Nvidia: The Machine Learning Company (2006-2022)

Ben Gilbert 0:02 Still got Swedish House Mafia, Greyhound in my head from the pump up.

David Rosenthal 0:05 Nice. Nice. [laughing]

Ben Gilbert 0:08 It is funny how all like GPU companies, like I was watching a bunch of NVIDIA keynotes and AMD keynotes to get ready for this, and everyone is so like techno, neon lighting.

David Rosenthal 0:19 [chuckles]

Ben Gilbert 0:19 Like, it's like crypto before crypto.

Speaker 0:20 [singing] Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Is it you? Is it you? Is it you? Sit me down, say it straight. Another story on the way. Who got the truth?

Ben Gilbert 0:37 Welcome to Season Ten, Episode Six of Acquired, the podcast about great technology companies and the stories and playbooks behind them. I'm Ben Gilbert, and I am the co-founder and managing director of Seattle-based Pioneer Square Labs, and our venture fund, PSL Ventures.

David Rosenthal 0:53 And I'm David Rosenthal, and I am an angel investor based in San Francisco.

Ben Gilbert 0:58 And we are your hosts. When I was a kid, David, I used to stare into backyard bonfires and wonder if that fire flickering was doing so in a random way, or if I knew about every input in the world, all the air, exactly the physical construction of the wood, all the variables in the environment, if it was actually predictable. And I don't think I knew the term at the time, but modelable. If I could know what the flame could look like if I knew all those inputs. And we now know, of course, it is indeed predictable, but the data and compute required to actually know that is extremely difficult. But that is what NVIDIA is doing today.

David Rosenthal 1:43 Ben, I love that intro. That's great!

Ben Gilbert 1:44 [laughing]

David Rosenthal 1:45 I was thinking, like, "Where is Ben going with this?"

Ben Gilbert 1:47 And this was occurring to me as I was watching Jensen sharing the omniverse vision for NVIDIA, and realizing NVIDIA has really built all the building blocks: the hardware, the software for developers to use that hardware, all the user-facing software now, and services to simulate everything in our physical world with their unbelievably efficient and powerful GPU architecture. And these building blocks, listeners, aren't just for gamers anymore. They are making it possible to recreate the real world in a digital twin, to do things like predict airflow over a wing, or simulate cell interaction to quickly discover new drugs without ever once touching a petri dish, or even model and predict how climate change will play out precisely. And there is so much to unpack here, especially in how NVIDIA went from making commodity graphics cards to now owning the whole stack in industries from gaming to enterprise data centers, to scientific computing, and now even basically off-the-shelf, self-driving car architecture for manufacturers. And at the scale that they're operating at, these improvements that they're making are literally unfathomable to the human mind. And just to illustrate, if you are training one single speech recognition machine learning model these days, one, just one model, the number of math operations, like adds or multiplies, to accomplish it is actually greater than the number of grains of sand on the Earth.

David Rosenthal 3:17 I know exactly what- [chuckles]

Ben Gilbert 3:19 [chuckles]

David Rosenthal 3:19 -part of the research you got that from, 'cause I read the same thing, and I was like: "You gotta be freaking kidding me." [chuckles]

Ben Gilbert 3:24 Isn't that nuts? I mean, there's just nothing better in all of the research that you and I both did, I don't think, to better illustrate just the unbelievable scale of data and compute required to accomplish the stuff that they're accomplishing, and how unfathomably small all of this is, the fact that that happens on one graphics card.

David Rosenthal 3:44 Yep. So great.

Ben Gilbert 3:46 Many of you already know this. Many of you have already RSVP'd, but if you have not, we would love to see you at our Arena show in Seattle. That's gonna be on May 4th at five PM. It's gonna be an awesome show. We've announced we'll have Jim Weber there, who's the CEO of Brooks Running, which is now amazingly a billion-dollar revenue business inside of Berkshire. We'll have other announcements coming as well. Go to acquired.fm/arenashow, or click the link in the show notes to RSVP. All proceeds are going to charity. Our huge thanks to our friends at PitchBook Data for putting this on with us. That's acquired.fm/arenashow, and we hope to see you there.

David Rosenthal 4:24 I get giddy every time you say that URL. [laughing]

Ben Gilbert 4:28 [chuckles] Yes, indeed, it is real. All right, now, before we dive in, we have a fun little Q&A from our presenting sponsor, Vanta, the leader in automated cloud security and compliance. We are huge fans of Vanta and their approach. They do everything from SOC 2, to HIPAA, to GDPR, and more, and we are back with CEO and co-founder Christina Cacioppo to talk about it. So Christina, based on our last few conversations, I'm getting the sense Vanta is becoming a lot more than just a SOC 2 compliance company. Is that true, and how do you think about that?

Speaker 5:02 Yes. I mean, the joke about Vanta, that's not a joke, is that we're a security company masquerading as a compliance company. And it comes from the early founding days when we wanted to start a security company and went around and asked all of our friends at startups, you know, what security problems they had. And they did have some, and then we're like: "Oh, you know, what happens if we solve them for you?" And our friends were like: "I'm not gonna use that, 'cause I have ten other problems ahead of that one. I just feel badly. I know I should be doing it, but it is hard for me to prioritize. I often need to get customers, so that's what I do." Which was a little demoralizing [chuckles] when you're trying to come up with a startup idea. But then we actually realized that compliance can be like the security feature someone is asking for, right? So generally, if you're selling, someone doesn't say: "Hey, will you be more secure?" They say: "Hey, are you SOC 2 compliant?" And so if we could build a product that help folks get more secure, help them prepare for and get through a compliance audit, it would accelerate their business, because they have their compliance certification, and leave them more secure.... One of our product team's goals is to help our users fix security issues we surface faster. So we can surface lots of misconfigurations, like proverbial doors and windows you leave open, but if no one fixes them, then we're not having the impact we're looking for, and we're not ultimately securing the company. And so we just have a substantial portion of our product team focused on building features that help companies fix misconfigurations faster. So the whole set of work that falls under that, but it's one of the things I'm most excited about.

Ben Gilbert 6:34 Thanks, Christina, and thank you to Vanta, the leader in automated security and compliance software. If you are looking to join Vanta's two thousand-plus customers and get compliance certified in weeks instead of months, click the link in the show notes or go to vanta.com/acquired for a sweet ten percent discount.

David Rosenthal 6:51 Woo!

Ben Gilbert 6:51 And after you finish this episode, come join the Slack acquired.fm/slack, and talk about it with us. And if you're dying for even more Acquired before we, uh, come back with our next season episode, search for Acquired LP Show in the podcast player of your choice. Our latest installment was a very fun, deep dive and close to home for me, diving in with Nick and Lauren, the creators of Trova Trip, on travel for the creator economy, where they talk about a very interesting business model that they have on their hands and a space that David and I know well in creator things.

David Rosenthal 7:24 Indeed.

Ben Gilbert 7:25 All right, David, without further ado, take us in. And as always, listeners, this is not investment advice. David and I may hold positions in securities discussed, and, uh, please do your own research.

David Rosenthal 7:35 That's good. I was gonna make sure that you said that this time, because we're gonna talk a lot about investing and investors in Nvidia stock over the years. It has been a wild, wild journey. So last we left our plucky heroes, Jensen Huang and Nvidia, in the end of our Nvidia, the GPU company years, ending kind of roughly, you know, 2004, 2005, 2006. They had cheated death, not once, but twice. The first time in the super overcrowded graphics card market when they were first getting started, and then once they sort of, you know, jumped out of that frying pan into the fire of Intel, now gunning for them, coming to commoditize them, like all the other, you know, PCI chips that plugged into the Intel motherboard back in the day. And they bravely fend them off. They team up with Microsoft. They make the GPU programmable. This is amazing. They come out with programmable shaders with the GeForce 3. They power the Xbox. They create the CG programming language with Microsoft. And so here we are. It's now 2004, 2005, and this is a pretty impressive company. Public company, stock is high-flying after the tech bubble crash. They've conquered the graphics card market. Of course, there's ATI out there as well, which will come up again. But there's three pretty important things that I think the company built in the first ten years. So one, we talked about this a lot last time, the six-month ship cycles for their chips. We talked about that, but we didn't actually say [chuckles] the rate at which they ship these things. I actually wrote down, like, a little list. So in the fall of 1999, they shipped the first GeForce card, the GeForce 256. In the spring of 2000, GeForce 2. In the fall of 2000, GeForce 2 Ultra. Spring of 2001, GeForce 3, that's the big one with the programmable shaders. Then six months later, the GeForce 3 Ti 500.

Ben Gilbert 9:38 I mean, the normal cycle, I think we said, was two years, maybe eighteen months, for most of their competitors, who just got largely left in the dust.

David Rosenthal 9:46 Well, I was just thinking, you know, yeah, the competitors are gone at this point, but I'm thinking about Intel. How often did Intel ship new products, let alone fundamentally new architecture? You know, there was the 286, and then the 386, and the Pentium, and it got up to Pentium, I don't know, five, whatever.

Ben Gilbert 10:02 Dude, I feel like the Intel product cycle is approximately the same as a new body style of cars.

David Rosenthal 10:09 Yes, exactly!

Ben Gilbert 10:10 Every five, six years, there seems to be a meaningful new architecture change.

David Rosenthal 10:15 And Intel is the driver of Moore's Law, right? Like, these guys ship and bring out new architectures at warp speed, and they've continued that through to today. Two, one thing that we missed last time that is super important and becomes a big foundation of everything Nvidia becomes today that we're gonna talk about, they wrote their own drivers for their graphics cards. And we owe a big thank you for this and many other things to a great listener, a very kind listener named Jeremy, who reached out to us in Slack and pointed us to a whole bunch of stuff, including, um, the Asianometry YouTube channel.

Ben Gilbert 10:50 So good. I've probably watched, like, twenty-five Asianometry videos this week. [chuckles]

David Rosenthal 10:54 So, so good. Huge shout-out to them. But all the other graphics cards companies at the time, and most peripheral companies, they let the further downstream partners write the drivers for what they were doing. Nvidia was the first one that said, "No, no, no, we wanna control this. We wanna make sure consumers who use Nvidia cards have a good experience on whatever systems they're on." And that meant, A, that they could ensure quality, but, B, they start to build up in the company this, like, base of really nitty-gritty, low-level software developers in this chip company, and there are not a lot of other chip companies that have capabilities like this.

Ben Gilbert 11:34 No, and what they're doing here is taking on a bigger fixed cost base. I mean, it's very expensive to employ all the people who are writing the drivers for all the different operating systems, all the different OEMs, all the different boards that it has to be compatible with. But they viewed it as it's kind of an Apple-esque view of the world. We want the control or as much control as we can get over making sure that people using our products have a great user experience, so they were sort of willing to, uh, take the short-term pain of that expense for the long-term benefit of that improved user experience with their products.

David Rosenthal 12:07 That their users-... high-end gamers that want the best experience, you know, they're gonna go out, they're gonna spend at the time, three, four, or five hundred dollars on an NVIDIA top-of-the-line graphics card. They're gonna drop it into the PC that they built. You know, they want it to work. I remember [chuckles] messing around with drivers back in the day and things not working, like, this is super important. So all this is focused, and then, of course, they have the third advantage in the company is programmable shaders. You know, which ATI copies as well, but, like, they innovated, like, they've, you know, done all this. So all of this at this time, it's all in service of the gaming market.

Ben Gilbert 12:40 And one seed to plant here, David, when you say the programmable shaders developers, the notion of a NVIDIA developer did not exist until this moment. It was people who wrote software that would run on the operating system, and then from there, maybe it would get- that compute load would get offloaded to whatever the graphics card was. But it wasn't like you were developing for the GPU, for the graphics card, with a language and a library that was specific to that card. So for the very first time now, they start to build a real direct relationship with developers so that they can actually start saying, "Look, if you develop for our specific hardware, there are advantages for you."

David Rosenthal 13:21 And really, our specific gaming card. It's like everything we're talking about, these developers, they're game developers. [chuckles] All of this stuff, it's all in service of the gaming market. So, you know, again, they're a public company. They have this great deal with Microsoft. They bring out CG together. They're powering the Xbox. You know, Wall Street loves them. They go from sub a billion-dollar market cap company after the tech crash, up to five to six billion dollars kind of by two thousand and four, two thousand and five. Stock keeps going on a tear. By mid-two thousand and seven, the stock reaches just under twenty billion dollar market cap. You know, this is great, and this, all the story is like, this is pure play gaming. These guys have built such a great advantage and a developer ecosystem in a large and growing market, clearly, which is video games.

Ben Gilbert 14:09 Which on its own, that would be a great wave to surf. I mean, I think, what's the gaming market today? A hundred and eighty billion or something, and when we talked to Trip Hawkins, who sort of like helped invent it, or Nolan Bushnell, you know, it was zero then. And so NVIDIA is sort of like on a wave that's at an amazing inflection point. They could totally just ride this gaming thing and be an important company.

David Rosenthal 14:30 It's not running out of steam. I mean, like, how could you not be, not just satisfied, but, like, more than satisfied with this as a founder?

Ben Gilbert 14:37 Yes.

David Rosenthal 14:37 You're like, "Yes, I am the leading company in this major market, this huge wave that I don't see ending anytime soon." [chuckles] You know, ninety-nine point nine percent of founders who are themselves as a class, like, you know, very ambitious, are gonna be satisfied with that. [chuckles]

Ben Gilbert 14:54 But not Jensen.

David Rosenthal 14:55 But not Jensen. [laughing] So while all this is happening, he starts thinking about, well, what's the next chapter? You know, [chuckles] I'm dominating this market. I wanna keep growing. I don't want NVIDIA to be just a gaming company. So we ended last time with the little, you know, almost a surely apocryphal story of a Stanford researcher, you know, sends the email to Jensen, and it's like, "Ah, you know, thanks to you, my son told me to go buy off-the-shelf, you know, GeForce cards at the local Fry's Electronics, and I stuffed them into my PC at work, and, you know, I ran my models on, on this." Uh, he's a-- I think it was a quantum chemistry researcher, supposedly. "It was ten times faster than the supercomputer I was using in, in the lab, and so thank you. I can get my life's work done in my lifetime." [chuckles]

Ben Gilbert 15:43 And Jensen loves that quote. It comes out at every GTC.

David Rosenthal 15:46 So that story, if you're a, uh, skeptical listener, might beg two questions. First is a practical one. You know, we just said everything's about gaming here, and here's, like, a researcher, like a scientific researcher, doing, you know, chemistry modeling using GeForce cards for that. What's he writing this in? Well, it turns out-

Ben Gilbert 16:07 Programmable shaders, right?

David Rosenthal 16:08 Yeah. [chuckles] They were shoehorning CG, which was built for graphics. They were translating everything that they were doing into graphical terms, even if it was not a graphical problem they were trying to solve, and writing it in CG. This is not for the faint of heart, so to speak.

Ben Gilbert 16:27 Right. So everything is sort of metaphorical. He's a quantum chemistry researcher, and he's basically telling the hardware, "Okay, so imagine this data that I'm giving you is actually a triangle, and imagine that this way that I wanna transform the data is actually like applying a little bit of lighting to the triangle, and then I want you to output something that you think is the right color pixel, and then I will translate it back into the result that I need for my quantum chemistry." Like, you can see why that's suboptimal.

David Rosenthal 16:56 Yep. So he thinks this is an interesting market. He wants NVIDIA to serve it. If you really wanna do that right, it is a massive undertaking. It was ten-plus years to get to the company to this point. You know, what CG was is like a small sliver of the stack of what you would need to build for developers to use GPUs in a general purpose way, like we're talking about. You know, it's kind of like, um... They worked with Microsoft to make CG. It's like the difference between working on CG and, like, Microsoft building the whole .NET framework for developing on Windows, [chuckles] you know? Or today, even better, Apple, right? Like, everything Apple gives to iOS and Mac developers-

Ben Gilbert 17:39 Right

David Rosenthal 17:39 ... to develop on Mac.

Ben Gilbert 17:40 Right. Yeah, the analogy's not perfect, but it's like instead of just Apple saying, "Okay, Objective-C is the way that you write code for our platforms. Good luck," they're like: "Okay, well, well, you need UI framework, so how about AppKit and Cocoa Touch? And how about all these other SDKs and frameworks like ARKit and like StoreKit and like HomeKit?" It's basically, you need the whole sort of abstraction stack on top of the programming language to actually make it very accessible to write software for domains and disciplines that you know are gonna be really popular using that hardware.

David Rosenthal 18:14 Exactly. So-... When Jensen commits himself and the company to pursuing this, he's biting off a lot. Now, we talked about they've been writing their own drivers, so they have actually a lot of very low-level, and I don't mean low-level like bad, I mean low-level like infrastructure, like close, very difficult systems-oriented programming talent within the company. So that kind of enables them to start here, but, like, still, this is big. [chuckles] So then the second question, if you're a discerning investor, particularly in Nvidia, that you want to ask at this point in time, is like: "Okay, Jensen, you're committing the company to a big undertaking. What's the business case for that? Show me the market." I mean, Don Valentine, at this point, would be sitting there listening to Jensen and being like, "Show me the market."

Ben Gilbert 19:02 And not only is it show me the market, but it's how long will the market take to get here? And it's how long is it gonna take us, and how many dollars and resources is it gonna take us to actually get to something that's useful for that market when it materializes? Because while CUDA development began in 2006, that was not a useful, usable f- platform for six-plus years at Nvidia.

David Rosenthal 19:29 Yep. This is closer to on the order of the Microsoft development environment [chuckles] or the Apple development environment than what Nvidia was doing before, which was like, "Hey, we made some APIs and worked with Microsoft so that you can program for my thing." [chuckles]

Ben Gilbert 19:45 Right. I'm gonna flash way forward just to illustrate the l- insane undertaking of this. I searched LinkedIn for people who work at Nvidia today and have the word "CUDA" in their title. There are eleven hundred employees dedicated specifically to the CUDA platform.

David Rosenthal 20:01 I'm surprised it's not eleven thousand.

Ben Gilbert 20:03 Yeah.

David Rosenthal 20:04 Okay, [chuckles] so, like, where's the market for this? Yes, Ben, you asked the, you know, the third question, which is, okay, the intersection of what does this take to do this, and when is the market gonna get there, and time and cost and all that, but even just put that aside. Is there a market for this? Is the first order question. And the answer to that is probably no, [chuckles] at this point in time.

Ben Gilbert 20:24 And what they're aiming at is scientific computing, right? It's researchers who are in science-specific domains, who right now need supercomputers or access to a supercomputer to run some calculation that they think is gonna take weeks or months, and wouldn't it be nice if they could do it cheaper or faster? Is that kind of the market they're looking at?

David Rosenthal 20:46 Yeah, they're attacking, like, the Cray market, like Cray supercomputers, like, that kind of stuff. You know, great company, right? But, like, that's no Nvidia today. [chuckles]

Ben Gilbert 20:55 Right.

David Rosenthal 20:55 And they were dominating the market. You know, yeah, it's scientific research, computing, you know, it's drug discovery. It's probably a lot of this work, they're thinking, "Oh, maybe we can get into more professional, like Hollywood and architecture and other professional graphics domains." Yeah, yeah, yeah, sure. But, you know, you sum all that stuff up, and, like, maybe you get to a couple billion-dollar market, maybe, like, total market.

Ben Gilbert 21:17 Mm-hmm.

David Rosenthal 21:17 And not enough to justify the time and the cost of what you're gonna have to build out to go after this, to any rational person. [chuckles] So, you know, here we come. Jensen and Nvidia, like, they are doing this. He is committed. He's drunk the Kool-Aid. 2006, 2007, 2008, they are pouring a lot of resources into building what will become CUDA, that we'll get to in a second. Um, like, it already is CUDA at this point in time.

Ben Gilbert 21:44 And I think Jensen's psychology here is sort of twofold. One is, he is enamored with this market. He loves the idea that they can develop hardware to accelerate specific use cases in computing that he finds sort of fanciful, and, and he likes the idea of making it more possible to do more things for humanity with computers. But the other part of it is certainly a business model realization, where he has spent the last, gosh, at this point, thirteen, fourteen years being commoditized in all these different ways, and I think he sees a path here to durable differentiation, where he's like, "Whoa!"

David Rosenthal 22:23 To own the platform.

Ben Gilbert 22:24 You know, it's kind of the Apple thing again, to own the platform and to build hardware that's differentiated by not only software, but relationships with developers that use that custom software. Like, then I can build a really sort of like, a, a company that can throw its weight around in the industry.

David Rosenthal 22:41 A hundred percent. Jensen, I don't know if he used it at the time, because he probably would've gotten pilloried, but maybe he did. I don't think he cared. [chuckles] Uh, he certainly, uh, has used it since. You know, the way he thought about this was, uh, it wasn't just like, if we build it, they will come, which is what was going on. The phrase he uses is: "If you don't build it, they can't come." [chuckles]

Ben Gilbert 23:00 [chuckles]

David Rosenthal 23:00 So it's not even like, "Yeah, I'm pretty sure if we build it, they will come." It's one step removed from that. It's like, "Well, if we don't build it, they can't even possibly come. I don't know if they will come, but they can't come if we don't build it." [chuckles]

Ben Gilbert 23:12 [chuckles]

David Rosenthal 23:12 So Wall Street is mostly willing to ignore this in 2006, 2007, 2008. The company's still growing really nicely. They had this great market cap run leading up to right before the financial crisis. But then, you know, you mentioned last time, I think it gets announced in 2006, maybe, and closes in 2007, AMD acquires ATI.

Ben Gilbert 23:39 Yep.

David Rosenthal 23:39 And ATI was a very legit competitor. It was the only standing legit competitor to Nvidia through its whole life. But now, AMD acquired it, and I think they acquired it for what? Six, seven billion dollars, something like that?

Ben Gilbert 23:51 Something like that.

David Rosenthal 23:52 So it was a lot of money, and then they put a lot of resources. Like, they weren't just acquiring this to, you know, get some talent. Like, they're like: "No, no, no, this is gonna be a big product line for us. We're putting a lot of weight behind this."

Ben Gilbert 24:02 We haven't done the research into AMD the way we have into Nvidia, but the AMD Radeon line, which used to be the ATI Radeon line, that is how you think about AMD as a company, is that they make these GPUs mostly for the gaming use case.

David Rosenthal 24:16 Yep. Before the acquisition, I think the first PC I built in, like, end of high school, beginning of college, I think I had a Radeon-... card in it. I think I was probably in the minority. I think Nvidia was bigger, but for whatever reason, I liked ATI at that point in time, so, like, they were legit. Well, so here's Nvidia now focusing on this whole other thing, and you're still in the gaming market, which, like we said, is like a massive rising tide. Your competitor now has all these resources, and AMD that's fully dedicated to going after it. Mid-2008, Nvidia whiffs on earnings. [chuckles] Like, this is natural. They took their eye off the ball. Of course, they did, and, uh, the stock gets just hammered.

Ben Gilbert 24:59 Because in anything that CUDA empowers is not yet a revenue driver, and they've totally taken their eye of- off of gaming.

David Rosenthal 25:06 Yes. So, you know, we said the high was around a twenty billion dollar market cap. It drops eighty percent, eight zero. [chuckles] This isn't just the financial crisis. It's almost quaint, I think, you know, for me, thinking back on the financial crisis now and, like, people freaking out, the Dow, you know, or the S&P dropping five percent in a day, and like, oh, that's a Thursday these days, [laughing] you know?

Ben Gilbert 25:26 It is literally the Thursday that we are recording.

David Rosenthal 25:29 Yes. For a company's stock to drop eighty percent, a technology company's stock, even during the financial crisis, they're not just in the penalty box, they're, like, getting kicked to the curb.

Ben Gilbert 25:40 Right. Are they done? The headlines at this point are, "Is Nvidia's run over?"

David Rosenthal 25:44 If you're most CEOs at this point in time, you're probably calling up Goldman or, you know, Allen & Company or Frank Quattrone, and you're shopping this thing, 'cause how are you gonna recover?

Ben Gilbert 25:59 But not Jensen.

David Rosenthal 26:00 But not Jensen, obviously. So instead, he goes and builds CUDA and continues to build CUDA. And, um, this is, you know, just to set context, like, we get excited about a lot of stuff on Acquired, but I think CUDA is, like, one of the greatest business stories of the last ten years, twenty years, more? I don't know. What do you think, Ben?

Ben Gilbert 26:23 I mean, I'd say it's one of the boldest bets we've ever covered, but so were programmable shaders, and so was Nvidia's original attempt to make a more efficient quadrilateral-focused-

David Rosenthal 26:36 Yeah

Ben Gilbert 26:36 ... graphics.

David Rosenthal 26:37 Those were big bets. I think this is, this is a bet on another scale, though. This is a bet that we don't cover that often on Acquired.

Ben Gilbert 26:42 Those were big bets relative to the company's size at the time, but this bet is like an iPhone-sized bet.

David Rosenthal 26:48 That's exactly what this is. It's an iPhone-sized bet.

Ben Gilbert 26:51 It is a bet the company when you are already a several billion dollar company.

David Rosenthal 26:55 Yes. An attempt to create something that if they are successful and this market materializes, this will be a generational company.

Ben Gilbert 27:04 Yep.

David Rosenthal 27:04 So [chuckles] what is CUDA? It is Nvidia's Compute Unified Device Architecture. It is, as we've referred to, you know, thus far throughout the episode, a full, and I mean full, development framework for doing any kind of computation that you would want on GPUs.

Ben Gilbert 27:26 Yeah, and in particular, it's interesting because I've heard Jensen reference it as a programming language. I've heard him reference it as a computing platform. It is all of these things. It's an API. It is an extension of C or C++, so there's a way that it's sort of a language, but importantly, it's got all these frameworks and libraries that live on top of it, and it enables super high-level application development, you know, really high abstraction layer development for hundreds of industries at this point to communicate down to CUDA, which communicates down to the GPU and everything else that they have done at this point.

David Rosenthal 28:06 Ah, this is what's so brilliant. So right after we released, right, the same day that we released part one-

Ben Gilbert 28:12 Yep

David Rosenthal 28:12 ... the first Nvidia episode we did a couple weeks ago, Ben Thompson had this amazing interview with Jensen on Stratechery. And, uh, Jensen, in this interview, I think, puts what CUDA is and, and how important it is, I think, better than I've seen anywhere else. So this is Jensen speaking to Ben: "We've been advancing CUDA and the ecosystem for fifteen years and counting. We optimize across the full stack, iterating between GPU, acceleration libraries, systems, and applications continuously, all while expanding the reach of our platform by adding new application domains that we accelerate. We start with amazing chips, but for each field of science, industry, and application, we create a full stack. We have over a hundred and fifty SDKs that serve industries from gaming and design to life and earth sciences, quantum computing, AI, cybersecurity, 5G, and robotics." And then he talks about what it took to make this. This is, like, the point we were trying to, like, hammer home here. He says, "You have to internalize that this is a brand-new programming model, and everything that's associated with being a program processor company or a computing platform company had to be created. So we had to create a compiler team. We had to think about SDKs. We had to think about libraries. We had to reach out to developers and evangelize our architecture and help people realize the benefits of it. And we even had to help them market this vision so that there would be demand for their software that they write on our platform," and on, and on, and on.

Ben Gilbert 29:35 It's crazy. It's amazing. And when he says that it's a whole new programming, I think he says maybe paradigm or way of programming, it is literally true because most programming languages up to this point, and most computing platforms, primarily contemplated serial execution of programs. And what CUDA did was it said, "You know what? The way that our GPUs work and the way that they're gonna work going forward is tons and tons of cores all executing things at the same time, parallel programming, parallel architecture." Today, there's over ten thousand cores on their most recent consumer graphics card. So insanely, uh, or dare I say, embarrassingly parallel, and CUDA is designed for-... parallel execution from the very beginning.

David Rosenthal 30:28 That's the, like, catchphrase in the industry of [chuckles] embarrassingly parallel.

Ben Gilbert 30:32 And it's actually kind of a technical term.

David Rosenthal 30:34 I don't know why it's embarrassing.

Ben Gilbert 30:35 It's basically the notion that this software is so parallelizable, which means that all of the computations that need to be run are independent. They don't depend on a previous result in order to start executing. It's sort of like it would be embarrassing for you to execute these instructions in order instead of finding a way to do it parallel.

David Rosenthal 30:54 Ah, it's not that it's parallel that's embarrassing, it's embarrassing if you were to do it the old way on CPUs, serially. [chuckles]

Ben Gilbert 31:00 I think that's the implication.

David Rosenthal 31:02 Got it. Got it.

Ben Gilbert 31:03 This is so obvious that it's embarrassingly parallel.

David Rosenthal 31:05 Okay, now it makes sense. Now, here's the coup de grâce. We're gonna spend a few minutes talking about how brilliant this was. Everything we just described, this whole undertaking, like, it's like building the pyramids of Egypt-

Ben Gilbert 31:19 Wow

David Rosenthal 31:19 ... or something here. It is entirely free. [chuckles] Nvidia, to this day... Now, this may be changing, we'll talk about this at the end of the episode, has never charged a dollar for CUDA.

Ben Gilbert 31:30 But-

David Rosenthal 31:31 Anyone can download it, learn it, use it, you know, blah, blah, blah, you know, all of this work, stand on the shoulders of everything Nvidia has done. But, Ben, what is the but?

Ben Gilbert 31:41 It is closed source and proprietary exclusively to Nvidia's hardware.

David Rosenthal 31:46 That's right. You do any of this work, you cannot deploy it on anything but Nvidia chips. [laughing] And that's not even just like, oh, Nvidia put in their, like, terms of service that you can't deploy this on, you know, AMD chips or, or whatever, like-

Ben Gilbert 31:59 Literally doesn't work.

David Rosenthal 32:00 Nope, it's full stack. It's like if you were to develop an iOS, uh, app and then try and deploy it on Windows, like, uh, it wouldn't work. [chuckles] It is integrated with the hardware.

Ben Gilbert 32:12 So OpenCL is sort of the main competitor at this point, and they do actually let OpenCL applications run on their chips, but nothing in CUDA is available to run elsewhere.

David Rosenthal 32:22 [laughing] It's so great. Okay, so now you can see this is just like Apple, and it's the Apple business model. Apple gives away all of this amazing platform ecosystem that they've built to developers, and then they make money by selling their hardware for very, very healthy [chuckles] gross margins. But this is why Jensen is so brilliant, because back when they started down this journey in 2006, even before that, when they started and then all through it, there was no iOS, there was no iPhone. Like, it wasn't obvious that this was a great model. In fact, most people thought this was a dumb model, [chuckles] that, like, Apple lost, and the Mac was stupid and niche, and, like, Windows and Intel is what won, the open [chuckles] ecosystem.

Ben Gilbert 33:08 Well, but Windows and Intel did have proprietary development environments and, you know, full stack dev tools.

David Rosenthal 33:15 Oh, yeah. There's a lot of nuance here. It's not like they were, like, open source per se, but it could run on any hardware.

Ben Gilbert 33:21 Well, except that it couldn't. It could only run on the Intel-IBM-Microsoft alliance world. It wasn't running on PowerPCs. It wasn't running on anything Apple made.

David Rosenthal 33:32 That's true.

Ben Gilbert 33:33 It's funny, in some ways, Nvidia is like Apple. In other ways, they're like the Microsoft-Intel-IBM alliance, except fully integrated with each other instead of being three separate companies.

David Rosenthal 33:46 Yeah, that's maybe a good way to put it. It is sort of somewhere in between. There is nuance here. Remember when Clay Christensen was bashing on Apple in the early days of the iPhone, being like-

Ben Gilbert 33:55 Yeah. Oh, yeah

David Rosenthal 33:56 ... "Open's gonna win. Android's gonna win. Apple is doomed," [chuckles] you know, "Closed never works. You gotta be modular. You can't be integrated." And, like, you know, Clay was amazing and one of the greatest strategic- But I think that's just representative to me of, like, everybody thought that, like, the Apple model sucked. [chuckles]

Ben Gilbert 34:14 Yeah. I mean, it sucks unless you're at scale, and at the time, there was very little to believe that Nvidia was going to have the scale required to justify this investment or that there was a market to let them achieve the scale to justify this.

David Rosenthal 34:32 That's the thing. Even if you were to say, "Okay, Jensen, I believe you, [chuckles] and I agree with you that this is a good model if you can pull it off," at the time, you could be Don Valentine or whoever looking around, and maybe Don was still looking around 'cause they probably still held the stock, being like, "Where's the market that's gonna enable the scale you need to run this playbook?"

Ben Gilbert 34:53 All right, so are you gonna take us to 2011, '12? Where are we hopping back in here?

David Rosenthal 34:59 [chuckles] Uh, if only the world were, uh, worked like fiction, and it were actually, like, a truly straight line. [chuckles] It's never a straight line. We will get there, and that is what saves Nvidia and makes this whole thing work. But they have some misadventures in between. So the stock's getting hammered, it's 2008, and, uh, I'm just completely speculating on my own, but they're in the penalty box. They're committed to continuing to invest in CUDA and making general purpose computing on GPU a thing. I do wonder if they felt like, "Well, we gotta do something to appease shareholders here. You know, we gotta show that we're trying to be commercial here."

Ben Gilbert 35:45 Mm-hmm.

David Rosenthal 35:46 So it's 2008. What's going on in 2008, you know, in the tech world? It's mobile. So in 2008, they launch the Tegra chip and platform within Nvidia.

Ben Gilbert 35:59 This may not be what saved the company. [chuckles]

David Rosenthal 36:01 This is not what saved the company. [laughing]

Ben Gilbert 36:02 [laughing]

David Rosenthal 36:02 This is more, uh, uh, clown car style. Uh, that's- maybe that's too rough on Nvidia, but what was Tegra? Uh, people might recognize that name. It was a full-on system on a chip for smartphones, competing directly with Qualcomm, with Samsung. Like, it was a processor, like an Arm-based CPU, plus all of the other stuff you would need for a system on a chip to power-... Android headsets. [chuckles] Uh, I mean, this is like a wild departure for-- it leverages none of Nvidia's core skill sets, except maybe graphics being part of smartphones. But, like, come on, if there's ever a use case for integrated graphics, it's smartphones. [laughing]

Ben Gilbert 36:46 Right. Right, low power, smaller footprint.

David Rosenthal 36:50 Yep, totally.

Ben Gilbert 36:51 Yeah.

David Rosenthal 36:51 Do you know... [chuckles] This is one of my favorite parts about the whole research. Do you know what the first product was that shipped using a Tegra chip?

Ben Gilbert 37:00 Uh, no.

David Rosenthal 37:02 It was the Microsoft Zune HD media player. [laughing]

Ben Gilbert 37:06 [laughing]

David Rosenthal 37:07 Uh, that just tells you pretty much everything you need to know. [laughing] Uh, it did, though, the Tegra system, it is still around, sort of, to this day. It powered the original Tesla Model S touchscreen. So, like, before any of the autopilot, autonomous driving stuff, they were the processor powering just the infotainment, the touchscreen infotainment in the Model S. And I think that actually starts to help Nvidia get into the automotive market. The Tegra platform still to this day is the main processor of the Nintendo Switch.

Ben Gilbert 37:41 Oh, they repurposed it for that?

David Rosenthal 37:42 Yeah, for that. And they s-- I think they still have their Nvidia Shield proprietary gaming device stuff that I don't know that anybody buys those.

Ben Gilbert 37:51 Oh, this makes so much sense because they basically have walked away from every console since the PlayStation 3.

David Rosenthal 38:00 Yep.

Ben Gilbert 38:01 And so it's interesting that they have this thriving gaming division that doesn't power any of the consoles except the Nintendo Switch, and I always sort of wondered, like, why did they take on the Switch business? 'Cause they kinda already had it done.

David Rosenthal 38:11 It's not for the graphics cards. It was as somewhere to put the Tegra stuff.

Ben Gilbert 38:16 Fascinating. Uh, quick aside, it's funny how these GPU companies have not been good at transitioning to mobile. There's, like, a funny naming thing-

David Rosenthal 38:26 Yeah

Ben Gilbert 38:26 ... but do you know what happened to-- so there's the ATI Radeon, which became the AMD Radeon desktop series. They tried to make mobile GPUs. It didn't go great, and they ended up spinning that out and selling all that IP to another company. Do you know the company?

David Rosenthal 38:44 Ooh, I do not. Was it Apple?

Ben Gilbert 38:48 It is Qualcomm.

David Rosenthal 38:49 Oh.

Ben Gilbert 38:49 And it today is Qualcomm's mobile GPU division, and Qualcomm's good at mobile, and so it's a natural home for it. Do you know what that line of mobile GPU processors is called?

David Rosenthal 39:01 No.

Ben Gilbert 39:02 It is the Ardeno, A-R-D-E-N-O, processors. And do you know why it's called the Ardeno or Ardeno?

David Rosenthal 39:10 No. That sounds super familiar, but no.

Ben Gilbert 39:12 The letters are rearranged from Radeon.

David Rosenthal 39:16 [laughing]

Ben Gilbert 39:16 [laughing]

David Rosenthal 39:17 That's great.

Ben Gilbert 39:18 Yeah.

David Rosenthal 39:18 That's great.

Ben Gilbert 39:19 So you're saying Nvidia's mobile graphics efforts didn't quite pan out?

David Rosenthal 39:24 No. We didn't talk about this as much in the Sony episode, but my impression of the whole Android value chain ecosystem is that there's no profits to be made anywhere, and Google keeps it that way on purpose. [chuckles]

Ben Gilbert 39:39 Ironically, they make a lot of money now on the Play Store.

David Rosenthal 39:41 Ah, yeah, the Play Store, and ads.

Ben Gilbert 39:44 Right. I do think the primary way that they monetize it is not having to pay other people to acquire the search traffic.

David Rosenthal 39:50 Right. But, I mean, for, like, partners. Like, if you are making-

Ben Gilbert 39:53 Oh, yeah

David Rosenthal 39:53 ... everything from chips all the way up through hardware in the Android ecosystem, I don't think you're making... Like, maybe if you were the scale player, but, like, these things are designed to sell for dirt cheap as end products. Like, there's no margin to be had here. [chuckles]

Ben Gilbert 40:06 Yep. Yep.

David Rosenthal 40:08 Also, before we continue, you just did the sidebar on the AMD mobile graphics chip. I see your sidebar. I'm gonna raise you one more sidebar-

Ben Gilbert 40:16 [laughing]

David Rosenthal 40:16 ... that we have to include, that you know, because the NZS guys told us about this. So, when Nvidia is going after mobile, they buy a mobile baseband company called Icera, a British company called Icera, in 2011. You know where I'm going with this.

Ben Gilbert 40:32 Oh, yes, I do.

David Rosenthal 40:33 This is so good. [chuckles]

Ben Gilbert 40:33 It's a good seed plant to come back to later.

David Rosenthal 40:35 Uh, you know, 'cause they're investing in mobile, Tegra's gonna be a thing, blah, blah, blah. And, uh, then a few years later, when they end up with pretty much shutting down the whole thing, they, they shut down what they bought from Icera, they lay everyone off. The Icera founders, who made a lot of money when Nvidia bought them, they go off, and they found a company called Graphcore [chuckles] that, uh, we're gonna talk about a little bit at the end of the episode, as, you know, maybe one of the primary sort of, uh-

Ben Gilbert 41:02 Nvidia bear cases?

David Rosenthal 41:04 Nvidia bear cases, Nvidia killers out there. They've now raised about seven hundred million in venture capital and picked up some... [laughing] Ah, mobile.

Ben Gilbert 41:13 In some ways, it's kind of like Bezos and Jet.com-

David Rosenthal 41:16 Yes

Ben Gilbert 41:17 ... if Jet had been successful. I think that's sort of the Graphcore to Nvidia analogy.

David Rosenthal 41:21 Yes. Well, I mean, the jury's still out if, uh, anybody's gonna be really successful in competing with Nvidia. Although, I think the market now is probably, ironically, big enough that-

Ben Gilbert 41:30 Large, yeah. [laughing]

David Rosenthal 41:31 [chuckles] Nvidia can be the whale, and there can be plenty of big other companies, too, so... Anyway, okay, back to the story. So Nvidia's bumping along through all of this in the early- late 2000s, early 2010s. You know, some years, growth is, like, m- ten percent, maybe it's flat. In others, like, this company's completely gone sideways. In 2011, they whiff on earnings again. Stock goes through another fifty percent drawdown. It's cliché. I don't- I was gonna say it, I don't even know if you can say it about Jensen, like, "Here we are, the company is screwed again." [chuckles] Like, everybody else would've given up, but obviously not them. So what happens? Basically, a miracle happens. I don't know that there's any other way that you can describe this except like a miracle, so maybe this is actually not a great [chuckles] strategy case study of Jensen because it required a miracle. [chuckles]

Ben Gilbert 42:27 Well, Jensen would say it was intentional, that they did know the market timing, and that the strategy was right, and the investment was paying off, and that they were doing this the whole time.

David Rosenthal 42:36 Ah. [chuckles] ... Sure. [laughing] Sure, Jensen.

Ben Gilbert 42:38 In fact, even in the Ben Thompson interview, I think he said Ben basically lays out, like, "How did all these implausible things happen at exactly the right time?" And, and his response is, "Oh, yes, we planned it all. It was so intentional." [chuckles]

David Rosenthal 42:50 Jensen did not plan AlexNet or see it coming, 'cause nobody saw AlexNet coming. So in 2009, a Princeton computer science professor and also undergrad alum of Princeton, just like yours truly, woo, wonderful place, named Fei-Fei Li, their specialty is artificial intelligence and computer science, starts working on an image-classifying project that she calls ImageNet. Now, the inspiration for this was actually a way old project from, I think, the '80s at Princeton called WordNet, that was, like, classifying words, but this is classifying image, ImageNet. And her idea is to create a database of millions of labeled images, like images that they have the correct label applied to them, like, this is a dog, or this is a strawberry, or something like that. And that with that database, then artificial intelligence image recognition algorithms could run against that database and see how they do. So like, "Oh, look at this image of..." You know, you and I would look at it and be like, "That's a strawberry," but you don't give the answer to the algorithm, and the algorithm figures out if it thinks it's a strawberry or a dog or whatever. [chuckles] So she and her collaborators start working on this. It's super cool. They build the database. They use, uh, Mechanical Turk, Amazon Mechanical Turk, to build it. And then one of them, I'm not exactly sure who, if it was Fei-Fei or somebody else, has the idea of like, "Well, you know, we've got this database. We want people to use it. Well, let's make a competition." This is, like, a very standard thing in computer science academia of like, "Let's have a competition, an algorithm competition." So we'll do this annually, and anyone, any team, can submit their algorithms against the ImageNet database, and they'll compete, like, who can get the lowest error rate, like, the most number of images, percentage of the images correct. And, uh, this is great, so it brings her great renown, becomes popular in the AI research community. She gets poached away by Stanford the next year. I guess that's okay, 'cause I went there, too, so that's fine. [chuckles] And, uh-

Ben Gilbert 44:53 David!

David Rosenthal 44:53 ... She's still there to this-- I know.

Ben Gilbert 44:54 [laughing]

David Rosenthal 44:55 I, I couldn't resist. I couldn't resist. I was just... She's like a kindred spirit to me. Do you know... I know you do know, but I bet most listeners do not know, what her endowed tenure chair is at Stanford today?

Ben Gilbert 45:08 I do. She is the Sequoia Chair.

David Rosenthal 45:10 Yes, the Sequoia Capital Professor of Computer Science at Stanford. [inhales] So cool. Why did she become the Sequoia Capital chair, and what does all this have to do with NVIDIA? Well, in the 2012 competition, a team from the University of Toronto submits an algorithm that wins the competition, and it doesn't just win it by, like, a little bit. It wins it by a lot. So the way they measure this is, the hundred percent of the images in the database, what percentage of them did you get wrong? So it wins it by over ten percent. I think it had a fifteen percent error rate or something, and the next-

Ben Gilbert 45:48 Like, all the best previous ones have been, like, twenty-five-point-something percent.

David Rosenthal 45:53 Yes.

Ben Gilbert 45:54 This is like someone breaking the four-minute mile. Uh, actually, in some ways, it's more impressive than the four-minute mile thing because they just didn't brute force their way all the way there. They, like, tried a completely different approach-

David Rosenthal 46:06 Yes

Ben Gilbert 46:06 ... And then, boom, showed that we could get way more accurate than anyone else ever thought.

David Rosenthal 46:11 So what was that approach? Well, they called the team, which was composed of Alex Krizhevsky, uh, was the primary, uh, lead of the team, he was a PhD student, in collaboration with Ilya Sutskever and Geoff Hinton. Uh, Geoff Hinton was the PhD advisor of, of Alex. They call it AlexNet. What is it? It is a convolutional neural network, which is a branch of artificial intelligence called deep learning. Now, deep learning is new for this use case, but, Ben, as-- you weren't exactly right. It had been around for a long time, a very long time, and deep learning neural networks, this was not a new idea. The algorithms had existed for many decades, I think, but they were really, really, really computationally intensive. They required, to train the models to do a, a deep neural network, you need a lot of compute, like, on the order of, you know, like, grains of sand that exist on Earth. [chuckles] It was completely impossible with a traditional computer architecture that you could make these work in any practical applications.

Ben Gilbert 47:21 And people were forecasting, too, like, when with Moore's Law, when will we be able to do this? And it still seemed like the far future, because not only did Moore's Law need to happen, but you also needed the NVIDIA approach of massively parallelizable architecture, where suddenly you could get all these incredible performance gains, not just because you're putting, you know, more transistors in a given space, but because you're able to run programs in parallel now.

David Rosenthal 47:47 Yes. So AlexNet took these old ideas, and it implemented them on GPUs. And to be very specific, it implemented them in CUDA on NVIDIA GPUs. We cannot overstate the importance of this moment, not just for NVIDIA, but for, like, computer science, for technology, for business, for [chuckles] the world, for us staring at the screens of our phones all day, every day. This was the Big Bang moment for artificial intelligence, and NVIDIA and CUDA were right there. [chuckles]

Ben Gilbert 48:21 Yep. It's funny, there's another example within the next couple of years, 2012, 2013, where NVIDIA had been thinking about-... this notion of general purpose computing for their architecture for a long time. In fact, they even thought about, "Should we relaunch our GPUs as GP GPUs, general purpose graphics processing units?" And of course, they decided not to do that, but just built CUDA.

David Rosenthal 48:47 Which is code word for, like, "We've been searching for years for a market for this thing. We can't find a market, so we'll just say, 'You can use it for anything!'" [chuckles]

Ben Gilbert 48:55 Right. And so deep learning's generating a lot of buzz, you know, a, a lot from this AlexNet competition. And so in 2013, Brian Catanzaro, who's a research scientist at NVIDIA, published a paper with some other researchers at Stanford, which included Andrew Ng, where they were able to take this unsupervised learning approach that had been done inside the Google Brain team, where they had sort of- the Google Brain team had sort of published their work on this, and it had 1,000 nodes. And, you know, this is a big part of the sort of early neural network hype cycle of people trying cool stuff, and this team was able to do it with just three nodes. So totally different model, super parallelized, lots of compute for a super short period of time in a really high-performance computing way, or HPC, as it would sort of become known. And this ends up being the very core of what becomes cuDNN, which is the library for deep neural networks that's actually baked into CUDA, that makes it easy for data scientists and research scientists everywhere who aren't hardware engineers or software engineers to just pretty easily write high-performance deep neural networks on NVIDIA hardware. So this AlexNet thing, plus then Brian and Andrew Ng's paper, it just collapses all these sort of previously thought to be impossible lines to cross and just makes it way easier and way more performant and way less energy-intensive for other teams to do it in the future.

David Rosenthal 50:24 Yep, and specifically to do deep learning. So I think at this point, like, everybody knows that this is [chuckles] pretty important, but it's not that much of a leap to say if you can train a computer to recognize images on its own, that you can then train a computer to see on its own, to drive a car on its own, to play chess, to play Go, to make your photos look really awesome when you take them on the latest iPhone, even if you don't have everything right.

Ben Gilbert 50:55 To eventually let you describe a scene and then have a transformer model paint that scene for you in a way that is unbelievable that a human didn't make it.

David Rosenthal 51:05 Yep, and then most importantly, for the market that Jensen and NVIDIA are looking for, you can use this same branch of AI to predict what type of content you might like to see next show up in your feed [chuckles] of content and what type of ad might work really, really, really well on you. [chuckles] So basically, all of these people we were just talking about, I bet a lot of you recognize their names, they get scooped up by Google. Fei-Fei Li goes to Google.

Ben Gilbert 51:38 Brian went to Baidu, and he's back at NVIDIA now, doing applied AI.

David Rosenthal 51:42 Brian went to Baidu. Jeff Hinton goes to Facebook. So, you know, all the other markets-- Like, even throw out-- Say you don't believe in self-driving cars, you don't think it's gonna happen or any of this other stuff. Like, it just doesn't matter. Like, [chuckles] the market of advertising, of digital advertising that this enables is a freaking multi-trillion dollar market.

Ben Gilbert 52:01 And it's funny 'cause like, that feels like, ooh, that's the killer use case, but that's just the easiest use case. That's the most, like-

David Rosenthal 52:07 Yes

Ben Gilbert 52:08 ... obvious, well-labeled data set, that these models don't have to be amazingly good 'cause they're not generating unique output. They're just assisting in making something more efficient. But then, like, flash forward ten more years, and now we're into these crazy transformer models with, I don't know if it's hundreds of millions or billions of parameters. Things that we thought only humans could do are now being done by machines, and it's like it's happening faster than ever.

David Rosenthal 52:36 Yep.

Ben Gilbert 52:37 So I think to your point, David, it's like, oh, there was this big cash cow enabled by, you know, neural networks and deep learning in advertising. Sure, but that was just the easy stuff.

David Rosenthal 52:47 Right, but that was necessary, though. This was finally the market that enabled the building of scale [chuckles] and the building of technology to do this, and, uh-

Ben Gilbert 52:56 Yes

David Rosenthal 52:56 ... in the Ben Thompson, um, Jensen interview, Ben actually says this when he's sort of realizing this, talking to Jensen, he says... This is Ben talking: "The way value accrues on the internet in a world of zero marginal costs, where there's just an explosion in abundance of content, that value accrues to those who help you navigate the content." And he's talking about aggregation theory, duh. And then he says, "What I'm hearing from you, Jensen, is that, yes, the value accrues to people that help you navigate that content, but someone has to make the chips and the software so that they can do that effectively." [chuckles] And it's like it sort of used to be with Windows was the consumer-facing layer, and Intel was the other piece of the Wintel monopoly. This is Google and Facebook and a whole host of other companies on the consumer side, and they're all dependent on NVIDIA, and that sounds like a pretty good place to be. [chuckles] And indeed, it was a pretty good place to be.

Ben Gilbert 53:46 Amazing place to be.

David Rosenthal 53:47 Oh, my gosh. The thing is, like, the market did not realize this for years, and, I mean, I didn't realize this, and I- you probably didn't realize this. We were the class of people working in tech as venture capitalists that should have.

Ben Gilbert 53:58 Ooh, do you know the Marc Andreessen quote?

David Rosenthal 54:01 Ooh, no.

Ben Gilbert 54:02 Oh, this is awesome. Okay, so it's a couple years later, so it's, like, getting more obvious, but it's 2016, and Marc Andreessen gave an interview, and he said, "We've been investing in a lot of companies applying deep learning to many areas, and every single one effectively comes in building on NVIDIA's platforms. It's like when people were all building on Windows in the '90s or all building on the iPhone in the late 2000s." And then he says, "For fun, our firm has an internal game of what public companies we'd invest in if we were a hedge fund. We'd put in all of our money to NVIDIA."

David Rosenthal 54:32 This is like, uh... It was Paradigm, right? That called all of their capital in one of their funds and put it into Bitcoin when it was, like, three thousand dollars a coin or something like that.... We all should have been doing this. So literally, Nvidia's stock in twenty, like recent, like this is now known, twenty twelve, thirteen, fourteen, fifteen, it doesn't trade above, like, five bucks a share. And Nvidia today, as we record this, is I think about two twenty a share. The high in the past year has been well over three hundred. Like, if you realized what was going on, and, and, and again, in a lot of those years, it was not that hard to realize what was going on. Wow! Like, [chuckles] it was huge.

Ben Gilbert 55:11 It's funny. So there was even-- and we'll get to what happened in twenty seventeen and twenty eighteen with crypto in a little bit, but there was a massive stock run-up to like sixty-five dollars a share in twenty eighteen. And even a- as late as, I think, the very beginning of twenty nineteen, you could have gotten it... I tweeted this, and we'll put the graph on the screen in the YouTube version here. You could have gotten it in that crash for thirty-four bucks a share.

David Rosenthal 55:35 [exhales] In twenty nineteen!

Ben Gilbert 55:37 If you zoom out on that graph, which is the next tweet here, then you can see that, like, in retrospect, that little crash just looks like nothing. You don't even pay attention to it in the crazy run-up that they had to three fifty or whatever their, their all-time high was.

David Rosenthal 55:51 Yeah, it's wild. And a few more wild things about this: It's not until twenty sixteen, again, AlexNet happens in twenty twelve. It's not until twenty sixteen that Nvidia gets back to the twenty billion dollar market cap peak that they were in two thousand and seven when they were just a gaming company. [chuckles] That's almost ten years.

Ben Gilbert 56:10 I really hadn't thought about it the way that you're describing it, but the breakthrough happened in twenty ten, twenty eleven, twenty twelve. Lots of people had the opportunity, especially because freaking Jensen's talking about it on stage, he's talking about it at earnings calls at this point.

David Rosenthal 56:25 He's not keeping this a secret.

Ben Gilbert 56:26 No, he's, like, trying to tell us all that this is the future, and people are still skeptical. Everyone's not rushing to buy the stock. We're watching this freaking magic happen, using their hardware, using their software on top of it, and, like, even semiconductor analysts, who are like students of listening to Jensen talk and following the space very closely, sort of think he sounds like a crazy person when he's up there espousing that the future is neural networks, and we're gonna go all in, and we're not pivoting the business, but from the amount of attention that he's giving in earnings calls to this versus gaming, I mean, everyone's just like, "Uh, are you off your rocker?"

David Rosenthal 57:06 Well, I think people had just lost trust and interest, [chuckles] you know, after, like... There were so many years of, like, they were so early with CUDA and early tech, and they didn't even know that this-- like, they didn't know AlexNet was gonna happen.

Ben Gilbert 57:19 Right.

David Rosenthal 57:20 Jensen felt like the GPU platform could enable things that the CPU paradigm could not, and he, like, had this faith that something would happen. But, like, he didn't know this was gonna happen. [chuckles] And so for years, he was just saying that, like: "We're building it, they will come," you know? And-

Ben Gilbert 57:38 And to be more specific, it was that... Well, look, the GPU has accelerated the graphics workload, so we've taken the graphics workload off of the CPU. The CPU is great, it's your primary workhorse for all sorts of flexible stuff, but we know graphics needs to happen in its own separate environment and have all these fancy fans on it and get super cooled, and it needs these matrix transforms. The math that needs to be done is matrix multiplication, and there was starting to be this belief that, like, oh, well, because the, you know, professor, the apocryphal professor, told me that he was able to use these program, the matrix transforms, to work for him, you know, maybe this matrix math is really useful for other stuff. And sure, it was for scientific computing. And then, honestly, like, it fell so hard into Nvidia's lap that the thing that made deep learning work was massively parallelized matrix math, and they're like, Nvidia's just, like, staring down at their GPUs, like, "Uh, I think we have exactly what you are looking for." [laughing]

David Rosenthal 58:38 Yes. [laughing] There's, uh, that same, uh, interview with Brian Catanzaro. He says about when all this happened, he says, the deep learning happened to be the most important of all applications that need high-throughput computation. Understatement of the century. [chuckles] And so once Nvidia saw that, it was basically instant. The whole company just latched onto it. There's so many things to laud Jensen for. You know, he was painting a vision for the future, but he was paying very close attention, and the company was paying very close attention to anything that was happening, and then when they saw that this was happening, they were not asleep at the switch.

Ben Gilbert 59:14 Yeah, hundred percent. It's interesting thinking about the fact that i- in some ways, it feels like an accident of history, in some ways, it feels so intentional, that graphics is an embarrassingly parallel problem because every pixel on a screen is unique. I mean, they don't have a core to drive every pixel on the screen. There's only ten thousand cores on the most recent Nvidia graphics cards, but there's not... Which is crazy, right?

David Rosenthal 59:41 [chuckles]

Ben Gilbert 59:41 But there's way more pixels on a screen.

David Rosenthal 59:42 Right.

Ben Gilbert 59:42 So, you know, they're not all doing every single pixel at the same time, every clock iteration. But it worked out so well that neural networks also can be done entirely in parallel like that, where every single computation that is done is independent of all the other computations that need to be done, so they also can be done on this super parallel set of cores. It's just, you gotta wonder, like, when you kind of reduce all this stuff to just math, it is interesting that these are two very large applications of the same type of math. In the search space of the world, of what other problems can we solve with parallel matrix multiplication, there may be more. There may even be bigger markets out there.

David Rosenthal 1:00:30 Totally. Well, I think there probably will be. [chuckles] A big part of Jensen's vision that he paints for Nvidia now, which we'll get to in a sec, is this is just the beginning. [chuckles] You know, there's robotics, there's autonomous vehicles, there's the omniverse. It's all coming.... It's funny, we just joked about how, like, nobody saw this before the run-up in twenty sixteen, twenty seventeen. There were all these years where, like, Marc Andreessen knew, [chuckles] you know, whether he made money in his personal account or not, you know-

Ben Gilbert 1:00:57 [chuckles]

David Rosenthal 1:00:57 -we'll have to ask him. But then in twenty eighteen, another class of problems that are embarrassingly parallelizable is, of course, cryptocurrency mining. [chuckles] And so a lot of people were going out and buying consumer NVIDIA, you know, graphics cards and using them to set up crypto mining rigs in twenty sixteen and twenty seventeen. And then when the crypto winter hit in twenty eighteen and the end of the ICO craze and all that, the mining rig demand it fell off, and this had become so big for NVIDIA that their revenue actually declined.

Ben Gilbert 1:01:30 Right. Yeah, so a couple interesting things here. Let's talk about technically why. So the way crypto mining works is effectively guess and check. You're effectively brute forcing an encryption scheme, and when you're mining, you know, you're trying to discover the answer to something that is hard to discover. So you're guessing. If that's not the right thing, you're incrementing, you're guessing again, and that's a vast oversimplification and not technically exactly right, but that's the right way to think about it. And if you were gonna guess and check at a math problem, and you had to do that on the order of a few million times in order to discover the right answer, you could very unlikely discover the right answer on the first time, but, you know, that probabilistically is only gonna happen to you once, if ever. And so well, the cool thing about these chips is that, A, they have a crap ton of cores, so the problem like this is massively parallelizable because instead of guessing and checking with one thing, you can guess and check with ten thousand at the same time, and then ten thousand more, and then ten thousand more. And the other thing is, it is matrix math. So yet again, there's this third application beyond gaming, beyond neural networks. There's now this third application in the same decade for the two things that these chips are uniquely good at. And so it's interesting that, like, you could build hardware that's better for crypto mining or better for AI, and both of those things have been built by NVIDIA and their competitors now, but the sort of like general purpose GPU happened to be pretty darn good at both of those things.

David Rosenthal 1:03:05 Well, at least way, way, way better than a CPU.

Ben Gilbert 1:03:08 Yeah. As some of NVIDIA's startup competitors put it today, and Cerebras is the one that I'm thinking of, they sort of say, "Well, the GPU is a thousand times better or, you know, much, much better than a CPU for doing this kind of stuff, but it's like a thousand times worse than it should be." There exists much more optimal solutions for, you know, doing some of this, this AI stuff.

David Rosenthal 1:03:31 Interesting. Really begs the question of like, how good is good enough in these use cases? [chuckles]

Ben Gilbert 1:03:37 Right. And now, I mean, uh, to flash way forward, the game that NVIDIA and everyone else, all these upstarts are playing, is really... It's still the accelerated computing game, but now it's how do you accelerate workloads off the GPU instead of off the CPU?

David Rosenthal 1:03:50 Interesting. [chuckles] Well, back to crypto winter-

Ben Gilbert 1:03:54 [chuckles]

David Rosenthal 1:03:54 ... just 'cause this is so funny. Uh, A, well, crypto itself became like a real industry. [chuckles] I don't think that's a controversial statement at this point. Maybe it is, maybe it isn't, but certainly it's less controversial than it was in twenty eighteen.

Ben Gilbert 1:04:07 Yes.

David Rosenthal 1:04:07 What happens is [chuckles] the NVIDIA stock gets hammered again. It goes through another fifty percent drawdown. This is just like every five years, this has gotta happen.

Ben Gilbert 1:04:16 Which is fascinating because at the end of the day, it was a thing completely outside their control. Like, people were buying these chips for a use case that they didn't build the chips for. They had really no idea what people were buying them for, so it's not like they could even get really good market channel intelligence on, "Are we selling to crypto miners, or are we selling to, you know, people that are gonna use these for gaming?"

David Rosenthal 1:04:36 Right, they're selling to Best Buy, and then people go buy them in Best Buy.

Ben Gilbert 1:04:40 Right. And some people are buying them wholesale, like if you're actually starting a data center to mine, but a lot of people are just doing this in their basement with consumer hardware, so they don't have perfect information on this. And then, of course, the price crashing makes it either unprofitable or less profitable to be a miner, and so then your demand dries up for this thing that you, A, didn't ask for, and B, had poor visibility into knowing if people were buying in the first place. So the management team just looks terrible to the street at this point 'cause they had just no ability to understand what was going on in their business.

David Rosenthal 1:05:13 And I think a lot of the Street was still was [chuckles] still had this hangover of skepticism about, "Is this deep learning thing? Like, what? Jensen, okay." And so, you know, it was kinda any excuse to sell off it took, but [chuckles] anyway, that was short-lived to the fifty percent dip because, uh, with the use case, and specifically the enterprise use case for GPUs for deep learning, like, it just takes off. And so this is really interesting. If you look at NVIDIA's, um... Well, they

David Rosenthal 1:05:43 report financials a couple different ways, but one of the ways they break it out is a few different segments. There's the gaming consumer segment, and then their data center segment, and it's like data center. Like, what do they do in the data center? Well, all the use cases for-

Ben Gilbert 1:05:55 Data centers AI.

David Rosenthal 1:05:56 [chuckles] Right. All of the stuff we're talking about, it's all done in the data center. Like, Google isn't going and buying, [chuckles] you know, a bunch of NVIDIA GPUs and hooking them up to the laptops of their software engineers like-

Ben Gilbert 1:06:07 Is Stadia still a thing? Like, I think that's used for cloud gaming and some-- like, there, there are-

David Rosenthal 1:06:12 Oh, yeah, but it's all happening in the data center is, uh, my point.

Ben Gilbert 1:06:15 Right, right. I guess what I'm saying, and my argument is, every time I see data center revenue, I, in my mind, I sort of make it synonymous with, this is their ML segment.

David Rosenthal 1:06:23 Ah, yes, yes, that's what I'm saying. I, I agree.

Ben Gilbert 1:06:26 Yeah.

David Rosenthal 1:06:26 Now, the data center, this is really interesting, again, because they used to sell these cards that would get packaged, put on a shelf, a consumer would buy them. Yeah, they made some specialty cards for the scientific computing market and stuff like that, but this data center opportunity, like, man, do you know the prices that you can sell gear to data centers for? Like, it makes the RTX 3090 look like a pence.

Ben Gilbert 1:06:51 ... And the RTX 3090, which is their most expensive high-end graphics card that you can buy as a consumer, was $3,000, now it's like $2,000. But if you're buying, I don't know, what's the latest? It's not the A100, it's the H100?

David Rosenthal 1:07:04 Uh, so the A100, they just announced the H100.

Ben Gilbert 1:07:07 And that's what? Like 20 or 30 grand in order to just get one card.

David Rosenthal 1:07:12 Yeah. [chuckles] And people are buying a lot of these things.

Ben Gilbert 1:07:16 Yeah, it's crazy. It's crazy.

David Rosenthal 1:07:18 It's funny, I tweeted about this, and I was sort of wrong, but then, like everything, there's nuance. You know, Tesla has announced making their own hardware. They're certainly doing it for the on-the-car, the inference stuff, like the full self-driving computer on Teslas, they now make those chips themselves. The Tesla Dojo, which is the training center that they announced, they announced they were also gonna make their own silicon for that. They actually haven't done it yet, so they're still [chuckles] using NVIDIA chips for their training. The current compute cluster that they have, that they're still using, I wanna say I did the math and, like, assumed some pricing, I think they spent between $50 and $100 million, that they paid NVIDIA for all of the compute in that-

Ben Gilbert 1:08:00 Wow!

David Rosenthal 1:08:00 ... cluster. [chuckles]

Ben Gilbert 1:08:01 That's one customer.

David Rosenthal 1:08:02 That's one customer for one use case at that one customer.

Ben Gilbert 1:08:05 Crazy. I mean, you see this show up in their earnings. So we're at the part of the episode where we're close enough to today that it's best illustrated by the today numbers, so I'll, I'll just flash forward to what the data center segment looks like now. So two years ago, they had about $3 billion of revenue, and it was only about half of their gaming revenue segment. So gaming, you know, through all this, through 2006 to AlexNet, all the way, you know, another decade forward to 2020, gaming is still king. It generates almost $6 billion in revenue. The data center revenue segment was $3 billion, but had been pretty flat for a couple years. So then insanely, over the last two years, it 3X'd. The data center segment 3X'd, and is now doing over $10.5 billion a year in revenue, and it's basically the same size as the gaming segment. It's nuts. It's amazing how it was, like, sort of obvious in the mid-2010s, but when the enterprises really showed up and said, "We're buying all this hardware and putting it in our data centers," and whether that's the hyperscalers, the, like, cloud folks, Google, Microsoft, Amazon, putting it in their data centers, or whether it's companies doing it in their own private clouds or whatever they wanna call it these days, on-prem data centers, everyone is now using machine learning hardware in the data center.

David Rosenthal 1:09:25 Yep. And NVIDIA is selling it for very, very, very healthy [chuckles] gross margins. Apple-level gross margins.

Ben Gilbert 1:09:33 Yes, exactly.

David Rosenthal 1:09:35 So speaking of the data center, couple things. One, in... [chuckles] This is so NVIDIA. In 2018, they actually do change the terms of the user agreements of their consumer-

Ben Gilbert 1:09:48 [chuckles]

David Rosenthal 1:09:48 ... cards, of GeForce cards, that you cannot put them in data centers anymore. [laughing]

Ben Gilbert 1:09:52 They're like-

David Rosenthal 1:09:52 Of course

Ben Gilbert 1:09:52 ... "Uh, we really do need to start segmenting a little bit here, and, uh, we know that the enterprises have much more willingness to pay," and it, it is worth it. I mean, you buy these crazy data center cards, and they have, like, twice as many transistors, and actually, they don't even have video outputs. Like, you can't use the data center GPUs, like the A100 does not have video out, so they actually can't be used as graphic cards.

David Rosenthal 1:10:16 Oh, yeah, there was a-- there's a cool, um, Linus Tech Tips video about this, where they get a hold of an A100 somehow, and then they run some benchmarks on it, but they can't actually, like, drive a game on it.

Ben Gilbert 1:10:28 Oh, fascinating.

David Rosenthal 1:10:29 Yeah, so fun.

Ben Gilbert 1:10:30 Data center stuff is, like, super high horsepower, but of course, like, useless to run a game on, 'cause you can't pipe it to a TV or a monitor. But then it's interesting that they're sort of artificially doing it the other way around, and saying, "For those of you who don't wanna spend $30,000 on this and are trying to, like, make your own little rig at home, your own little data center rig at home, no, you cannot rack these things."

David Rosenthal 1:10:51 Yeah, don't think about going to Fry's and buying [chuckles] a bunch of GeForces. [laughing] Ironic, 'cause that's how the whole thing started, but anyway... In 2020, they acquire an Israeli data center compute company called Mellanox, that I believe focuses on, like, uh, networking compute within the data center-

Ben Gilbert 1:11:08 Yep

David Rosenthal 1:11:08 ... for about $7 billion, integrate that into, you know, their ambitions in building out the data center.

Ben Gilbert 1:11:14 And the way to think about what Mellanox enables them to do is, now they're able to have super high bandwidth, super low latency connectivity in the data center between their hardware.

David Rosenthal 1:11:24 Yep.

Ben Gilbert 1:11:24 So at this point, they've got NVLink, which is their [chuckles] it's like the... What does Apple call it? A proprietary interconnect, or I think AMD calls it the Infinity Fabric. It's the, like, super high bandwidth chip-to-chip connection. So think about what Mellanox lets them do is, it lets them have these extremely high bandwidth switches in the data center, to then let all of these different boxes with NVIDIA hardware in them communicate super fast to each other.

David Rosenthal 1:11:52 That's awesome, because, of course, these data centers, that's the other thing about, you know, customers, like that Tesla example I gave. They're not buying cards, the enterprise customers, they're buying solutions from [chuckles] NVIDIA. They're buying big boxes with lots of stuff in them. [chuckles]

Ben Gilbert 1:12:06 You say solutions, I hear gross margin.

David Rosenthal 1:12:10 [laughing] Yeah. [laughing] That's such a great quote. We should, like, frame that and put it on the wall-

Ben Gilbert 1:12:17 [laughing]

David Rosenthal 1:12:17 ... of the, uh, the Acquired museum.

Ben Gilbert 1:12:20 It is true that acquiring Mellanox not only, like, enables this, now we have this super high connectivity thing, but this is what leads to this introduction of this third leg of the stool of computing for NVIDIA, that they talk about now, which is, you had your CPU, it's great, it's your workhorse, you know, it's your general purpose computer. Then there's the GPU, which is really a GPGPU, that they've really beefed up, and they've really, like, for the enterprise, for these data centers, they've put tensor cores in it to do the machine learning specific four-by-four-by-four matrix multiplication super fast, and do that really well. And they've put all this other-... non-gaming data center specific AI modules onto these chips and, and this hardware. And now what they're saying is, "You've got your CPU, you've got your GPU, now there's a DPU." And this data processing unit that's, like, kind of born out of the Mellanox stuff is the way that you really efficiently communicate and transform data within data centers. So the unit of how you think about, like, the black box just went from a box on a rack to now you can kind of think about your data center as the black box, and you can write at a really high abstraction layer, and then NVIDIA will help handle how things move around the data center.

David Rosenthal 1:13:32 We have one more thing to talk about on data centers, but before we do-

Ben Gilbert 1:13:35 Can we tell our audience about one of our favorite things? One of our favorite things that will be in person at the Acquired Arena Show.

David Rosenthal 1:13:42 Yes! Yes, we can. It's time for our second sponsor of the episode and all of Season 10. Huge thank you to Vouch, the insurance of tech. In, uh, our Insurance 101 last time, on the first NVIDIA episode, we talked about directors and officers insurance, or D&O. You know, this sounds boring or whatever. Like, go listen to that. Like, you need this. You absolutely need this.

Ben Gilbert 1:14:07 This Insurance 101 stuff is so fun.

David Rosenthal 1:14:09 Yes.

Ben Gilbert 1:14:10 It's like it takes something very boring, like data centers, and makes it very practical. [laughing]

David Rosenthal 1:14:13 [laughing] It's the Acquired of insurance, you might say. Vouch is the Acquired of insurance.

Ben Gilbert 1:14:18 Yes.

David Rosenthal 1:14:19 That was great stuff. If you are a board director or, or an officer of a company, you must have that. Go to vouch.us/acquired, pick it up right now, take five minutes, come back. Today, we are gonna talk about employment practices liability insurance, or EPL. So EPL is insurance that protects your company from employment-related claims, anything from harassment and discrimination to improper hiring practices, wrongful termination, et cetera, et cetera. And not just, like, you know, there's the obvious bad stuff, like sexual harassment or discrimination. HR laws are super complex. [chuckles]

Ben Gilbert 1:14:56 And different state by state.

David Rosenthal 1:14:58 Yes. My wife, Jenny, both of her parents are labor and employment attorneys. There are whole classes of the law dedicated just to, like, dealing with all of this.

Ben Gilbert 1:15:07 With EPL, I used to think, like, "Oh, well, as long as the company's buttoned up and the managers are being good people, then, like, this isn't an issue." But really, it's just a matter of time before you end up with an EPL-related issue. Just as you're scaling, it's gonna happen.

David Rosenthal 1:15:20 If you have started a company, been on a board, you know this is the most frequent claim that you are going to experience. It's not if, it is when. And indeed, actually, Vouch told us they see this in their own data. This is by far the most common claim that comes up, and perhaps unsurprisingly, over the last two years, with everything that's happened, EPL incidents are up forty%, [chuckles] literally forty percent in the last two years in terms of volume. So the bottom line on this one, you could have the best company culture, the best managers, you could never do anything wrong, but if you hire enough employees, this is guaranteed to come up. So you definitely wanna have this. What's important to know is that EPL coverage protects the company regardless of whether the claim has merit. So if you actually did do something wrong and there's a, a judgment against you, EPL coverage, of course, covers you up to your limit. But if it's meritless, then a great insurer like Vouch, and Vouch does this, they can help you take care of that. They can act as an advisor. They see this stuff a lot more than you do [chuckles] . So when... Not if, but when this comes up in your company, pick up the phone, call Vouch, they can help you through it. So once again, Vouch, you guys are the best. We love you. Learn more at vouch.us/acquired, and everybody, if you use that link, you will get an extra five percent off your coverages.

Ben Gilbert 1:16:42 Great stuff. Thanks, Vouch.

David Rosenthal 1:16:44 Indeed, it is. Okay, so I said one more thing on the data center.

Ben Gilbert 1:16:49 Yes.

David Rosenthal 1:16:50 That one more thing is, uh... It's easy to forget now. I know because we've just been deep on this. NVIDIA was gonna buy Arm. [chuckles] Do you remember this?

Ben Gilbert 1:16:58 Yes, they were. And in fact, this is gonna be like a corporate communications nightmare. Everyone out there, Jensen, their IR person, different tech people who are being interviewed on various podcasts, were talking about the whole strategy and how excited they are to own Arm, and how NVIDIA's gonna be... You know, it's good on its own, but it could be so much better if we had Arm, and here's all the cool stuff we're gonna do with it, and then it doesn't happen.

David Rosenthal 1:17:21 They were talking about it like it was a done deal.

Ben Gilbert 1:17:23 And now you've got dozens of hours of people talking about the strategy, so you're almost like-- It's funny that now after listening to all that, I'm sort of, like, disappointed with NVIDIA's ambition on its own without having the strategic assets of Arm. [chuckles]

David Rosenthal 1:17:37 Yeah. We should revisit Arm at some point. We did do the SoftBank acquiring Arm episode years and years ago now. But, you know, you think Arm, like, they are a CPU architecture company [chuckles] whose primary use case is mobile and smartphones, right? So, like, everything that Intel screwed up on [chuckles] back in the misguided mobile era, now they're going and buying, like, the most important company in that space. You know, and it's interesting, like, again, in the Ben Thompson interview, Jensen talks all about this, and maybe this is just justifying in retrospect, but I don't think so. He's like: Look, it was about the data center. Yeah, like, everything Arm does is, like, great, and that's fine, but, like, we wanna own the data center. When we say we wanna own the data center, we wanna own everything in the data center, and we think Arm chips, Arm CPUs, can be really, a really important part of that. Arm is not focusing right now enough on that. Why would they? Their core market is mobile. We want them to do that. We think there's a huge opportunity. We wanna dial them in and do that. And indeed, this year, NVIDIA announced they are making a data center CPU, an Arm-based data center CPU called Grace, to go with the new Hopper architecture for their latest GPU. So there's Grace and Hopper. Uh, of course, the rear admiral, Grace Hopper, I think?

Ben Gilbert 1:18:54 I think that's right. Yeah.

David Rosenthal 1:18:55 She was in the Navy. Uh, great computer scientist, pioneer.... So yeah, like da- data center. [chuckles] It's, it's big.

Ben Gilbert 1:19:03 It's interesting. So the objectors to that acquisition, and it's a good objection, and this is ultimately, I think, why they abandoned it, 'cause they got the regulatory pressure on this, is Arm's business is simple. They make the IP, so you can license one of two things from them. You can license the instruction set, so even Apple, who designs their own chips, is licensing the Arm instruction set. And so in order to use that, I don't know what it actually is, 20 keywords or so, that, that can get compiled to assembly language to run on whatever the chip is, you know, if you wanna use these instructions, you have to license it from Arm, great. And if you don't wanna be Apple, and you don't wanna go build your own chips, or you don't wanna be NVIDIA or whatever, but you wanna use our- that instruction set, you can also license these off-the-shelf chip designs from us, and we will never manufacture any of them. But you take one of these two things you license from us, you have someone like TSMC make them, great, now you're a fabless semiconductor company. And they sell to everyone. And so of course, a regulatory body is gonna step in and being like, "Wait, wait, wait. So NVIDIA, you're a fabless chip company. You're a vertically integrated business model. Are you gonna stop allowing Arm licenses to other people?" And NVIDIA goes, "Oh, no, no, no, no. Of course, we would never do that." Over time, they might do some stuff like that. But the thing that they were sort of like... Which, which is believable, beating the drum on that the strategy was going to be, is right now, our whole business's strategy is that CUDA and everything built on top of it, our whole software services ecosystem, is just for our hardware. And how cool would it be if you could use that stuff on Arm-designed IP, either just the, using the ISA or also using the actual designs that people license from them? How cool would it be if, because we were one company, we were able to make all of that stuff available for Arm chips as well?

David Rosenthal 1:20:49 Yep.

Ben Gilbert 1:20:49 Plausible, interesting, but no surprise at all that they faced too much regulatory pressure to go through with this.

David Rosenthal 1:20:55 No. But clearly, that idea rattled around in Jensen's head a bunch [chuckles] and in NVIDIA's head, because, um... Well, let's catch us up to today. So they just did GTC at the end of March, the big, uh, developer- the big GPU developer conference that they do every year, that they started in 2009 as part of building the whole CUDA ecosystem. Um, it's so freaking impressive [chuckles] now. Like, there are now three million registered CUDA developers, 450 separate SDKs and models for CUDA. They announced 60, six-zero, new ones at this GTC. Yeah, we talked about the next-generation GPU architecture with Hopper, and then the Grace CPU to go along with it. I think Hopper, I could be wrong on this, I think Hopper is gonna be the world's first four-nanometer process chip using TSMC's new four-nanometer process, which is-

Ben Gilbert 1:21:48 I think that's right

David Rosenthal 1:21:49 ... amazing. To talk a lot about Omniverse, we're gonna talk about Omniverse in a second, but you mentioned this licensing thing. They usually do their investor day, their analyst day, at the same time as GTC. [chuckles] And in the analyst day, Jensen gets up there... It's just so funny, I've been going through the whole history of this now, of, like, looking for a market, trying to find some market of any size, and he's like, "We are targeting a trillion-dollar market." [chuckles] He's like a startup raising a seed round, walking in with a pitch deck.

Ben Gilbert 1:22:17 We'll put this graphic up on the screen for those watching the video. It's a articulation of what the segments are of this trillion-dollar addressable opportunity that NVIDIA has in front of it. My view of this is, if their stock price wasn't what it was, there's no way that they would try to be making this claim that they're going after a trillion-dollar market. I think it's squishy.

David Rosenthal 1:22:41 Oh, there's a lot of squish in there. [chuckles]

Ben Gilbert 1:22:43 But the fact that they're valued today... I mean, what's their market cap right now? Something like-

David Rosenthal 1:22:49 About half a trillion.

Ben Gilbert 1:22:50 Half a trillion dollars. They need to sort of justify that, unless they are willing to have it go down, and so they need to come up with a story about how they're going after this ginormous opportunity. Which maybe they are, but it leads to things like an investor day presentation of, "Let us tell you about our trillion-dollar opportunity ahead." And the way that they actually articulate it is, "We are going to serve customers that represent a $100 trillion opportunity, and we will be able to capture about 1% of that."

David Rosenthal 1:23:22 [chuckles] God, it's just like a freaking seed company pitch deck. [chuckles]

Ben Gilbert 1:23:25 If we just get 1% of the market. [chuckles]

David Rosenthal 1:23:27 Well, and that's the thing. We're gonna talk about this in narratives in a minute, but this is a generational company. This is unbelievable. This is amazing. There's so much to admire here. This company did, what, like 20-something billion in revenue last year and is worth half a trillion dollars?

Ben Gilbert 1:23:42 They did $27 billion last year in revenue.

David Rosenthal 1:23:46 Google AdWords revenue in the fourth quarter of 2021 [chuckles] was 43 billion.

Ben Gilbert 1:23:53 [laughs]

David Rosenthal 1:23:53 Google as a whole did 257 billion in revenue. So, like, you gotta believe if you're an NVIDIA shareholder.

Ben Gilbert 1:24:01 Right. They're the eighth largest company in the world by market cap, but these revenue numbers, you know, are in a different order of magnitude.

David Rosenthal 1:24:08 You gotta believe it's on the come.

Ben Gilbert 1:24:10 Yeah, you do. I mean, NVIDIA has literally three times the price-to-sales ratio of Apple, or price to revenue as Apple, and nearly 2X Microsoft, and that's on revenue. I mean, fortunately, this NVIDIA story is not speculative in the way that an early-stage startup is speculative. Like, even if you think it's overvalued, it is still a very cash-generative business.

David Rosenthal 1:24:34 Yes.

Ben Gilbert 1:24:34 They generate eight billion of free cash flow every year.

David Rosenthal 1:24:38 Yep.

Ben Gilbert 1:24:38 So I think they're sitting on 21 billion in cash, 'cause the last few years have been very cash generative very suddenly for them. So the takeaway there is, by any metric, price to sales, price to earnings, all that, they're much more richly valued than a, an Apple or a Microsoft or these FAANG companies, but it is, you know, extremely profitable business, even on an operating profits perspective.

David Rosenthal 1:24:59 ... Well, you sell enough of that [chuckles] enterprise, uh, data center goodness, and, uh, you're gonna make some money.

Ben Gilbert 1:25:04 It's crazy. They now have a sixty-six percent gross margin. So that illustrates to me how seriously differentiated they are and how much of a moat they have versus competitors in order to price with that kind of margin. 'Cause think back, we'll put it up on the screen here, but back in 'ninety-nine, they had a gross margin of thirty percent on their graphics chips, and then in twenty-fourteen, they broke the fifty percent mark. And then today, and this slide really illustrates it, it's architecture, systems, data center, CUDA, CUDA X. Like, it's like the whole stack of stuff that they sell as a solution, and then it's sort of all bundled together, and bundle is the right word. I think they get great economics because they're bundling so much stuff together. It's sixty-six percent gross margin business now.

David Rosenthal 1:25:50 Yep. Well, and, [chuckles] you know, thinking about increasing that gross margin further and what we were talking about a minute ago with Arm and the licensing. So at the Analyst Day at around GTC this year, they say that they're gonna start licensing a lot of the software that they make separately, licensing it separate from the hardware, like CUDA. [chuckles] And, uh, there's a quote from Jensen here: "The important thing about our software is that it's built on top of our platform. It means that it activates all of Nvidia's hardware, chips, and system platforms. And secondarily, the software that we do are industry-defining software. So we've now finally produced a product that an enterprise can license." [chuckles] They've been asking for it, and the reason for that is because they can't just go to open source and download all the stuff and make it work for their enterprise. No more than they could go to Linux, download open-source software, and run a multi-billion dollar company with it. [chuckles] You know, when you were-- we were joking a few minutes ago about, you say solution, and I see margin. [chuckles] You know? Yeah, like, open-source software companies have become big for this reason. You know, Databricks, Confluent, Elastic, like, these are big companies with big revenue based on open source because enterprises, they're like: "Oh, I want that software," but they're not just gonna, you know, go to... If you're J.P. Morgan, you're not gonna go to GitHub and be like, "Great, I got it now!" You know?

Ben Gilbert 1:27:15 Right.

David Rosenthal 1:27:15 You need solutions. So to Jensen and Nvidia, they see this as an opportunity to... I'm sure this isn't gonna be cannibalizing hardware customers for them. I think this is gonna be incremental selling on top of what they're already doing.

Ben Gilbert 1:27:29 That's an important point, and I think this is a playbook theme that I had. But oftentimes, when someone has hardware that is differentiated by software and services, and then they decide to start selling those software and services à la carte, it's a strategy conflict. It's your classic vertical versus horizontal problem, unless you are good at segmentation, and that's sort of what Nvidia is doing here, which is what they're saying: "Well, we're only gonna license it to people that there's no way that they would've just bought the hardware and gotten all this stuff for free anyway. So if we don't think it's gonna cannibalize, and they're a completely different segment, and we can do things in pricing and distribution channel and terms of service that clearly walls off that segment, then we can behave in a completely different way to that segment."

David Rosenthal 1:28:18 Yep, and get further, you know, returns on our assets that we've generated.

Ben Gilbert 1:28:23 Yep. It is a little Tim Cook, though, in, uh- [chuckles] you know, Tim Cook beating the services narrative drum. I mean, it is kinda... You hear a public company CEO who has a high market cap, and everyone's asking where the next phase of growth is gonna come from and saying, "We're gonna sell services, and look at this growing business line of licensing that we have." [chuckles]

David Rosenthal 1:28:41 Oh, my goodness. But who else is gonna do it wearing a leather jacket?

Ben Gilbert 1:28:45 That is a great point. It's a great point.

David Rosenthal 1:28:48 Frankly, Elon. [laughing] But well, we'll talk about cars in a second here.

Ben Gilbert 1:28:52 Let's hold the Elon. [chuckles]

David Rosenthal 1:28:54 Yeah.

Ben Gilbert 1:28:55 Uh, okay, so a few other things just to talk about the business today that I think are important to know, just as you sort of, like, think about-- sort of have a mental model for what Nvidia is. It's about twenty thousand employees. We mentioned they did twenty-seven billion in revenue last year. We talked about this very high revenue multiple or earnings multiple or however you wanna frame it, relative to FAANG companies. They're growing much faster than Apple, Microsoft, Google. They're growing at sixty percent a year. This is a thirty-year-old company that grew sixty percent in revenue last year.

David Rosenthal 1:29:29 Yeah.

Ben Gilbert 1:29:30 If you're not used to, like, wrapping your mind around that, like, startups double and triple, but, like, in the first five years that they exist. Google has had this amazing run where they're still growing at forty percent. Microsoft went from ten to twenty percent over the last decade. Again, amazing, they're accelerating, but, like, Nvidia is growing at sixty percent.

David Rosenthal 1:29:50 Right.

Ben Gilbert 1:29:51 I don't care what your discount rate is, having sixty percent growth in your DCF model versus twenty or forty will get you a lot more multiple.

David Rosenthal 1:29:59 Inflation be damned. [chuckles]

Ben Gilbert 1:30:00 Inflation be damned. Okay, a couple other things about specific segments of the business that I think are pretty interesting. So they have not slept on gaming. Like, we keep beating this Nvidia data center, enterprise-

David Rosenthal 1:30:14 Yeah

Ben Gilbert 1:30:14 ... machine learning argument.

David Rosenthal 1:30:16 Yeah, we haven't even talked about ray tracing and-

Ben Gilbert 1:30:19 Right. Yeah, this RTX set of cards that they came out with. The fact that they could do ray tracing in real time, holy crap! For anyone who's looking for sort of a fun dive on how graphics works, go to the Wikipedia page for ray tracing. It's very cool. You model where all the light sources are coming from, where all the paths would go in 3D. The fact that Nvidia can render that in real time at sixty frames a second or whatever, while you're playing a video game, is nuts. And one of the ways that they do that, they invented this new technology that's extremely cool. It's called DLSS, deep learning super sampling, and this, I think, is, like, where Nvidia really shines, bringing machine learning stuff and gaming stuff together.... where they basically have faced this problem of, well, we either could render stuff at low resolution with less frames, 'cause, uh, we can only render so much per amount of time, or we could render really high-resolution stuff with less frames. And nobody likes less frames, but everyone likes high resolution, so what if we could cheat death, and what if we could get high resolution and high frame rate? And they're sitting around thinking, "How on earth can we do that?" And they're like, "You know what? Maybe this 15-year bet that we've been making on deep learning can help us out."

David Rosenthal 1:31:37 Mm.

Ben Gilbert 1:31:37 And what they discovered here and, and invented in DLSS, and AMD does have a competitor to this, it's a similar sort of idea, but this DLSS concept is totally amazing. So what they basically do is they say, "Well, it's very likely that you can infer what a pixel is going to be based on the pixels around it."

David Rosenthal 1:31:57 Ah, that's awesome.

Ben Gilbert 1:31:58 "It's also pretty likely you can infer what a pixel is gonna be based on what it was in the previous frames. And so let's actually render it at a slightly lower resolution, so we can bump up the frame rate, and then when we're outputting it to screen, we will use deep learning to artificially- "

David Rosenthal 1:32:18 Ah, at the final stage of the graphics pipeline.

Ben Gilbert 1:32:21 Yes.

David Rosenthal 1:32:21 Yo, that's awesome.

Ben Gilbert 1:32:23 It's really cool, and when you watch the side by side on all these YouTube videos, it looks amazing. I mean, it does involve really tight, embedded development with the game developers. They have to sort of do stuff to make it DLSS-enabled, but it just looks phenomenal, and it's so cool that when you're looking at this 4K or even 8K output of a game at, you know, full frame rate, you're like, "Whoa! In the middle of the graphics pipeline, this was not this resolution, and then they magically upscaled it." It's basically making the, like, enhance joke, like a real thing.

David Rosenthal 1:32:58 That's so awesome. [chuckles] I'm remembering back to the Riva 128 in the beginning of- when they went to game developers, and they were like, "Yeah, yeah, yeah, all the blend modes in, in DirectX," you know, "You don't need all of them, just use these." [chuckles]

Ben Gilbert 1:33:10 Yes, exactly. Exactly. And they have the power to do it. I mean, they have the stick and the carrot with game developers to do it.

David Rosenthal 1:33:17 Oh, I mean, at this point, no game developer is not gonna make their games optimized for the latest NVIDIA hardware.

Ben Gilbert 1:33:25 The other thing that is funny that's within the gaming segment, because they didn't want to create a new segment for it, is crypto. So because they have poor visibility into it, and before they weren't liking the fact that it was actually reducing the amount of cards that were available to the retail channel for their gamers to go and buy, what they did was they artificially crippled the card to make it worse at crypto mining.

David Rosenthal 1:33:48 And then they came out with a dedicated crypto mining card. [chuckles]

Ben Gilbert 1:33:50 Yes. And so, like, the charitable PR thing from NVIDIA is, "Hey, you know, we really did- we love gamers, and we didn't want to make it so that the gamers couldn't get access to-

David Rosenthal 1:34:00 [chuckles]

Ben Gilbert 1:34:00 ... you know, all the cards they want." But really, they're like, "Hmm, people are just, like, straight-up performing an arbitrage by crypto mining on these cards. Let's make that more expensive on the cheap cards, and let's make dedicated crypto hardware for them to buy to do those."

David Rosenthal 1:34:15 Let's make that our arbitrage. [chuckles]

Ben Gilbert 1:34:17 Yes.

David Rosenthal 1:34:18 Your arbitrage is my opportunity. [chuckles]

Ben Gilbert 1:34:20 So magically, their revenue is more predictable now, and they get to make more money because, much like their sort of terms of service data center thing, they terms of serviced their way to being able to create some segmentation, and thus, more profitability.

David Rosenthal 1:34:33 Love it.

Ben Gilbert 1:34:33 Evil, evil, genius laugh. [chuckles] The last thing that you should know about NVIDIA's gaming segment is this really weird concept of add-in board partners. So we've been oversimplifying in this whole episode, saying, "Oh, you know, you go, and you buy your RTX 3090 Ti at the store, and you run your favorite game on it." But actually, you're not buying that from NVIDIA the vast majority of the time. You are going to some third-party partner, ASUS, MSI, uh, ZOTAC is one. They've-- there's also, like, a bunch of really low-end ones as well, who NVIDIA sells the cards to, and those people install the cooling and the branding and all this stuff on top of it, and you buy it from them, and it's really weird to me that NVIDIA does that.

David Rosenthal 1:35:26 I love how consumer gaming graphics cards have become the modern-day equivalent of a hot rod. [chuckles]

Ben Gilbert 1:35:31 Oh, dude, as you can imagine, for this episode, I've been hanging a lot on the NVIDIA subreddit, and, like, it's not actually about NVIDIA or NVIDIA the company or NVIDIA the strategy. It's like, show off your sick photos of your glowing rig, [chuckles] which is pretty funny. But, like, it feels like a remnant of old NVIDIA that they still do this. Like, they do make something called the Founders Edition card, and it's basically a reference design where you can buy it from NVIDIA directly, but I don't think the vast majority of their sales actually come from that.

David Rosenthal 1:36:03 Oh, it's like, um... What are the Android phones that Google makes, Pixel?

Ben Gilbert 1:36:07 Yeah, it's exactly like that.

David Rosenthal 1:36:08 Yeah.

Ben Gilbert 1:36:08 The Pixel.

David Rosenthal 1:36:09 It's exactly what it is, yeah.

Ben Gilbert 1:36:11 So I, I suspect that shifts more over time. I can't imagine a company that wants as much control as NVIDIA does loves the add-in board partner thing, but they've built a business on it, and so they're not really willing to cannibalize and alienate. But I bet if they had their way, and they're becoming a company that can more often have their way, they'll find a way to, to kinda just go more direct.

David Rosenthal 1:36:31 Makes sense.

Ben Gilbert 1:36:32 Two other things I wanna talk about, one is automotive. So this segment has been, like, very small from a revenue perspective for a long time and seems to not have a lot of growth.

David Rosenthal 1:36:41 But Jensen says in his pitch deck it's gonna be a $300 billion [chuckles] part of the TAM.

Ben Gilbert 1:36:47 And I think right now it's something like, is it a billion dollars in revenue? I think it's, like, a billion dollars, but it doesn't really grow.

David Rosenthal 1:36:53 I don't even know if it's that much.

Ben Gilbert 1:36:55 Don't quote me on that. So here's what's going on with automotive, which is pretty interesting.... What Nvidia used to do for automotive is what everyone used to do for automotive, which is make fairly commodity components that automakers buy and then put in there. Every technology company has had their fanciful attempt to try to create a meaningfully differentiated experience in the car. All have failed. You think about Microsoft and the Ford Sync.

David Rosenthal 1:37:19 Ford Sync. Oh, wow!

Ben Gilbert 1:37:21 You think about CarPlay, kind of maybe a little bit works. And the only company that's really been successful has been Tesla at starting, like, a completely new car company. That's the only way they're able to provide a meaningful, differentiated experience. Nvidia is-- my perception of what they're doing is they're pivoting this business line, this, like, flat, boring, undifferentiated business line to say, "Maybe EVs, electric vehicles, and autonomous driving is a way to break in and create a differentiated experience, even if we're not gonna make our own cars." And so I think what's really happening here is when you hear them talk about automotive now, and they've got this very fancy name for it, it's the something Drive platform.

David Rosenthal 1:38:08 Oh, Hyperion Drive, is that it? Something like that.

Ben Gilbert 1:38:11 Something like that. But dealing with Nvidia's product naming is maddening. But this Drive platform, it kind of feels like they're making the full EV, AV, hardware, software stack, except for the metal and glass and wheels, and then going to car companies and saying: "Look, you don't know how to do any of this. This thing that you need to make is basically a battery and a bunch of GPUs and cameras on wheels, and, like, you're issuing these press releases saying you're going in that direction, but is-- none of this is the core competency of your company, except the sales and distribution. So, like, what can we do here?" And if Nvidia is successful in this market, it'll basically look like, you know, an Nvidia computer, full software, hardware, with a car chassis around it that is branded by whatever the car company is.

David Rosenthal 1:38:58 Like the Android market.

Ben Gilbert 1:39:00 Yeah. And I think we will see if the shift to autonomous vehicles is, A, real, B, near-term, and, C, enough of a dislocation in that market to make it so that someone like Nvidia, a component supplier, actually can get to own a bunch of that value chain versus the auto manufacturer kinda forever stubbornly getting to keep all of it and control the experience.

David Rosenthal 1:39:26 Yep. Which, to do a mini bull and bear on this here before we get to the broader on the company, you know, the bull case for that is, we were, again, a friend of the show, Jeremy, messaging with in, in Slack. Lotus is one of their partners. Is Lotus gonna go build autonomous driving software? Like, I don't think so. [chuckles] Ferrari? No. [chuckles] You know?

Ben Gilbert 1:39:48 Not at all. They're gonna be Nvidia cars, effectively.

David Rosenthal 1:39:51 Yeah.

Ben Gilbert 1:39:53 Okay, last segment thing I wanna talk about is how we opened the show, talking about the Nvidia Omniverse. And this is not Omniverse like metaverse. It is similar in that it's kind of a 3D simulation-type thing, but it's not an open world that you wander around in, the same way that Meta is talking about or that you think about in Fortnite or something like that. What they mean by Omniverse is pretty interesting. So a good example of it is this, uh, Earth-2, this digital twin of Earth that they're creating that has these really sophisticated climate models that they're running, that basically is a proof of concept to show enterprises who wanna license this platform, "We can do super realistic simulations of anything that's important to you."

David Rosenthal 1:40:40 Mm-hmm.

Ben Gilbert 1:40:41 And what their pitch is to the enterprise is, "Hey, you've got something." Let's say it is a bunch of robots that need to wander around your warehouse to pick and pack, if it's, uh, Amazon, who actually, Amazon is a customer. They showcase Amazon in all their fancy videos, and they say, "You're gonna be using our hardware and software to train models to figure out the routes for these things that are driving around your data centers. You're gonna be licensing, certainly, some of our hardware to actually do the inference, to put on the robots that are driving around. When you wanna make a tweak to a model, you're not just gonna, like, deploy those to all the robots. You kinda wanna run that in the Omniverse first, and then when it's working, then you wanna deploy it in the real world." And their Omniverse pitch is basically, "It's an enterprise solution that you can license from us, where anytime you're gonna change anything in any of your real-world assets, first model it in the Omniverse." And I think that's a really powerful-- Like, I believe in the future of that in a big way, 'cause I think now that we have the compute, the ability to gather the data, and the ability to actually, you know, run these simulations in a, in a way that has a efficient way of running it and a good user interface to understand the data, people are gonna stop testing in production with real-world assets, and everything's gonna be modeled in the Omniverse first, before rolling out.

David Rosenthal 1:42:03 This is what an enterprise metaverse [chuckles] is gonna be. This is not designed for humans. Humans may interact with this. There will be UI, you'll be able to be part of it. The purpose of this is for simulating applications, and m- most of it, I think, is gonna run with no humans there.

Ben Gilbert 1:42:23 Yep, pretty crazy.

David Rosenthal 1:42:24 Yeah. It's a good idea. Sounds like a good idea.

Ben Gilbert 1:42:27 All right, you wanna talk bear and bull case on the company?

David Rosenthal 1:42:29 Let's do it. [sighs] Analysis.

Ben Gilbert 1:42:32 So, I mean, they paint the bull case for us when they say, "There's a $100 trillion future. We're gonna capture 1% of it. There's $300 billion from automotive. Here's the four or five segments that add up to a trillion dollars of opportunity." Sure, that's, like, a very neat way with a bow on it and a very wishy-washy, hand-wavy way of articulating it. So the question sort of becomes: Where does AMD fall in all this? They're a legitimate second-place competitor for high-end gaming graphics, and I think will continue to be. That feels like a place where th- these two are gonna keep going head-to-head. The bear case is that there's a TikTok rather than a durable, competitive advantage for Nvidia, but most high-end games you can play on both AMD and Nvidia hardware at this point. The question for the data center is...... Is the future these general purpose GPUs that NVIDIA continues to modify the definition of GPU to include specialized, you know, functions as well, all this other stuff they're putting on their, in their hardware? Or is there someone else who is coming along with a completely different approach to accelerated computing, where they're accelerating workloads off the GPU onto something new, like a Cerebras or like a Graphcore, that is gonna eat their lunch in the enterprise AI data center market? [chuckles] That's an open question.

David Rosenthal 1:43:55 You know, it's interesting. Like, people have been talking about that for a while. The other big bear case that people have been talking about, again, for a while now, is, you know, the big, big customers of NVIDIA that are paying them a lot of money, the Teslas, the Googles, the Facebooks, the Amazons, the Apples. And not just paying them a lot of money and getting, you know, assets of value of that, they're paying high gross margin dollars to [chuckles] NVIDIA for what they're getting, that those companies are gonna wanna say, "You know, it's not that hard to design our own silicon to bring all this stuff in-house. We can tune it to exactly our use cases," sorta similar to the Cerebras, uh, Graphcore bear case on NVIDIA. I think in both of these cases, you know, it hasn't happened yet. [chuckles]

Ben Gilbert 1:44:49 Well, there have been a lot of people who have made a lot of noise-

David Rosenthal 1:44:52 Yes.

Ben Gilbert 1:44:52 -but there have been few that have executed on it. Like, Apple has their own GPUs on the M1s. Tesla's switching... hasn't happened yet, but switching the, to their own, for the full self-driving, they're s- they're doing their own stuff on the car, and they're switching.

David Rosenthal 1:45:04 Yep, that is switch- on the inference side-

Ben Gilbert 1:45:06 Yes

David Rosenthal 1:45:07 ... on-device, yes, that has happened. But look, NVIDIA's probably strong in that, but I think the real thing to watch is the data center.

Ben Gilbert 1:45:13 And Google is probably the biggest bear case there.

David Rosenthal 1:45:17 Yeah.

Ben Gilbert 1:45:17 It's interesting to talk about these companies, and particularly Cerebras, 'cause what they're doing is such a gigantic swing and a totally different take than what everyone else has done. For folks who hasn't sort of followed the company, they're making a chip that's the size of a dinner plate. Everyone else's chip is like a thumbnail, but they're making a dinner plate-sized chip. And, you know, the yields on these things kinda suck, so, like, they need all the redundancy on those-

David Rosenthal 1:45:44 Oof

Ben Gilbert 1:45:44 ... huge chips to make it so that-

David Rosenthal 1:45:46 Oh, my God, the amount of expense to do that.

Ben Gilbert 1:45:48 Right, and you can put one on a wafer.

David Rosenthal 1:45:51 Oof.

Ben Gilbert 1:45:52 These wafers are crazy expensive to make.

David Rosenthal 1:45:55 Wow, so you get poor yields in the wrong places on a wafer, and, like, that whole wafer is toast.

Ben Gilbert 1:46:00 Right. So a big part of the design of Cerebras is this sort of redundancy and the ability to turn off different pieces that aren't working. They draw 60 times as much power. They're way more expensive. Like, if NVIDIA is gonna sell you a $20,000 or $30,000 chip, Cerebras is gonna sell you a $2 million chip to do AI training. And so it is this bet in a big way on hyper-specialized hardware for enterprises that wanna do these very specific AI workloads. And it's d- deployed in these beta sites in research labs right now, and, you know, not there yet, but it'll be very interesting to watch if they're able to meaningfully compete for what everyone thinks will be a very large market, these enterprise AI workloads. I mentioned Google, that made a bunch of noise about making their own silicon in the data center and then stayed the course and stayed really serious about it with their TPUs. Their business model is different, so nobody knows what the bill of materials is to create a TPU. Nobody knows really what they cost to run. They don't retail them. They're only available in Google Cloud. And so Google is sort of counter-positioned against NVIDIA here, where they're saying, "We wanna differentiate Google Cloud with this offering, that depending on your workload, it might be much cheaper for you to use TPUs with us than for you to use NVIDIA hardware with us or anyone else." And they're probably willing to eat margin on that in order to grow Google Cloud's share in the cloud market.

David Rosenthal 1:47:34 Mm-hmm. Interesting.

Ben Gilbert 1:47:35 So it's kind of the Android strategy but run in the data center.

David Rosenthal 1:47:40 One thing we haven't mentioned, but we should, is, uh, cloud is also part of the NVIDIA story, too. Like, you can get NVIDIA GPUs in AWS and Azure and, and Google Cloud, and that is part of the growth story for NVIDIA, too. [chuckles]

Ben Gilbert 1:47:53 And NVIDIA's starting their own cloud. You can get direct from NVIDIA, cloud-based GPUs.

David Rosenthal 1:47:57 Data center GPUs. Interesting.

Ben Gilbert 1:47:59 Yeah. It'll be very interesting to see how this all shakes out with, uh, with NVIDIA, the startups, and with Google.

David Rosenthal 1:48:05 [exhales] I mean, all that said, though, like, I think... But look, NVIDIA is very, very, very richly [chuckles] valued on a valuation basis right now. [chuckles] Very, with another very in there.

Ben Gilbert 1:48:17 It depends if you think their growth will continue. Are they a 60% growing company year over year over year for a while? Then they're not richly valued. But if you think it's a COVID hiccup or a crypto hiccup-

David Rosenthal 1:48:27 But to the, the bull bear case and kinda both the startups and the big tech companies doing this stuff in-house, uh, it's not so easy. You know, like, yeah, Facebook and Tesla and Google and Amazon and Apple are capable of doing a lot, but we just told this whole story. This is 15 years of CUDA [chuckles] and the hardware underneath it and the libraries on top of it that NVIDIA has built. To go recreate that and surpass it on your own is such an enormous, enormous bite to bite.

Ben Gilbert 1:49:05 Yes, and if you're not a horizontal player, and you're a vertical player, you better believe that the pot of gold at the end is worth it for you for this massive amount of cost to create what NVIDIA has created.

David Rosenthal 1:49:17 Yep.

Ben Gilbert 1:49:18 Like, NVIDIA has the benefit of getting to serve every customer. If you're Google, and their strategy is what I think it is, of not retailing TPUs at any point-... then your customer is only yourself, so you're constrained by the amount of people you can get to use Google Cloud.

David Rosenthal 1:49:31 Well, and at least with Google, they have Google Cloud that they can sell it through.

Ben Gilbert 1:49:35 Yep. Power.

David Rosenthal 1:49:37 Ooh, power.

Ben Gilbert 1:49:39 So the way I wanna do this section, because in our Nvidia episode, we covered the first thirteen years of the company, we talked a lot about what does their power look like up to two thousand and six. And now I wanna talk about what does their power look like today. What is the thing that they have that enables them to have a sustainable competitive advantage and continue to maintain pricing power over their nearest competitor, be it Google Cerebras in the enterprise or AMD in gaming?

David Rosenthal 1:50:07 Yep, and just to enumerate the powers again, as we always do, counter-positioning, scale economies, switching costs, network economies, process power, branding, and cornered resource.

Ben Gilbert 1:50:19 So there are definitely scale economies. The whole CUDA investment-

David Rosenthal 1:50:24 Yes

Ben Gilbert 1:50:25 ... Not at first, but definitely now, is predicated on being able to amortize that a thousand-plus employee spend over the base of the three million developers and all the people who are buying the hardware to use what those developers create.

David Rosenthal 1:50:39 This is the whole reason we spent twenty minutes talking about if you were going to run this playbook, you needed an enormous market to justify the CapEx you were gonna put in.

Ben Gilbert 1:50:47 Right. So very few other players have access to the capital and the market that Nvidia does to make this type of investment. So they're basically just competing against AMD for this.

David Rosenthal 1:51:00 Totally agree. Scale economies, to me, is, like, the biggest one that pops out. To the extent that you have lock-in to developing on CUDA, which I think a lot of people really [chuckles] have lock-in on CUDA, then that's major switching costs.

Ben Gilbert 1:51:17 Yep.

David Rosenthal 1:51:17 Like, if you're gonna boot out Nvidia, that means you're booting out CUDA.

Ben Gilbert 1:51:21 Is CUDA a cornered resource?

David Rosenthal 1:51:23 Oh, interesting. Maybe. I mean, it only works with Nvidia hardware.

Ben Gilbert 1:51:29 You could probably make an argument there's process power, or at least there was somewhere along the way with them having the six-month ship cycle advantage. That probably has gone away since people trade around the industry a lot, and that wasn't sort of a hard thing for other companies to figure out.

David Rosenthal 1:51:44 Yeah, I think process power definitely was part of the first instantiation of Nvidia's power, to the extent it had power.

Ben Gilbert 1:51:52 Right.

David Rosenthal 1:51:53 Yeah, I don't know as much today, especially because TSMC will work with anybody.

Ben Gilbert 1:51:57 In fact, TSMC is working with these new startup billion-dollar funded silicon companies.

David Rosenthal 1:52:03 Yes, they are. Yes.

Ben Gilbert 1:52:05 Yeah. It's funny, I actually heard a rumor, and we can link to it in the show notes, that the Ampere series of chips, which is the one immediately before the, the Hopper, the sort of A-series chips, are actually fabbed by Samsung, who gave them a sweetheart deal.

David Rosenthal 1:52:20 Ah.

Ben Gilbert 1:52:21 Nvidia likes to keep the lore alive around TSMC because they've been this, like, great longtime partner and stuff, but-

David Rosenthal 1:52:26 Yep

Ben Gilbert 1:52:27 ... they do play manufacturers off each other. I even think that Jensen said something recently like: "Intel has approached us about fabbing some of our chips, and we are open to the conversation."

David Rosenthal 1:52:41 Yes, yes, that did happen.

Ben Gilbert 1:52:44 So there was this big cybersecurity hack a couple of months ago by this group, Lapsus, and they stole access to Nvidia's source code. And actually, Jensen went on Yahoo Finance and talked about the fact that this happened. I mean, this is a very public incident, and i- it's clear from the demands of Lapsus where some of Nvidia's power lies, 'cause they-

David Rosenthal 1:53:04 Hmm

Ben Gilbert 1:53:04 ... demanded two things. They said, one, "Get rid of the crypto governors. Like, make it so that we can mine."

David Rosenthal 1:53:09 [laughing]

Ben Gilbert 1:53:10 Which may have been a red herring. That might have just been-

David Rosenthal 1:53:14 Right

Ben Gilbert 1:53:14 ... them trying to look like a bunch of, like, crypto miner people.

David Rosenthal 1:53:18 Hey, nothing wrong with being a crypto miner, but-

Ben Gilbert 1:53:20 Totally not, but I, I think there's a reputation around it. And the other thing they demanded is that Nvidia open source all of its drivers and make available-

David Rosenthal 1:53:29 Ah

Ben Gilbert 1:53:29 ... its source code. I don't think it was for CUDA. I think it was just the drivers, but it was very clear-

David Rosenthal 1:53:34 Interesting

Ben Gilbert 1:53:35 ... that, like, "We want you to make open your trade secrets so that other people can build similar things." And that, to me, is illustrative of the incredible value and pricing power that Nvidia gets by owning not only the driver stack, but, you know, all of CUDA and how tightly coupled their hardware and software is.

David Rosenthal 1:53:54 Nvidia is-- We just did this, our most recent episode with Hamilton and Chenyi. Nvidia is a platform, in my mind, no doubt about it. CUDA and Nvidia and general purpose computing on GPUs is a platform, so whatever, you know, all of the stew of powers that go into making that, that go into making Apple, Microsoft, you know, and the like [chuckles] go into Nvidia.

Ben Gilbert 1:54:21 Yep. I think the stew of powers is the right way to phrase that.

David Rosenthal 1:54:25 Yes. [chuckles]

Ben Gilbert 1:54:26 Anything else here, or you wanna move to playbook?

David Rosenthal 1:54:28 Let's move to playbook. So, man, I have-- I just wrote down in advance one that is [chuckles] such a big one for me, and I'm biased because I, I, I try to think about this in investing, particularly in public markets investing. But, like, man, you really, really want to invest in whoever is selling the picks and the shovels in a gold rush.

Ben Gilbert 1:54:51 Hmm.

David Rosenthal 1:54:52 The AI, you know, ML, deep learning gold rush, uh, those years, gosh! Oh, my gosh, like, we should all, all be kicking ourselves of twenty twelve, thirteen. Maybe not twenty twelve, but certainly twenty fourteen, twenty fifteen into twenty sixteen. Like, duh. [chuckles] You know, Marc Andreessen saying, "Every startup that comes in here wants to do AI and deep learning, and they're all using Nvidia. Like, maybe we should [chuckles] have bought Nvidia." Like, I don't know if any one of those startups, any given one, is gonna succeed, but I'm pretty sure Nvidia was gonna succeed back then.

Ben Gilbert 1:55:27 Yeah, it's such a good point. Kicking myself.... One I have is, uh, being willing to expand your mission. So it's funny how, uh, Jensen, early days, would talk about to enable graphics to be a storytelling medium. And, of course, this led to the invention of the pixel shader and the idea that everybody can sort of tell their own visual story their own way in a social, networked, real-time way. Very cool. And now it's much more that wherever there is a CPU, there is an opportunity to accelerate that CPU, and Nvidia will bring accelerated computing to everyone. And we will make all the best hardware, software, and services solutions to make it so that any computing workload runs in the most efficient way possible through accelerated computing. That's pretty different than enable graphics as a storytelling medium, but also, they need [chuckles] to sell a pretty big story around the TAM that they're going after.

David Rosenthal 1:56:22 I think there's also something to, uh, the whole Nvidia story, you know, across the whole arc of the company of, you know, it's sort of a trite, cliché thing at this point in startup land, but so few companies and founders can actually do it: just not dying.

Ben Gilbert 1:56:35 Yeah.

David Rosenthal 1:56:36 They should have died at least four separate times, and they didn't. And part of that was brilliant strategy, part of that was things going their way, but I think a large part of it, too, was just the company and Jensen, particularly in this, these most recent chapters, where they're already a public company, just being like, "Yeah, I'm willing to just sit here and endure this pain [chuckles] and I have confidence that, like, we will figure it out. The market will come. I'm not gonna declare game over."

Ben Gilbert 1:57:08 One that I have is, we mentioned at the top of the show, but the scale of everything involved in machine learning at this point, and anything semiconductors, is kind of unfathomable. You and I mentioned falling down the YouTube rabbit hole with that Asianometry channel, and I was watching a bunch of stuff on how they make the silicon wafers, and my God, floor planning is this just unbelievable exercise at this point in history, uh, especially with the way that they sort of overlay different designs on top of each other on different layers of the, the chip.

David Rosenthal 1:57:38 Yeah. Say more about what floor planning is. I bet a lot of listeners won't know.

Ben Gilbert 1:57:42 So it's funny how they keep appropriating these sort of real-world, large-scale analogies to chips. So floor planning, the way that an architect would lay out the 15 rooms in a house or five rooms in a house or two rooms in a house, on a chip, is laying out all of the circuitry and wires on the actual chip itself, except, of course, there's, like, ten million rooms, and so it's incredibly complex. And the stat that I was gonna bring up, which was just mind-bending to think about, is that there are dozens of miles of wiring on a GPU.

David Rosenthal 1:58:18 Wow, that is mind-bending. 'Cause these things are like, you know, I don't know, they're less than the size of your palm, right?

Ben Gilbert 1:58:24 Right! And it obviously is not wiring in the way you think about, like, a wire. I'm gonna reach down and pick up my Ethernet cable, but it's wiring in the EUV-etched substrate on chip... Exposure is probably the term that I'm looking for here, photolithography exposure. But it is just so tiny. I mean, you can say four nanometers all you want, David, but that won't register with me how freaking tiny that is until you're sort of faced with the reality of dozens of miles of, quote, unquote, "wires" on this chip.

David Rosenthal 1:58:55 Yeah, it's not like-- To me, that registers as like, "Oh, yeah, that's like a decal I put on my hot rod. Four nanometers!" [chuckles]

Ben Gilbert 1:59:00 [chuckles] Yes.

David Rosenthal 1:59:01 "I got the S version." [chuckles]

Ben Gilbert 1:59:03 [chuckles]

David Rosenthal 1:59:03 But yeah, like, that's what that means.

Ben Gilbert 1:59:05 Okay, here's one that I had that we actually even talked about, which I think will be fun. So I generated a CapEx graph.

David Rosenthal 1:59:12 Ooh, fun!

Ben Gilbert 1:59:14 We'll show it on screen here for those watching on video. Obviously, there's a very high-looking line for Amazon 'cause building data centers and fulfillment centers is very expensive, especially in the last couple of years when they're doing this massive build-out. But imagine without that line for a minute. Nvidia only has a billion dollars of CapEx per year.

David Rosenthal 1:59:32 Hmm, and this is relative, for people listening on audio, relative to a bunch of other, you know, FAANG-type companies?

Ben Gilbert 1:59:38 Yeah. So Apple has ten billion dollars of spend on capital expenditures per year. Microsoft and Google have twenty-five billion. TSMC, who makes the chips, has thirty billion. What a great capital-efficient business that Nvidia has on their hands, only spending a billion dollars a year in CapEx. It's like it's a software business, and it basically is.

David Rosenthal 2:00:01 Well, it is, right? Like, TSMC does the fabbing, Nvidia makes software and IP.

Ben Gilbert 2:00:06 Yep. So here, this is the best graph for you to very clearly see the magic of the fabless business model that Morris Chang was so gracious to invent when he grew TSMC.

David Rosenthal 2:00:19 Thank you, Morris.

Ben Gilbert 2:00:20 Another one that I wanted to point out: it's a freaking hardware company-- I know we didn't... They're not a hardware company, but they're a hardware company with thirty-seven percent operating margins, so this is even better than Apple. And for non-finance folks, operating margins... So we talked about their sixty-six percent gross margin, that's like unit economics, but that doesn't account for all the headcount and the leases and just all the fixed costs in running the business. Even after you subtract all that out, thirty-seven percent of every dollar that comes in gets to be kept by Nvidia shareholders. It's a really, really, really cash-generative business, and so if they can continue to scale and can keep these operating margins or even improve them, 'cause they think they can improve them, that's really impressive.

David Rosenthal 2:01:06 Wow, I didn't realize that's better than Apple's.

Ben Gilbert 2:01:08 Yeah. I think it's not as good as, like, Facebook and Google because they just run these, like-

David Rosenthal 2:01:13 Well, those are digital monopolies. Like, come on. [chuckles]

Ben Gilbert 2:01:15 ... Basically zero-cost [chuckles] digital monopolies in some of the largest markets in history, but, uh, it's still very good. All right, well, let's do grading. And before we actually grade, we wanna tell you about another one of our friends. For our final sponsor-... Let's talk about the SoftBank Latin America Fund. So you know this by now, these folks created the fund with a simple thesis: the region of Latin America was overflowing with innovative founders and great opportunities, but short on the ingredient of capital. SoftBank has invested eight billion dollars in seventy plus companies, and they have one gigantic takeaway. And I, I can't say this enough, because I think it's like you can keep hearing it, but I think the important thing is sort of like internalizing it, that technology in Latin America is not about disruption, it's about inclusion. So when you're thinking about economic opportunities in this region, you don't have to think like, "Ooh, how can we overthrow the incumbent?" It really is like if you're used to living your life or doing business in North America in a lot of the like, ways that you feel are, quote, unquote, "modern," a lot of these business models and a lot of this technology just has not happened yet to serve the vast populations in Latin America. You sort of have a case study in some businesses that have worked, and now you get to go and bring it to the masses. So just amazing, uh, opportunity for inclusion here. The vast majority of the population is underserved by every category, from banking to transportation to e-commerce. Businesses are not served by modern software solutions, as I was saying, and we want to highlight a great portfolio company, VTEX. Now, this is a crazy story. Speaking of high-growth companies recently, they saw ninety-eight percent growth during the pandemic as companies looked to VTEX for their digital commerce, native marketplace, and order management capabilities. Today, VTEX powers over three thousand online storefronts for global brands like Walmart, Coca-Cola, Nestle, and as we mentioned on our Sony episode, Sony. They were recently named the world's fastest growing e-commerce platform, and they are just one example of how SoftBank is partnering with great founders and bringing them the capital and expertise they need to bring the future and build it in Latin America now. To learn more, you can click the link in the show notes or go to latinamericafund.com.

David Rosenthal 2:03:39 So cool. And Shu and Paulo, who run it, are just-

Ben Gilbert 2:03:42 The best.

David Rosenthal 2:03:43 They are just the best.

Ben Gilbert 2:03:44 The best.

David Rosenthal 2:03:44 We love them. Become such great friends over the years, and, uh, can't say enough good things.

Ben Gilbert 2:03:50 Excited to see Shu, uh, as well at the Seattle Arena Show.

David Rosenthal 2:03:55 Yes!

Ben Gilbert 2:03:56 Acquired.fm/arenashow. Uh, okay, grading. So I think the way to do this one, David, is what's the A-plus case, what's the C case, what's the F case?

David Rosenthal 2:04:07 I think so.

Ben Gilbert 2:04:08 And there's sort of an interesting way to do this one, 'cause you could do it from a shareholder perspective, where you have to evaluate it based on where it's trading today, and sort of like what needs to be true in order to have a A-plus investment starting today, that sort of thing.

David Rosenthal 2:04:25 Hmm. You mean like a Michael Mauboussin expectations investing style?

Ben Gilbert 2:04:29 Yes, exactly. Or you could sort of close your eyes to the price and say, "Let's just look at the company." If you're Jensen, what do you feel would be an A-plus scenario for the company, regardless of the investment case? I kind of think you have to do the first one, though. Like, I kind of think it's a cop-out to not think about it like, what's the bull and bear investment case from here?

David Rosenthal 2:04:54 As we pointed out many times on the episode, there's a lot you gotta believe to be a bull on Nvidia at this share price. So...

Ben Gilbert 2:05:03 What are they? Well, one big one is that they continue their incredible dominance and their... What are they growing, like seventy-five percent or something year over year in the, the data center?

David Rosenthal 2:05:15 Yep.

Ben Gilbert 2:05:16 And they just sort of continue to own that market. I think there's a plausible story there around all the crazy gross margin expansion they've had from sort of selling solutions rather than, you know, fitting into someone else's stuff. I also think with the Mellanox acquisition, there's a very plausible story around this idea of a data processing unit and around being your one-stop shop for AI data center hardware. And I think rather than saying like, "Oh, the upstart competition will fail," I think you kind of have to say that Nvidia will find a way to learn from them and then integrate it into their strategy, too.

David Rosenthal 2:05:54 Which seems plausible.

Ben Gilbert 2:05:56 Yeah, but they've been very good at changing the definition of GPU over time to mean more and more robust stuff and accelerate more and more compute workloads. And I think you just have to kind of bet that because they have the developer attention, because they now have the relationships to sell into the enterprise, they're just gonna continue to be able to do their own innovation, but also fast follow when it makes sense to, uh, redefine GPU as something a little bit heftier and incorporate other pieces of hardware to do other workloads into it.

David Rosenthal 2:06:27 Yep. I think the question for me on an A-plus outcome for Nvidia from the shareholder perspective is: do you need to believe that all the real-world AI use cases are gonna happen? Do you need to believe that some basket, maybe not all of them, but that some basket of autonomous vehicles, the Omniverse, robotics, one or multiple of those three are gonna happen, they're going to be enormous markets, and that Nvidia is gonna be a key player in them?

Ben Gilbert 2:07:04 I mean, I think you do, because I think that's where all the data center revenue is coming from, is companies that are going after those opportunities.

David Rosenthal 2:07:11 I'm wrestling with whether that is something you have to believe or whether that's optionality. The reason it would be only optionality, only upside, is if the digital AI, we know that that's a big market.

Ben Gilbert 2:07:24 Mm-hmm.

David Rosenthal 2:07:24 There's no question about that at this point.

Ben Gilbert 2:07:26 Hmm.

David Rosenthal 2:07:27 Is that gonna continue to just get so big? Are we still only scratching the surface there? How much more AI is gonna be baked into all the stuff we do in the digital world, and will Nvidia continue to be at the center of that?... I don't know. I don't have a great way to assess how much growth is left there.

Ben Gilbert 2:07:45 That is kind of the right question, though. Yeah.

David Rosenthal 2:07:48 They're at an interesting point right now. You know, there was all the early company stuff that we talked about in the first episode, but at the beginning of this episode, you know, Jensen was really asking you to believe. It's like: Hey, we're building this CUDA thing. Just ignore that there's no real use case for it or market. [chuckles] Now, there is a real, real use case and market for it, which is machine learning, deep learning in the digital world.

Ben Gilbert 2:08:15 Mm-hmm.

David Rosenthal 2:08:15 Undeniable.

Ben Gilbert 2:08:16 Mm-hmm.

David Rosenthal 2:08:17 He's also pitching now that that will exist in the physical world, too.

Ben Gilbert 2:08:21 Yeah, the A+ is definitely that it does exist in the physical world, and they are the dominant provider of everything you need to be able to accomplish that.

David Rosenthal 2:08:29 Yep.

Ben Gilbert 2:08:29 And if the real world stuff, you know, these little robots that run around, uh, factory floors and the autonomous vehicles, and if that stuff doesn't materialize, then, yeah, there's no way that it can support the growth that it's been on.

David Rosenthal 2:08:42 [chuckles] I think that's probably right. That would be my hunch. Although, saying that, though, does feel like a little bit of a betting against the internet, you know? Like, I don't know, man. [chuckles] Digital world's pretty big, and it keeps getting bigger.

Ben Gilbert 2:08:55 Yeah, but I think we're saying the same thing. I think you're saying that these physical experiences will become more and more intertwined with your digital experiences.

David Rosenthal 2:09:03 Yeah. Yeah.

Ben Gilbert 2:09:04 I mean, autonomous driving in electric vehicles is an internet bet. In part, if you want to bet on the growth of the internet, it'll mean you'll drive less, but it also means that you're just going to be on the internet when you're driving.

David Rosenthal 2:09:19 Yep. Yeah.

Ben Gilbert 2:09:20 Or when you're in motion in the physical world.

David Rosenthal 2:09:22 [chuckles] That's actually... That's a bull case for Facebook, right? Is like, is autonomous vehicles, because if people are being driven instead of driving, that's more time they're on Instagram. [chuckles]

Ben Gilbert 2:09:31 Right. Ah, that's so true.

David Rosenthal 2:09:33 [laughing]

Ben Gilbert 2:09:35 Okay, what's the failure case? It's actually quite hard to imagine a failure case of the business in any short order. It's very easy to imagine a failure case for the stock in short order if there's a cascading set of events of people losing faith.

David Rosenthal 2:09:49 I think maybe the failure case is this amazing growth for the past couple years was pandemic pull forward. It's so hard for me to imagine that that's, like, to the de- degree of a Peloton or a Zoom or something like that.

Ben Gilbert 2:10:04 Right.

David Rosenthal 2:10:04 Both of which I think are great companies. They just got everything pulled forward. I don't think Nvidia got everything pulled forward. They probably got a decent amount pulled forward.

Ben Gilbert 2:10:14 Hard to quantify, hard to know, but it's the right thing to be thinking about.

David Rosenthal 2:10:18 Yep.

Ben Gilbert 2:10:19 All right, Carve Outs?

David Rosenthal 2:10:21 Ooh, Carve Outs. I've got a fun one, a small one. Well, a collection of small things. Longtime listeners probably know one of my favorite... I think my favorite series of books that have been written in the past ten years is The Expanse series. Amazing sci-fi, nine books, so great. The ninth book came out last fall. Ah, it was just-- Even with, like, a newborn, I made time to read this book. [chuckles]

Ben Gilbert 2:10:48 That's awesome.

David Rosenthal 2:10:48 Newborn plus Acquired, I was like: I gotta read this.

Ben Gilbert 2:10:51 That's how you know.

David Rosenthal 2:10:51 That's how you know. Recently, last month, so the authors have been writing short stories, like, companion short stories, alongside the main narrative over the last decade that they've been doing this, and they released a compendium of all the short stories, plus a few new ones, called Memory's Legion, and it's just really cool. Like, I mean, they're great writers, great short stories to read, even if you don't know anything about The Expanse story. But if you know the whole nine-book saga, and then these, like, just paint little... They give you little glimpses into corners and, like, characters that-

Ben Gilbert 2:11:25 Huh

David Rosenthal 2:11:25 ... just exist, and you don't question otherwise, but you're like: Oh, what's the backstory of that? So I've been really enjoying that.

Ben Gilbert 2:11:31 So it's like the Solo of the Fantastic Beasts and Where to Find Them?

David Rosenthal 2:11:35 Exactly. It's, like, nine or ten of those. [chuckles]

Ben Gilbert 2:11:38 Cool. Mine is a physical product. Actually, for the episode we did with Brad Gerstner on Altimeter, we needed a third camera, and so I went out and bought a Sony RX100, a little point-and-shoot camera, and, uh, recently took it to Disneyland, and I must say, it is so nice to have a point-and-shoot camera again. It's, like, funny how it's gone full circle. I, you know, was a DSLR person forever, and then I got a mirrorless camera, and then I became a mirrorless plus big, long zoom lens person, but it's kind of annoying to lug that around. And then once I started downgrading my phone from the massive, awesome iPhone with the 3X zoom, and I now have the iPhone 13 Mini, I think that's what it is, with the two cameras and no zoom lens. It's really disappointing. So it's pretty awesome. It fills a sort of spot in my camera lineup to have a point-and-shoot with a really long zoom lens on it. And of course, like, it's not as nice as having a, you know, full-frame mirrorless with, like, an actual zoom lens, but it really gets the job done, and it's nice to have that sort of, like, real-feeling, mirrorless-style image that is very clearly from a real camera and not from a phone. Uh, it's, uh, it's slightly more inconvenient to carry because you kind of need another pocket.

David Rosenthal 2:12:54 Yeah, I was gonna ask, can you put it in your pocket?

Ben Gilbert 2:12:57 Yeah, I put it in a pocket. I don't have to have a sort of, like, a rapid strap around my neck, which is nice.

David Rosenthal 2:13:02 Nice.

Ben Gilbert 2:13:03 So the Sony RX100, great little device. It's, like, the seventh generation of it, and they've really refined the industrial design at this point.

David Rosenthal 2:13:10 That's awesome. That's awesome. I actually just bought my first camera cube, like a travel camera cube thing for our, uh, uh, Alpha 7Cs now that we have, uh-

Ben Gilbert 2:13:22 Oh, nice!

David Rosenthal 2:13:22 Literally, it's for Acquired, for when after the Altimeter episode, I was like: Oh, wow.

Ben Gilbert 2:13:26 We gotta do more in person. Yeah.

David Rosenthal 2:13:27 Yeah, Ben brought his down, and I was like: For sure, I'm gonna need to bring this somewhere. These cameras are just... They are so good. They're so good.

Ben Gilbert 2:13:37 All right, listeners, thank you so much for listening. If you're contemplating coming to Seattle on May 4th, we would love to see you there. It's gonna be so fun. It'll be a, uh, a blast interviewing Jim, and maybe by the time this comes out, we will have announced, uh, some of our other little fun surprises, too.

David Rosenthal 2:13:54 I think we can say now there's gonna be an after-party.

Ben Gilbert 2:13:58 There is definitely gonna be an after-party. Thank yous to our friends at Vouch for renting out, uh, a bar basically across the street, a couple blocks away, with huge capacity. So that'll be really fun to have everyone wander over from the arena to the Vouch after-party, where they're gonna launch in Washington state at the event, which is very fun. I'm very excited for all my portfolio companies. And no matter what, whether you are attending that in person or not, you should come chat about this episode with us in the Slack. There's 11,000 other smart members of the Acquired community just like you. And if you want more Acquired content after this, and you are all caught up, go check out our LP show by searching Acquired LP Show in any podcast player. Hear us interview Nick and Lauren from Trova Trip most recently. And we have a job board, acquired.fm/jobs. Find your dream job curated just by us, the fine folks at the Acquired podcast. All right, with that, thank you to Vanta, Vouch, and the SoftBank Latin America Fund, and we will see you next time.

David Rosenthal 2:15:01 We'll see you next time.

Speaker 2:15:02 Who got the truth? Is it you? Is it you? Is it you? Who got the truth now? Huh. [singing]