go. Yeah, that’s it. Look at you.

JOE ROGAN: Same jacket.

JENSEN HUANG: Look at that. I haven’t aged. Not a lick of black hair though.

JOE ROGAN: The size of it is, it’s significantly smaller. That was the other day.

JENSEN HUANG: So yeah, there you go. Yeah, look at the difference. Exactly the same industrial design.

JOE ROGAN: He’s holding it in his hand.

JENSEN HUANG: Here’s the amazing thing. DGX-1 was one petaflops. That’s a lot of flops. And DGX Spark is one petaflops. Nine years later.

JOE ROGAN: Wow.

JENSEN HUANG: The same amount of computing horsepower in a much smaller, shrunken down. And instead of $300,000, it’s now $4,000 and it’s the size of a small book. Incredible. Crazy. That’s how technology moves. Anyways, that’s the reason why I wanted to give him the first one. Because I gave him the first one.

JOE ROGAN: 2016. It’s so fascinating. I mean, if you wanted to make a story for a film, I mean, that would be the story that, what better scenario? If it really does become a digital life form, how funny would it be that it is birthed out of the desire for computer graphics for video games?

Isn’t it kind of crazy? It’s kind of crazy, yeah. Kind of crazy when you think about it that way.

JENSEN HUANG: Because perfect origin story, computer graphics was one of the hardest computer supercomputer problems, generating reality.

JOE ROGAN: And also one of the most profitable to solve because computer games are so popular.

The Birth of NVIDIA and the Gaming Industry

JENSEN HUANG: When NVIDIA started in 1993, we were trying to create this new computing approach. The question is, what’s the killer app? And the problem we wanted to, the company wanted to create a new type of computing architecture, a new type of computer that can solve problems that normal computers can’t solve.

Well, the applications that existed in the industry in 1993 are applications that normal computers can solve. Because if the normal computers can’t solve them, why would the application exist?

And so we had a mission statement for a company that has no chance of success.

But I didn’t know that in 1993. It just sounded like a good idea. And so if we created this thing that can solve problems, you know, it’s like you actually have to go create the problem.

And so that’s what we did in 1993. There was no Quake. John Carmack hadn’t even released Doom yet. You probably remember that.

JOE ROGAN: Sure.

JENSEN HUANG: Yeah. And there were no applications for it. And so I went to Japan because the arcade industry had this at the time of Sega, if you remember. Sure. The arcade machines, they came up with 3D arcade systems. Virtual Fighter, Daytona, Virtual Cop. All of those arcade games were in 3D for the very first time.

And the technology they were using was from Martin Marietta, the flight simulators. They took the guts out of a flight simulator and put it into an arcade machine. The system that you have over here, it’s got to be a million times more powerful than that arcade machine. And that was a flight simulator for NASA.

JOE ROGAN: Whoa.

JENSEN HUANG: And so they took the guts out of that. They were using it for flight simulation with jets and, you know, space shuttle, and they took the guts out of that. And Sega had this brilliant computer developer. His name is Yu Suzuki. Yu Suzuki and Miyamoto. Sega and Nintendo, these were the incredible pioneers, the visionaries, the incredible artists. And they’re both very, very technical.

They were the origins really of the gaming industry. And Yu Suzuki pioneered 3D graphics gaming. And so I went, we created this company and there were no apps and we were spending all of our afternoons. We told our family we were going to work, but it was just the three of us, you know, who’s going to know? And so we went to Curtis’s, one of the founders went to Curtis’s townhouse. And Chris and I were married. We have kids. I already had Spencer and Madison. They were probably 2 years old.

The Birth of NVIDIA and the Sega Partnership

And Chris’s kids are about the same age as ours. And we would go to work in this townhouse. But, you know, when you’re a startup and the mission statement is the way we described, you’re not going to have too many customers calling you. And so we had really nothing to do.

And so after lunch, we would always have a great lunch. After lunch, we would go to the arcades and play the Sega, you know, the Sega Virtua Fighter and Daytona and all those games and analyze how they’re doing it, trying to figure out how they were doing that. And so we decided, let’s just go to Japan and let’s convince Sega to move those applications into the PC.

And we would start the PC gaming, the 3D gaming industry, partnering with Sega. That’s how NVIDIA started.

JOE ROGAN: Wow.

JENSEN HUANG: And so, in exchange for them developing their games for our computers in the PC, we would build a chip for their game console. That was the partnership. I build a chip for your game console, you port the SEGA games to us. And then they paid us at the time, quite a significant amount of money to build that game console. And that was kind of the beginning of NVIDIA getting started. And we thought we were on our way.

And so I started with a business plan, a mission statement that was impossible. We lucked into the Sega partnership. We started taking off, started building our game console. And about a couple years into it, we discovered our first technology didn’t work. It was a flaw.

And all of the technology ideas that we had, the architecture concepts were sound, but the way we were doing computer graphics was exactly backwards. You know, instead of, I won’t bore you with the technology, but instead of inverse texture mapping, we were doing forward texture mapping. Instead of triangles, we did curved surfaces. So other people did it flat, we did it round.

The Wrong Technology Choices

The technology that ultimately won, the technology we use today has Z buffers. It automatically sorted. We had an architecture with no Z buffers. The application had to sort it. So we chose a bunch of technology approaches that three major technology choices. All three choices were wrong.

Okay, so this is how incredibly smart we were. And so, 1995, we realized we were going down the wrong path. Meanwhile, the Silicon Valley was packed with 3D graphics startups because it was the most exciting technology of that time. And so 3D effects and rendition and Silicon Graphics was coming in. Intel was already in there.

And, you know, gosh, what added up eventually to a hundred different startups we had to compete against? Everybody had chosen the right technology approach and we chose the wrong one. And so we were the first company to start. We found ourselves essentially dead last with the wrong answer.

And so the company was in trouble.

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

And so that argument, you know, do we change and therefore be dead? Don’t change and make this technology work somehow, or go do something completely different?

That question stirred the company strategically and was a hard question I eventually advocated for. We don’t know what the right strategy is, but we know what the wrong technology is. So let’s stop doing it the wrong way and let’s give ourselves a chance to go figure out what the strategy is.

The second thing, the second problem we had was our company was running out of money and I was in a contract with Sega and I owed them this game console. And if that contract would have been canceled, we’d be dead. We would have vaporized instantly.

The Meeting with Sega’s CEO

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

And he was already kind of elder. And I went to him and I said, “Listen, I’ve got some bad news for you. And first, the technology that we promised you doesn’t work.”

And second, we shouldn’t finish your contract because we’d waste all your money and you would have something that doesn’t work. And I recommend you find another partner to build your game console.

JOE ROGAN: Whoa.

JENSEN HUANG: And so I’m terribly sorry that we’ve set you back in your product roadmap. And third, even though I’m asking you to let me out of the contract, I still need the money. Because if you didn’t give me the money, we’d vaporize overnight.

And so I explained the two humbly, honestly. I gave him the background. Explained to him why the technology doesn’t work, why we thought it was going to work, why it doesn’t work. And I asked him to convert the last $5 million that they were going to complete the contract to give us that money as an investment.

Instead. And he said, “But it’s very likely your company will go out of business even with my investment.”

And it was completely true. Back then, 1995, $5 million was a lot of money. It’s a lot of money today. $5 million was a lot of money. And here’s a pile of competitors doing it right. What are the chances that giving NVIDIA $5 million, that we would develop the right strategy, that he would get a return on that $5 million or even get it back? Zero percent. You do the math. It’s zero percent.

If I were sitting there, right there, I wouldn’t have done it. $5 million was a mountain of money to Sega at the time. And so I told him that. If you invested that $5 million in us, it is most likely to be lost. But if you did invest that money, we’d be out of business and we would have no chance. And I told him that.

I don’t even know exactly what I said in the end, but I told him that I would understand if he decided not to, but it would make the world to me if he did. He went off and thought about it for a couple days and came back and said, “We’ll do it.”

JOE ROGAN: Wow. Strategy to how to correct what it was doing wrong. Did you do that to him?

JENSEN HUANG: Oh man. Wait until I tell you the rest of it. It’s scary. Even scarier.

JOE ROGAN: Oh no.

JENSEN HUANG: And so, so what he decided was, was Jensen was a young man he liked. That’s it.

JOE ROGAN: Wow. To this day, that’s nuts. I was, boy, do you owe, but the world owes that guy.

JENSEN HUANG: No doubt. Right? He celebrated today in Japan. And if he would have kept that the investment, I think it’d be worth probably about a trillion dollars today.

I know, but the moment we went public, they sold it. They goes, “Wow, that’s a miracle.” They sold it. Yeah, they sold it at NVIDIA valuation about $300 million. That’s our IPO valuation, $300 million.

JOE ROGAN: Wow.

JENSEN HUANG: And so anyhow, I was incredibly grateful.

Rebuilding from Scratch

And then now we had to figure out what to do because we still were doing the wrong strategy, wrong technology. So unfortunately, we had to lay off most of the company. We shrunk the company all back, all the people working on the game console, you know, we had to shrunk it all, but shrink it all back.

And then somebody told me that, “But Jensen, we’ve never built it this way before. We’ve never built it the right way before. We only know how to build it the wrong way.”

And so nobody in the company knew how to build this supercomputing image generator, 3D graphics thing that Silicon Graphics did. And so I said, “Okay, how hard can it be? You got all these 30 companies, 50 companies doing it. How hard can it be?” And so luckily there was a textbook written by the company, Silicon Graphics.

And so I went down to the store, I had $200 in my pocket, and I bought three textbooks. The only three, they had $60 apiece. I bought the three textbooks, I brought it back, and I gave one to each one of the architects. And I said, “Read that and let’s go save the company.”

And so they read this textbook, learned from the giant at the time, Silicon Graphics, about how to do 3D graphics. But the thing that was amazing and what makes NVIDIA special today is that the people that are there are able to start from first principles. Learn best known art, but re-implement it in a way that’s never been done before.

And so when we reimagined the technology of 3D graphics, we reimagined it in a way that manifests today the modern 3D graphics. We really invented modern 3D graphics, but we learned from previous known arts and we implement it fundamentally differently.

JOE ROGAN: What did you do that changed it?

The Breakthrough Innovation

JENSEN HUANG: Well, you know, ultimately, ultimately the simple answer is that the way Silicon Graphics works, the geometry engine is a bunch of software running on processors. We took that and eliminated all the generality. The general purposeness of it, and we reduced it down into the most essential part of 3D graphics and we hard coded it into the chip.

And so instead of something general purpose, we hard coded it very specifically into just the limited applications, limited functionality necessary for video games. And that capability that super, and because we reinvented a whole bunch of stuff, it’s supercharged the capability that one little chip, and our one little chip was generating images as fast as a $1 million image generator.

That was the big breakthrough. We took a million dollar thing and we put it into the graphics card that you now put into your gaming PC. And that was our big invention.

And of course, the question is, how do you compete against these 30 other companies doing what they were doing? And there we did several things. One, instead of building a 3D graphics chip for every 3D graphics application, we decided to build a 3D graphics chip for one application. We bet the farm on video games.

The needs of video games are very different than needs for CAD needs for flight simulators. They’re related, but not the same. And so we narrowly focused our problem statement so I could reject all of the other complexities. And we shrunk it down into this one little focus, and then we supercharged it for gamers.

And the second thing that we did was we created a whole ecosystem of working with game developers and getting their games ported and adapted to our silicon so that we could get turn essentially what is a technology business into a platform business, into a game platform business. So GeForce is really, today, it’s also the most advanced 3D graphics technology in the world. But a long time ago, GeForce is really the game console inside your PC.

It runs Windows, it runs Excel, it runs PowerPoint. Of course, those are easy things, but its fundamental purpose was simply to turn your PC into a game console. So we were the first technology company to build all of this incredible technology in service of one audience, gamers.

Now, of course, in 1993, the gaming industry didn’t exist. But by the time that John Carmack came along and the Doom phenomenon happened, and then Quake came out, as you know, that entire community, boom, took

The Origin of Doom

JOE ROGAN: Do you know where the name Doom came from? It came from this. There’s a scene in the movie The Color of Money where Tom Cruise, who’s this elite pool player, shows up at this pool hall and this local hustler says, “What do you got in the case?” And he opens up this case, he has a special pool cue. He goes in here and he opens it up, he goes—

JENSEN HUANG: Doom. That’s where it came from.

JOE ROGAN: Yeah, because Carmack said that’s what they wanted to do to the gaming industry: Doom. That when Doom came out, everybody would be like, “Oh, we’re f*ed.”

JENSEN HUANG: Oh, wow.

JOE ROGAN: This is Doom. Isn’t that amazing? That’s amazing because it’s the perfect name for the game. And yeah, the name came out of that scene in that movie.

JENSEN HUANG: That’s right. Well, and then of course, Tim Sweeney and Epic Games and the 3D gaming genre took off. And so in the beginning there was no gaming industry. We had no choice but to focus the company on one thing, that one thing.

A Pivotal Moment

JOE ROGAN: It’s a really incredible origin story. It’s amazing. You must look at that disaster—what, $5 million? That pivot with that conversation with that gentleman. If he did not agree to that, if he did not like you, what would the world look like today? That’s crazy.

JENSEN HUANG: Then our entire life hung on another gentleman. And so now here we are. We built—so before GeForce was RIVA 128. RIVA 128 saved the company. It revolutionized computer graphics. The performance cost, performance ratio of the 3D graphics for gaming was off the charts amazing.

And we’re getting ready to ship it. But we’re so—as you know, $5 million didn’t last long. And so every single month we were drawing down. You have to build it, prototype it, you have to design it, prototype it, get the silicon back, which costs a lot of money, test it with software. Because without the software testing the chip, you don’t know the chip works.

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

The Emulator Gamble

And so the math, the spreadsheet doesn’t allow us to do that. And so I heard about this company, and this company built this machine. And this machine is an emulator. You could take your design, all of the software that describes the chip, and you could put it into this machine. And this machine will pretend it’s our chip.

So I don’t have to send it to the fab, wait until the fab sends it back, test. I could have this machine pretend it’s our chip, and I could put all of the software on top of this machine called an emulator, and test all of the software on this pretend chip, and I could fix it all before I send it to the fab.

And if I could do that, when I send it to the fab, it should work. Nobody knows, but it should work. And so we came to the conclusion that let’s take half of the money we had left in the bank—at the time, it was about a million dollars. Take half of that money and go buy this machine. So instead of keeping the money to stay alive, I took half of the money to go buy this machine.

Well, I called this guy up. The company’s called IKOS. Call this company up. And I say, “Hey, listen, I heard about this machine. I’d like to buy one.” And they go, “Oh, that’s terrific, but we’re out of business.” I said, “What? You’re out of business?” He goes, “Yeah, we had no customers.”

I said, “Wait, hang on a second. So you never made the machine?” He goes, “No, no, no, we made the machine. We have one in inventory if you want it, but we’re out of business.” So I bought one out of inventory.

After I bought it, they went out of business. I bought it out of inventory and on this machine, we put NVIDIA’s chip into it, and we tested all of the software on top. And at this point, we were on fumes. But we convinced ourselves that chip is going to be great.

The TSMC Bet

And so I had to call some other gentleman. So I called TSMC and I told TSMC—listen, TSMC is the world’s largest foundry today. At the time, they were just a few hundred million dollars large. Tiny little company.

And I explained to them what we were doing. And I explained to them, I told them I had a lot of customers. I had one, you know, Diamond Multimedia, probably one of the companies you bought the graphics card from back in the old days. And I said, you know, we have a lot of customers and the demand’s really great, and we’re going to tape out a chip to you. And I’d like to go directly to production. Because I know it works.

And they said, “Nobody has ever done that before. Nobody has ever taped out a chip that worked the first time. And nobody starts out production without looking at it.” But I knew that if I didn’t start the production, I’d be out of business anyway. And if I could start the production, I might have a chance.

And so TSMC decided to support me. And this gentleman is named Morris Chang. Morris Chang is the father of the foundry industry, the founder of TSMC, really great man. He decided to support our company. I explained to them everything. He decided to support us, frankly, probably because they didn’t have that many other customers anyway, but they were grateful, and I was immensely grateful.

And as we were starting the production, Morris flew to United States. And he did—so many words—asked me so. But he asked me a whole lot of questions that was trying to tease out, do I have any money? But he didn’t directly ask me that. And so the truth is that we didn’t have all the money, but we had a strong PO from the customer. And if it didn’t work, some wafers would have been lost. I’m not exactly sure what would have happened, but we would have come short. It would have been rough, but they supported us.

With all of that risk involved, we launched this chip. Turns out to have been completely revolutionary, knocked the ball out of the park. We became the fastest growing technology company in history to go from zero to $1 billion.

JOE ROGAN: So wild that you didn’t test the chip.

JENSEN HUANG: I know. We tested afterwards. Yeah, we tested afterwards. Went into production already. But by the way, that methodology that we developed to save the company is used throughout the world today.

JOE ROGAN: That’s amazing.

JENSEN HUANG: Yeah, we changed the whole world’s methodology of designing chips. The whole world’s rhythm of designing chips. We changed everything.

Sleepless Nights

JOE ROGAN: How well did you sleep those days? It must have been so much stress.

JENSEN HUANG: You know, what is that feeling where the world just kind of feels like it’s flying? You have this—what do you call that feeling you can’t stop? The feeling that everything’s moving super fast and, you know, you’re laying in your bed and the world just feels like, you know, and you feel deeply anxious, completely out of control.

I’ve felt that probably a couple of times in my life. It’s during that time.

JOE ROGAN: Wow.

JENSEN HUANG: Yeah, it was incredible.

JOE ROGAN: What an incredible success story.

Lessons Learned

JENSEN HUANG: But I learned a lot. I learned several things. I learned how to develop strategies. And when I—you know, our company learned how to develop strategies. What are winning strategies? We learned how to create a market. We created the modern 3D gaming market.

We learned how—and so that exact same skill is how we created the modern AI market. It’s exactly the same. Yeah, exactly the same skill, exactly the same blueprint. And we learned how to deal with crisis, how to stay calm, how to think through things systematically. We learned how to remove all waste in the company and work from first principles and doing only the things that are essential. Everything else is waste because we have no money for it. To live on fumes at all times.

And the feeling—no different than the feeling I had this morning when I woke up—that you’re going to be out of business. You know, the phrase “30 days from going out of business” I’ve used for 33 years.

JOE ROGAN: You still feel that?

JENSEN HUANG: Oh, yeah. Oh, yeah. Every morning.

JOE ROGAN: Every morning? But you guys are one of the biggest companies on planet Earth.

JENSEN HUANG: But the feeling doesn’t change.

JOE ROGAN: Wow.

JENSEN HUANG: The sense of vulnerability, the sense of uncertainty, the sense of insecurity. It doesn’t leave you.

JOE ROGAN: That’s crazy. We were—you know, we had nothing. We had nothing. We were dealing with that.

JENSEN HUANG: Oh, yeah. Oh, yeah. Every day, every moment.

Fear as Fuel

JOE ROGAN: Do you think that fuels you? Is that part of the reason why the company’s so successful? That you have that hungry mentality. That you never rest, you’re never sitting on your laurels, you’re always on the edge?

JENSEN HUANG: I have a greater drive from not wanting to fail than the drive of wanting to succeed.

JOE ROGAN: Isn’t that like success coaches would tell you that’s completely the wrong psychology?

JENSEN HUANG: The world has just heard me say that out loud for the first time, but it’s true. Well, that’s how fascinating. The fear of failure drives me more than the greed or whatever it is.

JOE ROGAN: Well, ultimately, that’s probably a more healthy approach, now that I’m thinking about it.

JENSEN HUANG: Because the feeling—I’m not ambitious, for example. You know, I just want to stay alive, Joe. I want the company to thrive. I want us to make an impact.

JOE ROGAN: That’s interesting. Well, maybe that’s why you’re so humble. Maybe that’s what keeps you grounded, you know, because with the kind of spectacular success the company’s achieved, it would be easy to get a big head.

Right. But isn’t that interesting? It’s like, though, if you were the guy that your main focus is just success, you probably would go, “Well, made it. Nailed it. I’m the man.”

JENSEN HUANG: Drop the mic.

JOE ROGAN: Instead, you wake up, you’re like, “God, we can’t f* this up.”

JENSEN HUANG: No, every morning. Every morning. No, every moment. Yeah, that’s before I go to bed.

JOE ROGAN: Well, listen, if I was a major investor in your company, that’s what I’d want running it.

The Work Never Stops

JENSEN HUANG: I want a guy who’s—that’s why I work out. Yeah, that’s why I work. That’s why I work seven days a week. Every moment I’m awake.

JOE ROGAN: You work every moment?

JENSEN HUANG: Every moment I’m awake. Wow. I’m thinking about solving a problem. I’m thinking about how—

JOE ROGAN: Can you keep this up?

JENSEN HUANG: I don’t know, but could be next week.

JOE ROGAN: Sounds exhausting.

JENSEN HUANG: It is exhausting.

JOE ROGAN: It sounds completely exhausting.

JENSEN HUANG: Always in a state of anxiety.

JOE ROGAN: Wow.

JENSEN HUANG: Always in a state of anxiety.

JOE ROGAN: Well, kudos to you for admitting that. I think that’s important for a lot of people to hear because, you know, there’s probably some young people out there that are in a similar position to where you were when you were starting out that just feel like all those people that have made it, they’re just smarter than me, and they had more opportunities than me, and it’s just like it was handed to them or they’re just in the right place at the right—

JENSEN HUANG: Time. Joe, I just described to you somebody who didn’t know what was going on actually did it wrong.

JOE ROGAN: Yeah. Yeah. And the ultimate diving catch, like two or three times.

JENSEN HUANG: Crazy.

JOE ROGAN: Yeah. The ultimate diving catch is the perfect way to put it. It’s just like the edge of your glove. It probably bounced off of somebody’s helmet and landed at the edge of it.

God, that’s incredible. It’s incredible. But it’s also, it’s really cool that you have this perspective, that you look at it that way, because, you know, a lot of people that have delusions of grandeur or they have, you know, and their rewriting of history oftentimes had them somehow extraordinarily smart, and they were geniuses, and they knew all along, and they were spot on.

The business plan was exactly what they thought. They destroyed the competition and, you know, and they emerged victorious. Meanwhile, you’re like, I’m scared every day.

JENSEN HUANG: Exactly. Exactly. It’s so funny. Oh, my God. That’s amazing. It’s so true. Though. It’s amazing. It’s so true. It’s amazing.

But I think there’s nothing inconsistent with being a leader and being vulnerable. You know, the company doesn’t need me to be a genius, right, all along, right, all the time. Absolutely certain about what I’m trying to do and what I’m doing. The company doesn’t need that. The company wants me to succeed.

You know, the thing that, and we started out today talking about President Trump, and I was about to say something and listen, he is my president. He is our president. We should all, and we’re talking about, just because it’s President Trump, we all want him to be wrong. I think the United States, we all have to realize he is our president. We want him to succeed.

JOE ROGAN: Because no matter who’s president, that’s right. That’s right. We want him to succeed. We need to help him succeed because it helps everybody, all of us succeed.

Leading Through Vulnerability

JENSEN HUANG: And I’m lucky that I work in a company where I have 40,000 people who wants me to succeed. They want me to succeed. And I can tell. And they’re all, every single day to help me overcome these challenges. Trying to realize what I described to be our strategy, doing their best, and if it’s somehow wrong or not perfectly right to tell me so that we could pivot.

And the more vulnerable we are as a leader, the more able other people are able to tell you. You know that, Jensen, that’s not exactly right. Or have you considered this information or. And the more vulnerable we are, the more able we’re actually able to pivot.

If we put ourselves into this superhuman capability, then it’s hard for us to pivot strategy because we were supposed to be right all along. And so if you’re always right, how can you possibly pivot? Because pivoting requires you to be wrong.

And so I’ve got no trouble with being wrong. I just have to make sure that I stay alert. But I reason about things from first principles all the time, always break things down to first principles, understand why it’s happening, reassess continuously.

The reassessing continuously is kind of partly what causes continuous anxiety, you know, because you’re asking yourself, were you wrong yesterday? Are you still right? Is this the same? Has that changed? Has that condition? Is that worse than you thought?

JOE ROGAN: But God, that mindset is perfect for your business, though, because this business is ever changing all the time. You got competition coming from every direction. So much of it is kind of up in the air.

JENSEN HUANG: And you have to invent a future where a hundred variables are included and there’s no way you could be right on all of them. And so you have to be, you have to surf.

JOE ROGAN: Wow. You have to surf. That’s a good way to put it. You have to surf.

JENSEN HUANG: Yeah. You’re surfing waves of technology and innovation. That’s right. You can’t predict the waves. You got to deal with the ones you have. Wow. But skill matters. And I’ve been doing this for 30. I’m the longest running tech CEO in the world.

JOE ROGAN: Is that true? Congratulations. That’s amazing.

JENSEN HUANG: You know, people ask me how just one, don’t get fired. I’ll stop it short in a heartbeat. And then two, don’t get bored.

JOE ROGAN: Yeah. Well, how do you maintain your enthusiasm?

JENSEN HUANG: The honor of truth is not always enthusiasm. Sometimes it’s enthusiasm, sometimes it’s just good old fashioned fear and then sometimes a healthy dose of frustration. Whatever keeps you moving.

Yeah, just all the emotions, I think, you know, CEOs, we have all the emotions, right? You know, and so probably jacked up to the maximum because you’re, you’re kind of feeling it on behalf of the whole company. I’m feeling it on behalf of everybody at the same time. And it kind of, you know, encapsulates into somebody.

And so I have to be mindful of the past, I have to be mindful of the present, I’ve got to be mindful of the future. And, you know, it can’t, it’s not without emotion. It’s not just a job. Let’s just put it that way.

Anticipating the Future of Technology

JOE ROGAN: It doesn’t seem like it at all. I would imagine one of the more difficult aspects of your job currently, now that the company is massively successful, is anticipating where technology is headed and where the applications are going to be. Yeah. So how do you try to map that out?

JENSEN HUANG: Yeah. There’s a whole bunch of ways and it takes a whole bunch of things. But let me just start. You have to be surrounded by amazing people. And NVIDIA is.

Now, if you look at the large tech companies in the world today, most of them have a business in advertising or social media or, you know, content distribution. And at the core of it is really fundamental computer science. And so the company’s business is not computers. The company’s business is not technology. Technology drives the company.

NVIDIA is the only company in the world that’s large whose only business is technology. We only build technology, we don’t advertise. The only way that we make money is to create amazing technology and sell it. And so to be that, to be NVIDIA today, the number one thing is you’re surrounded by the finest computer scientists in the world.

And that’s my gift. My gift is that we’ve created a company’s culture, a condition by which the world’s greatest computer scientists want to be part of it. Because they get to do their life’s work and create the next thing, because that’s what they want to do. Because maybe they’re not, they don’t want to be in service of another business, they want to be in service of the technology itself. And we’re the largest form of its kind in history of the world.

JOE ROGAN: Wow. I know, it’s pretty amazing. Wow. And so one.

JENSEN HUANG: We have got a great condition, we have a great culture, we have great people. And now the question is, how do you systematically be able to see the future, stay alert of it, and reduce the likelihood of missing something or being wrong?

And so there’s a lot of different ways you could do that. For example, we have great partnerships. We have fundamental research, we have a great research lab, one of the largest industrial research labs in the world today. And we partner with a whole bunch of universities and other scientists. We do a lot of open collaboration.

And so I’m constantly working with researchers outside the company. We have the benefit of having amazing customers. And so I have the benefit of working with Elon and, you know, and others in the industry. And we have the benefit of being the only pure play technology company that can serve consumer Internet, industrial manufacturing, scientific computing, health care, financial services, all the industries that we’re in, they’re all signals to me.

And so they all have mathematicians and scientists. And so, because I have the benefit now of a radar system that is the most broad of any company in the world, working across every single industry, from agriculture to energy to video games. And so the ability for us to have this vantage point, one, doing fundamental research ourselves, and then two, working with all the great researchers, working with all the great industries. The feedback system is incredible.

And then finally, you have to have a culture of staying super alert. There’s no easy way of being alert except for paying attention. I haven’t found a single way of being able to stay alert without paying attention. And so, you know, I probably read several thousand emails a day.

JOE ROGAN: How, how do you have the time for that?

JENSEN HUANG: I wake up early this morning. I was up at 4 o’clock.

JOE ROGAN: How much do you sleep?

JENSEN HUANG: Six, seven hours.

JOE ROGAN: Yeah. And then you’re up at four. Read emails for a few hours before you get going.

JENSEN HUANG: That’s right, yeah. Wow. Every day. Every single day. Not one day missed. Including Thanksgiving, Christmas.

JOE ROGAN: Do you ever take a vacation?

JENSEN HUANG: Yeah, but there, my definition of a vacation is when I’m with my family. And so if I’m with my family, I’m very happy. I don’t care where we are.

JOE ROGAN: And you don’t work then, or do you work a little?

JENSEN HUANG: No, no, I work a lot. Even like if you go on a trip somewhere, you’re still working?

JOE ROGAN: Oh sure. Oh sure.

JENSEN HUANG: Wow. Every day. Every day. Well, my kids work every day.

JOE ROGAN: You make me tired saying this.

JENSEN HUANG: My kids work every day. Both of my kids work in video. They work every day. Wow. Yeah, I’m very lucky. Wow. Yeah, it’s brutal now because, you know, it’s just me working every day. Now we have three people working every day and they want to work with me every day. And so it’s, it’s a lot of work.

JOE ROGAN: While you’ve obviously imparted that ethic into them, they work incredibly hard. I mean, there’s nothing else.

JENSEN HUANG: But my parents work incredibly hard. Yeah, I was born with the work gene, the suffering gene.

An Immigrant Success Story

JOE ROGAN: Well, listen, man, it has paid off. What a crazy story. It’s really an amazing origin story. It really. I mean, it has to be kind of surreal to be in the position that you’re in now when you look back at how many times that it could have fallen apart and humble beginnings.

JENSEN HUANG: But, Joe, this is a great country, and I’m an immigrant. My parents sent my older brother and I here first. We’re in Thailand. I was born in Taiwan, but my dad had a job in Thailand. He was a chemical and instrumentation engineer. Incredible engineer.

And his job was to go start an oil refinery. And so we moved to Thailand, lived in Bangkok, and in I guess, 1973, 1974 timeframe.

Growing Up in Thailand and Coming to America

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

JOE ROGAN: How old were you?

JENSEN HUANG: I was about to turn 9, and my older brother almost turned 11. And so the two of us came to United States, and we stayed with our uncle for a little bit while he looked for a school for us. And my parents didn’t have very much money, and they never been to United States. My father was, I’ll tell you that story in a second.

So my uncle found a school that would accept foreign students and affordable enough for my parents. And that school turns out to have been in Oneida, Kentucky. Clark County, Kentucky, the epicenter of the opioid crisis today, coal country.

Clark County, Kentucky was the poorest county in America when I showed up. It is the poorest county in America today. And so we went to the school. It’s a great school.

Life at Oneida Baptist Institute

Oneida Baptist Institute, in a town of a few hundred. I think it was 600 at the time that we showed up. No traffic light. And I think it has 600 today. It’s kind of an amazing feat, actually. The ability to hold your population for when it’s 600 people. It’s quite a magical thing.

However they did it, and so the school had a mission of being an open school for any children who would like to come. And what that basically means is that if you’re a troubled student, if you have a troubled family, whatever your background, you’re welcome to come to Oneida Baptist Institute, including kids from international who would like to stay there.

JOE ROGAN: Did you speak English at the time?

JENSEN HUANG: Yeah. And so we showed up. My first thought was, gosh, there are a lot of cigarette butts on the ground. 100% of the kids smoked. So right away, you know, this is not a normal school.

JOE ROGAN: Nine year olds?

JENSEN HUANG: No, I was the youngest kid.

JOE ROGAN: Okay, 11 year olds.

JENSEN HUANG: My roommate was 17 years old.

JOE ROGAN: Wow.

JENSEN HUANG: Yeah, he just turned 17 and he was jacked. I don’t know where he is now. I know his name, but I don’t know where he is now. But anyways, that night we got, and the second thing I noticed when you walk into your dorm room is there are no drawers and no closet doors. Just like a prison.

There are no locks so that people could check up on you. And so I go into my room and he’s 17 and, you know, get ready for bed. And he had all this tape all over his body. And turned out he was in a knife fight. And he’s been stabbed all over his body. And these were just fresh wounds. And the other kids were hurt much worse.

And so he was my roommate, the toughest kid in school. And I was the youngest kid in school. It was a junior high, but they took me anyways, because if I walked about a mile across the Kentucky river, the swing bridge, the other side is a middle school that I could go to, and then I can go to that school and I come back and then I stay in the dorm.

And so basically, Oneida Baptist Institute was my dorm. When I went to this other school, my older brother went to the junior high. And so we were there for a couple of years.

Every kid had chores. My older brother’s chore was to work in the tobacco farm, you know, so they raised tobacco so that they could raise some extra money for the school. Kind of like a penitentiary.

JOE ROGAN: Wow.

JENSEN HUANG: And my job was just to clean the dorm. And so I was nine years old. I was cleaning toilets for a dorm of a hundred boys. I cleaned more bathrooms than anybody. And I just wish that everybody was a little bit more careful.

But anyways, I was the youngest kid in school. My memories of it was really good. But it was a tough town. Town kids, they all carried. Everybody had knives. Everybody had knives. Everybody smoked. Everybody had a Zippo lighter. I smoked for a week.

JOE ROGAN: Did you?

JENSEN HUANG: Oh, yeah. Sure.

JOE ROGAN: How old were you?

JENSEN HUANG: I was nine.

JOE ROGAN: Yeah, when you, nine, you were nine, you tried smoking?

JENSEN HUANG: Yeah, I got myself a pack of cigarettes. Everybody else did.

JOE ROGAN: Did you get sick?

JENSEN HUANG: No, I got used to it, you know, and I learned how to blow smoke rings and, you know, breathe out of my nose, you know, take it in through my nose. All the different things that you learned.

JOE ROGAN: Yeah. At nine?

JENSEN HUANG: Yeah.

JOE ROGAN: Wow. You just did it to fit in or?

JENSEN HUANG: Yeah, because everybody else did it, right? And then I did it for a couple of weeks, I guess, and I just rather have, I had a quarter, you know, I had a quarter a month or something like that. I just rather buy popsicles and fudge sickles with it. I was nine, you know, I chose the better path.

JOE ROGAN: Wow.

JENSEN HUANG: That was our school. And then my parents came to the United States two years later and we met them in Tacoma, Washington.

JOE ROGAN: That’s wild.

JENSEN HUANG: It was a really crazy experience.

JOE ROGAN: What a strange, formative experience. Tough kids, Thailand to one of the poorest places in America, or if not the poorest, as a nine year old.

Staying Connected Through Cassette Tapes

JENSEN HUANG: That was my first experience with your brother. Yeah. No, I, and what breaks my heart, probably the only thing that really breaks my heart about that experience was, so we didn’t have enough money to make, you know, international phone calls every week. And so my parents gave us this tape deck, this IWHA tape deck and a tape.

And so every month we would sit in front of that tape deck and my older brother Jeff and I, the two of us would just tell them what we did the whole month.

JOE ROGAN: Wow.

JENSEN HUANG: And we would send that tape by mail. And my parents would take that tape and record back on top of it and send it back to us.

JOE ROGAN: Wow.

JENSEN HUANG: Could you imagine if for two years, wow, that tape still existed of these two kids just describing their first experience with United States? I remember telling my parents that I joined the swim team and my roommate was really buff. And so every day we spent a lot of time in the gym. And so every night, 100 push ups, 100 sit ups, every day in the gym. So I was nine years old, I was getting, I was pretty buff. And I’m pretty fit.

And so I joined the soccer team, I joined the swim team. Because if you join the team, they take you to meets and then afterwards you get to go to a nice restaurant. And that nice restaurant was McDonald’s.

And I recorded this thing I said, “Mom and dad, we went to the most amazing restaurant today. This whole place is lit up. It’s like the future. And the food comes in a box. And the food is incredible. The hamburger is incredible.” It was McDonald’s. But anyhow, wouldn’t it be amazing? Oh, my God. Two years. Yeah. Two years.

JOE ROGAN: Yeah. What a crazy connection to your parents, too. Just sending a tape and them sending you one back. And it’s the only way you’re communicating for two years.

JENSEN HUANG: Yeah.

JOE ROGAN: Wow.

Parents’ Journey to America

JENSEN HUANG: Yeah. My parents are incredible, actually. They just, they grew up really poor, and when they came to the United States, they had almost no money. Probably one of the most impactful memories I have is we, they came and we were staying in an apartment complex.

And they had just rent back in the, I guess people still do rent, rent a bunch of furniture. And we were messing around. And we bumped into the coffee table and crushed, was made out of particle wood and we crushed it.

And I just still remember the look on my mom’s face, you know, because they didn’t have any money and she didn’t know how she was going to pay it back. But anyhow, that kind of tells you how hard it was for them to come here. But they left everything behind, and all they had was their suitcase and the money they had in their pocket and they came to the United States.

JOE ROGAN: How old were you?

JENSEN HUANG: Pursued the American dream. They were in their 40s. Yeah. Late, late 30s. Pursue the American dream. This is the American dream. I’m the first generation of the American dream.

JOE ROGAN: Wow.

JENSEN HUANG: Yeah. It’s hard not to love this country. It’s hard not to be romantic about this country.

JOE ROGAN: That is a romantic story. That’s an amazing story.

JENSEN HUANG: Yeah. And my dad found his job, literally, in the newspaper, you know, the ads, and he calls people got a job.

JOE ROGAN: What did he do?

JENSEN HUANG: He was a consulting engineer in a consulting firm. And they helped people build oil refineries, paper mills and fabs. And that’s what he did. He was, he’s really good at factory design. Instrumentation engineer. And so he’s brilliant at that. And so he did that. And my mom worked as a maid and they found a way to raise us.

JOE ROGAN: Wow. That’s an incredible story.

JENSEN HUANG: It really is.

JOE ROGAN: Everything, all of it, from your childhood to the perils of Nvidia almost falling. It’s really incredible, man.

JENSEN HUANG: It’s a great story. Yeah. I’ve lived a great life.

The American Dream and Nvidia’s Journey

JOE ROGAN: You really have. And it’s a great story for other people to hear too. It really is.

JENSEN HUANG: You don’t have to go to Ivy League schools to succeed. This country creates opportunities, has opportunities for all of us. You do have to strive. You have to claw your way here.

JOE ROGAN: Yeah. But if you put in the work you can succeed.

JENSEN HUANG: Nobody works for, there’s a lot of luck and a lot of good decision making and the good graces of others.

JOE ROGAN: Yes, that’s really important.

JENSEN HUANG: Yeah. You and I spoke about two people who are very dear to me. But the list goes on. The people at Nvidia who have helped me. Many friends that are on the board, the decisions, you know, them giving me the opportunity.

Like when we were inventing this new computing approach, I tanked our stock price because we added this thing called CUDA to the chip. We had this big idea, we added this thing called CUDA to the chip, but nobody paid for it, but our cost doubled. And so we had this graphics chip company and we invented GPUs, we invented programmable shaders, we invented everything, modern computer graphics.

We invented real time ray tracing. That’s why it went from GTX to RTX.

The Birth of CUDA and Market Rejection

JENSEN HUANG: We invented all this stuff, but every time we invented something, the market doesn’t know how to appreciate it. But the cost went way up. And in the case of CUDA, that enabled AI, the cost increased a lot. But we really believed it.

If you believe in that future and you don’t do anything about it, you’re going to regret it for your life.

And so we always, you know, I always tell the team, “Do you believe what? Do we believe this or not?” And if you believe it and grounded on first principles, not random, you know, hearsay, and we believe it, we’ve got to. We owe it to ourselves to go pursue it. If we’re the right people to go do it. If it’s really, really hard to do, it’s worth doing. And we believe it. Let’s go pursue it.

Well, we pursued it, we launched the product. Nobody knew. It was exactly like when I launched DGX1 and the entire audience was like complete silence. When I launched CUDA, the audience was complete silence. No customer wanted it, nobody asked for it, nobody understood it. NVIDIA was a public company. What year was this? This is, let’s see, 2006. 20 years ago.

JOE ROGAN: 2005.

JENSEN HUANG: Wow. Our stock prices went poof. I think our valuation went down to like 2 or 3 billion dollars.

JOE ROGAN: From?

JENSEN HUANG: From about 12 or something like that. I crushed it in a very bad way.

JOE ROGAN: What is it now though?

JENSEN HUANG: It’s…

JOE ROGAN: Higher.

Very humble of you.

JENSEN HUANG: It’s higher, but it changed the world. Yeah, that invention changed the world.

JOE ROGAN: It’s an incredible story, Jensen. It really is. Thank you.

JENSEN HUANG: I like your story. It’s incredible.

JOE ROGAN: My story’s not as incredible. My story’s more weird. You know, it’s much more fortuitous and weird.

Joe Rogan’s Journey to Podcasting

JENSEN HUANG: Okay, what are the three milestones that most important milestones that led to here?

JOE ROGAN: That’s a good question. What was step one?

I think step one was seeing other people do it. Step one was in the initial days of podcasting, like in 2009, when I started podcasting had only been around for a couple of years. The first was Adam Curry, my good friend who was the podfather. He invented podcasting. And then I remember Adam Carolla had a show because he had a radio show. His radio show got canceled, and so he decided to just do the same show but do it on the Internet. And that was pretty revolutionary. Nobody was doing that.

And then there was the experience that I had had doing different morning radio shows, like Opie and Anthony in particular, because it was fun. And we would just get together with a bunch of comedians. You know, I’d be on the show with, like, three or four other guys that I knew, and it was always just looked forward to it, which was just such a good time. And I said, “God, I miss doing that. It’s so fun to do that. I wish I could do something like that.”

And then I saw Tom Green set up. Tom Green had a setup in his house, and he essentially turned his entire house into a television set studio, and he did an Internet show from his living room. He had servers in his house and cables everywhere. He had to step over cables. This is, like 2007. I’m like, “Tom, this is nuts. Like, this is…”

JENSEN HUANG: And I’m like, “You got to figure out a way to make money from this. I wish everybody on the Internet could see your setup. It’s nuts. I just want to let you guys know that it’s not just this.”

JOE ROGAN: So that was the beginning of it, is just seeing other people do it and then saying, “All right, let’s just try it.” And then, so the beginning days, we just did it on a laptop. Had a laptop with a webcam and just messed around, had a bunch of comedians come in. We would just talk and joke around. And I did it, like, once a week. And then I started doing it twice a week. And then all of a sudden, I was doing it for a year, and then I was doing it for two years. Then it was like, “Oh, it’s starting to get a lot of viewers, a lot of listeners,” you know, and then I just kept doing it.

JENSEN HUANG: That’s all it is.

JOE ROGAN: I just kept doing it because I enjoyed doing it.

JENSEN HUANG: Was there any setback?

JOE ROGAN: No, no, there’s never really a setback.

JENSEN HUANG: Really. No, it must have been, or you saw the same kind of… You’re just resilient or you’re just tough.

JOE ROGAN: No, no, no, no. It wasn’t tough or hard. It was just interesting. So I just…

JENSEN HUANG: You were never once punched in the face?

JOE ROGAN: No, not in the show? No, not really. Not doing the show.

JENSEN HUANG: You never did something that… that big blowback?

JOE ROGAN: Nope. Not really. No. It all just kept growing. It kept growing, and the things stayed the same from the beginning to now. And the thing is, I enjoy talking to people. I’ve always enjoyed talking to people.

JENSEN HUANG: I could even tell just when we walked in, the way you interacted with everybody, not just me. Yeah, it’s cool. People are cool. Yeah, that’s cool.

The Gift of Conversation

JOE ROGAN: You know, I… It’s an amazing gift to be able to have so many conversations with so many interesting people because it changes the way you see the world, because you see the world through so many different people’s eyes, and you have so many different people, have different perspectives and different opinions and different philosophies and different life stories, and, you know, it’s an incredibly enriching and educating experience, having so many conversations with so many amazing people.

And that’s all I started doing, and that’s all I do now. Even now, when I book the show, I do it on my phone and I basically go through this giant list of emails of all the people that want to be on the show or the request to be on the show. And then a factor in another list that I have of people that I would like to get on the show that I’m interested in, and I just map it out and that’s it. And I go, “Ooh, I’d like to talk to him.”

JENSEN HUANG: If it wasn’t because of President Trump, I wouldn’t have been bumped up on that list.

JOE ROGAN: No, I wanted to talk to you already. I just think, you know, what you’re doing is very fascinating. I mean, how would I not want to talk to you? And today, it proved to be absolutely the right decision.

From Immigrant to Industry Leader

JENSEN HUANG: Well, you know, listen, it’s strange to be an immigrant one day, going to Oneida Baptist Institute with the students that were there, and then here. NVIDIA is one of the most consequential companies in the history of companies. It is a crazy story.

JOE ROGAN: It has to be. That journey is… And it’s very humbling, and I’m very grateful.

It’s pretty amazing.

JENSEN HUANG: Surrounded by amazing people.

JOE ROGAN: You’re very fortunate. And you’ve also… You seem very happy, and you seem like you’re 100% on the right path in this life.

JENSEN HUANG: You know? You know, everybody says you must love your job. Not every day.

JOE ROGAN: That’s not… You know what I’m saying.

JENSEN HUANG: That’s kind of the beauty of everything.

JOE ROGAN: Yeah. Is that there’s ups and downs.

JENSEN HUANG: That’s right.

JOE ROGAN: It’s never just like this giant dopamine high.

The Reality of Success: Suffering and Hard Work

JENSEN HUANG: We leave this impression. Here’s an impression I don’t think is healthy. People who are successful leave the impression often that our job gives us great joy. I think largely it does that our jobs… We’re passionate about our work. And that passion relates to… It’s just so much fun. I think it largely is, but it distracts from… In fact, a lot of success comes from really, really hard work.

JOE ROGAN: Yes.

JENSEN HUANG: There’s long periods of suffering and loneliness and uncertainty and fear and embarrassment and humiliation. All of the feelings that we most not love. That creating something from the ground up. And Elon will tell you something similar. Very difficult to invent something new. And people don’t believe you all the time. You’re humiliated, often disbelieved most of the time.

And so people forget that part of success. And I don’t think it’s healthy. I think it’s good that we pass that forward and let people know that it’s just part of the journey.

JOE ROGAN: Yes.

JENSEN HUANG: And suffering is part of the journey.

JOE ROGAN: You will appreciate it so much. These horrible feelings that you have when things are not going so well. You’ll appreciate it so much more when they do go well.

JENSEN HUANG: Deeply grateful. Yeah. Yeah. Deep, deep pride. Incredible pride. Incredible, incredible gratefulness. And surely incredible memories.

JOE ROGAN: Absolutely. Jensen, thank you so much for being here. This was really fun. I really enjoyed it. And your story is just absolutely incredible and very inspirational. And, you know, I think it really is the American dream.

JENSEN HUANG: It is the American dream.

JOE ROGAN: It really is.

JENSEN HUANG: Thank you so much.

JOE ROGAN: Thank you. All right, bye, everybody.

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