Nvidia Part II: The Machine Learning Company (2006-2022)

0:00 Still got Swedish House Mafia Greyhound in my head from the pump up. Nice. Nice. 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. Like it's like crypto before crypto. Who got the truth? Is it you, is it you, is it you Who got No. Is it you, is it you, is it you?

0:30 Down! Straight. Another story on the Welcome to Season 10, Episode 6 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. And I'm David Rosenthal, and I'm an angel investor based

0:55 In San Francisco. And we Are your hosts. When I was a kid, David, I used to stare into backyard bonfires and wonder If that fire flickering.

1:06 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

1:20 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

1:35 is extremely difficult. But that Is what NVIDIA? is doing today. Ben, I love that intro. That's great. I was thinking like where is Ben going with this. And this was occurring to me as I was watching Jensen showing the

1:49 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

2:10 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.

2:32 Precisely. And there was 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

2:52 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. I know exactly what part of the research you got that from because I read the same thing and I was like, You gotta be freaking kidding me. 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.

3:34 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. Yeah. So great.

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6:08 And after you finish this episode, come join the Slack, acquired.fm slash Slack, and talk about it with us. 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. That's good. I was gonna make sure that you said that this time because We're gonna talk a lot.

6:29 About Investing in investors. In NVIDIA stock over the years. It has been a wild Wild Journey. So last

6:38 We left our Plucky heroes. Jensen Huang and NVIDIA. in the end of our NVIDIA the GPU company years, and in kinda roughly, you know, two thousand four, two thousand five, two thousand six, they had Cheated death.

6:55 Not once. But twice. The first time in the Super overcrowded graphics card market when they were first getting started. And then

7:04 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 G force three, they power the Xbox.

7:29 They create the CG. Programming language. With Microsoft. And so here we are. It's now two thousand four, two thousand five.

7:37 And this is a pretty impressive company. Public company Stock is High flying after the tech bubble crash. Conquered the graphics card market. Of course there's ATI out there as well.

7:48 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. These six month ship cycles for They're chips.

8:01 We talked about that, but we didn't actually say the rate at which they ship these things. I actually wrote down like a little list, so In the fall of nineteen ninety nine. They ship the first Ge Force card, the Ge Force two fifty six. In the spring of two thousand, G Force Two

8:17 In the fall of two thousand G Force Two Ultra. Spring of two thousand one, G Force three, that's the big one with the programmable shaders. Then Six months later. The G Force three T I five hundred. I mean, the normal cycle, I think we said was two years, maybe eighteen months for most other competitors who just got largely left in the dust. Well, I was just thinking, you know, yeah, the competitors are gone at this point, but I'm thinking about Intel.

8:40 How often did Intel Ship. New products, let alone fundamentally new architecture. You know, there was the two eighty six and then the three eighty six and the Pentium, and it got it to Pentium, I don't know, five, whatever. Dude, I feel like the

8:53 Intel product cycle is approximately the same as a new body style of cars. Yes, exactly. Every five, six years there seems to be a meaningful new architecture change. 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.

9:14 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.

9:27 And we are a Big thank you for this and many other things too. Uh great Listener, 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. So good. I've probably watched like twenty five Asianometry videos this week.

9:44 So so good. Huge shout out to them. But All the other graphic cards companies at the time And most peripheral companies, they let the further downstream partners write the drivers for what they were doing.

9:57 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

10:13 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.

10:23 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

10:49 take the short term pain of that expense for the long term benefit of that improved user experience with their products. That their users high end gamers that want the best experience, you know, they're gonna go out, they're gonna spend the time three, four, five hundred dollars on an NVIDIA top of the line graphic card. They're gonna drop it into the PC that they built. You know, they want it to work. I remember messing around with drivers back in the day and things not working like

11:14 This is super important. So all this is focused on, of course, they have the third advantage in the company is programmable shaders. copies as well, but like they innovated, like They've, you know, done all this. So all of this at this time. It's

11:27 All in service of the gaming market. 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.

11:45 Maybe it would get that compute load would get offloaded to whatever the graphic 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.

12:04 So that they can actually start saying, look, if you develop for our specific hardware, there are advantages for you. And really. gaming cards. Like everything we're talking about. These developers, they're game developers. All of this stuff, it's all in service to the gaming market. So you know, again, they're a public company.

12:22 They have this great deal with Microsoft. They bring out CG together. They're powering the Xbox. Yeah, 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 four, two thousand five. Stock keeps going on a tear. By mid two thousand seven the stock

12:43 reaches just under twenty billion dollar market cap. Yeah, this is great. And this is 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

12:58 video games. Which on its own, that would be a great wave to surf. I mean, I think what's the gaming market today? Hundred eighty billion or something. And when we talk to Trip Hawkins, who sort of like helped invent it or l 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 can totally just ride this gaming thing and be an important code. 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.

13:27 Yes, I am the leading company in this major market, this huge wave that I don't see ending. Any time soon. 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. But not Jensen. But not Jensen.

13:47 Mm So while all this is happening, he starts thinking about well, what's the next chapter, you know, I'm dominating this market. I want to 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.

14:03 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, D Force 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. He's a I think it was a quantum chemistry researcher, supposedly.

14:24 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. And Jensen loves that quote. It comes out at every GTC. So that Story. If you're a uh Skeptical listener. My big

14:42 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 doing, you know, chemistry modeling. Using G Force cards for that.

14:54 What's he writing this in? Well it turns out Programmable shaders, right? Yeah. They were shoehorning CG. Which was built for Graphics. They were translating everything that they were doing.

15:06 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. 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

15:25 is actually a triangle. And Imagine that this way to that I want to 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.

15:46 Yep. So he thinks this is an interesting market. He wants NVIDIA to serve it. If you really want to do that right. It is a massive undertaking. It was ten plus years to get to the company to this point. You know

15:59 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.

16:12 You know, it's kinda 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. You know, or today, even better, Apple, right? Like everything Apple gives to iOS and Mac developers. Right. To develop on Mac. Right. Yeah. The analogy's not perfect.

16:32 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, will you need UI framework, so how about AppKit and Coco Touch? And how about all these other SDKs and frameworks like AR kit and like store kit and like home kit. It's basically you need the whole sort of abstraction stack on top of the programming language to actually make it very accessible to right software for

17:00 domains and disciplines that you know are going to be really popular using that hardware. Exactly. So When Jensen commits himself and the company to pursuing this, He's biting off a lot.

17:12 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.

17:25 within the company. So that kinda enables them to start here. But like still this is big. 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.

17:44 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. 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 it gonna take us. to actually get to something that's useful for that market when it materializes. Because

18:06 Well CUDA development began in 2006. That was not a useful usable f platform for six plus years. Add Nvidia? Yep.

18:19 This is Closer to on the order of the Microsoft development environment or the Apple development environment than What? NVIDIA was doing before. Which was like, Hey

18:30 We made some APIs and worked with Microsoft so that you can Program for my thing. Right. I'm gonna flash way forward just to illustrate the 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. I'm surprised it's not eleven thousand.

18:53 Yeah. Okay. 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 in time and cost and all that. But even just put that aside. Is there a market for this?

19:07 Is the first order question. And the answer to that is probably no at this point in time. 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

19:30 Weeks or months and wouldn't it be nice if they could do it cheaper or faster. Is that kinda the market they're looking at? Yeah, they're attacking like the Cray market, like Cray supercomputers. Like that kinda. stuff. You know, great company. Right. But like That's no NVIDIA today. Right. 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.

20:00 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. Mm-hmm. And not enough to justify the time and the cost of what you're gonna have to build out to go after this.

20:13 to any rational person. So You know, here we come. Denson and N video like they are doing this. He is Committed. He's drunk the Kool Aid. Two thousand six, two thousand seven, two thousand eight.

20:25 They're Pouring a lot of resources. into building what will become CUDA that we'll get to in a second. Um I guess already is CUDA at this point in time. And I think Jensen's psychology here is sort of twofold. One is He is enamored with this market. He loves the idea.

20:42 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

21:01 Gosh, at this point. thirteen, fourteen years. Being commoditized. in all these different ways. And I think he sees a path here to

21:10 durable differentiation where he's like whoa to own the platform. 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 uh a company that can throw its weight around in the industry.

21:30 A hundred percent. Jensen. I don't know if he used it at the time because he probably would have gotten bill read, but maybe he did. I don't think he cared. Uh he certainly uh has used it since the 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. 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

21:55 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. So Wall Street is mostly willing to ignore this in two thousand six, two thousand seven. Two thousand eight.

22:10 The company's still Growing really nicely. the this great market cap run leading up to right before Financial crisis. But then.

22:20 You know, you mentioned last time. I think it gets announced in two thousand six, maybe and closes in two thousand seven, AMD. Acquires. A TI. Yep. 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. Something like that. So it was a lot of money. And then they put

22:43 a lot of resources. Like they weren't just acquiring this to, you know get some talent. Like they're like, No, this is gonna be a big pract line for us. We're putting a lot of weight behind this. We haven't done the research into AMD the way we have into NVIDIA, but the AMD Radion Line, which used to be the ATI Radion Line, that is how you think about AMD as a company, is that they make these GPUs

23:04 Mostly for the gaming use case. Yep. Before the acquisition, I think the first P C I built. in like end of high school, beginning of college, I think I had a Radion.

23:14 Card in it. I think I was probably in the minority. I think NVIDIA was bigger, but for whatever reason I Like A T I at that point in time, so like They were legit. Well.

23:23 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 Massive rising tide. Your competitor now has all these resources and AMD that's fully dedicated to going after it.

23:38 Mid two thousand eight. NVIDIA. whiffs on earnings. Like this is natural. They took their eye off the ball. Of course they did. And uh The stock gets

23:48 Hammered. Because an anything that CUDA empowers is not yet a revenue driver and they've totally taken their eye up off of gaming. Yes. So Yeah, we said the high was around a twenty billion dollar market cap It drops.

24:01 Eighty percent, eight zero. 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, during the SP dropping five percent in a day. I'm like, oh that's a Thursday these days, you know. It is literally the Thursday that we are recording. Yes. For a company stock to drop eighty percent. A technology company stock, even during the financial crisis. They're not just in the penalty box. They're like Getting kicked to the curb. Right. Are they done? The headlines at this point are is NVIDIA's run over.

24:34 If you're most CEOs. At this point in time. You you're probably calling up. Goldman or, you know, Allen Company or Frank Quatron and You're shopping this thing.'Cause

24:47 How are you gonna recover? But not Jensen. But not. Jensen, obviously. So Instead he goes and builds Cuda and continues to build Cuda and um

24:57 This is And just set context. Like we get excited about a lot of stuff unacquired. I think Cuda is like One of the greatest.

25:07 Business stories of the last. Ten years, twenty years, more? I don't know. What do you think, then? 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

25:23 more efficient quadrilateral focused graphics. 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. Those were big bets relative to the company's size at the time, but this bet is like an iPhone sized bet. That's exactly what this is. It's an iPhone sized bet. It is a bet the company when you are already a several billion dollar company. Yes. An attempt to create something that if they are successful and this market materializes.

25:52 This will be a generational company. Yeah. So What is Cuda? It is

25:58 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.

26:09 development framework for doing any kind of computation that you would want on GPUs. 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.

26:24 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

26:44 hundreds of industries at this point. To communicate down to Cuda, which communicates down to Lee GPU.

26:52 And everything else that they have done at this point. This is what's so brilliant. So right after we released right the same day that we released part one. Yep. The first NVIDIA episode we did a couple of weeks ago. Ben Thompson had this amazing interview with Jensen on Strategic 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.

27:18 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.

27:45 to Life and Earth Sciences, Quantum Computing, AI, Cybersecurity, Five G, and Robotics. And then he talks about what it took. This is like The point we we're trying to like hammer home here. He says you have to internalize.

27:58 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. It's crazy.

28:26 It's amazing. And when he says that It's a whole new Programming. I think he says maybe paradigm or way of programming.

28:34 It is literally true because most programming languages up to this point and most Computing platforms. Primarily contemplated serial. execution of programs.

28:47 And what CUDA did was it said You know what? The way that our GPUs work and the way that they're going to work going forward is Tons and tons of cores all executing things at the same time, parallel programming, parallel architecture. Today there's

29:02 over 10,000 cores on their most recent consumer graphics card. So insanely uh or dare I say embarrassingly parallel, and CUDA is designed Four. parallel execution from the very beginning. That's the like catchphrase in the industry of embarrassingly parallel.

29:21 And it's actually kind of a technical term. I don't know why it's embarrassing. 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. Uh it's not that it's

29:45 Parallel that's embarrassing. It's embarrassing if you were to do it the old way on CPUs. Serially. I think that's the implication. Got it. Got it. This is so obvious that it's embarrassingly parallel. Okay, now it makes sense. Now here's the coup de gras. We're gonna spend a few minutes talking about how Brilliant this was.

30:03 Everything we just described, this whole undertaking the like it's like building the pyramids of Egypt or something here. It is entirely free. 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.

30:18 For Cuda. But Anyone can download it, learn it, use it, you know, blah blah blah. All of this work stand on the shoulders of everything NVIDIA has done. But then what is The butt.

30:31 It is closed source and proprietary exclusively to NVIDIA's hardware. That's right. You do any of this work? You cannot deploy it. On anything. But NVIDIA chips. And that's not even just like, oh, NVIDIA put in the like terms of service that you can't deploy this on, you know, AMD chips or or whatever. Like literally doesn't work. Nope, it's full stack. It's like if you were to develop an iOS uh

30:54 app and then try and deploy it on Windows. Like uh It wouldn't work. It is Integrated. With the hardware. So OpenCL is sort of the main competitor at this point, and they do actually let OpenCL applications run on their chips.

31:08 But Nothing in CUDA is available to run elsewhere. It's so great. You can see this is just like Apple. And it's the Apple business model. Apple gives away all of this

31:20 amazing platform ecosystem that they built to developers and then they make money by selling. their hardware for very, very healthy gross margins. But this is why Jensen is so brilliant. Because back when They started down this journey in Two thousand six, even before that when they started and then all through it.

31:40 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, that like Apple lost and the Mac was stupid and niche and like Windows and Intel is What one? The open. Ecosystem.

31:57 Well, but Windows and Intel did have proprietary development environments and you know full stack. Dev tools. 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. Well, except that it couldn't. It could only run on the Intel, IBM, Microsoft alliance world. It wasn't running on power PCs. It wasn't

32:20 Running on anything Apple made. That's true. It's funny, in some ways NVIDIA is like Apple, in other ways they're like The Microsoft Intel IBM Alliance except

32:32 fully integrated with each other instead of being three separate companies. 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 Yeah, oh yeah. Open's gonna win, Android's gonna win, Apple is doomed, you know, close never works, you gotta be modular, you can't be integrated. And like, you know, Clay was amazing and one of the greatest strategic th but I think that's just representative to me of like Everybody thought that like the Apple model sucked.

33:04 Yeah, I mean It sucks unless you're at scale. And at the time there was very little to believe. That N video. was going to have the scale required to justify this investment, or that there was a market to let them achieve the scale.

33:20 To justify this. That's the thing. Even if you were to say, Okay, Jensen, I believe you 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. All right, listeners, now is a great time to tell you about a longtime friend of the show, Vanta.

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35:33 Two thousand eleven, twelve, where are we hopping back in here? Uh, if only the world were uh It works like fiction and it were actually like a Truly straight line. It's never a straight line. We will get there and that is what Saves NVIDIA and makes this whole thing.

35:51 Work. But they have some misadventures. In between. So Stock's getting hammered. It's two thousand eight.

35:58 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

36:10 General purpose computing on GPU. A thing. I do wonder if they felt like well, we gotta do something to appease shareholders here. Yeah, we gotta show that we're trying to be commercial here.

36:24 So it's two thousand eight. What's going on in two thousand eight, you know, in the tech world. It's Mobile. So in two thousand eight. They launch

36:33 The Tegra. Yep. And platform within NVIDIA. This may not be what saved the company. This is not what saved the company. This is more uh uh clown car style. Oh that's maybe that's too rough on NVIDIA, but what was Tegra? Uh people might recognize that name.

36:49 It was a full on System on a chip for smartphones. competing directly with Qualcomm. With Samsung like it was a Processor like a an ARM based CPU.

37:02 Plus all of the other stuff you would need for a system on a chip to power. Android headsets. 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

37:18 Come on, if there's ever a use case for integrated graphics, it's Smartphones. Right. Right. Low power, smaller footprint. Yeah. Totally. Do you know? This is one of my favorite parts about the whole Research.

37:32 Do you know what the first product was that shipped using a Tegra. Chip. Uh no. It was the Microsoft Zune H D Media Player. Uh, that just tells you

37:47 Pretty much everything you need to know. Uh it did though. The Tega system. It is still around, sort of, to this day. Empowered the original Tesla model S. Touch screen.

37:59 So like before any of the Autopilot, autonomous driving stuff. They were the processor powering just the infotainment. the touch screen infotainment in the model S. And I think that actually starts to help.

38:11 NVIDIA get into the automotive market. The Tegra platform still to this day is the main processor of the Nintendo Switch. Oh, they repurposed it for that? 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. Oh, this makes so much sense because they basically have walked away from every console since the PlayStation Three.

38:37 Yep. 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. It's not for the graphics cards. It was as Somewhere to put the Tegra stuff. Fascinating. Quick aside, it's funny how these

38:57 GPU companies have not been good. Hat. transitioning to mobile. There's like a funny naming thing, 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.

39:14 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? Oh I do not. Was it

39:24 Apple? It is Qualcomm. And it today is Qualcomm's mobile GPU division and Qualcomm's good at mobile and so is a natural home for it. Do you know what that line of mobile GPU processors is called? No.

39:40 It is the Ardino. A R D E N O Processors. And do you know why it's called the Ardeno or Ardenno? No, that sounds super familiar, but no. The letters are rearranged from Radion. Ah That's great.

39:55 That's great. So you're saying NVIDIA's mobile. Graphics efforts didn't quite pan out. No.

40:02 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.

40:16 Ironically, they make a lot of money now on the Play Store. Ah yeah, the Play Star. And ads. Right. I do think the primary way that they monetize it is not having to pay other people to acquire the search traffic. Right. But I mean for like partners, like if you are making everything from chips all the way up through hardware in the Android ecosystem, I don't think you're making it. Like maybe if you were the scale player, but like

40:38 These things are designed to sell for dirt cheap as in products. Like there's no margin to be had here. Yep. Yeah. Also. Before we continue, you just did the sidebar.

40:48 on the AMD mobile graphics chip. I see your sidebar. I'm gonna raise you. One more sidebar 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 Isera.

41:05 Uh a British company called Icerra in twenty eleven. You know where I'm going with this. Oh yes, I do. This is so good. It's a good seed plant to come back to later. Uh, you know,'cause they're investing in mobile integrity is gonna be a thing, blah, blah, blah. And uh then a few years later when they end up Pretty much shutting down the whole thing. They they shut down what they bought from my Sarah, they lay everyone off. The ISER founders who made a lot of money when

41:27 Invita. Bought them. They go off and they found a company called Graphcore that uh We're gonna talk about A little bit at the end of the episode is, you know, maybe one of the primary sort of uh NVIDIA bear cases.

41:41 NVIDIA bearcases, NVIDIA killers out there. They've now raised about seven hundred million in venture capital and pick up some mobile. In some ways it's kind of like Bezos and Jet.com. Yes. If Jet had been successful. I think that's sort of the graph core to NVIDIA analogy. Yes. Well I mean jury's still out if uh

42:01 Anybody's gonna be really successful in competing with NVIDIA. Although I think the market now is probably ironically big enough that Large. Yeah. 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 two thousands, early twenty tens, you know, some years

42:24 Growth is like Mm, ten percent, maybe it's flat in others like This company is completely gone sideways. In two thousand eleven. They whiff on earnings again.

42:35 Stock goes through another fifty percent. Draw down. It's cliche. I d 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. Like Everybody else would have given up.

42:49 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 strategy case study of Jensen because it required a miracle. 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.

43:13 Yeah. Sure. Sure. 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. Jensen did not plan AlexNet or see it coming'cause nobody saw. Alex net coming, so

43:33 In two thousand nine. A Princeton computer science. Professor and also Undergrad alum of Princeton. Just like yours, truly. Woo.

43:42 Wonderful place. Named Fei Fei Li. Their specialty is artificial intelligence and computer science. Starts working on an image classifying Project.

43:51 That she calls ImageNet. Now the inspiration for this was actually Uh way old. Project from I think the eighties. at Princeton called WordNet.

44:01 That was like classifying words. This is classifying image. Image. No idea. is to create a database of millions of labeled images. Like images that they have a correct label applied to them, like this is a dog or this is a strawberry or

44:15 Something like that. And that with that database, then artificial intelligence image recognition algorithms could run against that database and see how they do.

44:26 So like Oh, look at this image of you know, you and I were looking at me like that's a strawberry. But you don't give the answer to the algorithm and the algorithm Figures out if it thinks a strawberry or a dog or Whatever.

44:38 So senior 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 Feife 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.

45:02 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.

45:16 And uh 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. And uh she's still there to I know. I I couldn't resist. I couldn't resist. I'm 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.

45:40 What her endowed tenure chair is at. Stanford. Today. I do. She is the Sequoia chair. Yes, the Sequoia Capital Professor of Computer Science.

45:51 Hat. Stanford. So cool, why does she become the Sequoia Capital Chair? And what does all this have to do with NVIDIA? Well, In the twenty twelve competition.

46:01 A team From the University of Toronto. Submits an algorithm. That wins the competition. And it doesn't just win it by like

46:11 A little bit. It wins it by A lot. So the way they measure this is a hundred percent of the images in the database, what percentage of them did you get wrong? So

46:21 It wins it by over ten percent. I think it had a fifteen percent error rate or something in the next Like all the best previous ones have been like twenty five point something percent. Yes. This is like someone breaking the four minute mile. Actually, in some ways it's more impressive than the four minute mile thing'cause they just didn't brute force their way all the way there.

46:40 They like tried a completely different approach. Yes. And then boom showed that we could get way more accurate than anyone else ever thought. So what was that approach? Well.

46:51 They called the team. Which was composed of Alex Krzebsky, uh was the primary uh lead of the team. He was a PhD student. And collaboration with Ilya Sutzkever and Jeff Hinton. Uh, Jeff Hinton was the PhD advisor of of Alex.

47:06 They call it AlexNet. What is it? It is a convolutional neural network. Which is a branch of artificial intelligence. Called.

47:16 Deep learning. Now deep learning. Is new for this use case, but Ben is You weren't exactly right. It had been around for a long time.

47:25 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

47:39 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. It was completely impossible.

47:53 with a traditional computer architecture that you could make these work in any practical Applications. 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

48:18 you know, more transistors in a given space, but because you're able to run programs in parallel now. Yes. So Alex Nett took these old ideas. And it implemented them on GPUs.

48:31 And to be very specific. You've implemented them. In Cuda? On NVIDIA GPUs. We cannot overstate.

48:39 the importance of this moment. Not just for NVIDIA, but for like computer science, for technology, for business, for the world, for us staring at the screens of our phones all day, every day. This was the Bing Bang moment for artificial intelligence and NVIDIA and Cuda? Word.

48:57 Right there. Yep. It's funny, there's another example within the next couple of years, twenty uh twelve, twenty thirteen. Where Nvidia had been thinking about

49:09 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 Kuda. 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 do use it for anything. Right. And so deep learning's generating a lot of buzz, you know, a a lot from this Alexnet competition. And so in twenty thirteen, Brian Catanzaro, who's a research scientist at NVIDIA published a paper with some other researchers at Stanford, which included Andrew Ng.

49:48 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 a thousand 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.

50:09 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 at the core of what becomes coup DNN. which is the library for deep neural networks that's actually baked into CUDA that makes it

50:32 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

50:47 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. Yep. And specifically to do Deep learning. So

51:06 I think at this point, like everybody knows that this is Pretty important, but it's not That much of a leap. If you can

51:15 Train a computer. To recognize images on its own. That you can then train a computer to C on its own, to drive a car on its own, to play chess, to play go.

51:26 To make your photos look really awesome when you take them on the latest iPhone. Even if you don't have everything right. 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.

51:43 Yep. And Then most importantly, Four. The market that Jensen and Nvidia are looking for the

51:50 You can use the same branch of AI to predict what type of content you might like to see next show up in your feed of content. And what type of And Might work.

52:02 Really. Really. really well on you. 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.

52:14 Fife Lee goes to Google. Brian went to Baidu. And he's back at NVIDIA now doing applied AI. Jeff Hinton goes to Facebook.

52:23 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 Just g it doesn't matter like the the market of advertising of digital advertising that this enables. is a freaking multi trillion dollar market. 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 obvious well labeled data set that

52:48 These models don't have to be amazingly good because they're not generating unique output. They're just assisting in making something more efficient. But then like flash forward 10 more years and now we're in 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

53:12 It's happening faster than ever. Yep. 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.

53:24 Right, but that was necessary though. This was finally the market that enabled the building of scale and the building of technology to do this. And uh in the Ben Thompson um Denton interview. Ben actually says this when he's sort of realizing this talking to Denson, 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.

53:48 That value accrues to those who help you navigate the content. He's talking about aggregation theory, the 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. 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.

54:19 And that sounds like a pretty good place to be. And indeed it was a pretty good place to be. Amazing place to be. 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 realise this. We were the class of people working in tech as venture capitalists that should have. Ooh, do you know the Mark Andreessen quote? Ooh, no.

54:39 Oh, this is awesome. Okay, so it's a couple of years later, so it's like getting more obvious, but it's twenty sixteen. And Mark Andreessen gave an interview. 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 nineties or all building on the iPhone in the late two thousands. 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. This is like uh it was paradigm, right, that called all of their capital and one of their funds and put it into Bitcoin when it was like three thousand dollars a coin or something like that.

55:19 We all should have been doing this. So literally NVIDIA stock in Twenty Like recent like this is now. known 2012, 13, 14, 15. 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

55:39 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 It was huge. 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

55:54 There was a massive stock run up to like sixty five dollars a share in twenty eighteen. And even y as late as I think the very beginning of 2019, 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.

56:13 In twenty nineteen. If you zoom out on that graph, which is the next tweet here, that you can see that like In retrospect, that little crash. This 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. Yeah. It's wild. And a few more wild things about this.

56:32 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 seven when they were just a gaming company. That's almost ten years. 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.

56:54 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. He's not keeping this a secret. No, he's like trying to tell us all that this is the future. And

57:08 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 No.

57:33 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? I think people. Lost.

57:46 Trust and interest, you know, after like There were so many years of like they were so early with CUDA. And early to you know again, they didn't even know that this like they didn't know Alexnet was gonna happen. Right. Jensen felt like The GPU platform

58:01 could enable things that the CPU paradigm could not, and he like had this faith that Something would happen. Like he didn't know this was gonna happen. And so for years he was just saying that like We're building it, they will come, you know. And to be more specific, it was that

58:18 Well look the GPU has accelerated the graphics workload. So we've taken the graphic workload off of the CPU. The CPU's 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.

58:41 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.

59:11 I think we have exactly what you are looking for. Yes. There's uh that same uh interview with Brian Catazaro. 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. And so once NVIDIA saw that It was basically instant.

59:35 the whole company just latched onto it. There's so many things to law 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.

59:51 Yeah. Hundred percent. It's interesting thinking about The fact that In some ways it feels like an accident of history, in some ways it feels so intentional that

1:00:02 Graphic 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 nothing. Which is crazy, right? But there's way more pixels on a screen. So, you know, they're not all doing every single pixel at the same time, every clock iteration.

1:00:26 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

1:00:45 You gotta wonder like When you kinda 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.

1:01:00 With parallel matrix multiplication. There may be more. There may even be bigger markets out there. Totally. I think they probably will be.

1:01:10 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. Robotics, there's

1:01:20 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 Mark Andreessen knew, you know, whether he made money in his personal account or not. You know, we'll have to ask him. But then in twenty eighteen, another class of problems that are embarrassingly paralyzable.

1:01:42 is of course cryptocurrency mining. And So a lot of people We're going out and buying Consumer NVIDIA, you know, graphics cards.

1:01:52 And using them to set up crypto mining rigs in twenty sixteen, twenty seventeen. And then when the crypter hit in twenty eighteen and the end of the ICO craze and all that. The mining rig demand. Fell off and this had become so big for NVIDIA that their revenue actually declined. Right.

1:02:08 Yeah, so a couple of 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.

1:02:25 So you're Guessing, if that's not the right thing, you're incrementing your 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.

1:02:40 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 going to 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

1:02:57 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

1:03:23 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 happen to be pretty darn good.

1:03:41 at both of those things. Well at least way, way, way better than a CPU. Yeah. As some of NVIDIA's startup competitors put it today. And Cerebrus is the one that I'm thinking of. They sort of say, Well the GPU is A thousand times better.

1:03:58 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 exist much more optimal solutions for, you know, doing some of this this AI stuff. Interesting. Really begs the question of like How good is good enough in these use cases.

1:04:14 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. Interesting. Well,

1:04:30 Back to crypto winter. The NVIDIA stock gets hammered again. It goes through another Fifty percent drawdown. This is just like every five years this has got to happen. 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.

1:04:47 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. 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

1:05:29 Just looks terrible to the street at this point because they had just no ability to understand what was going on in their business. And I think a lot of Street was still was still 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 Anyway, that was short lived the fifty percent dip because uh

1:05:54 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.

1:06:04 If you look at NVIDIA's um But they report financials a couple different ways, but one of the ways they break it out is few different segments. This is the gaming consumer segment. And then their data center segment. And it's like data center. Well, all the instances for right. All of the stuff we're talking about, it's all done in the data center. Like

1:06:23 Google isn't going and buying, you know, a bunch of NVIDIA GPUs and Hooking them up to the laptops of their software engineers like Is Stadia still a thing? Like I think that's used for cloud gaming and some s like they're But if it's all happening in the data center is uh My point. 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. Ah, yes, yes, that's what I'm saying. I I agree. Yeah.

1:06:49 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 card 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 thirty ninety look like uh pittance.

1:07:14 And the RTX thirty ninety, which is their most expensive high end graphics card that you can buy as a consumer, was three thousand dollars. Now it's like two thousand dollars. But if you're buying I don't know, what's the latest? It's not the A one hundred, it's the H one hundred. Uh so the A one hundred they just announced the H one hundred. And that's what, like twenty or thirty grand in order to just get one card.

1:07:35 Yeah. And people are buy a lot of these things. Yeah, it's crazy. It's crazy. 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

1:07:51 the full self driving computer on Tesla's. They now make those chips themselves. the Tesla Dojo, which is the training center that they announced. They announced they were also going to make their own silicon for that. They actually haven't done it yet. So they're still using and video chips for their training.

1:08:08 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 fifty and a hundred million dollars.

1:08:20 That they paid NVIDIA for. all of the compute in that cluster. One customer. It's one customer for one use case at that one customer. Crazy. I mean it 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.

1:08:40 So two years ago they had about three billion of revenue and it was only about half of their gaming revenue segment. So gaming, you know, through all this through two thousand six to AlexNet, all the way, you know, another decade forward to twenty twenty. Gaming is still king, it generates almost six billion in revenue. The data center revenue segment was three billion, but had been pretty flat for a couple of years. So then insanely over the last two years

1:09:06 It three X'd. The data center The segment three X. It is now doing over ten and a half billion a year in revenue, and it's basically the same size as the gaming segment. It's nuts.

1:09:19 It's amazing how it was like sort of obvious in the mid twenty tens. But when the enterprises really showed up and said we're buying all this hardware and putting it in our data centers. And then 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 want to call it these days, on prem data centers.

1:09:42 Everyone is now using machine learning hardware in the data center. Yep. And NVIDIA is selling it for Very, very, very healthy. Gross margins. Apple level gross margins. Yes.

1:09:57 Exactly. So speaking of the data center. One. In This is so NVIDIA.

1:10:05 in twenty eighteen they actually Do change the terms of the user agreements of their consumer cards of G Force cards, that you cannot put them in data centers anymore. They're like of course 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 A one hundred does not have video out.

1:10:37 So they actually can't be used as graphic cards. Oh yeah, there was a there's a cool um Linus Tech Tips video about this where they get a hold of an A one hundred somehow. And then they run some benchmarks on it, but they can't actually like drive a game on it. Oh fascinating. Yeah. So fun.

1:10:53 Data center stuff is like super high horsepower, but of course like useless to run a game on because you can't Just 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 want to spend thirty thousand dollars 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. Don't think about going to Fry's and buying a bunch of T forces.

1:11:17 Ironic,'cause that's how the whole thing started, but anyway. In twenty twenty. They acquire an Israeli data center compute company called Melanox that I believe focuses on like uh networking compute within the data center.

1:11:31 Yep. For about seven billion. Integrate that into You know, their ambitions and Building out the data center. And the way to think about what Malinox 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. So at this point, they've got NV Link, which is their it's like the

1:11:52 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 and them communicate super fast to each other. That's awesome because of course these data centers, that's the other thing about

1:12:18 You know, customers like that Tesla example I gave. They're not buying cards, the Enterprise Cards. They're buying solutions from NVIDIA. They're buying Mm. Big boxes with lots of stuff in them. You say solutions? I hear gross margin.

1:12:33 Yeah. That's such a great quote. We should like frame that and put it on the wall of the uh the acquired museum. It is true that acquiring Melanox not only like enables this now, we have the 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 GP GPU.

1:13:02 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.

1:13:23 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

1:13:40 Like the black box. Just went from A box on a rack. And 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.

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1:15:43 Okay, so I said one more thing on the data center. Yes. Now 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. Do you remember this? 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.

1:16:05 were talking about the whole strategy and how excited they are to own ARM and how NVIDIA is 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 this cool stuff we're gonna do with it. And then it doesn't happen. They were talking about it like it was a done deal. And now you've got dozens of hours of people talking about the strategy. So you're almost like

1:16:25 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. Yeah. We should revisit ARM at some point. We did do the South Bank acquiring ARM. Episode. Years and years ago now.

1:16:40 But You know, you think arm, like they are a CPU. Architecture company. Whose primary use case is mobile and smartphones, right? So like everything that Intel screwed up on back in the misguided mobile era.

1:16:56 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 want to own The data center. When we say we want to own the data center, we want to own

1:17:15 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 wanted to own them and And do that.

1:17:32 And indeed. This year. NVIDIA announced they are making a data center. CPU, an arm based data center CPU called GRECE. To go with the new.

1:17:42 Hopper architecture for Their latest GPU, so there's Greece and Hopper. Of course the Rear Admiral, Grace Hopper, I think. I think that's right. Yeah, she was in the Navy. Uh great computer scientist, pioneer.

1:17:55 So yeah, like Data center. It's It's big. 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 I get the regulatory pressure on this is Arms business is simple. They make the IP

1:18:10 So you can license one of two things from them. You can license the instruction set. So even Apple, who designs their own ships, 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 want to use these instructions, you have to license it from ARM, great. And if you don't want to be Apple and you don't want to go build your own chips or you don't want to be NVIDIA or whatever, but you want to 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 fabulous semiconductor company. And

1:18:48 They sell to everyone. And so Of course a regulatory body is gonna step in and being like Wait, wait, wait. So

1:18:57 Nvidia, you're a fabulous 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.

1:19:16 Is Right now, our whole business of strategy is that Kuda 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

1:19:31 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. Yep. Plausible, interesting, but no surprise at all that they face too much regulatory pressure to go through with this. No. But clearly that idea

1:19:54 rattled around in Jensen's head a bunch 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 two thousand nine as part of Building the whole CUDA ecosystem.

1:20:13 I mean it's so freaking impressive now. Like there are now three million registered CUDA developers, four hundred and fifty separate SDKs and models. For Cuda, they announced sixty six zero new ones. At this GTC Did we talk 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.

1:20:38 is gonna be the world's first four nanometer process chip using T SMC's new four nanometer. Process which is amazing. 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.

1:20:54 At the same time as GTC. And in the analyst day, Jen gets up there. It's just so funny. I've gone 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. He's like a startup raising a seed round, walking in with a pitch stick.

1:21:13 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

1:21:27 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. Oh, there's a lot of squish in there. But The fact that they're valued today, I mean, what's their market cap right now? Something like that. half a trillion dollars.

1:21:48 They need T. 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

1:22:08 Articulate it is. We are going to serve customers that represent a hundred trillion dollar opportunity and we will be able to capture about one percent of that. God, it's just like a freaking seed company pitch deck. If we just get one percent of the market. 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 twenty something billion in revenue last year and is worth half a trillion dollars.

1:22:38 They did. twenty seven billion dollars last year in revenue. Google AdWords revenue in the fourth quarter of twenty Twenty one was forty three billion. Google as a whole did two hundred and fifty seven billion in revenue. So like You gotta believe if you're an NVIDIA shareholder.

1:22:57 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. You gotta believe it's on the come. Yeah, you do. I mean

1:23:07 NVIDIA has literally three times the price to sales ratio of Apple or price to revenue as Apple, and nearly two X 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. Yes.

1:23:31 They generate eight billion of free cash flow every year. So I think they're sitting on twenty one 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 of sales, price earnings, all that, they're much more richly valued. than uh an Apple or Microsoft or these fame companies.

1:23:51 But it is, you know, extremely profitable business, even on an operating profits perspective. Well, you stole enough of that enterprise uh data center goodness and uh you can make some money. 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.

1:24:21 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 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.

1:24:43 It's sixty six percent gross margin business now. Yeah. Well. And You know, thinking about increasing that gross margin further.

1:24:51 And what we were talking about a minute ago with ARM in the licensing. So at the Analyst day and around GTC this year. They say Th they're gonna start Licensing.

1:25:04 a lot of the software that they make separately. It's licensing it separate from the hardware, like Cuda. 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.

1:25:23 And secondarily The software that we do are industry defining software. So we've now finally produced a product that an enterprise can license. 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. You know, when you were d we were joking a few minutes ago about you say solution and I see margin, you know. Yeah. Like open source software companies.

1:25:54 Have you gone Big for this reason, you know, data bricks, confluent, elastic, like These are big companies with big revenue. Based on open source because Enterprises.

1:26:04 They're like, Oh, I want that software, but they're not just gonna You know, go to give your JP Morgan. You're not gonna go to GitHub and be like, Great, I got it now. You know. Right. You need solutions. So To Jensen and NVIDIA, they see this as an opportunity to I'm sure this isn't gonna be Cannibalizing.

1:26:20 Hardware customers for them. I think this is gonna be Incremental selling. on top of what they're already doing. That's an important point. And I think this is a playbook theme that I had, but Oftentimes when someone has

1:26:34 hardware that is differentiated by software and services, and then they decide to start selling those software and services a 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 have just bought the hardware and gotten all this stuff for free anyway. So

1:26:59 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 clear walls off that segment, then we can behave in a completely different way to that segment. Yeah, I'm good. further, you know, returns on our Assets that we've generated.

1:27:20 Yeah. It is a little Tim Cook, though, in uh you know, Tim Cook beating the services narrative drum. I mean, it is kinda you hear 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. Oh my goodness. But who else is gonna do it wearing a leather jacket? At is a great point.

1:27:44 But I think it yeah. 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 want to frame it relative to fame companies.

1:28:12 They're growing much faster. than App, Microsoft, Google. They're growing at sixty percent a year. This is a 30 year old company. That grew sixty percent in revenue last year. Yeah.

1:28:26 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

1:28:44 NVIDIA is growing as 60%. Right. 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. Inflation be damned. Inflation be damned.

1:28:59 Okay, a couple other things about specific segments of the business that I think are pretty interesting. So the have not slept on gaming. Like we keep beating this NVIDIA data center, enterprise, machine learning argument. Yeah, we haven't even talked about ray tracing and

1:29:15 Right. Yeah. This RTX set of cards that they came out with. The fact that they can 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 three D. 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

1:29:44 Extremely cool is called DLSS. Deep learning super sampling. And this I think is like where NVIDIA really shines. bringing machine learning stuff and gaming stuff together.

1:29:57 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.

1:30:11 Yeah. 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

1:30:23 high resolution and high frame rate. And they're sitting around thinking, how on earth could 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. 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

1:30:45 Well It's very likely. That you can infer what a pixel is going to be based on the pixels around it. It's awesome. Also pretty likely you can infer what a pixel is going to be based on what it was in the previous frames. And so let's actually render it at a slightly lower resolution.

1:31:06 So we can bump up the frame rate. And then when we're outputting it to screen. We will use deep learning to artificially at the final stage of the graphics pipeline. Yes. It's really cool. And when you watch the side by side on all these YouTube videos

1:31:24 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.

1:31:44 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 enhanced joke like a real thing. That's so awesome. I'm remembering back to the Riva one twenty eight 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.

1:32:04 You don't need all of them, just use these. 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. Oh, I mean at this point no game developer is not gonna make their games optimized for The latest NVIDIA hardware.

1:32:22 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.

1:32:39 Was they artificially crippled the card to make it worse at crypto mining. And then they came out with a dedicated crypto mining card. 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 you know all the cards they want. But really they're like

1:32:59 Hm. 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. Let's make that. Our arbitrage. Yes. Your arbitrage is my opportunity. 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.

1:33:29 Evil evil genius laugh. The Last thing That

1:33:36 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 thirty nine ETI at the store and You run your favorite game on it.

1:33:55 But actually you're not buying that from NVIDIA the vast majority of the time. You are going to some third party partner, ACES, MSI uh Zotac is one. They've there's also like a bunch of really low end ones as well. Who Nvidia sells the card to and those people install the cooling and the branding and

1:34:16 all this stuff on top of it and you buy it from them. And it's really weird to me that NVIDIA does that. I love how consumer gaming graphics cards have become the modern day equivalent of a hot rod. E o Dude.

1:34:29 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. Which is pretty funny.

1:34:45 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. Oh, it's like um What are the Android phones that Google makes? Pixel. Yeah, it's exactly like that. The pixel. 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.

1:35:19 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. Make sense? Two other things I want to talk about. One is automotive.

1:35:31 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. But Jensen says in his pitch deck it's gonna be a three hundred billion dollar part of the dam. 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 I don't even know if it's that much. 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

1:36:05 Put in there. Every technology company has had their fanciful attempt to try to m create a meaningfully differentiated experience in the car. All have failed. You think about Microsoft and the Ford Sync. Ford sync. Oh wow. 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.

1:36:32 Nvidia is My perception of what they're doing is they're pivoting this business line, this like flat boring, undifferentiated business line.

1:36:43 Maybe EVs, electric vehicles, And autonomous driving. is a way to break in and create a differentiated experience, even if we're not going to make our own cars. And so I think what's really happening here.

1:36:56 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. Oh, Hyperion drive, is that it? Something like that? Something like that. But dealing with Nvidia's product naming is maddening. But this drive platform

1:37:14 It kinda feels like they're making The full E V, A V hardware software stack except for the metal and glass and wheels. And then going to car companies and saying look

1:37:26 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 that direction, but is n none of this is the core competency of your company except the sales and distribution. So like what can we do here?

1:37:42 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. Like the Android market. Yeah.

1:37:57 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

1:38:13 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. Yeah. Which

1:38:24 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, friend of the show, Jeremy messaging with in in Slack. Lotus is one of their partners. Lotus gonna go? Build autonomous driving software? Like, I don't think so. Ferrari? No. Not at all.

1:38:45 They're gonna be NVIDIA cars, effectively. Yeah. 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.

1:38:59 It is similar in that it's kind of a three D 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.

1:39:17 Is this uh earth to 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 want to license this platform We can do super realistic simulations.

1:39:34 of anything that's important to you. Mm-hmm. And what their pitches 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 a Amazon, who actually

1:39:50 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 want to make a tweak to a model, you're not just going to like deploy those to all the robots. You kinda want to run that

1:40:15 In the omniverse first. And then when it's working, then you want to deploy it in the real world. And their omniverse pitch is basically it's an enterprise solution that you can license from us where any time you're going to 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. Because I think

1:40:39 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

1:40:55 everything's gonna be modeled in the omniverse first before rolling out. This is what an enterprise meter 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.

1:41:15 And m most of it, I think, is gonna run With no humans there. Yep, pretty crazy. Yeah. Good idea. Sounds like a good idea. All right.

1:41:24 You wanna talk bear and bull case on the company? Let's do it. Analysis. So I mean they paint the bullcase for us when they say there's just a hundred trillion dollar future, we're gonna capture one percent of it. There's three hundred billion from automotive. Here's the four five segments that add up to a trillion dollars of opportunity.

1:41:41 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

1:41:54 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 bare case is that there's a tick tock 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

1:42:13 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 in there. in their hardware.

1:42:30 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 Cerebris. or like a graph core that is gonna eat their lunch in the enterprise AI data center market.

1:42:49 That's a open question. You know, it's interesting, like People have been talking about That's For a while. The other big bear case that

1:43:00 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 Apple's.

1:43:15 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 NVIDIA. For what they're getting.

1:43:25 That those companies are gonna wanna say, you know, it's not that hard to design our own silicon to Yeah. We can tune it to exactly our use cases, sort of similar to the Cerebrus uh graph core. Bear case on NVIDIA.

1:43:41 I think in both of these cases, you know. It hasn't happened yet. Well There have been a lot of people who have made a lot of noise, but there have been few that have executed on it. Like Apple has their own GPUs on the M1s. Tesla is 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 Yep, that is switched. On the inference side. Yes. On device, yes, that has happened. But

1:44:05 Look, NVIDIA is probably strong in that, but I think the real thing to watch is the data center. And Google is probably the biggest bear case there. Yeah. It's interesting to talk about these companies and particularly Cerebrus,'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

1:44:24 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 size chip. And you know, the yields on these things kinda suck. So like they need all the redundancy on those huge chips to make it so that Oh my God, the amount of expense to do that. Right. And you can put one on a wafer.

1:44:47 Mm. These wafers are crazy expensive to make. Wow. So you get poor yields in the wrong places on a wafer and like that whole wafer is toast. Right. So a big part of the design of Cerebrus is this sort of redundancy and the ability to turn off different pieces that aren't working. They draw sixty times as much power. They're way more expensive. Like if NVIDIA is gonna sell you a twenty or thirty thousand dollar chip, Cerebrus is gonna sell you a two million dollar chip. to do AI training. And so it is this bet in a big way on hyper specialized hardware for enterprises that want to do these very specific AI workloads.

1:45:23 And It's 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.

1:45:40 I mentioned Google. That made a bunch of noise about making their own silicon. in the data center. And then Stayed the course.

1:45:49 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.

1:46:00 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 want to differentiate Google Cloud with this offering that depending on your workload, it might be much cheaper for you to use TPUs with us.

1:46:20 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. Interesting. So it's kind of the Android strategy. but run in the data center. One thing we haven't.

1:46:37 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. And NVIDIA starting their own cloud. You can get direct from NVIDIA cloud based GPUs. Data center GPUs. Interesting. Yeah. It'll be very interesting to see how this all shakes out with uh the NVIDIA, the startups and with Google. I mean, all that said though, like

1:47:04 I think look, NVIDIA's very, very, very richly valued on a valuation basis right now. Very with another very in there. It depends if you think their growth will continue. Are they a sixty percent growing company year over year over year for a while? Then they're not rich valued. But if you think it's a covet hiccup or a crypto hiccup. But to the the Bull Bear case and Kenneth, both the startups and The big tech companies doing this stuff in house.

1:47:32 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've just told this whole story. This is fifteen years of CUDA and the hardware underneath it. And the libraries on top of it. The

1:47:50 NVIDIA has built. To go. recreate that and surpass it on your own. is such an enormous, enormous Bite to bite.

1:48:02 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. Yep. 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.

1:48:23 then your customer is only yourself. So you're constrained by the amount of people you can get to use Google Cloud. Well, and at least with Google, they have Google Cloud that they can sell it through. Yep. Power. Power. So the way I want to do this section because in our

1:48:38 NVIDIA episode we covered the first thirteen years of the company. We talked a lot about what is their power look like. up to two thousand and six. And now I want to 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 Cerebrus in the Enterprise or AMD in gaming.

1:49:04 Yep. And just to enumerate the powers again, as we always do. Counter positioning, scale economies, switching costs. Network economies, process power. Branding. And

1:49:13 Cornered Resource. So there are definitely scale economies. The whole CUDA investment Yes. Not at first, but definitely now.

1:49:23 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. 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. Right. So very few other players have access to the capital and and the market that NVIDIA does to make this type of investment.

1:49:52 So They're basically just competing against AMD for this. Totally agree. Scale economies to me is like the biggest one. That pops out. To the extent that

1:50:04 You have lock in To developing on CUDA. Which I think a lot of people really. have lock in on CUDA, then that's major switching costs. Yep. Like if you're gonna boot out NVIDIA, that means you're booting out Cuda.

1:50:17 Is Cuda a cornered resource? Oh, interesting. Maybe I mean it only works with NVIDIA hardware. You could probably make an argument there's process power.

1:50:28 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. Yeah, I think process power definitely was part of

1:50:43 The first instantiation of NVIDIA's power. To the extent it had. Power. Right. Yeah, I don't know as much today, especially because

1:50:52 T S M C will work with anybody. In fact, T SM C is working with these new startup billion dollar funded Silicon companies. Yes, they are. Yes. 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

1:51:08 immediately before the The hopper, the sort of A series chips. are actually fabbed by Samsung, who gave him a sweetheart deal. NVIDIA likes to keep the lore alive around T S M C'cause they've been this like great longtime partner and stuff, but Yeah.

1:51:23 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. Yes, yes, that did happen.

1:51:40 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.

1:51:54 And it it's clear from the demands of Lapsis where some of Nvidia's power lies. Cause it they demanded two things. They said one, get rid of the crypto governors. Like make it so that we can mine Which may have been a red herring. That might have just been them trying to look like a bunch of like crypto minor People. And the other thing they demanded is that NVIDIA open source all of its drivers.

1:52:20 And make available its source code. I don't think it was for CUDA. I think it was just the drivers. But it was very clear 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

1:52:42 you know, all of CUDA and how tightly coupled their hardware and software is. NVIDIA is we just did this our most recent episode with Hamilton and Ten Yi. NVIDIA is a platform, in my mind. No doubt about it. Kuda

1:52:55 And NVIDIA and general purpose computing on GPUs. As a platform. So whatever You know, all of the stew of powers that go into making that, that go into making

1:53:08 Apple. Microsoft, you know, and the like. Go into NVIDIA. Yeah. I think the stew of powers is the right way to phrase that.

1:53:17 Yes. Anything else here or you want to move to Playbook? Let's move to playbook. So man, I have I just wrote down in advance one that is such a big one for me. And I'm biased because I I I

1:53:29 Try to think about this in investing. particularly in public markets investing. Like man. You really, really want to invest in whoever Is selling the picks and the shovels.

1:53:41 In a gold rush. Hm. The AI. You know, M L deep learning. Gold Rush.

1:53:47 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. You know, Mark Andreessen saying every startup that comes in here. Wants to do AI and deep learning and they're all using NVIDIA.

1:54:09 Like maybe we shouldn't 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. 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

1:54:29 to enable graphics to be a storytelling medium. And of course this uh 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 We'll bring accelerated computing to everyone and we will make all the

1:54:53 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. that enable graphic as a storytelling medium.

1:55:08 But also They need to sell a pretty big story around the TAM that they're going after. 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 trait cliche thing at this point in startup land, but so few companies and founders can actually do it. Just

1:55:25 Not dying. Yeah. They should have died. At least four separate times. And

1:55:32 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.

1:55:43 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. And I have confidence that like We will figure it out. The market will come.

1:55:57 Not gonna declare game over. 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. Yeah. Say more about what floor planning is. I bet a lot of listeners won't know. So

1:56:34 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 fifteen 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.

1:56:51 on the actual chip itself, except of course there's like 10 million rooms. And so it's incredibly complex. And the stat that I was going to bring up, which was just mind bending to think about is that there are dozens of miles Of wiring. on a GPU.

1:57:09 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? Right. And it obviously it's 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.

1:57:26 On ship. 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. 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. Yeah. I got the S version. But yeah, like that's what that means.

1:57:57 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. Ooh, fun. We'll show it on screen here for those watching on video. Obviously there's a very high looking line for Amazon'cause

1:58:11 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.

1:58:24 Mm. And this is relative for people listening on audio. Relative to a bunch of other, you know, fang type companies. 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 trips has thirty billion.

1:58:42 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 Well it is, right? Like T SM C does the fabbing. Nvidia makes software and I B. Yep.

1:58:58 So Here, this is the best graph for you to very clearly see. the magic of the fabulous business model that Morris Chang was so gracious to invent when he Crew T S M C

1:59:11 Thank you, Morris. 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 nonfinance 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.

1:59:35 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

1:59:50 And can keep these operating margins or even improve them because they think they can improve them. That's really impressive. Wow, I didn't realize that's better than apples. Yeah. I think it's not as good as like Facebook and Google because they just run these like digital monopolies, like come on. Basically zero cost digital monopolies in some of the largest markets in history.

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2:01:18 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. I think so. 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

2:01:33 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. You mean like a Michael Mobis and expectations investing style? Yes, exactly.

2:01:46 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 kinda think you have to do the first one, though.

2:02:02 Like I kinda think it's a cop out to not think about it like What's the Bull and bare investment case from here. As we pointed out many times on the episode. There's a lot you gotta believe to be

2:02:15 A ball. On NVIDIA at this share price. So What are they? Well, one big one is that

2:02:23 they continue their incredible dominance and they're what are they growing like 75% or something year over year in the the data center. Yep. 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.

2:02:44 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 kinda have to say that NVIDIA will find a way to learn from them. And then integrate it into their strategy too.

2:03:10 Which seems plausible. 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

2:03:32 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. Yep. I think the question for me On an A plus outcome for NVIDIA. from the shareholder perspective is

2:03:52 Do you need to believe? Mm-hmm. All the real world AI use cases are gonna happen. Do you need to believe

2:04:04 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 then NVIDIA is going to be a key player in them. 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.

2:04:26 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 Is if you're not going to be able to The digital AI. We know that that's a big market. Mm-hmm. There's no question about that at this point.

2:04:42 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

2:04:59 Assess how much growth is left there. That is kind of the right question, though. Yeah. They're at an interesting point right now. You know, there's all the early company stuff that we talked about in the first episode. But at the beginning of this episode.

2:05:12 You know, Jensen was really asking you to believe. It's like, hey, we're building this cuna thing. Just ignore that there's no real use case for it or market. Now There is a real, real use case and market for it, which is Machine learning, deep learning in the digital world. Mm-hmm. Undeniable.

2:05:32 Mm-hmm. He's also pitching now. That that will exist in the physical world too. Yeah, the A plus 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. Yep.

2:05:45 And if the real world stuff, you know, these Little robots that run around uh factory floors and uh 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. I think that's probably right. That would be my hunt. Although saying that though does feel like a little bit of a Betting against the internet, you know, like

2:06:07 I don't know, man. Digital world's pretty big and it keeps getting bigger. 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. Yeah. Yeah. I mean Autonomous

2:06:22 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. Well it also means that you're just going to be on the internet when you're driving.

2:06:35 Yeah. Yeah. When you're in motion in the physical world. 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 there on Instagram. Right. Oh, it's so true. 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. I think maybe the failure case is

2:07:08 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 degree degree of a Peloton or a Zoom or something like that.

2:07:19 Right. 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. Hard to quantify, hard to know, but it's the right thing to be thinking about.

2:07:34 Yeah. All right, carve outs. Ooh, Carvat. I've got a fun one. Small one.

2:07:39 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 says the expanse.

2:07:52 Serious. Amazing sci fi, nine bucks. So great. The ninth book came out last fall. Oh, it was just even with like a newborn, I made time. to read this book. That's awesome. Newborn plus acquired now I was like, I gotta read this book.

2:08:07 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.

2:08:19 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

2:08:38 You give you little glimpses into corners and like characters that just exist and you don't question otherwise, but you're like, Oh, what's the backstory of that? I've been really enjoying that. So it's like the solo. Of the fantastic beasts and where to find them. Exactly. It's like nine or ten of those.

2:08:54 Cool. Mine is a physical product, actually for the episode we did with Brad Gersner on Ultimeter, we needed a third camera. And so I went out and bought a Sony R X one hundred 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.

2:09:14 It's like funny how it's gone full circle. I've you know, f was a D S L R 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 three X zoom and I now have the iPhone

2:09:35 There. I think that's what it is. with the two cameras and no zoom lens is 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

2:09:49 It's not as nice as having uh 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 is uh it's slightly more inconvenient to carry because you kind of need another pocket. Yeah, I was gonna ask, can you put it in your

2:10:12 Pocket. Yeah, I put it in a pocket. I don't have to have a sort of like a wrap and strap around my neck, which is nice. Nice. So the Sony Arcs One hundred, great little device. It's like the seventh generation of it, and they've really refined the industrial design at this point. That's awesome. That's awesome. I actually just bought.

2:10:29 My first Camera cube. Like a travel. Kimber Cube thing for our uh uh Alpha seven C is now that we have uh Literally it's for acquired for when after the Altimeter episode I was like, Oh wow. We're gonna do more in person. Yeah. Yeah, Ben brought his

2:10:45 Down is like for sure. I'm gonna need to bring. This somewhere. These cameras are just They're so good. They're so good.

2:10:53 All right, listeners. Thank you so much for listening. You should come chat about this episode with us in the Slack. There's eleven thousand other smart members of the acquired community. Just like you.

2:11:04 And if you want more acquired content after this and you were all caught up, go check out our LP show by searching acquired LP show in any podcast player. Here us interview Nick and Lauren from Trova Trip most recently. And we have a job board. Acquire.fm slash jobs. Find your dream job curated. Just by us the fine folks at the Acquired Podcast.

2:11:27 And we will see you next time. We'll see you next time. Who got the truth? Is it you, is it you, is it you Who got the truth now

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