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TECH002: Jensen Huang & NVIDIA w/ Seb Bunny - Review of The Thinking Machine by Stephen Witt
01:07:38
Preston and Seb kick off their tech book review series with The Thinking Machine by Stephen Witt, diving into NVIDIA’s evolution, Jensen Huang’s leadership, and AI’s accelerating future.
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Podcast Announcer
You're listening to tip.
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Preston Pysh
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. And 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 that we learn from studying Jensen Huang. And without further ado, let's jump right into the book.
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Podcast Announcer
You're listening to Infinite Tech by the Investors Podcast Network. Hosted by Preston Pysh. 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 Pysh.
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Preston Pysh
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.
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Seb Bunny
Oh, man, I'm super excited. Preston and I, for those who don't know, we've just kind of spent a week in the mountains together, and one thing we kind of tend to always fall back on is that our 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.
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Preston Pysh
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, like, 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.
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Seb Bunny
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 Nvidia and Jensen Huang the CEO and kind of the rise of Nvidia. And I just had no idea to what extent Nvidia plays a role in basically the world which we live in today, AI technology, all of this stuff. So to me it was mind blowing, really. Eye opening.
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Preston Pysh
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 OpenAI or XAI or you name it, AI company. And I didn't even think to 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 Nvidia. 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. Sep. 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 I'm just like, good God, that's crazy. 10 months to render a 3 second clip. Is there anything that captured your attention like right out of the gate?
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Seb Bunny
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. It 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?
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Preston Pysh
Yeah, yeah. The lithography process in general and all 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 themes 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, you know, help guide me here. Sebastian. 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 it did in the early days, I would probably emphasize. And what's interesting is the company stood up. He was a electrical engineer and he was just fascinated with parallel processing, doing things in parallel as opposed to serial. Intel back in the day was the king 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, Seb, or is that kind of accurate or.
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Seb Bunny
You know, 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, 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 could 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.
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Preston Pysh
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, their GPUs were 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, 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, Seb, but around that timeframe, 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 Nvidia cards It was? Like 30.
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Seb Bunny
It was a ton of them. There was a ton of them side by side. Massive parallel processing this guy had.
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Preston Pysh
He, 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, Seb, 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 CUDA 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 in 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 GPUs could do something more than just render a 3D environment. Anything you want to add on that part? Yeah, go ahead, Seb.
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Seb Bunny
I would add that. So I did a little bit of research outside of the book, listened 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 a symbology between the gamers, the researchers, and then Nvidia, 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 hacking them to use them in things other than gaming. And so I think this is where cuda, and if I kind of get this correct, CUDA kind of stands for. 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 CUDA 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 like, profound. This wasn't possible prior to GPUs because of all of this sequential processing in traditional CPUs, central processing units.
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Preston Pysh
Yeah, I had the same exact moment as I was reading it. It kept coming up, this cuda, this CUDA thing. And I was like, okay, let me rewind the tape and relisten 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 a dominating force in the market was actually because of the software in the CUDA 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 CUDA layer. So I found that really important. It's something I never thought new or entertained before reading the book. But then I would just, yeah, go ahead.
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Seb Bunny
So 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 cuda, 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, 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 Nvidia chips.
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Preston Pysh
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 2023. 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 Nvidia is playing. It goes through some of, like the. The shareholders meetings and how Jensen basically becomes the celebrity business man 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.
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Seb Bunny
The book, I think it does it. 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 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 I was able to learn from its mistakes and it could change how it thinks about things, which obviously 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 they 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 text, if you were to just basically ask the AI, hey, what comes next in this sequence? Like green ribbit, lily pad, like amphibian, it would say frog. It doesn't need to know what a frog is, it doesn't need to know what an amphibian is. But it's recognizing 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.
- [18:11]
Preston Pysh
No, that was, that was really good. The only thing I would add to what your point here is, Seb, the paper that led to the use of Transformers, there was a Google engineer, Vaswani et al, I think is 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, 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, 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 us? 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?
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Seb Bunny
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've 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, 0 to 1 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 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 his book 0 to 1, going from 1 to n, say 1 to 2, 2 to 3 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 0 to 1. 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 GPUs, parallel processing. @ 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.
- [21:37]
Preston Pysh
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Learn more about unchained signature@ Unchained.com Preston use code PRESTON10 at checkout to get 10% off your first year. Bitcoin isn't just for life, it's for generations. 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 0 to 1 in the market. But when you look early 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 like killed this thing like overnight. Like this thing had 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.
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Seb Bunny
Was going to fail.
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Preston Pysh
And this was like the final Hail 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, you know, 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. The. 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. If 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?
- [28:39]
Seb Bunny
There's one point before we jump on his personality, 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, they've built this thing called Cosmos. And Cosmos is basically just this like 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 moving into is one where we're able to get so far and whether it's our robotics or whatnot, 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 0 to 1. 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 Nvidia 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, Nvidia was kind of overtaking them because it was not about kind of 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 really dominated the market as a result of that strategy.
- [31:42]
Preston Pysh
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, 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 this, 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?
- [33:31]
Seb Bunny
Let's do it.
- [33:31]
Preston Pysh
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. 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 to Seb? 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.
- [34:33]
Seb Bunny
I had the exact same takeaway. It was my background. I, like, 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 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.
- [35:21]
Preston Pysh
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 his. 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. 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. So he's leading through almost a similar scheme as the GPUs that he's making, which is. 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, especially with a company? I mean, how many. How many employees do they got? Hundreds of thousands of employees. I just couldn't imagine how you manage that. It seems like chaos.
- [36:31]
Seb Bunny
Oh. But 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.
- [36:43]
Preston Pysh
Yeah.
- [36:43]
Seb Bunny
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 you pay. From that point on, they have 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.
- [37:04]
Preston Pysh
Yeah.
- [37:05]
Seb Bunny
It's not the worst time to invest. It's the best time because the probability of 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. Yeah. And so I think he sees that very much and ends up building that cohesivity in the team when they see these are lessons for all of us to learn.
- [37:29]
Preston Pysh
Yeah, he paid for that learning lesson, and now he's going to make sure he gets his money's worth in the future.
- [37:35]
Seb Bunny
Right.
- [37:36]
Preston Pysh
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 them to 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 whatever other factor. It's kind of hard to know whether 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.
- [38:51]
Seb Bunny
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 talks about kind of this flat system as opposed to this hierarchical 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 in on these meetings too. So anybody can share their opinion. He even has this thing and I may butch this, 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. Let's take a quick break and hear from today's sponsors.
- [40:16]
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- [43:41]
Seb Bunny
All right, back to the show.
- [43:42]
Preston Pysh
I find it interesting, and this is just more of a comedic comment than anything else. People, please don't read into this. When Elon was there with Doge working for the government, he required all government employees to send, like, the top five things or whatever. Exactly what Jensen. You know what they talk about in this book that he does at Nvidia? I guess Elon did this with government employees. 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, Seb, with Cuda, and how this was really important. What I think we failed to 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 industry. So, like, there was no money to be made by him creating this software CUDA 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 gonna be needed because he understood the engineering. And a lot of the times you hear in the VC world, or just entrepreneurs in general, they 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 even talked in the book about how the shareholders and he would take so much heat from people as to, like, the R and 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.
- [46:18]
Seb Bunny
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 hierarchical system allows them to pivot. Because if you look at Most S&P 500 companies, 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 dimension. And I think that throughout Nvidia'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 the NV1 chip, the first chip you 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, 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 hierarchical system enabled him to kind of like follow this instinct.
- [48:15]
Preston Pysh
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 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 4am 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.
- [50:10]
Seb Bunny
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 Nvidia is. Yeah, I had a few friends ask me, hey, I'm going to go record press and we're going to talk about this book. And they're like, what's Nvidia? Yeah, and I'm talking about people that have PlayStations 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 and 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 looked 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 asked customers, hey, what do you want? Everyone would have said, I want a BlackBerry. I want to have a full size keyboard on my phone. And then the moment the iPhone came out, it was something like within 3/4, BlackBerry 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.
- [51:40]
Preston Pysh
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. 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 writing. Apple does a terrible job at this, by the way. But it's completing your sentence for you. And like, how is that happening? It'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 Truck. 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 scenes. And we don't see any of it because it's not something the normal person sees ever.
- [53:16]
Seb Bunny
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 with the graphics processing units and parallel processing. They set the stage for this neural net and 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 Nvidia 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 Nvidia seeing the benefits of parallel processing. So then Nvidia 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 GeForce 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, 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 the 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.
- [55:51]
Preston Pysh
Another just kind of ad hoc comment on what you're bringing up there is this, is this the idea of compression? So when you take a WAV 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 WAV 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, and 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, where some of this compression and AI is going to take us.
- [56:50]
Seb Bunny
So this, again, this is one that blew me away. I started digging into. In the book, it talks about this thing called the DG X1. 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 OpenAI. 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 1/10,000th 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 have reduced the energy expenditure by 10,000 times. That's just mind blowing.
- [57:55]
Preston Pysh
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?
- [58:06]
Seb Bunny
Refresh my memory.
- [58:06]
Preston Pysh
Okay. So he was trying to figure out 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 long or 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, you know, 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, you know, 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. 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 Jensen the answer that he wanted, which was they could have it. And I'm exaggerating because I don't remember the exact 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 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 it's 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, sab or anything else you want to.
- [61:50]
Seb Bunny
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. Yeah, 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 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.
- [62:35]
Preston Pysh
Yeah, it definitely speaks to his. How proactive he is, as opposed to a passive leader. You know, if you're a passive leader, this guy would just eat your lunch. He would destroy you. He'd destroy you.
- [62:46]
Seb Bunny
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 psychoanalyze 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 would not have the technology we have today, would not have the AI we have today, if it wasn't for Nvidia. I had no idea to what extent they've completely shaped this world. But I wonder. There's an argument, 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?
- [63:21]
Preston Pysh
I wanted to cover this. Yeah, this is huge. Go ahead. Sorry.
- [63:24]
Seb Bunny
He gets slammed. Absolutely slammed. 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. It was 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.
- [64:13]
Preston Pysh
Yes.
- [64:13]
Seb Bunny
And there's another one quote I'll quickly share, which is, the author goes and speaks to other people in the company as well. And the other people said. I recalled the discipline of Nvidia's executives. I talked 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 recalled too, with sudden clarity how disinclined those same executives had been to discuss the potential future implications of the technology they were building. The disinclination that sensed it spilled over from the discomfort, even fear from Jensen. And so you wonder, 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 he came to Canada. Sorry, he 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.
- [65:21]
Preston Pysh
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 react. Everything else that he does is just very balanced and like, oh, you know, I don't know. Yeah, like I, 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 interesting.
- [66:16]
Seb Bunny
And there's. In psychology, there's this 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? Why are people successful?
- [66:37]
Preston Pysh
We covered this on the show as a Dan Daniel Coyle. We interviewed the author on this.
- [66:42]
Seb Bunny
Yeah, yeah, yeah, yeah, yeah. And so this book came out, I don't know, maybe like seven, eight years ago. It's a phenomenal book and it talks about how they looked at the world's fastest hundred meter sprinters and out of the world's fastest hundred meter sprinters, on average they were one of 4.6 siblings and they were on average the fourth sibling. So 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 that. And I don't want to diminish the change and the profound technology that brought into this world, but I find it really fascinating.
- [67:25]
Preston Pysh
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 at Nvidia 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 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, Seb and 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 did I want to cover? Oh, I wanted to tell people about the next book that Seb and I are going to work on. The name of this book is empire of AI, and this is about the inner story of OpenAI and Sam Altman. And I have a bit of a bias, I gotta 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 OpenAI is mind blowing. Totally mind blowing. So this book was written by Karen. Oh, 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, where we highly encourage that. Seb, anything you want to say about the next book real fast before we wrap this up?
- [69:36]
Seb Bunny
Oh, you know what? You basically took the words out of my mouth, which is, don't get me Wrong, I use ChatGPT. I think OpenAI 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. It's profoundly changed the world. But then you see some of the ways that 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.
- [70:04]
Preston Pysh
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 Nightmares 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 set, Bonnie, 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. 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 gotta read his book. He, 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?
- [71:07]
Seb Bunny
No, I just. Again, 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 for 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 world is changing. I feel incredibly grateful.
- [71:33]
Preston Pysh
So I think we need. I think we need to tell Stig to read one of these and he can join us in on the conversation too. He needs the needs to get back in the mix here. Seb, thank you so much. We're gonna have all the links to this in the show notes if people want to check out anything that we talked about. And thanks for joining us.
- [71:50]
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