We Study Billionaires - The Investor’s Podcast Network - TECH002: Jensen Huang & NVIDIA w/ Seb Bunney - Review of The Thinking Machine by Stephen Witt

Episode Date: September 24, 2025

Preston and Seb launch their tech book review series with a deep dive into The Thinking Machine, a book about NVIDIA and its CEO Jensen Huang. They explore NVIDIA’s transformation from a gaming hardware company to a key player in AI, discussing CUDA, leadership strategy, robotics, and the speed of innovation. The episode ends with a preview of their next review, Empire of AI. IN THIS EPISODE YOU’LL LEARN: 00:00 - Intro 05:29 – How NVIDIA transitioned from gaming GPUs to leading AI infrastructure 09:26 – Why CUDA was a turning point in GPU development for AI research 15:37 – The role of NVIDIA in enabling modern AI models, including transformers 19:55 – Jensen Huang’s leadership style and strategic market thinking 20:14 – The significance of creating new markets versus competing in existing ones 24:44 – How NVIDIA trains robots in hyper-realistic digital environments 27:47 – The impact of LiDAR and simulation on robotics advancement 38:53 – Whether Jensen's success is due to luck, skill, or strategic foresight 50:30 – The meaning behind Jensen’s "speed of light" principle 01:01:00 – What’s coming next in the book review series, starting with Empire of AI BOOKS AND RESOURCES Related Book: The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip. Seb’s Website and book: The Hidden Cost of Money. Related ⁠⁠books⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to engage in meaningful stock investing discussions with Stig, Clay, Kyle, and the other community members. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X (Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bitcoin Fundamentals Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Browse through all our episodes (complete with transcripts) ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance Tool⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Get smarter about valuing businesses in just a few minutes each week through our newsletter, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠⁠⁠sponsors⁠⁠⁠: Simple Mining HardBlock AnchorWatch Human Rights Foundation Linkedin Talent Solutions Vanta Unchained Onramp Netsuite Shopify Abundant Mines Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

You're listening to TIP. Hey everyone, welcome to this Wednesday's release of Infinite Tech. Today I'm joined by my good friend in dissecting technology book reviews, Mr. Seb Bunny. In this week, we dive into Stephen Witt's The Thinking Machine. We cover how Nvidia evolved from a gaming graphics to the center of the AI revolution and what Jensen Huang's leadership can teach us about building markets and shaping the future of tech. This is surely an episode you won't want to miss. There's so many interesting things.

things that we learn from studying Jensen Huang. And without further ado, let's jump right into the book. You're listening to Infinite Tech by the Investors Podcast Network, hosted by Preston Pish. We explore Bitcoin, AI, robotics, longevity, and other exponential technologies through a lens of abundance and sound money. Join us as we connect the breakthroughs shaping the next decade and beyond, empowering you to harness the future today. And now, here's your host. Preston Pish. Hey, everyone, welcome to the show. I am here with the one and only, Seb Bunny.

And we're excited to talk about where we're going with not only this episode, but with other episodes in the future with Infinite Tech. And Seb, welcome to the show. Oh, man. I'm super excited. Preston and I, for those that don't know, we just kind of spent a week in the mountains together. And one thing we kind of tend to always fall back on is that I love for books. And so you kind of mentioned a couple books to me. We chatted and we're like, you know what, let's talk about these books.

Let's kind of burn through some books and talk about kind of what comes up. I'm super excited. So when Stig and I first started the show, one of the things that we did quite often was read investing books and just talk about what we learned. And Seb and I are going to try to do that with tech. And the first book that we chose is a book called The Thinking Machine. And this is by Stephen Witt. And wow, this was pretty awesome.

I'm assuming, because I haven't really talked to you much, Seb, about what your thoughts are, but this book was amazing. I really enjoyed this. Just kind of your initial thoughts or what you were thinking when you were reading through this. I have to say, like, I would like to think that I've been relatively familiar with kind of the tech industry. And I just had no idea to what extent, and we'll get into it. But for those that don't know, it's called the Thinking Machine and it's about kind of Navidia and Jensen Huang, the CEO and kind of the rise of Nevis. And I just had no idea to what extent Navidia plays a role in basically the world of which we live in today, AI technology, all of this stuff.

So to me, it was mind-blowing, really eye-opening. Well, just right off the top with your comment there, the thing that I hadn't thought about is everybody's familiar with the hundreds of billions of dollars that are being plowed into AI, whether that's through open AI or X AI or you name it AI company. And I didn't even think to like go a step deeper than that. It's like, what funnel is then collecting all of this investment capital or all of this money that's being poured into this space? And at the bottom of that funnel is Invidia, right?

Like, Nvidia is literally harvesting any type of revenue that is hitting any of these AI companies. And it's just being funneled down into these chips. And then obviously the energy companies that are then powering the chips are really the net beneficiary of all of this. And it shouldn't be any surprise as to like why the market cap is in the trillions of dollars. But here's the opener that I want to start with. In the book, I read this line that said in the mid-90s, Nvidia had powered one of the chips that was being used to render Jurassic Park. And they said that it took 10 months to render a three-second clip of Jurassic Park

back in the day. And we're just looking at what they're doing now from like the mid-90s. And this is obviously when Nvidia first got their start and they were making their parallel processors to do this type of thing. And where we are now, it's just so mind-blowing. So I'm kind of curious, just to kind of kick this thing off, was there any moment in the book that just kind of grabbed you like that? Like, I remember reading this and it's just like, good God, that's crazy. 10 months to render a three-second clip. Is there anything that captured your attention, like, right out of the gate? I would say, like, similar to you, there's little sentences you come across that just blow your mind. And so I'll read one little quote from the book.

It says, and this is kind of the detail of the chips that we're using today. Basically, it says these crystal canyons were not so much printed as sculpted with ultraviolet light at a level of precision, which would have had impressed a Renaissance master. Engineers compared the manufacturing process to shooting a laser from the surface of the moon and hitting a quarter on a sidewalk in Arkansas. To me, I'm just like, this absolutely mind-blowing is in the intricacy of these chips. Mind-blown. Yeah, yeah.

The lithography process in general and all of that is just mind-blowing. Okay, let's tell a little bit of the story for folks, kind of do a real compressed overview of the book, and then we'll kind of get into some of the bigger theme. and things like that. So I'll take a stab at it. And if I'm off in any kind of way, just interrupt or help guide me here, Seb. But in general, Nvidia started off back in the early 90s. Jensen Huang was the founder of the company. And they take you through a little bit. The author takes you through his kind of journey very early on, total overachiever, somebody who has a lot of balance, I guess in his personality. He's never somebody out there like gallivanting around like he's somebody

special. He's just, he thinks very modestly of himself, or at least he did in the early days, I would probably emphasize. And what's interesting is the company stood up. He was an electrical engineer and he was just fascinated with parallel processing, doing things in parallel as opposed to serial. Intel back in the day was, you know, the king of. of serial processing. And so it walks you through this journey of somebody who is just very intriguing, very driven, somebody who's very intelligent. They talk about some of his early employment and where he worked and how he was a real standout. And it goes through the evolution of him then founding Nvidia, starting to build graphical processors, which they didn't even

call it a GPU when they first started out. But what they found very early on was this was really important for gaming, for rendering gaming environments for people playing computer games. And it was able to make a more realistic model for people to view on their display because they were doing this parallel processing of three-dimensional space. As the book progresses, anything else you want to add there, CEP? Or is that kind of accurate? You know what? I would just expand on just how big of a leap this parallel processing was. And I think this really tries to hammer home in the book, which is this idea that up until that point, if a computer wanted to do a task, it was kind of sequential. So, it massively limited the

ability to kind of crunch numbers or in games to render complex environments. And so you used to have a lot of these 2D games. You'd have Mario, you'd have whatever, a little ping pong. But they were very limited in their environments because you could only process information sequentially. And he was like, you know what? I think we can do a much better job at this. What happens if we can process all of this information to render these environments in parallel, so all of a sudden we can create fluid dynamics. We can have shadows in games. We can have more realism. And so this rise of parallel processing completely transformed the gaming industry, because all of a sudden you can have a lot more detail in these games, which created so much more engagement. But from

that, and I'm sure you're going to get into it, it completely changed the world which we live in because people started using these parallel processing chips or these GPUs. They started using them for tasks other than gaming. And so I'll kind of let you take over from there. Yeah. So they, and very early on, as they're making these GPUs for all these video games, it is total cutthroat, extremely difficult competition. You had an interesting buyer where the intels of the world didn't really want to take on this market because it was too small. And a lot of the people that were buying their GPUs where these hardcore gamers that, you know, was a really specialized customer base that wasn't very lucrative for some of the intels of the

world that existed back there in the 90s. But they continued to compete. I would say they had, what, 30 or 40 competitors in the 90s as they were kind of going through this, this phase of the company's evolution. But then in the mid, around 2005, I want to say it is, correct me if I'm wrong there on the timeline, sub. But around that time frame, Jensen was interested in making this, more accessible to a broader audience. And so he had this one gamer who had stitched together, do you remember how many of these Nvidia cards? It was like 30.

It was a ton of them. Side by side. Side by side. Mass of parallel processing. This guy had, he was gaming and he made like a large screen display of the game that he was playing. And in order to do this, he had to buy a bunch of these Nvidia cards. And when he stitched them all together, he's there playing the game and he's like looking at

like this amazing rendering on like a full screen, like full wall display. But then the guy was like, hold on, like how many calculations is this thing doing at any given moment to put this, I'm going to call it a stupid video game on the wall? And what he found was that the amount of computations that were happening per second were off the chart, like out of this world level of computation. And so he, I believe if I'm remembering this right, sub again, correct me if I'm misleading the audience here. But he then contacted Jensen and Nvidia and was like, is there any other way or is there any other use for all this computation beyond just like doing video games and putting this first action player game on my wall? And it really kind of captured the attention

of Huang, who was leading Nvidia. And what they did is they ended up creating this Kuda software to try to make the GPUs more accessible to something other than just rendering video games. And this became a huge effort within the company to create software to do things other than just rendering video game environments and 3D environments. This was a major turning point and major incentive for them to be in the right place at the right time when eventually the deep learning and AI came along because the GPU could do something more than just render a 3D environment.

Anything you want to add on that part? Yeah, go ahead. I would add that, so I did a little bit of research outside of the book, listen to a few different podcasts. And one of the things that stood out to me was the fact that basically gamers were obviously using these things to process complex environments. And then you had a bunch of researchers, which were essentially just like, well, in the back end, if you're doing fluid dynamics and thermodynamics in these realistic gaming environments,

You're basically doing math. You're basically crunching numbers. How do we take this information and use this for research? How do we crunch big data sets to try and figure out the complexity of the world? Science, physics, like mathematics, you name it. And so there was a bit of symbiology between the gamers, the researchers, and then the video in hearing this and recognizing, hey, people are trying to use our chips for things other than gaming.

These GPUs, these graphics processing units, they're basically having. hacking them to use them in things other than gaming. And so I think this is where QDA, and if I kind of get this correct, QDA kind of stands for those, it's kind of this compute unified device architecture. And at first, when I was reading the book, I was a little lost, I was like, what is this QA thing? I don't quite understand. And from my understanding, and again, correct me if I'm wrong, it's basically a platform that sits on top of a GPU that enables anyone to interact with the GPU in languages they're familiar with, like Python and C-sharp, And so then all of a sudden, they can get the GPU to do what they want and use it in ways

that's other than just traditional graphics processing. So this completely opened up the world and kind of revolutionized the research space, which ultimately led to kind of computer vision for autonomous driving, speech recognition, real-time translation. It's like profound. This wasn't possible prior to GPUs because of all of this sequential processing in traditional CPUs, central processing units. Yeah, I had the same exact moment as I was reading it.

It kept coming up this kuda, this kuda thing. And I was like, okay, let me rewind the tape and re-listen this section because like, what is this? And near the end of the book, there was one of the people that were being interviewed. And their comment was the irony for the outside observer was that they look at Nvidia and they're like, oh, yeah, it's a hardware company. But his opinion was that it's actually the essence of why it became so popular and became just the dominating force in the market was actually because of the software and the Kuda interface that allowed anybody to go and access the power of the GPU underneath of it.

And so their argument was it was just as much, maybe even more so, a software company than it was just a hardware company because of the access and the network effect of this Kuda layer. So I found that really important. It's something I never knew or entertained before reading the book. But then I would just say, yeah, go ahead. I was just going to add as well, like to that point, I was kind of thinking throughout this, like, what is, and maybe this is my value investor mind. I was like, what is their moat? And I think it's to that point, which is everyone was using Kuda, which was essentially anyone could have access to it.

It was free. It was built on top of their GPUs, but people were creating these packages. And so if you were a machine learner, if you were a physics professor, if you were a whatever, a data scientist and I don't. I don't know, some form of industry, you were creating these unique packages that spoke to your industry, but they're all free. And so people had this stickiness. They were getting used to these packages. So everyone in their various industries were all using Navidia chips.

Yeah, exactly. The stickiness there with the software interface was massive. Okay, so then if I was going to kind of wrap up the end of the book, it was really kind of, I think the book takes you up to about 20, 23. So a lot of the newer things that have happened with AI, which there's a lot since 2023 is not covered in the book. But you really kind of get an essence for like how powerful AI is then becoming at the end of the book, how much of a key role in Vida is playing.

It goes through some of like the shareholders meetings and how Jensen basically becomes this celebrity businessman in the making. And it kind of tracks that journey and just how big the company had become at the end of the book. Anything else you want to add on that as far as the tail end of the book? I think it does. So there's one more advancement that I think it touches on very briefly, and I did a little more of a deep dive into this because I think that it's one thing that's really fascinating is just kind of seeing the change in AI over time. And it briefly mentions these little snapshots from kind of like the early 40s where we saw what are called nervous nets.

nervous nets were like these single layer networks that solve basic problems. This is kind of of the foundation for neural nets, which we use today at the basis of AI. And then it goes into how there's this thing called back propagation. And we can dive into this stuff if we feel like it. And this idea that all of a sudden, AI was able to learn from its mistakes. And it could change how it thinks about things, which obviously is it's kind of how the human brain works. We're able to learn from our mistakes. And then from there, we started to get GPUs, graphics processing units for parallel processing. This was huge because up until that point, neural nets are kind of, they were struggling. They weren't really the dominant player in AI because

there were just too complex. We couldn't process enough information to be able to get neural nets to really work. And then in 2017, there was something else that really changed the way we think about AI. And this was the introduction of something called Transformers. And I did a little bit of digging again into Transformers and tried to understand them because I was like, what are these things? And from my understanding, like transformers were huge and kind of pun intended, they transformed the industry. Up until that point, if you wanted to train AI, they were massive memory intensive data heavy programs. You used to have to train these AIs on very specialist subjects and tasks. And then with the rise of Transformers, what it basically did is it changed

it from kind of really training an AI on a specialist task to more generalist tasks, because rather than trying to teach AI, let's say our language, the English language and the meaning of each word, instead what it started to do was look at words in context to one another. And so if you just basically gave AI the English dictionary without giving it any of the meanings, and then you started to give it a whole bunch of texts, if you were to just basically give RCAI, hey, what comes next in this sequence? Like green, ribbit, lily pad, like amphibian, it would say frog. It doesn't It doesn't need to know what a frog is. It doesn't need to know what an amphibian is, but it's recognizing that these words in

English tend to be used together. And so this was contextual AI. It doesn't need the meaning of things. It just needs it in context to everything else. And so I've probably done a poor example of trying to explain that. No, that was really good. The only thing I would add to what your point here is, said, the paper that led to the use of transformers.

There was a Google engineer Vaswani at all, I think is a, is a, is a, you. his name is the person who wrote a paper and it's called attention is all you need. I'm sure if people Google that or they, we can put it in the show notes, PDF to this paper, is exactly what Seb's talking about is this contextual association of letters, words, sentences, paragraphs have these contextual associations together. And when you run them through these GPUs to put this contextual mapping together, you get fantastic things that kind of pop out of it as anybody that's used AI can attest to. So yeah, I think that that's probably the really core milestone in the book where you kind of go from the middle part of it, where you're talking

about this CUDA piece and you kind of transform into the last part of the book. And I would say that this attention is all you need part is really kind of what takes you into that, the final part of the book. Okay. So that's the roll up. That's the overview. If you don't have time to go read the book, I would highly encourage you to read this book. This book is really good. Go out there and give it the full attention because there's a lot that we're not talking about. We're just kind of hitting the core chunks of it. But, Seb, I want to go into kind of the different themes that were throughout. Do you have one that you want to, if you don't have one that you want to start off with, I've got plenty here to kind of throw out. But do you have a theme that you want to go through? Oh, man.

I have, so I wrote down kind of like four core main themes, but I'm sure we'll go through both of these and I'm sure we'll probably got similar themes. Yeah. And I would say the first theme that really stood out to me is this visionary strategy, he talks a lot about like zero to one markets. And we see many people like Peter Thiel with his book, zero to one. And we also see like there's another book talking about finite games versus infinite games. And so it's this idea that he really does.

doesn't want to fight in these red ocean battles, which is you're going into a market that already exists and you're trying to take market share. To him, he's just like, I don't care about that. What I care about is I want to be a market creator, not a competitor. I want to completely reshape how we explore this world. And so this is kind of that difference. As Peter Thiel talks about in his book zero to one, going from one to end, say one to two, two, two to three is horizontal progress. You're basically taking something that already exists. You're replicating it, you're scaling it, you're improving it incrementally. As opposed to going from zero to one, this is vertical progress. You're creating something entirely new, new technology, new product,

new idea that did not exist before. And so this is, it really stood out to me that there's many times throughout the book it talks about these different industries that he would go into, and he completely reshape it, such as GPU's parallel processing. At one point, he talks about how he wanted to think about entering the phone or the cell phone chip market. And then he realized he doesn't have an edge here. The market already exists. And so he basically sunk costs, gave them up and transitioned into a new market. And so I really appreciate his idea of, I want to change the world in which we live in and think big as opposed to just trying to compete in markets that already exist. Let's take a quick break and hear from today's sponsors.

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sign up for your $1 per month trial today at Shopify.com slash WSB. Go to Shopify.com slash WSB. That's Shopify.com slash WSB. All right, back to the show. I would argue, and I think it's pretty well laid out in the book, is he probably developed this over time. And another book to kind of just go to this theme that you're talking about is Blue Ocean Strategy as a book that talks to this piece of finding a zero to one in the market.

But when you look in the early days, they came out with this NV1 chip was like their first thing that they brought to market. And it sold actually pretty well, considering their size and whatnot. But then Microsoft came out with a new graphics protocol and killed this thing, like, overnight. Like this thing would have only been on the market for call it a year. And the competition had already stepped in and just obliterated them. They came out with this NV2 chip, which was a total flop because of just the sheer timing

and all the competitors that are bringing other things to market. And like I said, there was like 30 or 40 competitors against. them at this point in time. And this was a really cool part of the book is they talk about then they came out with their NV3 chip and to try to stay in business. And not only did they come out with this chip, but they couldn't even build a prototype of it to stay in business. They had to do the entire thing through simulation with the hope and the prayer that there wouldn't be any mistakes when they actually went to the foundry to produce the chip and materialize it, they had no idea. They hadn't tested it on any type of real material before.

It was complete simulation. And the book does a really good job showing like the company was failing. Like it was going to fail. It was pretty much assured that it was going to fail. And this was like the final hell Mary like pass into the end zone. And the NV3 chip came out and it was successful and it didn't have issues. and it kept the company on life support for their next thing that they had to do. And I'm looking at this and I'm just thinking the amount of stress that Jensen and anybody else

that was participating in the company, I just can't even imagine the cycle time of producing this hardware and going to the foundries and having no clue. And the final thing that I would say, I find it crazy ironic that they emulated this in a simulated environment to create the hardware. And when you look at what they do right now, they create the hardware to create these simulated intelligent simulations of reality. And I find that really just like mind blowing that that's what saved them back in, you know,

in the very early days. We're talking like mid-90s that they were really on the cusp of death for quite a while. So I think that's probably why he's always looking at the company. And this is another theme that kind of touches on what Seb was talking about, this idea that he's always looking at the company as if it's going to die tomorrow. And that is culturally huge at Nvidia. I'm assuming if you work at Nvidia, that you are very well aware of this idea that he's constantly looking at the company like it could die tomorrow. A lot of it goes back to these early days of like that's what reality was for the company. Do you want to talk about his personality, Seb?

There's one point before we jump on his personality, but really you very briefly touched on it, which is this idea of simulating these environments. There was a point that he mentions at the end of the book that I thought was really, really fascinating. And it's this idea that the challenge with robots today is that if you want to go and train robots, it takes a ton of time. And if the robot falls over, damages itself, cool. You're back to square one.

You've got to rebuild the robot and such. And so now, in order to be able to train robots that built this thing called Cosmos, And Cosmos is basically just this hyper-realistic environment that abides by similar laws to the real world. So you've got physics, fluid dynamics, gravity, cause and effect, physical permanence. If you look at an object and then you look away from it, it's still there. And the idea is it's meant to be training robots in a digital environment, hyper-realistic digital environment. So by the time they actually enter the real world, they're far more proficient than trying to train them in the physical world. And so this is where he really is ahead of his time in the way that he thinks about things. And so, To give an example, if you had a warehouse and you wanted to train your robot on all of the various

different paths it could take to go and say pick packages throughout this warehouse, if you were to do that in real time, it would take a ton of time. But with a digital hyper-realistic environment, you're able to, within a split second, have it map out every potential possible path throughout their warehouse without ever having to set foot in the real warehouse. So by the time it sets foot in the real warehouse, it's good to go. And so I think that the world, which we're which we're moving into is one where we're able to get so far in whether it's our robotics or what not understanding of the world in these hyper-realistic digital environments before ever touching the physical world. And that's something that's kind of really coming into existence

today. And from my understanding, cosmos is free. Anyone can go and play around in Cosmos because he wants to support science, advancements in technology, advancements in robotics, advancement in AI, which I think is really, really cool. But again, it just kind of goes back to the fact that I think when it comes to the way that he thinks about things, he really is like a zero to one. He thinks about things in a way that there's no one else doing this thing. I want to go into this space and create. And his focus is not necessarily on perfection. His focus is on, let's just test it, iterate, iterate, iterate. There's a quote in the book,

and it basically says, someone who came into the, into Navidia and started looking at the code, he was just like, this thing is like cancer. Like, what is this thing? This is so poorly written. but it does what it's meant to be doing. And he ultimately ends with the saying, there was a brilliance to it all. Just iterate, iterate, iterate, execute, execute, execute. And so rather than its competitors

that were trying to create this super clean, professional-looking code, the video was kind of overtaking them because it was not about clean professional code. It was just like, let's try and minimize our execution times and getting this to market. They changed it from one year, two-year cycles to six-month cycles, and they were just trying to get it over out into the market, and they'd really dominated the market as a result of that strategy. Yeah.

Yeah, almost like just get in the room and start sensing the room so you can come up with a mental model or map it as fast as possible. We can iterate faster. We can get to what we think the truth is if we can just start sensing the environment is really kind of the approach. Yeah. One other thing that I wanted to kind of hit at with what you were saying there as far as simulation and where it's going, I find LIDAR. so fascinating and important to just how quickly and how accurately we can model things in physical reality for the simulation. So for people that aren't familiar with LiDAR technology, you can go out, you can get an emitter and sensor, a LiDAR sensor. It's almost like it is light.

It's light energy. It's just in a certain frequency that you can't see with your own eyes. But you could go into a room, you emit and you sense the return of the light energy as it comes back to the emission and you can map in 3D with super high precision down to millimeters of depth. And you could go into like this room I'm in right now. You could come in with a LiDar sensor. You could shine it around. You could and again, you can't see it, but you can emit the energy into the room and then

the feedback. And you can get a pristine mapping depending on how much energy you emit and how much you collect back. You can get a really hyper-realistic depth map of everything in the room. And so then you can take these models and you can apply it to AI to train, call it a robot that you wanted to be in the room with you. So some of the technology as you look at the convergence of it all is beyond exciting, beyond just mind melting of like where this is going to go as you start applying it to call

humanoid robots and whatnot. Okay. You want to go to Jensen? Let's do it. Let's go to Jensen. Okay. He's really interesting, right? Like, I don't really know how else to describe it other than I'll watch an interview of him on YouTube or wherever, you know, an interview just within the past couple years. And he's crazy humble, like, almost attributes nothing to like his skill.

It's almost always like, well, I don't know. Maybe I'm good at it. Maybe I'm not good at it. And it's like that I find that really fascinating. And then when I read the book, I was taken back a little bit in this idea of him dressing down employees and like just lambasting people in public, but not all the time. It was kind of sparingly here and there, but had this side to him where he could be almost explosive and just, and is that how you read it too? Is that kind of your take on how the book laid out his personality? Because, I mean, that was kind of my takeaway.

I was a little surprised because I wasn't expecting that from all the public interviews that I had seen him do, I would have never expected that kind of side of him. I had the exact same takeaway. It was my background. I studied to be a somatic therapist. So there's a part of me, and I never want to say like I'm psychoanalyzing people, but I'm always curious. I'm like, where does this come from? Like his ability to see the future, but at the same time, it sounds like he has a bit of a temper at times and will unleash. But he wants to make sure, and I think it's strategic, because it very much makes the point in the book that he believes in public feedback. And so he wants to turn one person's kind of mistake

into a collective learning and builds this kind of shared wisdom. And there's a few times where he made it a very like pertinent point to do it in front of the team, which would be hard to work in that kind of environment. But I understand to a certain extent why he's doing it. So think about this. What is his creation with all this? It's parallel processing, right? And so when you look at him doing this in public in front of everybody, what is he doing? He's making sure every other person that's standing there can learn all at the same time. And I'm not trying to promote this in the public workspace or anything like that. All I'm trying to do is what he created, which is parallel processing, is also the way he operates just as a human in the way he leads.

And when you look at his staff, I'm sure you are familiar with the way he runs his staff. there isn't one. Like, it's him and his interactions with everybody and anybody in the organization, regardless of like what level you're at. And so he's leading through almost a similar scheme as the GPUs that he's making, which is just it's all happening in parallel all the time, which as a person, you know, out of the military leadership side, that's just like beyond comprehension for me to think of like how would you manage that,

especially with a company. I mean, how many employees? do they got, hundreds of thousands of employees? I just couldn't imagine how you manage that. It seems like chaos. Oh, bad. I couldn't agree more. And one thing that I thought was really interesting is that he rarely fires.

And I think I've got a quote that says, like, he tortures to greatness. Like, there's almost this idea. Yeah. And actually, I'm trying to remember, there was a book I read a few years back. I think it was fooled by randomness by Nassim Taleb. And one of the things he talks about is if an employee makes a mistake, the last thing you should do in that moment is fire them because it is from that point on, that they've now understood, oh man, I should not be doing that thing. It's kind of like the best

time to invest is right after a recession. That's not the worst time to invest. It's the best time because the probability that happening again is very low. And so I think that he sees this. He sees when an employee makes a mistake, the instinct might be to fire them. But in doing so, you're just letting go of someone who just learned the lesson that they'll never repeat again. And so I think he sees that very much and it ends up building that cohesivity in the team when they see these are lessons for all of us to learn. Yeah, he paid for that learning lesson and now he's going to make sure he gets his money's worth in the future, right? Yeah, I was really surprised by that in the book too in that he like people that want to stay there can stay there what seems like forever. Like he just doesn't get rid of people.

but he may throttle you from time to time is really kind of the takeaway. And the other thing that they talked a lot about at the end of the book was just how people love working for him. Like he is a celebrity within the company itself where the people love him the death. But where I think it's hard to kind of understand the why is it also talks about how, you know, everybody that's working there that has stock in the company gets another zero added to their net worth. on what feels like every two to four years. And I'm wondering, like, how much of the love is just because he's made a lot of people

there, like, fabulously rich, and how much of it is because they actually respect him as a leader or, you know, whatever other factor. It's kind of hard to know whether, like, this type of leadership is repeatable or what. I don't know. I kind of left the book not really feeling like I could have an opinion on any of that. I felt almost more confused before I started the book. I was very similarly, I was kind of confused at the end because it's just like, is this, to your point, is it repeatable? And the thing that's interesting is that I think when companies grow to a certain size, you almost need hierarchy.

Otherwise, it's really hard to sort through the signal versus the noise. And I think to that point, he very much throughout the book, it talks about kind of this flat system as opposed to this hierarchy. system. And there was one point that I thought was really interesting. And he, in order to be able to constantly support the individual, he doesn't really have executive only meetings. Like junior engineers can sit on these meetings too, so anybody can share their opinion. He even has this thing, and I may butcher this, so correct me if I'm wrong, but at the end of each week, everyone in the business sends an email to him, and he very much like supports this idea of conciseness, but tell me the top five things you're interested in and working on right now.

And he then goes and picks at random a whole bunch of these emails and reads through them. And this is where a lot of his inspiration comes from. And so he wants this flat organizational structure. He doesn't want the hierarchy or he doesn't want this telephone where you're losing the signal, the more people it touches. He wants to hear directly from the individual that's coming up these ideas and helping to push that individual, reinforcing visibility, approachability and the cross-pollination of ideas. I think it's so cool.

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And who knows whether like what was sent back or what was even read. I think it was actually Elon just kind of poking everybody just like letting them know who was in charge. But anyway, as a side comedic note. Okay, let's talk about this other thing that we talked about earlier in the show, Seth, with Kuda and how this was really important. What I think we failed the capture as we were just kind of quickly going through the summary was this was not popular when he first started doing it. A lot of people were like, why are you doing this?

This has like five customers. It was like four, you know, academic people and one person who needed it for industries. said, like, there was no money to be made by him creating this software Kuta interface for the GPUs. There was no demand. And one of the things that they talked about in the book, which I really liked, was he would create things based on what he thought was going to be needed because he understood the engineering. And a lot of the times you hear in the VC world or just entrepreneurs is in general to say, well, what does the customer want? What's the customer demand? What's the proof that there's something here? And when he started doing this CUDA thing, there literally was not

like any type of market demand. It was a total distraction compared to where they were making revenues. And it was almost like this leap of faith that he was just kind of looking at the sheer horsepower of computation that he was producing. And he just kind of had this gut feeling or intuition that there was something way bigger here and like leaned into it. And it even talked in the book about how the shareholders and he would take so much heat from people as to like the R&D that was being dumped into this, especially when they're looking at the revenues that it was generating. So I guess my question to you is like where in the world? Because I'm left, like reading that and saying, well, I just don't know if I was in that position in time,

whether I would have made the same decision that he made, which was obviously the right decision. So is there some skill in there that we're supposed to extract out? Or is the takeaway that you just got to be lucky, which I hate like saying that's what it is, but maybe it is what it is. What was your take? Or maybe you read something in the book that kind of illustrated it better than what I picked up on. Well, I think it's a really interesting point. And I think building on what we were just talking about, this flat system, I think this flat non-hierarchial system allows them to pivot because if you look at most S&P 500 companies, like when you have this huge employee bloat with 10 different layers of bureaucracy, it becomes incredibly hard for you to pivot away from your main

product. Whereas I think when you've created this flat system with you at the top, ultimately, you're dictating, you can turn on a dime. And I think that throughout Navidia's history, it's shown a few times he's willing to turn on a dime. He starts going down an avenue and then immediately cuts it. So I would like to think that it was more than just luck because first off with like the NV1 chip, the first chip you kind of mentioned at the start of the book, he realized pretty quickly he was trying to do something called quadratic processing with the way to visualize gaming environments. And no one else was doing this. And it really, it was crashing all the Windows computers. It really was not doing what it was meant to be doing. And he basically was

30 days from going out of business. And they got like a $5 million. injection of capital from Sega, and they immediately were like, rather than continue to try and make it better, they dropped it onto the next thing. And then you saw, again, I mentioned it very briefly, with they saw the rise of the mobile phone market. So they were like, well, maybe we want to go into mobile chips. But there was already a lot of competition, Nokia, BlackBerry and stuff. And as a result, he was just like, you know what, this is not a zero to one market. It already exists. We don't want to take from them. And so he pivoted. And so I would like to think there was a bit of foresight in seeing this is where the world is heading.

as opposed to trying to compete in the prevalent markets that already exist. But it's definitely, it's a fascinating one. I think there's a point. I'm trying to remember the point. I've lost it. Anyway, I find it really, really interesting. I think there's definitely a skill to a certain extent, but I think that his ability to pivot his flat non-hierarchial system

enabled him to kind of like follow this instinct. Yeah. We often talk about, I know in the Bitcoin community, like show me the incentives and I can kind of show you the most probable path of, of like what's going to come next or who's going to try to attack the network. And, you know, for somebody that's just so deep in this particular space, parallel processing, the manufacturing process of all this hardware, the software that goes on top of it from an optimization standpoint so that it can be used for other things.

I think when you just, because he's so deep, another thing in the book that they talked about is his work ethic is mind blowing, like waking up at 4 a.m. for decades on and like just communicating with everybody. I think at that level, maybe he can just understand the incentives of what he's building, the incentives of the market, all the people that are out there using the hardware. And I think maybe when you're just sitting in that spot and you have humility like at your core of who you are, you're not doing things for the wrong reasons. You're just doing it because you're deeply curious and you're, you have just operational

excellence, it allows you to see the incentives and the vectors to where they're pointing of where things are going next. And you probably won't get that out of him because I think he always errs on the side of when he's speaking in public to be ultra humble to the point where if he does have any secrets, he's guarding them and not telling anybody he's just kind of like making you think that he really doesn't know where things are going, but maybe inside he knows very deeply where he thinks things are going to go. And I think it's probably more of that than anything else. I think he puts on this perception in the public eye that he is just maybe lucky. But under the hood, he's like deeply skilled and deeply knowledgeable on so many different domains

that the person looking at it from the outside just cannot possibly comprehend. When I think we're also, we're trying to look at this industry to the average individual, to the layperson, most people have no idea what Navidia is. I had a few friends ask me, hey, I'm going to go record a press and we're going to talk about this book. And they're like, what's Navidia? Yeah. I'm talking about people that have PlayStation's and gaming consoles and computers.

And the average individual just has no idea. And so I think that to that point, when you're so deep in an industry and then all of a sudden you start to see the rise of neural networks, and then you start to see the rise of like, you create GPUs. you can do parallel processing, and all of a sudden you can ask a question to an AI and it responds back to you, I think it becomes very clear, this is the path we need to go down, because you're seeing this stuff before anyone else. It's kind of like when you're in Bitcoin, it makes sense why are we going down this path

and Bitcoin keeps going up because of printing money, but people that have never spent five minutes looking into Bitcoin, it doesn't make any sense. And so I think it's just because he's in that world. He's in that world so immersed in it. And I think like the analogy that kind of comes to mind is kind of, I'd say like Apple with the iPhone versus BlackBerry. At the time, if you'd ask customers, hey, what do you want? Everyone would have said, I want a BlackBree. I want to have a full-size keyboard on my phone.

And then the moment the iPhone came out, it was something like within three quarters, BlackBree had pretty much gone bust. And so it really shows that people don't know what they want because they can't envision things that they've never used before. They can only envision a future of things which they've used before. And to that point, people are using this and they don't even realize they're using it. Like, it's completely masked out of their purview. They understand the iPhone because they are literally holding it and they hear it in the news and they use it literally on an hourly basis. But what they don't see is this computation, this parallel computation that's happening

in the background that's like completing the word that you're right. And Apple does a terrible job at this, by the way. But it's completing your sentence for you. And like, how is that happening? That's happening because you have AI that's assisting in some of these computations in the background that are being run on Nvidia chips. For all these people that have no idea what Nvidia is, and I agree with you, I think most people are clueless to this company. And what's so crazy to me is this company in market cap value is a trillion dollars more than Apple. A trillion dollars for today when we're recording this.

It's like $4.2 trillion in video. Apple's like $3.1 trillion. Just to kind of give people an idea of the sheer size and just think, how many of these things are there on the planet right now, Seb? And I'm holding up an iPhone for people that are listening. How many of these things exist in the world? And when you think through that and you're saying, wow, like just take New York City alone. Like how many iPhones are there floating around New York City?

Think about how many Nvidia chips there are. And it's freaking mind-blowing, man. It's crazy how big this company and what they're making must be behind the scene. And we don't see any of it because it's not something the normal person sees ever. Totally. And I think the other thing is when you read this book and even the way that we're talking about it, sometimes it may sound like it is a one-directional thing. He's thinking up, this is where the market is going.

And I'm putting these products out into the market. But I was listening to a talk by him and a lady called Cleo Abrams. If you just type in Jensen Huang on YouTube, it's the first one that comes up. It's got like 3.7 million views. So I highly recommend anyone going and listening to it. And one of the things he says is this is a reciprocal relationship. Like with their graphics processing units and parallel processing, they set the stage for this neural net and like being able to process large amounts of data so AI could kind of start to emerge.

But they're not the ones that enabled AI to emerge. They just gave the tools to enable AI to merge. Then you saw things like there was this contest called ImageNet. And ImageNet was essentially, the whole goal was we want teams to be able to take pictures and categorize pictures based on what's in the picture. And so if you've got your Google photos and you look through your photos and you want to go and search, find a photo with a cat. Rather than having someone going and tagging cat, how do we get AI to go and do all of this categorization and tagging. And so there's a team called AlexNet, and they used Navidia GPUs, and they trained them through a neural net AI to start to recognize photos, and they went into this contest in 2012, blew away the competition. Very low error rates, completely

blew away the competition. And so this was someone external to Navidia seeing the benefits of parallel processing. So then Navidia then takes this technology, this advancement in AI, and then looks back, okay, how can we start using this with our GPUs? And so in this podcast, he talks about how the G-Force GPU, which is kind of their top-of-the-line kind of gaming GPU, today, when they're rendering a 4K, let's say, a gaming screen, or a realistic world, that 4K screen, there's 8 million pixels on the screen. Well, traditionally, you would have had to have rendered all 8 million of those pixels using the GPU. Well, today, they only render 500,000 of the pixels. The rest are all rendered by AI. And so what that means is because their focus is now only on 500, they can

put way more effort into that 500, make far more detail in the 500,000, and then AI is able to kind of take that and create a phenomenally realistic screen. But it makes it far more efficient. And so there's kind of this symbiosis where they're creating the technology. The technology is being used for AI. AI is then being used back on their technology. And so it is reciprocal in this advancement as well. Another just kind of add comment on what you're bringing up there is this is the idea of compression. So when you take a wave file, an audio file that is really big and has like all the raw data in there and you compress it into an MP3 and you play it on a device, it sounds exactly the same. But it's just compression. It's a

compression algorithm that you use to take the wave file and make it much smaller without our ears really being able to notice the difference. And so what's AI doing? AI is compressing data. If it's taking something that's a 4K image that has like all these megapixels like Seb was laying out, then you're able to compress that into a process and procedure to render it in a way that puts it up there. You're effectively doing the same thing. You're just using different means of compression that can be applied across almost any type of file type. And I think that that's really like beyond fascinating. I can only imagine where some of this compression and AI is going to So, again, this is one that blew me away. I started digging into, in the book, it talks about

this thing called the DGX1. And at the time, the DGX1 was kind of, this was in 2016, it was top of the line GPU processing and correct me if I'm wrong in its function. But basically, it was being used for AI to basically train these neural nets. And it was $250,000 and the first one was sold to open AI. Elon Musk received it into the office. And it was like absolute top of the line at the time. And what's fascinating is on this podcast with this lady, Cleo, he brings in, and this is eight years later, this is 2024, the podcast, he brings in a mini version, which is one-tenth of the size, it's got six times the processing power, and it uses one 10,000th of the energy expenditure. This is in eight years. And so we talk about this problem, which is, where is all this

energy going to come from for all of these AIs? Where is all this energy, these massive data centers that are crunching these numbers, but in eight years, we've reduced the energy expenditure by 10,000 times. Like, that's just mind-blowing. Yeah, that's nuts. The last thing that I want to talk about, Seb, was this idea of his speed of light principle. Do you remember this in the book?

The speed of light principle that he brought up. Refresh my memory. Okay. So he was trying to figure out this. This is in production. And this is probably one of the reasons I like this because, guys, producing anything that's physical is super difficult. especially when you're competing against other people that might bring something else to market

and that makes your product obsolete. And we talk about this a lot in Bitcoin mining and how it's so difficult to compete in that space. So you're thinking through Jensen and he's building all of this hardware and the competition is crazy fierce. Well, he had one of his employees that was looking at the entire production line of all the parts and pieces to make these really complex end items. And he asked the executive, he said, how much would it cost to have this to us at the most breakneck pace that you could produce it?

And the person came back and they were like, it would be this many days and it would cost this much. And Huang was just like, there's no way. It's faster than that and it's going to cost more than that. There's no way that that's the timeline. And the person that was working for him was somewhat taken back. And they're like, no, that's what it is. I asked the suppliers and the vendors and this is what it is. And he says, that's not right.

And he was like, you know, public lambasting, boom, you're done. Get me the right answer. So the person comes back and they said, you know, as they talk to each one of the vendors, the vendors could do it faster, but the price was so outrageous that they didn't even quote them that price. They got Genton the answer that he wanted, which was they could have it. And I'm exaggerating because I don't remember the exact, you know, numbers from the book. But it was something like we could have it there within a week or three days.

but the cost would literally be this crazy insane amount of money. And Jensen was like, that's the answer. That's the answer I wanted. I don't want the vendors to come up with what is, you know, what they think the answer is for us because maybe we have a buyer that would want it in the three days and not the two weeks that you were telling me it would take. And he came up with this principle, which he called the, I think it's called the speed of light principle or the price from physics is really kind of what he's getting at.

And the reason he wants to know this number is because it's almost like in the universe, the speed of light is the one number you can't exceed. He wants to know that when he's manufacturing something because, hey, maybe he might have an Elon Musk that comes knocking at his door and say, hey, I want to buy $10 billion worth of GPUs. How many does that get me? I don't care about, you know, how many I get. I just care about the time.

Or I have somebody that's very price sensitive and they don't care about the time. But knowing that number in production is so vital in program management land. They call this the critical path. But I think this idea that he's talking about in the book goes beyond the idea of critical path because a lot of people just kind of take the quotes that their vendors give them and they plug it in and they figure out what the serial and parallel tasks are. And they say, okay, this is my critical path and this is what is going to take. But Jensen's like, no, I want to know the speed of light.

I want to know, like, absolutely the best you can possibly do. And whatever the cost is, I don't care. Just tell me that number. And then he pieces that together. And what this gives him is the ability to actually figure out, like, what pricing should be by dissecting each one of these swim lanes at each one of these things. And as a, you know, if you're a listener and you're a program manager, I think that this is a really important idea because it forces you to figure out what you think the cost should be

versus what you're being quoted the costs are by the vendors. But yeah, no, I found that really interesting. Anything you want to add on that particular idea, Seb, or anything else in the book? Previously, it was a bit of a throwaway comment, and we kind of touched on it, which is this idea that along those lines, as a result of this, he completely changed the industry. Because I think up until that point, from my understanding, chip cycles tended to be yearly every two years, and he managed to cut it down to every six months new chips were coming out. Because this kind of gets back to that point of just like, iterate, iterate, iterate, execute, execute, execute.

It's just like, we can completely change the world we live in. But we've got to constantly be pushing the limits. We've got to constantly be pushing the limits. And I think it's a phenomenal mind to try and actually figure out what are the boundaries of my ability to create change, as opposed to just taking for granted what other people are telling me my boundaries are, even when those are not really the boundaries. Yeah. It definitely speaks to his how proactive he is as opposed to a palmer.

You know, if you're a passive leader, this guy would just eat your lunch. He would destroy you. He'd destroy you. I'm definitely, so you know what? Like the one thing that I'm curious about, and this is again, like I don't want to psychoanalize. I think what he has done, and I'm going to preface it by saying what he has done is truly profound. Like the world we live in today would not be the world that we are kind of, that would not have the technology we have today, would not have the AI we have today if it wasn't

for Navidia. I had no idea to what extent they have completely shaped this world. But I wonder, there's not necessarily an argument, but there's an interview at the very end of the book, basically in the last two or three pages. And the author asks him about what do you think of the risks of AI in the world we live? I wanted to cover this. Yeah, this is huge. Go ahead.

Sorry. He gets slammed. Slammed. Yeah. And one of the questions he kind of says is, and I think to quote, he says, we invented agriculture and then made the marginal cost of producing food zero. good for society. We manufactured electricity at scale and it caused the marginal cost of chopping down trees, lighting fires, carrying fires and torches around to approximately zero. And we went off to do

something else. And then we made the marginal cost of doing calculations, long division. We made it zero. This company is not a manifestation of Star Trek. We are not doing those things. We are serious people doing serious work and it's just a serious company. And I'm a serious person just doing serious work and he kind of reiterated that. And so there's a part of me that wonders, like, where does this come from this? There's almost a fear to talk about what are the repercussions. Yes. And there's another one quote of quickly share, which is the author goes and speaks to other people in the company as well. And the other people said, I recall the discipline of Navidio's executives I'd talk to. Jensen had them wound as tight as piano strings. They were confident,

intelligent, and exceptionally well prepared down to the smallest detail and ever once caught one slipping. I recall too with sudden clarity how disinclined those same executives have been to discuss the potential future implications of the technology they were building. The disinclination it sensed, it spilled over from the discomfort, even fear from kind of Jensen. And so you wonder, like, I wonder where this came from. And what comes to mind, and I'm curious to hear your thoughts, is it mentions at the start of the book a few times, like he came to Canada, sorry, came to the US when he was 10 years old. And he quotes, like, you're always, an immigrant. I'm always Chinese. He was the younger of two brothers. And so he's kind of always

looking up to his younger brother. And I have a sense that he feels he needs to prove himself. He needs to prove himself, hence the comment, I'm a serious person doing serious work, as opposed to just like being able to step back without taking it personally. But I'm curious to hear your thoughts on that. I found this so interesting that anytime the implications of AI and where this is all leading came up. He went out of his way to just like almost make the person asking the question feel super small, like they're really stupid for asking such a question. And he's just hiding. He's hiding from this question. He hates this question, like really hates this question. And I guess that hatred for the question is probably one of the most interesting things about this entire book. And I

almost missed covering it. So I'm glad you brought it up. Yeah, why? It's a fear. It's definitely fear driving this because it's not a normal reaction. Everything else that he does is just very balanced and like, oh, you know, I don't know. Yeah, like I'm very successful. And I, you know, it was hard work, but, you know, I don't even know if that's it. Like, it's just this very casual response to everything but this question. Isn't that crazy? In psychology, there's just kind of question you ask yourself, which is anytime you get worked up, ask yourself the question, is my response in line with the stimulus. And if it is not, then I'm probably responding from some past event. Yes.

And there's a book that kind of comes to mind that I loved way back when. It was called The Talent Code. And it was like, why are people successful? We covered this on the show. As a Daniel Coyle, we interviewed the author on this. Yeah. Yeah, yeah, yeah. Yeah. And so this book came out, I don't know, maybe like seven, eight years ago. It was a phenomenal book. And it talks about how they looked at the world's fastest 100-meter sprinters. And out of the world's fastest 100-meter sprinters, on average, average, they were one of 4.6 siblings. And they were on average, the fourth siblings. They were nearly always the youngest. And so I was curious. I looked up, was he the younger

of his brother? And he was? So there's a part like, is he trying to, he wants to prove himself. He wants to add value to this world. But it's almost kind of like clouding this question around, like, what are the repercussions of AI? And I don't want to diminish the change and the profound technology that brought into this world. But I find it really fascinating. That is really fascinating. I found it so bizarre because that theme came up multiple times in the book. And that question just kept coming up. And then his response each time was just so just aggressively, like, putting the person down for asking it.

And yeah, so I agree with everything you're saying there. I just don't know why he's so scared to answer the question because he's clearly like it makes him upset. So I don't know. If you're a listener, if you're a listener and you know you work it in video or you know maybe more behind this, Throw it in the comments when we post this up on X. We'd love to hear what you've got. Seb, we have a lot more we could cover here, but you know what? I don't want to cover it. I want people to read this book. This book was really, really good. We'll have a link in the show notes to the book. Again, the name is The Thinking Machine, and this is by Stephen Witt. Stephen Witt,

Bravo, you did a phenomenal job. For our listeners that are tuning into more book reviews, I just having fun reading all these things that we find fascinating. We're going to try to get the authors to come on with us. And if they don't, we're going to record anyway because we don't care. We might have more fun without the authors. I don't know. But we're going to invite the authors on the show from time to time. What else that I want to cover?

Oh, I wanted to tell people about the next book that Seven and I are going to work on. The name of this book is Empire of AI. And this is about the inner story of Open AI and Sam Altman. And I have a bit of a bias. I got to say this bias up front for people. I'm not a fan. I'm not a fan of him. I really don't.

Everything that I've read online, and again, I haven't researched them all that much. But the little bit that I've read online, he just doesn't. And what I've seen is that he's not really the best person. But regardless of that, like what they've done at Open AI is mind blowing. totally mind-blowing. So this book was written by Karen Owa, something like that. But anyway, that's the next book we're going to read. So if you guys want to read it and you want to be prepared to hear our conversation have at it, we highly encourage that. Seb, anything you want to say about the next book real fast before we wrap this up? Oh, you know what? You basically

took the words out of my mouth, which is, don't get me wrong. I use chat GPT. I think open AI have done such a phenomenal job and it really has laid the foundation for this AI revolution since they released in what? The end of 2022, early 2023, has profoundly changed the world. But then you see some of the ways of Sam Altman acts in society in the way that he talks about what's happening and it brings up questions. And so I'm curious to see what this book talks about and whether it goes into some of these things. Yeah, the subtitle on the book is one of the reasons that I was sold as soon as I read it. The subtitle is Dreams and Nightmairs in Sam Altman's OpenAI. There was really good reviews online, so that we're plowing into that one next.

With all of that said, how awesome is Seb Bunny, right? Seb, we are so excited to have you on the show. I don't know what our frequency of doing this is going to be, but regardless of what it is, I love having these conversations with you because you and I have these conversations in real life. And when we get together and hang out from time to time, and I just knew you were the perfect person to kind of do these book reviews with. And you have your own book. It's called The Hidden Cost of Money.

And if people haven't checked it out, this is a Bitcoin book, of course. And if you haven't checked out Seb's book, you've got to read his book. As you can see on the show, he's crazy thoughtful. He has read tons. You see the books behind me. He has, I'm sure, just as big of a library somewhere in his home. But, Seb, thanks for making time and coming on the show. Anything else you want to highlight or point people to before we finish this up?

No, I just, again, like, you're too kind, Preston, and I just feel so lucky to be on the show. And I shared this, I think, the first time I came on the podcast, which is, I've been listening to Preston since probably over a decade now. And they're going from listening to you and Stig talking about the books, seeing the evolution of the show to bring it back to talking about the books. I just absolutely love it. Being able to share information, talk about these things, talking about how the books. world is changing. I feel incredibly grateful. I think we need to tell Stig to read one of these, and he can join us on the conversation, too. He needs to get back in the mix here. Seb, thank you so much. We're going to have all the links to this in the show notes if people

want to check out anything that we talked about, and thanks for join us. Thank you for listening to TIP. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on our episodes. To access our show notes and courses, go to the Investorspodcast.com. This show is for entertainment purposes only. Before making any decisions, consult a professional. This show is copyrighted by the Investors Podcast Network. Written permissions must be granted before syndication or rebroadcasting.