[00:01:45] 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 Steven 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.

[00:02:15] Preston Pysh: Just your initial thoughts or what you were thinking when you were reading through this.

[00:02:20] Seb Bunney: 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 dunno it, it’s called the Thinking Machine.

[00:02:32] Seb Bunney: And it’s about kind of NVIDIA and Jensen, Huang, the CEO, and 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.

[00:02:48] Preston Pysh: Just right off the top with your comment there.

[00:02:50] Preston Pysh: 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.

[00:03:16] Preston Pysh: 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.

[00:03:34] Preston Pysh: 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-nineties, 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.

[00:04:03] Preston Pysh: And we’re just looking at what they’re doing now from like the mid-nineties. 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 curious just to kick this thing off, was there any moment in the book that just grabbed you like that?

[00:04:23] Preston Pysh: Like, I remember reading this and I was just like, good god, that’s crazy. 10 months to render a three second clip. Is there anything that captured your attention like out of the gate?

[00:04:31] Seb Bunney: 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.

[00:04:39] Seb Bunney: It says, and this is the detail of the chips that we are using today. Basically it says, these crystal canyons were are not so much printed as sculpted with ultraviolet light. At a level of precision, which would’ve 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.

[00:05:03] Seb Bunney: To me, I’m just like, this absolutely mind blowing is in the intricacy of these chips. Mind blowing.

[00:05:09] Preston Pysh: 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. Do a real compressed overview of the book, and then we’ll get into some of the bigger themes and things like that.

[00:05:25] Preston Pysh: 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, Seb. In general, NVIDIA started off back in the early nineties. 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.

[00:05:45] Preston Pysh: Total overachiever somebody who has a lot of balance, 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.

[00:06:06] Preston Pysh: He was a electrical engineer, and he was just fascinated with parallel processing, doing things in parallel as opposed to serial. You know, Intel back in the day was, you know, 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.

[00:06:30] Preston Pysh: 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.

[00:06:56] Preston Pysh: 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? You know what? I would just expand on just how big of a leap this parallel processing was.

[00:07:16] Seb Bunney: 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 sequential. So it massively limited the ability to crunch numbers or in games to render complex environments. And so you used to have a lot of these 2D games.

[00:07:32] Seb Bunney: 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?

[00:07:50] Seb Bunney: 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. ’cause 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 let you take over from there.

[00:08:19] 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 ’cause it was too small.

[00:08:36] Preston Pysh: And a lot of the people that were buying 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 existed back there in the nineties. But they continued to compete. I would say they had what, 30 or 40 competitors in the nineties as they were going through this phase of the company’s evolution.

[00:09:00] Preston Pysh: 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 these NVIDIA cards? It was like 30. It was a ton of them. It was a ton of them. ’em are side by side.

[00:09:24] Seb Bunney: Yeah. Massive parallel processing.

[00:09:26] Preston Pysh: This guy had 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.

[00:09:46] Preston Pysh: 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 we’re off the chart. Like. Out of this world level of computation.

[00:10:05] Preston Pysh: 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?

[00:10:27] Preston Pysh: And it really 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 and 3D environments.

[00:10:53] Preston Pysh: This was a major turning point in major. Incentive for them to be in the right place at the right time, when eventually the deep learning and AI came along. ’cause the GPUs could do something more than just render a 3D environment. Anything you want to add on that part? Yeah, go ahead Seb.

[00:11:11] Seb Bunney: 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.

[00:11:25] Seb Bunney: And then you had a bunch of researchers, which were essentially just like, well. In the backend, 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?

[00:11:41] Seb Bunney: 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 symbiology 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.

[00:12:01] Seb Bunney: These GPUs, these graphics processing units, they’re basically hacking them to use them and things other than gaming. And so I think this is where cda, and if I get this correct, CDA kind of stands for those, it’s this compute unified device architecture. And at first when I was reading the book, I was a little lost.

[00:12:18] Seb Bunney: I was like, what? What is this CDA 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. It 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.

[00:12:42] Seb Bunney: So this completely opened up the world and 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 and traditional CPUs. Central processing units.

[00:13:03] Preston Pysh: Yeah, I had this same exact moment as I was reading it. It kept coming up this Cuda thing and I was like, okay, let me rewind the tape and relist this section. ’cause 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.

[00:13:25] Preston Pysh: 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 kuda layer.

[00:13:56] Preston Pysh: So I found that really important. It’s something I never knew or entertained before reading the book, but then I would just, so I was just going to add as well, like to that point, I was thinking throughout this like what is, and maybe this is my value investor mind, I was like, what is their moat?

[00:14:10] Preston Pysh: And I think it’s to that point, which is yes. 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, and I don’t know, some form of industry, you were creating these unique packages that spoke to your industry, but they’re all free.

[00:14:35] Preston Pysh: And so people had this stickiness, they were getting used to these packages. So everyone in the, in their various industries were all using NVIDIA chips.

[00:14:42] Preston Pysh: Yeah, exactly. The stickiness there with the software interface was massive. Okay. So then if I was going to wrap up the end of the book, it was really I think the book takes you up to about 2023.

[00:14:55] Preston Pysh: 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 get an essence for like how powerful AI is then becoming at the end of the book, how much of a key role NVIDIA’s playing. It goes through some of like the, the shareholders meetings and how Jensen basically becomes this celebrity business man in the making and it tracks that journey and just how big the company had become at the end of the book.

[00:15:25] Preston Pysh: Anything else you want to add on that as far as the tail end of the book?

[00:15:28] Seb Bunney: 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. ’cause I think that it’s, one thing that’s really fascinating is just seeing the, change in AI over time.

[00:15:41] Seb Bunney: And it briefly mentions these little snapshots from like the early forties where we saw what are called nervous nets. Nervous nets were like these single layer networks that solve basic problems. This is 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.

[00:16:01] Seb Bunney: 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 it’s how the human brain works. Yes. We’re able to learn from our mistakes and then from there we started to get GPUs graphics processing units for parallel processing.

[00:16:19] Seb Bunney: This was huge because up until that point, neural nets are 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.

[00:16:40] Seb Bunney: 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 pun intended, they transformed the industry. Up until that point. If you wanted to train AI, they were massive memory intensive data heavy programs.

[00:16:59] Seb Bunney: 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 really training an AI on a specialist task to more generalist tasks. ’cause 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.

[00:17:24] Seb Bunney: 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, RC AI, Hey, what comes next in the sequence? Like green rip it lily pad. Like amphibian, it would say frog. It doesn’t need to know what a frog is.

[00:17:45] Seb Bunney: 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.

[00:18:00] Preston Pysh: No, that was, really good. The only thing I would add to what your point here is, the paper that led to the use of transformers, there was a Google engineer, Vaswani at all, 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.

[00:18:22] Preston Pysh: PDF to this paper is exactly what Sebs talking about is this contextual association of letters, words, sentences, paragraphs, have. These contextual associations together, and when you run ’em 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.

[00:18:48] Preston Pysh: So yeah, I think that’s probably the really core milestone in the book where you go from the middle part of it where you’re talking about this Cuda piece and you transform into the last part of the book. And I would say that this attention is all you need. Part is really what takes you into that, the final part of the book.

[00:19:08] Preston Pysh: 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. ’cause there’s a lot that we’re not talking about. We’re just hitting the core chunks of it.

[00:19:22] Preston Pysh: 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 throw out, but do you have a theme that you want to go through?

[00:19:35] Seb Bunney: Oh man, I have, so I wrote down kinda like core four core, main themes. But, we’ll, I’m sure we’ll go through both of these and I’m sure we’ve probably got similar themes.

[00:19:43] Preston Pysh: Yeah.

[00:19:43] Seb Bunney: 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 0 1, 0 1, and we also see like there’s another book talking about like finite games versus infinite games.

[00:20:02] Seb Bunney: 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. 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 that difference.

[00:20:26] Seb Bunney: As Peter, the talks about his book, zero to one, going from one to N, say one to 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.

[00:20:42] Seb Bunney: You’re creating something entirely 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.

[00:20:59] Seb Bunney: 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.

[00:21:21] Preston Pysh: 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 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 early in the early days, they came out with this ENV one chip was like their first thing that they brought to market.

[00:21:43] Preston Pysh: 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 would’ve only been on the market for call it a year, and the competition had already stepped in and just obliterated them.

[00:22:01] Preston Pysh: It came out with this NV two 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 NV three chip.

[00:22:20] Preston Pysh: 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.

[00:22:43] Preston Pysh: 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.

[00:23:00] Preston Pysh: And the NV three 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.

[00:23:27] Preston Pysh: 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’s what saved them back in, you know, in the very early days we’re talking like mid-nineties, that they were really on the cusp of death for quite a while.

[00:23:58] Preston Pysh: 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.

[00:24:22] Preston Pysh: 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?

[00:24:31] Seb Bunney: There’s one point before we jump on his personality. Yeah. That 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, 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, damage itself, core, you’re back to square one, you’ve have got to rebuild the robot and such.

[00:24:54] Seb Bunney: 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 hyperrealistic 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.

[00:25:13] Seb Bunney: 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.

[00:25:29] Seb Bunney: 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.

[00:25:46] Seb Bunney: You are 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 are able to get so far and whether it’s our robotics or whatnot, understanding of the world in these hyperrealistic digital environments before ever touching the physical world.

[00:26:12] Seb Bunney: And that’s something that’s really coming into existence today. And from my understanding, cosmos is free. Anyone can go and play around in Cosmos ’cause he wants to support science, advancements in technology, advancements in robotics, advancement in AI, which I think is really cool. But again, it just goes back to the fact that I think when it comes to.

[00:26:30] Seb Bunney: 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.

[00:26:46] Seb Bunney: 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.

[00:27:04] Seb Bunney: Just iterate, iterate, iterate, execute. And so rather than it’s competitors that were trying to create this super clean, professional looking code, the video was 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.

[00:27:24] Seb Bunney: They changed it from one year, two year cycles to six months 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.

[00:27:33] 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.

[00:27:41] Preston Pysh: We can iterate faster, we can get to what we think the truth is. If we can just start sensing the environment is really the approach. Yeah. One other thing that I wanted to 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.

[00:28:07] Preston Pysh: 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.

[00:28:28] Preston Pysh: 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.

[00:28:53] Preston Pysh: 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.

[00:29:19] Preston Pysh: Okay. you want to go to Jensen? Let’s do it. Let’s go to Jensen. Okay. He’s really interesting. 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 is crazy humble, like almost attributes, nothing to like his skill.

[00:29:40] Preston Pysh: 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.

[00:29:59] Preston Pysh: It was 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, Seb? Is that kind of your take on how the book laid out his personality? ’cause I mean that, that was my takeaway. I was a little surprised ’cause I wasn’t expecting that from all the public interviews that I had seen him do. I would’ve never expected that kind of side of him.

[00:30:22] Seb Bunney: 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?

[00:30:35] Seb Bunney: Like his ability to see the future, but at the same time, it sounds like he has a bit of a temper at times.

[00:30:42] Seb Bunney: 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 like 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 it would be hard to work in that kind of environment. But I understand to a certain extent why he is doing it.

[00:31:10] Preston Pysh: So think about this, what is his creation with all of 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.

[00:31:25] Preston Pysh: 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.

[00:31:43] Preston Pysh: 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.

[00:32:20] Seb Bunney: So 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 torches to greatness.

[00:32:29] Seb Bunney: Like there’s almost this idea. And actually, I’m trying to remember. There was a book I read a few years back, I think it was Fool By Randomness by Nasem Tulip.

[00:32:38] Seb Bunney: 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 paid for it.

[00:32:44] Seb Bunney: It’s from that point on Yeah. That they’ve now understood. Oh man, I should not be doing that thing. It’s like the best time to invest is right after a recession. Yeah. That’s not the worst time to invest. It’s the best time. ’cause 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. 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.

[00:33:18] 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. Yeah. I was really surprised by that in the book too, in that he like, people that want to stay there can stay there.

[00:33:31] Preston Pysh: What seems like forever, like he just doesn’t get rid of people. But he may throttle you from time to time, is really 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 to death.

[00:33:51] Preston Pysh: But where I think it’s hard to 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.

[00:34:18] Preston Pysh: It’s hard to know whether like this type of, leadership is repeatable or what. I don’t know. I left the book not really feeling like I could have an opinion on any of that. I felt almost more confused than before I started the book.

[00:34:37] Seb Bunney: I was very similarly, I was confused at the end ’cause it’s just like, is this to your point, is it repeatable? Yeah. 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 this flat system as opposed to this hierarchical system.

[00:35:03] Seb Bunney: 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, 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 likes, supports this idea of conciseness.

[00:35:28] Seb Bunney: 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.

[00:35:48] Seb Bunney: He wants to hear directly from the individual that’s coming up with these ideas and helping to push that individual reinforcing visibility, approachability, and the cross pollination of ideas. I think it’s so cool.

[00:35:59] Preston Pysh: I find it interesting, and this is just more of a comedic comment than anything else.

[00:36:04] Preston Pysh: 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 his book that he does it NVIDIA, I guess Elon did this with government employees and who knows whether like what was sent back or what was even read.

[00:36:25] Preston Pysh: I think it was actually Elon just 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 talk about earlier in the show set, with Cuda and how this was really important. What I think we failed to capture as we were just quickly going through the summary was this was not popular.

[00:36:45] Preston Pysh: 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.

[00:37:03] Preston Pysh: 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 in general that say, well, what does the customer want?

[00:37:23] Preston Pysh: 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 looking at the sheer horsepower of computation that he was producing.

[00:37:45] Preston Pysh: And he just 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.

[00:38:03] Preston Pysh: 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’ve 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 have got to be lucky, which I hate like saying that’s what it is, but maybe it is what it is.

[00:38:27] Preston Pysh: What was your take? Or maybe you read something in the book that kind of illustrated it better than what I picked up on.

[00:38:33] Seb Bunney: 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 ’em to pivot.

[00:38:42] Seb Bunney: Because if you look at most s and p 500 companies, like when you have this huge employee bloat with 10 different layers of group 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 are dictating, you can turn on a dime.

[00:39:00] Seb Bunney: 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 like the NV one chip, the first chip you mentioned at the start of the book.

[00:39:19] Seb Bunney: 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.

[00:39:40] Seb Bunney: 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 you want to go into mobile chips. But there was already a lot of competition, no Kia Blackberry and stuff.

[00:39:56] Seb Bunney: 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.

[00:40:12] Seb Bunney: 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, 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 follow this instinct.

[00:40:30] Preston Pysh: Yeah. We often talk about, I know in the Bitcoin community, like show me the incentives and I can 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.

[00:40:58] Preston Pysh: I think when you, just because he’s so deep, and another thing in the book that they talked about is his work ethic is mind blowing. Like waking up at 4:00 AM 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.

[00:41:16] Preston Pysh: 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.

[00:41:34] Preston Pysh: 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 airs 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.

[00:41:55] Preston Pysh: He’s just 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. I 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.

[00:42:24] Seb Bunney: When I think we’re also, we’re trying to look at this industry to the average individual, to the lay person. Most people have no idea what NVIDIA is. Yeah. 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 NVIDIA? Yeah. And I’m talking about people that have.

[00:42:41] Seb Bunney: PlayStations and gaming consoles and computers and the average individual just has no idea. And so I think the, 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.

[00:43:05] Seb Bunney: I think it becomes very clear, this is the path we need to go down. ’cause you’ve seen this stuff before anyone else. It’s like when you’re in Bitcoin, it makes sense. Why are we going down this path? And Bitcoin keeps going up because we’re printing money. But people that have never looked, spent five minutes looking into Bitcoin, it doesn’t make any sense.

[00:43:22] Seb Bunney: And so I think it’s just because he is 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 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’ve said, I want a Blackberry.

[00:43:37] Seb Bunney: 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, 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. Yes, they can only envision a future of things which they’ve used before.

[00:43:53] 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 ’cause 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 writing on.

[00:44:15] Preston Pysh: 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.

[00:44:32] Preston Pysh: 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 trillion. Today when we’re recording this, it’s like $4.2 trillion n video. Apple’s, like 3.1 trillion. Just to 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?

[00:45:06] Preston Pysh: 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.

[00:45:26] Preston Pysh: Because it’s not something the normal person sees ever.

[00:45:29] Seb Bunney: 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.

[00:45:43] Seb Bunney: Well, I was listening to a talk by him and a lady called Cleo Abrams. If you just type in Jensen Wang 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 the graphics processing units and parallel processing.

[00:46:02] Seb Bunney: They step set the stage for this neural net and like being able to process large amounts of data. So AI could start to emerge. But they’re not the ones that enabled AI to emerge. They just gave the tools to enable AI to emerge. Then you saw things like, there was this contest called ImageNet, and ImageNet was essentially the whole goal was we want teams.

[00:46:25] Seb Bunney: 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?

[00:46:40] Seb Bunney: 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.

[00:47:00] Seb Bunney: 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 GForce, GPU, which is 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.

[00:47:27] Seb Bunney: Well, traditionally, you would’ve 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.

[00:47:38] Seb Bunney: 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.

[00:47:47] Seb Bunney: And then AI is able to take that and create a phenomenally realistic screen, but it makes it far more efficient. And so there’s 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.

[00:48:04] Preston Pysh: Another just ad hoc comment on what you’re bringing up there is this is just 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 MP three and you play it on a device, it sounds exactly the same, but it’s just compression.

[00:48:21] Preston Pysh: 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.

[00:48:45] Preston Pysh: 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’s really like beyond fasting. I can only imagine where some of this compression and

[00:49:00] Seb Bunney: AI’s going to take us. Well man, so this be nuts.

[00:49:02] Seb Bunney: This again, is one that blew me away. I started digging into, in the book it talks about this thing called the DGX one. And at the time, the DGX one was 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.

[00:49:21] Seb Bunney: 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 10th of the size.

[00:49:41] Seb Bunney: It’s got six times the processing power and it uses one 10000th 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? Yeah. 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.

[00:50:04] Seb Bunney: Like that’s just mind blowing.

[00:50:06] 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?

[00:50:17] Seb Bunney: Refresh my memory.

[00:50:18] Preston Pysh: 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.

[00:50:26] Preston Pysh: 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.

[00:50:46] Preston Pysh: 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.

[00:51:10] Preston Pysh: 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’s the timeline. 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.

[00:51:27] Preston Pysh: 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, could do it faster, but the price was so outrageous that they didn’t even quote them that price. They got jenen the answer that he wanted, which was they could have it.

[00:51:49] Preston Pysh: And I’m exaggerating ’cause 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.

[00:52:18] Preston Pysh: 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 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.

[00:52:36] Preston Pysh: 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.

[00:52:57] Preston Pysh: 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. ’cause a lot of people just 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.

[00:53:19] Preston Pysh: But jenssen’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.

[00:53:40] Preston Pysh: 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 costs 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 you want to,

[00:54:00] Seb Bunney: it was previously, it was a bit of a throwaway comment and we touched on it, which is this idea that along those lines, as a result of this, he completely changed the industry.

[00:54:10] Seb Bunney: ’cause 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. It, this kind of gets back to that point of just like iterate, iterate, iterate, execute, It’s just like we can completely change the world we live in.

[00:54:29] Seb Bunney: But we’ve have got to constantly be pushing the limits. Yeah. We’ve have 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.

[00:54:45] Preston Pysh: 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.

[00:54:54] Seb Bunney: He’d destroy you. So 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.

[00:55:05] Seb Bunney: Like the world we live in today would not be the world that we are that would not have the technology we have today. It would not have the AI we have today if it wasn’t for NVIDIA. I had no idea to what extent they have completely shaped this world. But I wonder, there’s an argue, 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 are the risks of AI in the world we live in?

[00:55:30] Preston Pysh: Oh yeah. I wanted to cover this. This is huge. Go ahead.

[00:55:32] Seb Bunney: And he gets slammed. Absolutely. Slammed. Slammed. Yeah. And. One of the questions he 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.

[00:55:56] Seb Bunney: 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 reiterated that.

[00:56:12] Seb Bunney: 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 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 recall the discipline of NVIDIA’s executives I talked to Jensen had them wound as tight as piano strings.

[00:56:34] Seb Bunney: They were confident, intelligent, and exceptionally well prepared down to the smallest detail and never once caught one slipping. I recall two with sudden clarity how disinclined those same executives have 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 kind of Jensen.

[00:56:55] Seb Bunney: 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, but 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.

[00:57:12] Seb Bunney: He was younger of two brothers. And so he is 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.

[00:57:29] 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.

[00:57:46] Preston Pysh: 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.

[00:58:04] Preston Pysh: I, it’s definitely fear driving this ’cause 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, 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? That’s interesting.

[00:58:24] Seb Bunney: 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? Yeah. 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? We covered this on the show.

[00:58:46] Preston Pysh: Is it Dan Daniel Coyle? We interviewed the author on this something. yeah,

[00:58:51] Seb Bunney: And so this book came out, I don’t dunno, maybe like seven, eight years ago. A phenomenal book. And it talks about how they looked at the world’s fastest a hundred meter sprinters.

[00:58:59] Seb Bunney: And out of the world’s fastest a hundred meter sprinters. On average, they were one of 4.6 siblings and they were on average the fourth siblings. Yeah. They were nearly always the youngest. And so I was curious, I looked up, was he the younger of his brother and he was. Yeah. And so there’s a part, like is he trying to, he wants to prove himself, he wants to add value to this world.

[00:59:19] Seb Bunney: But it’s almost 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.

[00:59:31] Preston Pysh: That is really fascinating. And I found it so bizarre ’cause 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. ’cause he’s clearly like, it makes him upset.

[00:59:55] Preston Pysh: So I don’t know. If you’re a listener, if you’re a listener and you know you work it 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’ve got. Ahs, 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.

[01:00:11] Preston Pysh: This book was 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 Steven Witt. Steven Witt Bravo. You did a phenomenal job for our listeners that are tuning into more book reviews, seven, just having fun reading all these things that we, you know, find fascinating.

[01:00:31] Preston Pysh: 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 ’cause 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 seven and I are going to work on.

[01:00:49] Preston Pysh: 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 have got to say this bias upfront 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 him all that much, but the little bit that I’ve read online, he just doesn’t.

[01:01:15] Preston Pysh: 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 oa, 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.

[01:01:36] Preston Pysh: Well, we highly encourage that. Seb, anything you want to say about the next book real fast before we wrap this?

[01:01:42] Seb Bunney: Oh, you know what? You basically took the words outta my mouth, which is, don’t get me wrong, I use chat GBT. Yeah. 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.

[01:01:57] Seb Bunney: 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.

[01:02:11] Preston Pysh: Yeah. The subtitle on the book is one of the reasons I that I was sold as soon as I read it. The subtitle is Dreams and Nightmares in Sam Altman’s Open AI. There was really good reviews online so that we’re plowing into that one next.

[01:02:24] Preston Pysh: With all of that said, how awesome is Seb Bunney, right?

[01:02:28] Preston Pysh: Seb, we are so excited to have you on the show. You know, 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, do these book reviews with.

[01:02:48] Preston Pysh: 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 have got to 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.

[01:03:07] Preston Pysh: 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?

[01:03:13] Preston Pysh: No, I just, again, like you’re too kind, Preston, and I just feel so lucky to be on the show and it’s, 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.

[01:03:24] Preston Pysh: 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. Sir, I think we

[01:03:39] Preston Pysh: need, I think we need to tell Stig to read one of these and he can join us in the on the conversation too.

[01:03:44] Preston Pysh: He needs the, 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 joining us.

[01:03:55] Outro: Thank you for listening to TIP. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on our episodes.

[01:04:04] Outro: To access our show notes and courses, go to theinvestorspodcast.com. This show is for entertainment purposes only. Before making any decisions, consult professional. This show is copyrighted by The Investor’s Podcast Network. Written permissions must be granted before syndication or rebroadcasting.

[01:03:55] Outro: Thank you for listening to TIP. Make sure to follow Infinite Tech on your favorite podcast app and never miss out on our episodes.

[01:04:04] Outro: To access our show notes and courses, go to theinvestorspodcast.com. This show is for entertainment purposes only. Before making any decisions, consult professional. This show is copyrighted by The Investor’s Podcast Network. Written permissions must be granted before syndication or rebroadcasting.