Chapter 1: What is discussed at the start of this section?
Joe Rogan Podcast, check it out. The Joe Rogan Experience.
Train by day, Joe Rogan Podcast by night, all day.
Good to see you again. We were just talking about, was that the first time we ever spoke? Or was the first time we spoke at SpaceX? SpaceX. SpaceX the first time. When you were giving Elon that crazy AI chip. Right, DGX Spark. Yeah, ooh, that was a big moment. That was a huge moment. That felt crazy to be there. It was like watching these wizards of tech exchange ideas.
information and you're giving him this crazy device. And then the other time was I was shooting arrows in my backyard and randomly get this call from Trump and he's hanging out with you. President Trump and I called you. We were talking about you.
He was talking about the UFC thing he was going to do in his front yard. Yeah. And he pulls out. He said, Jensen, look at this design. He's so proud of it. And I go, you're going to have a fight in the front lawn in the White House?
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Chapter 2: What was the significance of the DGX Spark chip?
He goes, yeah. Yeah, you're going to come. This is going to be awesome. And he's showing me his design and how beautiful it is. And he goes, and somehow your name comes up. He goes, do you know Joe? And I said, yeah. I'm going to be on his podcast. He said, let's call him.
He's like a kid.
I know.
Let's call him. He's like a 79-year-old kid. He's so incredible. Yeah, he's an odd guy. Just very different. You know, like what you'd expect from him, very different than what people think of him, and also just very different as a president. A guy who just calls you or texts you out of the blue. Also, when he texts you, you have an Android, so it won't go through with you.
But with my iPhone, he makes the text go big. Is that right? USA is respected again. All caps and it makes the text enlarge. It's kind of ridiculous.
Well, the one-on-one Trump, President Trump is very different. He surprised me. First of all, he's an incredibly good listener. Almost everything I've ever said to him, he's remembered.
Yeah, people only want to look at negative stories about him or negative narratives about him. You can catch anybody on a bad day. There's a lot of things he does where I don't think he should do. I don't think he should say to a reporter, quiet piggy. That's pretty ridiculous. objectively funny. I mean, it's unfortunate that it happened to her.
I wouldn't want that to happen to her, but it was funny. Just ridiculous that the president does that. I wish he didn't do that. But other than that, like, he's an interesting guy. Like, he's a lot of different things wrapped up into one person, you know?
You know, part of his charm, well, part of his genius is he says what's on his mind.
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Chapter 3: How does AI impact the future of jobs and industries?
Without a computer in the car, how would you do any of that? Right. And that little computer, the computers that you have doing your traction control is more powerful than the computer that went to Apollo 11. And so you want that technology, channel it towards safety, channel it towards functionality.
And so when people talk about power, the advancement of technology, oftentimes I feel what they're thinking and what we're actually doing is very different.
What do you think they're thinking?
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 into It's towards more refined thinking, more reflection, more planning, more options.
I think the big fears that people have is one, a big fear is military applications. That's a big fear. Because people are very concerned that you're going to have AI systems that make decisions that maybe an ethical person wouldn't make or a moral person wouldn't make based on achieving an objective versus based on how it's going to look to people.
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.
Yeah, we had Palmer Lucky on the podcast. He was demonstrating some of the stuff with his helmet on. And he showed some videos of how you could see behind walls and stuff. Like, it's nuts. He's actually the perfect guy to go start that company, by the way. A hundred percent. Yeah, a hundred percent. It's like he was born for that. Yeah. He came in here with a copper jacket on. He's a freak.
It's awesome. He's awesome. But it's also – it's an unusual intellect channeled into that very bizarre field is what you need.
And I think it's – 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.
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Chapter 4: What are the implications of AI and energy production?
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.
And it also seems like you'd be way better at detecting where these threats are coming from and neutralizing them too.
Exactly. Because the moment you detect it somewhere, you're going to find out right away.
It'll be really hard to hide.
That's right. Yeah. That's how it works. That's the reason why it's safe. That's why I'm sitting here right now instead of, you know, locking everything down in video.
Yeah.
Not only am I watching my own back, I've got everybody watching my back, and I'm watching everybody else's back. It's a bizarre world, isn't it, when you think about that, cyber threats? This idea about cybersecurity 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... It's 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.
We'll use the same AI technology to go defend against it.
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Chapter 5: What significant technological advancements did NVIDIA achieve?
Yeah. It was, it was really a great, great moment.
Oh yeah. There you go. Yeah. That's it. Look at you bro. Same jacket. Look at that. I haven't aged. Not a lick of black hair, though. The size of it is significantly smaller. That was the other day at SpaceX. Okay, so there you go. Yeah, look at the difference. That's crazy. Exactly the same industrial design. He's holding it in his hand.
Here's the amazing thing. DGX-1 was one petaflops, okay? That's a lot of flops. And DGX-Spark is one petaflops. Nine years later. Wow. The same amount of computing horsepower. In a much smaller. Shrunken down. Yeah. And instead of $300,000, it's now $4,000. And it's the size of a small book.
Chapter 6: How did Jensen Huang's early experiences shape his perspective on success?
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 in 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, like, 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? Exactly. It's kind of crazy. Yeah. Kind of crazy when you think about it that way. Because...
It's a perfect origin story. Computer graphics was one of the hardest supercomputer problems. Generating reality is hard.
And also one of the most profitable to solve because computer games are so popular.
When NVIDIA started in 1993, we were trying to create this new computing approach. The question is, what's the killer app? And The 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. Right.
And so if we created this thing that can solve problems, 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.
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Chapter 7: What challenges did NVIDIA face in its early years?
Sure. 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 out 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. Whoa. And so they took the guts out of that. They were using it for flight simulation with jets and space shuttle.
And they took the guts out of that. And Sega had this brilliant computer developer. His name was 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. Who's gonna 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.
Chapter 8: How does Jensen Huang view the role of fear in his leadership?
They were probably two years old. And Chris's kids are about the same age as ours. And we would go to work in this townhouse. But 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 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.
Wow.
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... 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, it would have been a flaw. 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. 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.
Other technology, 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. And so we chose a bunch of technology approaches that three major technology choices, all three choices were wrong. Okay, so this is how incredibly smart we were.
And so in 1995, mid-95, 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. And ultimately, we had to make several decisions. The first decision is, well, if we change now, we will be the last company.
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