Tae Kim (2:52)

I've been following Nvidia from its exception. I started my career in consulting, went to Wall street for a while, and then moved to media. And I've been a video gamer my whole life, from the Atari 2600 move on to Nintendo, Super Nintendo. And I also played a lot of computer Games with Commodore. 64 was my first one, and Commodore made this machine called the Commodore Amiga. And then the early 90s, I got into PC gaming. 3A6, 4A6. I built my own computers, the Pentium. So all over the early 90s, and that's when the 3D graphics took off. And Nvidia actually wasn't the first one that did well. It was this company called Rendition Verity who got the first port of Geoquake going. And then this other company called 3DFX, which I cover a lot in the book, kind of dominated the mid to late 90s 3D graphics. But that was the glory days of PC gaming. You started off with Doom, and then you went to Quake, Unreal Tournament. All these amazing games with these great 3D graphics cards that really pushed the envelope graphics. So I was a passionate user of all these 3D graphics cards. I knew Nvidia from the early days, so I had that early technology knowledge. So in terms of research process, I got this email in May of 2003. This is actually a couple weeks before Nvidia had the blowout guidance quarter. And it was a cold email from a publisher saying, Nvidia question mark. And I read it, and it was a business publisher called Norton. And it was from an editor there saying that Matthew Ball, who wrote the Metaverse book, recommended me to write a book on Nvidia. I was like, Nvidia. Like there has to be like five books on Nvidia. Even by then it was a large technology company. It was the largest chip maker in the world. I went straight to Amazon and then I found, like, there are no books on Nvidia. Every other tech company has a ton of books and no one has written a book on Nvidia. So I thought about it for a few minutes and wait a minute. I know the technology, I know the business and finance stuff with my Wall street background. And I've been in Media for 10 years. So I have these three things where I felt I could write an Amazing book on Nvidia. I know it really well from the beginning, so I replied quickly. I met with the publisher at Bryant Park. It was a short meeting. He's like, you need to get a book agent. I got the best book agent through the recommendations of friends. And within a few weeks I had a book deal and I was off and running. I didn't know how to write a book, but I figured first thing to do is talk to as many Nvidia employees as you can. And the great thing is there's this thing called LinkedIn where if you start talking to Nvidia employees and you become their friends, you can look into their networks. So that's all I did. I just talked to dozens and dozens of Nvidia employees. And after every conversation, usually an hour or so, they were so excited to talk to me because they want the Nvidia story out there. And they too were kind of confused that this massive success story, the largest shipmaker in the world, and no one has written a book about it. So they are super enthusiastic and very open. And that's all I did. I just kept on talking to people and going through people's networks. YouTube is another amazing resource. Jensen probably has like dozens of interviews, 30 years of interviews, the Computer History museum. That's how I got in touch with the three DFX people. So there's just so much information on the Internet and LinkedIn was a godsend. The art of the cold email and Reaching out through LinkedIn just did wonders for the book. And that's the only reason I was able to turn the book around in about a year.

Jensen Huang (6:18)

Yeah, I mean, there's just so many great lessons we can tap into. So with 3D FX, I mean, it's a lesson of just not resting on your laurels. And Jensen, throughout the past 30 plus years, he's never just rested on his laurels. And just with the employees just being so interested in sharing their story, I think it just points to the culture where he's attracted people that are just so passionate about what Nvidia is doing and how they're building out the future. And I'm curious just to learn more about your interactions with Jensen, what's sort of your takeaways and how you felt interacting with him and interviewing him and. Yeah, what stuck out to you?

Tae Kim (6:52)

So I actually met him back in the early 2000s when he was doing the first kind of secondary on Nvidia. I was at a hedge fund then, and Nvidia was actually my largest investment at the hedge fund. And when I told him this when I met him, he's like, oh, that's really funny, because Nvidia is my biggest success story too. When I told him that was my first big winner, he's like, oh, Nvidia is my first big winner too. So that was a fun interaction. He's very blunt and direct, which I think a lot of Nvidia employees grew to appreciate. Because when you go to a different company, large bureaucratic companies, people play a lot of games. There's a lot of gaming of metrics, there's a lot of internal politics. People don't tell people the intellectual, like, blunt truth about things. So you're always dancing around people's feelings. Nvidia, that doesn't happen. Like, it's a culture of telling you the unpleasant truths, being blunt and direct. So my interaction with Jensen, like, there was a period where I'll. I'll be asking questions or going down a certain line of questioning, he'd be like, tay, you're not understanding Nvidia here. And he'd just be very blunt and tell me off. So he's very serious. He's very blunt. I mean, first of all, you get a little emotional reaction, but then you realize it's a good thing because we can save time, we can focus on the right things and you know exactly where he is at all times. And that's the same thing employees have said to me all the time. They appreciate Jensen being very blunt and direct because they know exactly where he is and they can focus on important things and improve instead of playing this game of coddling emotions that happens at most other companies. He's very honest, transparent. I've seen him being interviewed like, you know, dozens oftimes on YouTube and I was afraid he would go off. A lot of times he just goes off in the same 10, 12 stories that I could repeat verbatim. He didn't do that. Thankfully, every question I asked, he answered honestly. Even the stuff that is pretty personal, like the fallout between him and his co founder with Kurtis Supreme. So that was fantastic. There was a period where he was very down on the first 10, 15 years of Nvidia and even said that if Jensen wasn't involved for the first 10, 15 years, he'd be happy with that. Speaking in third person. He was just bashing the politics and the problems that he saw early on. And I was like, wait a minute. Like, you could say that you weren't fully formed the first 10 years, but it still was like one of the fastest growing chip companies. When it went public after 1999, I mean, it beat Broadcom, I think, to hit, like, that $1 billion annualized revenue number. So it wasn't all bad. They did really well. But like, Jensen is a perfectionist. When he thinks about the past or history, he always sees the flaws. And Nvidia back then had a lot of flaws, a lot of politics, he would say. And, yeah, he wants to improve and be perfect at all times and win at all times.

Jensen Huang (9:38)

Obviously, one of the things that really sticks out to me just about Nvidia is just the culture and how that's been a key driver in their success. And Jensen, you explained in the book how he had a management style just unlike anything else in corporate America, which has really enabled them to prevent this complacency and prevent these office politics from creeping in. I mean, how is it possible for someone like Jensen to keep such a strong culture intact despite them being tens of thousands of employees and 60 billion in revenue today? How has he been able to push that through to so many people?

Tae Kim (10:13)

A lot of it is just. It comes straight from the top. I mean, I've been involved with large bureaucratic companies, and what happens is it becomes more dysfunctional. There's this thing at Nvidia called Mission is the Boss make decisions, do actions that serve the customer and nothing else. And most large organizations and bureaucracies, you start spending a significant portion of your time, maybe 30, 40% on gaming metrics, on internal politics, on serving your boss's boss instead of the company. So you're kind of in a business unit or a division, and it's almost like you're competing against other parts of the company and you want to get your boss's bosses promoted or have him hit his numbers right. At Nvidia, if you start playing politics like that and serve your boss's boss, you will get dressed down in public like Jensen. If he just smells the sniff of politics on what you're doing, he'll just rip you apart and embarrass you in front of everyone. And he told me, if you do that once or twice, people will stop doing it. And he does it. So that sniff of internal politics, of meeting after meeting, indecisiveness that is prevalent at every other large company that I've heard when you talk about Microsoft, Google, when I talk to these employees that go to these companies, these former Nvidia employees, they can't adjust to that kind of company where it's where endless meetings, you need to get five stakeholders to say yes. At Nvidia Jensen gathers the 20 people that he needs to make a decision. They hash it out. There's something called honing the sword where they're, you know, friction brings the best result. So they're yelling at each other, hashing out with data and arguments. And at the end of the meeting, Jensen makes a decision and they go and execute. Right. At other companies, they might take five meetings along PowerPoints with five different general managers and nothing gets done. Like some of the times, the managers are incentive to just throw quicksand into the gears and slow everything down. And that's the opposite of the culture at Nvidia. The other thing that is really special about Nvidia is that I've heard at Apple, everything is siloed and information is guarded and people don't share information at all. Nvidia is the exact opposite. Jensen says he would actually want it to be err on the side of oversharing because if everyone in the company knows the strategic direction where Nvidia wants to go at all times and you're transparent with information, they're going to make decisions to push Nvidia in the right direction. There's this large software company executive that I talked to. It's a very large company and he talked about how when he meets executives at other companies, sometimes those and they're talking about partnership or deal, those two executives would argue back and forth on what the partner company wants to do. He said, tay, that never happens in Nvidia. Like if I, I'm talking to two people on Nvidia, they have the same tune, the same vision, same strategic direction. And that doesn't happen to other companies. The other thing that's really important is this thing called top five email, which has been around since the early 90s of Nvidia. So basically every Nvidia employee for every week or every two weeks sends an email to their coworkers on their team, their manager. What's the top five most important things that are happening in my job right now? It might be, oh, I read a paper that is affecting all the AI technology in our area. It might be, oh, I'm falling behind on this project, I need some help. It might be some competitive development or industry development. What are the top five things that are most important right now? And you send that to your team members and your manager and Jensen is able to somehow, in his outlook, read 100 of these emails across the company a day. So it gives them a perfect sample of what's exactly happening inside the company. Jensen headsets, JPURI which is executive. Most companies don't do this if they have these slow status reports where employee sends information or emails to their manager and the manager sanitizes it, takes out all the bad stuff and sends that status report to his manager and by the time he gets to the CEO that's three, four levels up, it's completely useless. First of all, it's too late. Second of all, all the negative things are polished out because the lower manager doesn't want this higher manager to know any of the bad news that's potentially happening. So this top five emails like takes care of the slowness of the SaaS reports. And there's a culture of Nvidia that something bad is happening, like a competitor has a better product and it's doing better or there's a customer that is upset. With Nvidia you have to share that like in these emails. So at all times Jensen has really real time view of what's happening inside Nvidia and that way he can allocate time, energy and resources Almost like an F1 race car driver, perfectly able to steer the race car, which is Nvidia, at all times. So this kind of real time control of Nvidia is what helps Nvidia be successful because Jensen has almost like perfect intelligence at all times and he's able to steer resources, steer the company in a way that is the right thing to do in the current technology landscape and market and most companies don't do that at all. This is a completely different way of, of running a company and steering a company. Let's take a quick break and hear from today's sponsors.

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Jensen Huang (18:46)

That'S fintool.com all right.

Tae Kim (18:50)

Back to the show.

Jensen Huang (18:51)

And one of the other clear things that sticks out to me just about Jensen, that was also one of my favorite parts. Just to learn more about in the book is just his pure drive to succeed. So he knew that he was going head to head with a lot of people who are probably a lot smarter than him but he always said that nobody was going to outwork him. And you have a number of great stories and quotes in the book. For example, when he goes on the rare vacation, he's pretty much working the whole time. The last 30 years, he's pretty much spent about every waking hour working. And even the friendly office ping pong games or whatever, he just hates to lose. That's the last thing he wants. So he also told you when you interviewed him that Nvidia's success had much more to do with hard work and resilience than it had to do with intelligence and genius, which is quite fascinating. So I'm curious to just learn more. Where do you think this intense dedication and motivation to succeed, where does it come from?

Tae Kim (19:47)

I think he's just had it innately since the beginning. I mean, when I talk to co workers and his friends, he's always been like this. And it's just a competitive drive that's inside him. I think he also has a chip on his shoulder. Like I compare him to Michael Jordan. Like, he would create like things that make him angry and then work even harder and give him more drive. When I talked to him, he like brought up this article a journalist wrote like 30 years ago. That person didn't even write Nvidia on the list of graphics chip companies in the 1990s. Like, he's still carrying that, like that slight against him that gives him more drive to work even harder. I think the work ethic is really important because he talks about how if he goes to a movie theater, he never remembers a movie because he's constantly thinking about Nvidia and like what he needs to do at all times. So it's this obsession that Michael Jordan had with basketball that Jensen has with the business of Midea that I don't think you can teach. It's just something that he's so passionate and obsessed about. Like the funny story about like one of his early CFOs who used to be like a top 50 chess player when he was younger. And he knew this, but he had to beat him. He would like study all the chess moves and all the openings and the CFO would just crush him every time because he knew exactly what Jensen was doing. He would do something a little off. Then Jensen memorized an opening. He couldn't react because he wasn't as good as chess. And then every single time he lost, he would flip over the chessboard and knock the pieces off and then force the CFO to play him in ping pong because he's better him at ping pong. So he needed to beat him at something like that kind of competitive drive. Like, I think it's just innate. It's like people have this drive to win. There's this other story about the marketing manager that came from S3, which was a good competitor. And the first review spread by a major computer PC magazine ranked Nvidia number two out of the top three. Right. And normally at S3, the market manager said, if we got number two, that was like, hooray, great job. We're number two. And there's dozens of graphics companies. That's great. So when he let Jensen know that we were in second place, Jensen got upset, angry, and was like, you know what second place is? Their first loser. And the market manager was stunned. Like, what second place? Their first. Like nothing is acceptable unless you're in first place. Later I found actually after I wrote the book, that line is like from Ferrari. Like the founder of Ferrari used that line decades ago. That kind of mentality where you have to be first place, the winner at all times, at all things. I think it's just innate from the beginning. Like, that's just who he is. And some people have that chip on their shoulder and other people don't. And I think that's actually rare. I've talked to probably dozens of CEOs in my life and not a lot of people are at a hundred, you know, a thousand RPMs at all times, like Jensen is.

Jensen Huang (22:38)

Yeah. And that's something he just really tries to push on all of his employees. He would constantly tell his employees that Nvidia's 30 days from bankruptcy. So he would want to hold everyone around him to the same standard that he held himself, which is an extremely high bar. And I think a lot of people knew going into Nvidia that you were entering a workplace where you were working a lot of Saturdays. You were probably working a lot of Sundays too. Even. So, let's jump back to the 1990s and tell a little bit more about their story. So they were founded in 1993. The idea was conceived in Denny's with the three co founders when they decided they wanted to start Nvidia. So after a few years of early growth and success, they went to raise capital in the year 2000, and they went public in 99. And one potential investor had asked them, why would we invest in a graphics company? Because they'd seen dozens of graphics companies just fade off into irrelevance. So how about you just speak to the significance of what they pulled off in those early years and some of the things that, that they did to practically be the only graphics company to survive from that time.

Tae Kim (23:45)

Yeah, I mean, they literally are the only graphics company that survived out of, I think someone counted 200 to 300. I mean, the accurate number is probably around 60 or 70. If you think about it, this is a market, a computer industry, where intel pretty much commoditizes any piece of hardware and then incorporates it on the. Onto the motherboard or cpu. Microsoft, anytime there's a piece of productivity application, Microsoft would make something that's competitive and kind of bundle it into their Microsoft Office. So it's an industry that a lot of companies do well for a while, and then they get crushed, either by the two gorillas, Microsoft and Intel, or by other competitors. And Jensen kind of had to think about that. Like, why are the PC graphics companies, they always do well for about like a year and a half, two years, and then they lose their leadership perch. Right. So he had to solve that, and he talked to his executives and then he realized, I figured it out. The PC makers decide which graphics chip to put in their PCs twice a year, in the spring and the fall, two seasons. And the reason why graphics chip companies can't stay on top is that the PC makers just choose what's best twice a year. And whoever's best at that time, they incorporate that chip into their computers. And then he realized the reason why that happens is most graphics chips take about 18 months to two years to make. Like, you design the chip and then you kind of tape it out and fix all the bugs. And then at the end of two years, you have a chip that you can sell to a computer maker. And Jensen was like, hmm, what if we, instead of 18 months, we make three chips in the 18 months and make a new chip every six months. And then the employees are like, huh, that's impossible. But he figured out a way to make an architecture every 18 months and then two derivatives. So you can have like three chips in 18 months. So one new chip architecture, in six months you have a faster derivative, and in six months you have another faster derivative. So he figured it out, and then he convinced the PC makers to say, you should stick with us because we're going to keep the drivers the same, the software that you need to run the graphics chips, and it's going to be more reliable and it's better because everything is backwards compatible, so you don't have to. The drivers are just going to be unified and you don't have to worry about that. And the drivers are a huge headache for PC computer makers. And by executing that six month cycle instead of 18 month cycle, it pretty much blew everyone out of the water. And that kind of thinking that's completely out of the box. No one has done before. Time and time again, Jensen figures something out like that and comes out with a business strategy that is smarter than anyone else and gives Nvidia a huge advantage. This other thing that Curtis Prim, one of the co founders, he's like a technical genius, he created this pseudo operating system that sits on top of the chip that let them put things in hardware and then pull it out if they didn't need it anymore and put it and kind of emulate in software. So it allowed them to just do that every six month cycle. Because if something didn't work like they're developing a hardware feature, they could just put it in software, right? And then if they had enough power, they could put it in hardware. So it gave them a lot more flexibility. These new graphics chip features that other companies didn't have, they didn't have this operating system that sat on top of the graphics hardware. So they just had these smart advantages that other companies didn't have. Another thing they did was this concept called ship the whole cow. They built in redundancies into the chip where if the yield, some of the parts of the chip didn't work, they could sell that chip as a lower end part for really cheap. And that would like block a competitor from coming up from the bottom right. The big Clayton Christensen getting disrupted by the high volume, low price supplier. He blocked that off with this concept that everyone does now. We have different bins of different chips, a low end part, a medium end part, high end part. So he just create these amazing business strategies that let him survive. And the other thing is he's just super resilient. Like Curtis told me he couldn't believe the rabbits that he would come out of the hat like at the last minute. They needed a 2D graphics capability for the Revo 128, which is their third chip, the first one that was successful. And he just got a 2D graphics license from their main competitor. And he's like, how did you do that? Jensen found a way. And then a few weeks later he hires the top 2D graphics engineer from that competitor that he just licensed the technology from. Like he's completely ruthless hiring the best talent from the competition. There was this time where they're running outta money. Thirty days from going out of business in 1998, intel was breathing down their neck. They're spreading fear, uncertainty, doubt about the i740 chip that they told PC makers is going to destroy Nvidia. He was running out of money. They're having problems with TSMC in terms of yields. Literally, they're weeks away from going bankrupt. And he somehow convinced his three board partners, why don't you invest in us? We'll give you a 10% discount when we IPO. You know, we're good at the technology stuff. Just give us some money and we'll figure it out. And you know, we're the best. So give us some money right now to tide us over in the middle of the Asian financial crisis in 1998. But he was able to convince his three main board partners to put in some money when they needed the most. Like most CEOs aren't able to be resilient like that. Never say die. Figure it out. Their first two chips were complete disasters. The MV1. MV2 lost tons of money, got refunded by all the retailers. The MV2 didn't even take off. It wasn't even sold. But somehow he figured out ways. He had to lay off half the company, but somehow he figured out a way to raise some money and survive and then launch the chip that did really well. So that kind of resilience and business genius. And then on top of that, his technical expertise is a combination that led to Nvidia's success. Yeah.

Jensen Huang (29:49)

And I can imagine that just marketing and selling chips in the 90s was very difficult, especially with how important the relationships were with the big players like the IBM's and the Microsofts of the world. This would bring Nvidia to essentially invent the GPU market. So I was curious if you could just tell this story and its significance.

Tae Kim (30:10)

So they were scared about intel coming in and incorporating basic level graphics capability onto the motherboard or onto the chip. Jensen is a student of history. He knows technology companies get disrupted all the time. So he was always thinking, how can I make what we do more performant, higher value, that we could protect our margins and not be taken over by a bigger company. And the thing they came up with was a programmable GPU or programmable shaders. So before programmable shaders, which actually came with the GeForce 3, before that, graphics were a very fixed function. You couldn't really program it. You had to do a certain way that everyone did it. So most graphics and computer games all look the same. They all look like Quake, a little bit dreary, whatever. So they built this programmability into the GeForce 3 that allowed developers and PC game makers to have complete artistic freedom, how to program all the graphics and art styles. So what that did was give the developers reason to create all these amazing art styles. But also it led to what actually happened later with cuda, when all these non graphics people start to see all the computing power that's possible in these GPUs and kind of hack into the programmable shader language where you could program the art styles and they would hack that language and use it for non graphics algorithms applications. So that led to using Nvidia's GPU computing power in non gaming, non graphics simulations work that happened later. And Jensen actually Curtis told me he knew this from 1983. He had this kind of long term vision that he called accelerating computing. That it wasn't just going to be video games, it wasn't just going to be graphics. That this, this style of parallel computing where a computing workload is split up and all these cores are going to attack that and solve that workload at the same time. He kind of foresaw that that was going to be the future of computing because it was so much more performant and faster at doing this high performance computing. So eventually it did happen. So this is why. Just a basic overview. Most computer and processors at that time, the microprocessor which was started by intel, was a thing called a CPU. And CPUs usually have about four to eight kind of processor cores, but they do things serially where they follow the program and then you do one after another follow that program with about four to eight cores at the time. What a GPU does and what Jensen foresaw was each graphics chip would have hundreds of processor cores and then eventually it goes to thousands and tens of thousands. So they would break down the program and split up the work between hundreds and thousands of processor cores. So when you do like scientific problems or things like that, you can calculate that all at once across hundreds and thousands of of course, and that would lead to tremendous speed ups, a hundred to a thousand times faster than running the same program on CPUs. So he foresaw that the computing whole paradigm would change and the world will move from CPUs to GPUs. And that started with graphics with the programmable shaders and then that programmable shaders, which was the GPU people, they thought of the Cuda language in 2006, programming platform that lets non graphics people use the computing power of GPUs using programming extensions on The C language. And that's how things were off and running. The AI stuff actually did not really hit till I guess 15 years after CUDA came out. So if you think about it, Cuda came out 2006. It didn't really take off for like a decade. But Jensen believed that this was the future of computing so much that he dedicated parts of the chip to accelerate the CUDA operations, these things called CUDA cores, and then later tensor cores. And Wall street was upset because this hit their gross margins like their gross margins plummeted in the years after they put the CUDA cores onto the graphics chips. And but Jensen was just like, no, this is the future of computing. I know what's going to happen. I know the world is going to use these GPUs in this way at a certain point. And he told his people to make libraries, these math libraries, these math science libraries for every single vertical, and to help accelerate these developers to use these GPUs whatever they want to do, whether it be for MRI imaging, figuring out the thermodynamics of clouds, figuring out stock options, pricing, whatever they want to do. Jensen wanted Nvidia engineers to help make the software libraries to accelerate that process.

Jensen Huang (35:02)

And it was with the release of CUDA around 2006 that Jensen really was doubling down on the expanded use of GPUs for non graphical purposes. You can think of this of using it for more real world applications like scientific, technical, industrial sectors. And you described this as a technology that would take them to a trillion dollar company. He was always focused on clearly defining the market opportunities and developing these long term business strateg. I think the other interesting thing we should probably touch on with Cuda is this is what really allowed Nvidia to build a moat around their business as it created a platform for millions of developers to start using it. So maybe you could talk more to that just to help people understand sort of the moat and competitive advantage for a company like Nvidia.

Tae Kim (35:50)

So this actually plays into Jensen's kind of long term thinking, right? Most companies are looking out to the next quarter or the next year. Jensen just wants to create the perfect platform, the perfect ecosystem possible for the long term development of the system. So what he did was create these math libraries for every single vertical, whether it be science or industrial medicine. Like he wants Nvidia engineers to talk to all the customers and developers in each sector and say, what do you need? How can I make your life easier? So by creating all these hundreds of libraries, whether it be like ray tracing For Hollywood animators to the math libraries I talked about, it just relieves a lot of programming burden for these developers. So they could just focus on fixing the problem instead of creating these things that just take a lot of work and effort that aren't focused on fixing the problem. So he did that from the beginning. They would run these sessions where they invite all their customers and they would stay and talk to all the CUDA engineers and say, what do you need? What can I do to make your life easier? And they would take all the input and then make libraries or make the hardware circuits to be optimized to run their software faster. So this constant process every year and ironing out the bugs is also a huge unlock here. And you just develop an ecosystem where all the developers rely on the libraries, so they build all their applications on top of the CUDA libraries and all they do is learn how to program in Cuda. So you have these millions of developers who learn Cuda rely on all these libraries and it just creates that kind of ecosystem where it's really hard for a developer that learned on Cuda and built all their programs on top of Cuda to switch, import that to say AMD ROCKM now or Cerebrus, which is an AI chip startup. The other problem is when I talk to these AI startups is it's not easy. Like there's always technical problems when you port your software from one chip platform to another chip platform, right? So back when the Mac was running on PowerPC and Windows was running at intel, porting a Windows application to PowerPC on a Mac was a huge endeavor. And there's always problems after you do it. So that's what the ecosystem, that's what the moat is, right? People aren't going to be inclined to switch over to a chip that might be 30% cheaper because why risk your business on doing that, right? It's not worth it. I rather focus on making the software or making my AI model better. Plus Nvidia is making a new chip in 12 months that's going to be much faster and it's going to be backwards compatible. So every piece of soft that you wrote in the past you could use going forward. So a combination of these things, there's a technical risk. Nvidia chips are usually that fastest performing, highest performing chip. And you don't want to rely on another vendor that is either going to abandon you, which Google has done a million times. You're going to build your business on Google platform that might not have a track record of sticking with you in the end or a startup that might go bankrupt in two years. Like you said, the safer thing is to stick with Nvidia. They're going to be around the number one player and you're not going to have the technical issues that you had because all the bugs and stuff have been optimized from like 15 years of work of these millions of developers going on stack overflow, sharing their tricks and tricks, and Nvidia software engineers kind of ironing out and making sure everything works in the best way possible. So that's why like every other week there's a headline about how Hyperscaler is going to make their own AI chip and that a startup is going to come with a better chip that's going to be faster than Nvidia. And I always like roll my eyes because Amazon and Google have been doing this for five, 10 years and then the Amazon Web Services CEO goes on television and says, yeah, Nvidia still has 95% market share. You've been doing this for five, 10 years. And the reason is what I just said. It's just Nvidia makes the best performing chips. Everyone builds their stuff on Nvidia, so there's no point in switching and risking your entire business by switching to a different platform. And it's going to continue that way until we go into completely new computing paradigm, which is it's going to move from GPUs to whatever, like it could be quantum computing or whatever. And then that gives an opportunity for another company to be the dominant player in that competing paradigm.

Jensen Huang (40:22)

If we ahead to 2013, Jensen believed that deep learning, machine learning, artificial intelligence was going to be a big market as well and they should bet big on it, which of course helped them get an early lead. And you actually make a pretty interesting point in your book that he really isn't this brilliant fortune teller might come across that way. Looking in hindsight, someone who can just predict the future. He's very measured in allocating Nvidia's resources and not just making all these moonshot bets. So when he sees strong evidence that things are changing, then he's willing to adapt. And yeah, this helped enable them to fully capitalize on AI when that strong demand eventually came around. So I was curious if you could just talk about the transition they went through since then that's allowed them to be where they're at today.

Tae Kim (41:09)

So I mean I would push back a little bit. Like he is a fortune teller in the sense that he sees the end thing happening. So. But he doesn't know the exact timing. So Even like the 2013, the big bet in AI, it took another nine years for it to really take off where the data center revenue in the last six quarters went from like 5 billion to 30 billion. But he just foresees what's going to happen and when he's very confident that that's going to be the end state, he's willing to keep investing for 5, 10, 15 years. Ray tracing, DLSS and obviously AI are examples of this. And I actually compared him to Reed Hastings. Like Reed Hastings intuitively knew that video would go to Internet streaming someday. And when he knew this, like the technology wasn't ready, American households didn't have broadband, everyone's on dial up. But he knew he could invest, stay on top of the technology, do a DVD rental business by mail and just stay around and keep investing and be the first guy there when the market was ready to go to Internet video streaming. And Jensen does that time and time again. Like programmable GPUs. Cuda, the steel for Mellanox which gave Nvidia the ability to build out these massive AI data center scale networking GPU clusters. That's exactly what's happening today with these hundred thousand GPU clusters that are being built going to 500,000 to 1 million. Like all that was enabled by Jensen seeing what's going to happen someday and then positioning Nvidia to be perfectly positioned to take advantage of it. So it's a combination of both. It's. He sees the end goal, but he's willing to stick around and stay there until the end state happens. The other thing is like ChatGPT took off in late 2022. I talk about this podcast where the head of Cuda, ian Buck in 2019 talks about all these things, the natural language processing, AI model, scaling laws. He actually like sees exactly what happens like two, three, four years later. When the transformer architecture paper came out from Google, Jensen and the Nvidia team is all over it. They're like, this is a big deal, this is going to change everything. And they actually built in a transformer engine in the hopper GPU that came out a month before Chap GPT was released. So they're like, these guys are super technical nerds. They're on top of all the technology trends and papers and they position Nvidia to be there like years before it actually happens. And I think exact timing is impossible to predict, but the technology stuff, they're on top of it and they've proven that time and time again. Let's take a quick break and hear from today's sponsors.

Clay Fink (43:48)

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Jensen Huang (46:29)

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Tae Kim (47:09)

All right, back to the show.

Jensen Huang (47:12)

So you have a chapter in your book titled the Big Bang. That's what everyone's sort of been talking about the past couple of years and especially talking about Nvidia. How about you walk through just the significance of the big bang?

Tae Kim (47:24)

So ChatGPT came out in November or October of 2022 and it didn't really affect Nvidia's revenues for a couple quarters. People were talking about, oh, it might help Nvidia, maybe it'll help a little bit. They reported a quarter, it wasn't anything special about. It was like in line. And then they reported their financial quarter in May of 2023. So this is about six or seven months after ChatGPT first came out. Like, I have this thing where a trader's waiting for these results and the Bloomberg headline goes across the screen and he like blinks. He's like, that can't be right. That can't be right. So Nvidia's what the street was forecast was like 7 billion. And Nvidia said we're going to do 11 billion. Like it was insane. People are just like stunned. Like it just blew everyone's heads off. Like I quote all these fund managers and they couldn't believe the number. It was like the biggest number they've ever seen. Literally the largest upside. Like you said, there's this chip analyst at Bernstein called Stacy Razgahn and he just titled his note the Big Bang. Like, this was the Big Bang and it just, it was off to the races. And it kind of reminds me of Netscape when IPO'd back in 1997 and the stock like doubled and tripled the first day. And everyone's like, wow, this is real. This Internet thing is going to be huge. And you know, all these Internet startups started going public. This was the same effect. So every company was like, oh my gosh, this is a game changer. The whole financial Wall street industry Just opened their eyes saying this is going to be huge. And it almost like every company also had to realize, holy crap, every other company is investing in this stuff. This is an existential threat for me. Like if my competitor incorporates these AI models and has better customer service, is able to figure out better product or better service, I might get disrupted. Like my competitor might destroy me. So it just, it was kind of like a gun that went off and everyone had to play ball and invest. That's exactly what happened. I mean literally, like I said, Nvidia's data center revenue went from like 5 billion to over 30 billion in six quarters. I mean that's like one of the largest kind of technology ramps in history. And this is not like Google or Facebook or software company where you could just copy paste the bits. This software is easy to scale, right? If you have demand for a hundred billion of your product, you could just sell your software. It's no big deal. This is hardware, this is stuff that has 35,000 parts. So Nvidia had to like go crazy with this. Taiwan suppliers, talk to them. What do you need to meet this tidal wave of demand that we're seeing in our pipeline? So it's literally one of the biggest kind of accomplishments in technology industry. I don't think people really recognize how amazing it is to go from 5 to 30 billion in six quarters. It looks like it's going to be even larger over the next four quarters. So that may report where they gave guidance for the next quarter. $4 billion above expectations was the thing that set everything off and the whole AI arms race was on. I think the next day the stock went up like $180 billion in market value. It's like one of the biggest one day gains ever. That one day gain alone was bigger than Intel's market cap. So it was a big event and it's going to be looked back on history as a big deal.

Jensen Huang (50:46)

Yeah, I wrote down one data point here. For fiscal year 2024, data center revenue rose 427% to 22.6 billion, driven primarily by that AI chip demand. And it sort of reminds me of the Barron's article you recently wrote which was titled the the Battle for the AI King Won't be Close. I wanted to give you a chance to share some of your thoughts related to this.

Tae Kim (51:12)

Yeah, so like I said, the Cuda ecosystem is just so ingrained in all the developers that it's really hard to shake that out. So Broadcom, Marvell, there's all these custom chip programs inside These big hyperscalers, Jensen has said, you know, we'll see what they make in two, three years. That's what they're developing now. But if you're spending few hundred million dollars doing R and D, Nvidia is spending $10 billion in R& D. Like it's at a different scale. Nvidia has the networking expertise from the mellanox acquisition in 2019. So right now, in the past, even just a year ago, a typical AI server would have 8 GPUs. It was a Hopper AI server. Now the current Blackwell AI server that's shipping right now is 72 GPUs in the same space. It's like one rack. It weighs one and a half tons. Like, this is why AMD can't compete. Like they don't have a 72 GPU AI server that has all the interconnects, all the networking, everything optimized. And then you could stack a row of these together and build out a hundred thousand GPU cluster, right? So the competitors don't have cuda, they don't have all the optimized libraries and all the bugs ironed out after 10 years of battle testing. They don't have just a networking hardware and software expertise all optimized and combined at once. So that's why Nvidia has one and is going to keep winning. It just has the resources and all that foundation built up over the last 10 plus years. It just looks, you know, as long as the AI training laws and scaling laws hold and all these new innovations that require even more compute. I've talked about reasoning models, AI agents, multimodal models that can work with audio and video, just like the AI models are so good with text today. So all these like AI innovations are just ramping at the same time. And all that requires massive amount of compute from GPUs. And Nvidia is really the only one that has these scalable systems that can meet that demand if we turn back.

Jensen Huang (53:24)

To just the culture that's enabled them to get such a big lead. Jensen, he puts a lot of energy into communicating the company's strategy and his vision to employees to make sure everyone's aligned, everyone's steering the ship in the same direction. And at the end of the book you share many Jensen isms. The one I'm reminded of that you already mentioned during this interview is the mission is the boss. And I also love the one you shared that states strategy is about the things you give up. I think it's such a critical insight because saying yes to one thing is simultaneously saying no to a thousand other things that you could be working on. So how would you describe Jensen's long term vision for Nvidia?

Tae Kim (54:06)

I think right now they're doing what they've been doing, like the whole accelerated computing, making all the GPU parallel processing attack, all these different non graphics and non gaming problems, they're still on that curve up. I mean, the two things I talk about are drug discovery and robotics. These are very early technologies. It really is a computation problem, It's a computation simulation problem. And if we're able to put a thousand times more AI processing power to these problems and simulate how all the drugs and proteins interact with each other, you can literally solve what drug molecule will be most effective at treating this disease or cancer. And I think this is something that really drives Nvidia employees today. The stakes are so much higher than, you know, making nice computer graphics on a screen or playing a video game. And I think they're really excited and passionate about what can happen over the next few years on that front.

Jensen Huang (55:03)

And if we look at the corporate structure for Nvidia, I think Jensen embraces a quite unconventional corporate structure and prefers to have a flatter organization. I think he would describe it much differently than I would here, but essentially he wants people to be able to operate independently, more independently than other large organizations. And I was surprised to hear that he has more than 60 direct reports today, whereas other companies his size would have maybe a handful of people reporting to. The CEO talk more about how he structures Nvidia and his overall management style and how he makes this all work.

Tae Kim (55:40)

So the board members, Nvidia told me every time a new board member comes, the first thing they say is, this doesn't make any sense. Jensen needs a chief operating officer. And then Jensen gets all upset. It's like, no, I don't. This is the way I do things and it's more effective. And like I said before, he just wants his hands and everything. Like he wants to know what's happening everywhere. So that's why he talks, has 60 direct reports underneath him. One thing that he doesn't do is coddle and do career coaching. So a lot of CEOs meet one on one to talk about their careers or how things are going. Just, you know, a lot of hand holding. He doesn't do any of that. So that saves him a lot of time. The other part of it is just there's this crazy email culture Nvidia, where he's constantly peppering all his executives with emails. What are you doing here? What are you doing that? What do you think about this? Here's a paper, do this, do that. He's doing that with all 60 executives. One of the AI executives I talked to when I interviewed him, he's like, Yeah, I got 13 emails from Jensen today. Like he's constantly emailing people left and right, up and down all day long, right? So that email culture where he's constantly like has his finger on the pulse of what's going on is how he manages Nvidia. And I don't think most companies are like that. I don't think most corporate CEOs are emailing. A dozen emails per executive a day, especially on the top projects like that is how he manages Nvidia, just so closely, better than anyone else. And then he does these meetings. Like every meeting at Nvidia is live, right? I've been in big companies, a lot of meetings are completely worthless. There's a lot, like I said, there's a lot of indecision. There's a lot of, you know, people just talking for the sake of talking is a complete waste of time. And Nvidia, that doesn't happen. It's either a decision meeting where he, the executives get in a room, including junior employees, they share, they hash it out and make the decision, this is what we're going to do, or it's a problem solving meeting. So they're working on a project. He brings all the top players that are working on that project and they go through the biggest problem, then the second biggest problem, then the third biggest problem. We need to fix that. We're not leaving here until we figure out a way to fix the biggest problem on that project. So these meetings and things that, how things are done at Nvidia, it's just action oriented, it's process oriented, it's getting to an end result. The other thing besides mission is the boss, which I already discussed. The second biggest catchphrase that Nvidia that talks about their culture is speed of light. Speed of light is a mentality of everything. Nvidia has to be done at the fastest way possible, in a quality way, but the fastest way possible. So at most companies, if you say, oh, I did this 10% faster than the competitor or 10% faster than I did last time. Yay. Props to you. You did a good job. If you did that Nvidia, you get dressed down, they yelled at because they don't care about how you did versus last time. They don't care about how you're doing. Versus your competitor. They want to know how you're doing versus the physical limits of reality, right? So you break down this process into the component parts, you take out all the possibilities in terms of lag and downtime between each step and then tell me how close are you are to the ultimate speed run only held back by the laws of physics. So if you do stuff at the speed of light, like if you do make a product at the speed of light, you're doing it at the absolute maximum possible, the fastest way possible, because you're taking away all the slowdown, all the cues, all the downtime. And if you do FSP light, your competitor can't beat you, right? If you're doing it at the fastest way possible, your competitor can't beat the laws of physics. So if you have that mentality on everything, right? Instead of two years to make an AI gpu, we're going to do it every year and we're going to figure out a way to make it every year. Like your competitors can't compete. And that's what Nvidia does. That extreme velocity of getting things done at the speed of light is like another huge competitive advantage for Nvidia.

Jensen Huang (59:51)

So speaking of competitors, the more recent headlines that everyone is pretty focused on is what was released related to Deep Seek. So you mentioned before we hit record that it's been keeping you quite busy at Barron's. What do you make of that announcement and that news?

Tae Kim (60:08)

There were a couple narratives that came out when Deep Seek kind of first hit the mainstream media. The first narrative was somehow China magically found a way to create a cutting edge model without using a lot of resources, right? The number everyone threw around was about 5 to 6 million dollars. They figure out make a model that almost as good as OpenAI's best model for 5 to 6 million dollars. And that freaked everyone out. Like oh my gosh, like China figured out some magic alchemy. If you actually looked at where that figure came from, it was from a paper on December 26. It wasn't even the last few weeks, it was like long time ago. And that five to six million dollars figure was just on the final training run. On a final theoretical training run. If they rented that 2000 GPUs from a cloud provider, it wasn't even real. It was a theoretical rental cost. If they did a final perfect training run for that model. And then you read this next line and it says this does not incorporate any employee costs. The pre training costs, all the research and experiments we did before, like it was just a final training run. But all the research, all the employees, all the infrastructure costs to lead you to the final perfect training run was not included. So the actual cost is like an order of magnitude higher. The week before all this stuff hit the wires, the Deep Seq CEO met with the Chinese Premier and said, we need more GPUs. If you've magically figured out a way to create cutting edge models for 5 to 6 million, why, why would you go to the Chinese premiere and say you need more GPUs? Right? It doesn't make sense. And a few days before this deep sea craziness, the Chinese government announced they're going to invest 140 billion in AI over the next few years. So all these things happened at once. Everyone just went off with the misleading narrative that China somehow figured out a way to recreate OpenAI with 5, $6 million, which is not even what the paper says. So that's the first part of it. The second part of it, which is actually true, is that Deep SEQ model is very optimized and efficient. Like compared to the OpenAI model, it's probably uses 90% less compute resources. So it is very efficient in the use of compute. And the way it did that, it kind of combined all these AI innovations that other AI startups and companies have already done in the past and combined it in a very smart way, a novel way, mixture of experts, number, precision, all these things, right? But if you just step back, that compute efficiency, 90% is completely normal. It's something Jensen talks about in his speeches all the time, how Nvidia and the industry has driven down the cost of computing by a thousand times, and he hopes another thousand times will happen over the next decade. So it's just a fact of computing history where the cost of computing will come down and developers will find a way to use that extra computing power to make new AI applications. It'll drive AI adoption. There's all the things you can do with extra compute power and it creates innovation, it sparks innovation. So the second part where people start freaking out that maybe we don't need AI compute power anymore because Deep SEQ is more efficient than other models. That doesn't make any sense either, because this has been happening for the last few decades and this is just a continuation of what's going to happen. And what happened is after all this chaos with Deep Seq, every major technology company in the US erase their guidance in terms of their AI infrastructure capital spending this year. I mean, Google, Meta, Amazon, all of them said that AI demand is off the charts. We don't have enough capacity to serve that demand and we're going to actually increase what we spend later this year. So all these, like, narratives that people kind of freaked out about just simply aren't true. And it shows by from what the large technology companies are saying with AI demand and the capacity they need going forward.

Jensen Huang (64:08)

Yeah, it's pretty crazy how much markets move just based on a headline or a narrative. I wanted to wrap up the discussion here just on the conclusion from your book. The conclusion is titled the Nvidia Way. How about you just break down the essence of what you see as the Nvidia Way?

Tae Kim (64:25)

I think the first part of the Nvidia is just extreme work ethic. Like, there are no shortcuts. If you outwork your competitor, you outwork your rival, it's going to give you a huge edge. And Jensen just talks about this all the time, like, no one is going to outwork me. Someone might be smarter than me, but no one's going to outwork me. So if you work really hard, it's going to give you an edge. The second part is just talent cultivation. Jensen knows the way Nvidia is going to beat this competition is by having and hiring and retaining the best talent. So he looks at stock allocation like his blood. A guy from human resources executive told me, like, if he sees an engineer that's a rock star, that's doing a great job, that is adding a ton of value, he'll double the stock grant on the spot. Like, and this kind of meritocracy and this culture of winning and just retaining people. There's so many Nvidia executives that are there 25, 30 years. It's kind of amazing. The turnover rate is 3% in the industry. That's usually 15%. And the last part of the UA is just the extreme velocity of how things get done. People just move fast in the best way possible and beat their rivals the speed of light. Like, just get stuff done quickly. Don't get bogged down. You can make mistakes, but learn from your mistakes. So those are three main components of what I termed the Nvidia Way.

Jensen Huang (65:48)

Wonderful. Well, that turnover piece certainly surprises me given how much work is required to work at a company like that. Yet I'd imagine, you know, a number of people get burnt out, but it's just a matter of attracting the right person and that person attracts a certain type of individual into the company. So, Tay, I really appreciate you joining me here. I want to give you the final handoff to let the audience know how they can get in touch with you if they'd like and pick up the book.

Tae Kim (66:12)

I'm First Adopter on X. The book is available everywhere, Amazon local bookstores. I also have a website@takeim.com t a e k I m.com so I would love to hear from you guys and thanks so much for having me. Clay thank you for listening to tip. Make sure to follow we study billionaires on your favorite podcast app and never miss out on episodes. To access our show notes, transcripts or courses, go to theinvestorspodcast.com this show is.

Clay Fink (66:43)

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Tae Kim (66:44)

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