Nvidia: The Machine Learning Company (2006-2022)

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 kenos 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 the truth now is it you is it you is it you sit me down say it straight another story on the way who got the truth 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 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 was doing so in

a random way or if i knew about every input in the world all the air exactly the physical construction of the wood all the variables in the environment if it was actually predictable and i don't think i knew the term at the time but modelable if i could know what the flame could look like if i knew all those inputs and we now know of course it is indeed predictable but the data and compute required to actually know that is extremely difficult but that is what nvidia is doing today ben i love that ninja that's great i was thinking like where's ben going with this and this was occurring to me as i was watching jensen ensuring the omniverse vision for nvidia and realizing nvidia has really built all the building blocks the hardware the software for developers to use that hardware all the user-facing software now and services to simulate everything in our physical world with their unbelievably efficient and powerful gpu

architecture and these building blocks listeners aren't just for gamers anymore they are making it possible to recreate the real world in a digital twin to do things like predict airflow over a wing or simulate cell interaction to quickly discover new drugs without ever once touching a petri dish or even model and predict how climate change will play out precisely and there is so much to unpack here especially in how nvidia went from making commodity graphics cards to now owning the whole stack in industries from gaming to enterprise data centers to scientific computing and now even basically off-the-shelf self-driving car architecture for manufacturers and at the scale that they're operating at these improvements that they're making are literally unfathomable to the human mind and just to illustrate if you are training one single speech recognition machine learning model these days one just one model

the number of math operations like ads 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 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 yep so great many of you already know this many of you have already rsvp'd but if you have not we would love to see you at our arena show in seattle that's going to be on may 4th at 5 00 pm it's going to be an awesome show we've announced we'll have jim weber there who's the ceo of brooks running which is now amazingly a

billion dollar revenue business inside of berkshire we'll have other announcements coming as well go to acquire.fm arena show or click the link in the show notes to rsvp all proceeds are going to charity are a huge thanks to our friends at pitchbook data for putting this on with us that's acquired.fm slash arena show and we hope to see you there i get giddy every time you say that url yes indeed it is real all right now before we dive in we have a fun little q a from our presenting sponsor vanta the leader in automated cloud security and compliance we are huge fans of vanta and their approach they do everything from sock 2 to hipaa to gdpr and more and we are back with ceo and co-founder christina cassiopo to talk about it so christina based on our last few conversations i'm getting the sense of vanta as becoming a lot more than just a sock to compliance company is that true and how do you think about that

yes i mean the joke about vanta that's not a joke is that we're a security company masquerading as a compliance company and it comes from the early founding days when we wanted to start a security company and went around and asked all of our friends at startups you know what security problems they had and they did have some and then we're like oh you know what happened if we solved them for you and our friends were like i'm not going to use that because i have 10 other problems ahead of that one i just feel badly i know i should be doing it but it is hard for me to prioritize i often need to get customers so that's what i do which was a little demoralizing when you're trying to come up with a startup idea but then we actually realized that compliance can be like the security feature someone is asking for right so generally if you're selling someone doesn't say hey will you be more secure they say hey are

you sock too compliant and so if we could build a product that help folks get more secure help them prepare for and get through a compliance audit it would accelerate their business because they have their compliance certification and leave them more secure one of our product team's goals is to help our users fix security issues we surface faster so we can surface lots of misconfigurations like proverbial doors and windows you leave open but if no one fixes them then we're not having the impact we're looking for and we're not ultimately securing the company and so we just have a substantial portion of our product team focused on building features that help companies fix misconfigurations faster so the whole set of work that falls under that but it's one of the things i'm most excited about thanks christina and thank you to vanta the leader in automated security and compliance software if you

are looking to join vanta's 2 000 plus customers and get compliance certified in weeks instead of months click the link in the show notes or go to vanta.com acquired for a sweet 10 discount and after you finish this episode come join the slack acquired dot fm slack and talk about it with us and if you're dying for even more acquired before we uh come back with our next season episode search for acquired lp show in the podcast player of your choice our latest installment was a very fun deep dive and close to home for me diving in with nick and lauren the creators of trovatrip on travel for the creator economy where they talk about a very interesting business model that they have on their hands and a space that david and i know well in greater things indeed 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 going to make sure that you said that this time because we're going to talk a lot about investing in investors in nvidia stock over the years it has been a wild wild journey so last we left our plucky heroes jensen huang and nvidia in the end of our nvidia the gpu company years and in kind of roughly you know 2004 2005 2006 they had cheated death not once but twice the first time in the super overcrowded graphics card market when they were first getting started and then once they sort of you know jumped out of that frying pan into the fire of intel now gunning for them coming to commoditize them like all the other you know pci chips that plugged into the intel motherboard back in the day and they bravely fend them off they team up with microsoft they make the gpu programmable this is amazing they come out with programmable shaders with the

geforce three they power the xbox they create the cg programming language with microsoft and so here we are it's now 2004 or 2005. and this is a pretty impressive company public company stock is high flying after the tech bubble crash they've conquered the graphics card market of course there's ati out there as well which will come up again but there's three pretty important things that i think the company built in the first 10 years so one we talked about this a lot last time these six-month ship cycles for their chips 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 1999 they shipped the first geforce card the geforce 256 in the spring of 2000 geforce 2 in the fall of 2000 geforce 2 ultra spring of 2001 geforce 3 that's the big one with the programmable shaders then six months later the geforce three

ti-500 i mean the normal cycle i think we said was two years maybe 18 months for most of their 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 how often did intel ship new products let alone fundamentally new architecture you know there was the 286 and then the 386 and the pentium and it got it to pentio about 05 whatever dude i feel like the 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 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 going to

talk about they wrote their own drivers for their graphics cards and we owed a big thank you for this and many other things to a 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 agenometry youtube channel so good i've probably watched like 25 asian amateur videos this week so so good huge shout out to them but all the other graphics cards companies at the time and most peripheral companies they let the further downstream partners write the drivers for what they were doing and video was the first one that said no no we want to control this we want to make sure consumers who use nvidia cards have a good experience on whatever systems they're on and that meant a that they could ensure quality but b they start to build up in the company this like base of really nitty gritty low-level software developers in this chip company and there are not a

lot of other chip companies that have capabilities like this no and what they're doing here is taking on a bigger fixed cost base i mean it's very expensive to employ all the people who are writing the drivers for all the different operating systems all the different oems all the different boards that it has to be compatible with but they viewed it as it's kind of an apple-esque view of the world we want the control or as much control as we can get over making sure that people using our products have a great user experience so they were sort of willing to uh take the short-term pain of that expense for the long-term benefit of that improved user experience with their products that they're users high-end gamers that want the best experience you know they're gonna go out they're gonna spend the time three four or five hundred dollars on an nvidia top of the line graphics card they're gonna drop it into

the pc that they built you know they want it to work i remember messing around with drivers back in the day and things not working like this is super important so all this is focused and of course they have the third advantage in the company is programmable shaders you know which ati copies as well but like they innovated like they've you know done all this so all of this at this time it's all in service of the gaming market and one seed to plant here david when you say the programmable shaders developers the notion of a nvidia developer did not exist until this moment it was people who wrote software that would run on the operating system and then from there maybe it would get that compute load would get offloaded to whatever the graphics card was but it wasn't like you were developing for the gpu for the graphics card with a language and a library that was specific to that card so for the very first time now they

start to build a real direct relationship with developers so that they can actually start saying look if you develop for our specific hardware there are advantages for you and really our specific gaming cards like everything we're talking about these developers they're game developers all of this stuff it's all in service of the gaming market so you know again they're a public company they have this great deal with microsoft they bring out cg together they're powering the xbox you know wall street loves them they go from sub a billion dollar market cap company after the tech crash up to five to six billion dollars kind of by 2004 2005 stock keeps going on a tear by mid 2007 the stock reaches just under 20 billion dollar market cap you know this is great and this is all the stories like this is pure play gaming these guys have built such a great advantage in a developer ecosystem in a large and growing market clearly

which is video games which on its own that would be a great wave to surf i mean i think what's the gaming market today 180 billion or something and when we talked to tripp hawkins who sort of like helped invent it or nolan bushnell you know it was zero then and so nvidia's sort of like on a wave that's at an amazing inflection point they could totally just ride this gaming thing and be an important 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 fast i feel like yes i am the leading company in this major market this huge wave that i don't see ending anytime soon you know 99.9 percent of founders who are themselves as a class like you know very ambitious are going to be satisfied with that but not jensen but not jensen 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 of a stanford researcher you know sends the email to jensen and it's like uh you know thanks to you my son told me to go buy off the shelf you know geforce cards at the local fry's electronics and i stuffed them into my pc at work and you know i ran my models on on this he's a i think it was a quantum chemistry researcher supposedly it was 10 times faster than the super computer 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 skeptical listener might beg two questions first is a practical one you know we just said everything's about gaming here and here's like a researcher like a scientific researcher doing you know chemistry modeling

using g-force cards for that 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 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 is actually a triangle and imagine that this way 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 sub-optimal yup 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 10 plus years to get to the company to this point you know what cg was is like a small sliver of the stack of what you would need to build for developers to use gpus in a general purpose way like we're talking about you know it's kind of like um they worked with microsoft to make cg it's like the difference between working on cg and like microsoft building the whole net framework for developing on windows 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 is not perfect but it's like instead of just apple saying okay objective c is the way that you write code for our platforms good luck they're like okay well you need ui frameworks so how about app kit 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 write software for 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 now we talked about they've been writing their own drivers so they have actually a lot of very low level i don't mean low level like bad i mean low level like infrastructure like close very difficult systems oriented programming talent within the company so that kind of enables them to start here but like still this is big so then the second question if you're a discerning investor particularly in nvidia that you want to ask at this point in time is like okay jensen you're committing the company to a

big undertaking what's the business case for that show me the market i mean don valentine at this point would be sitting there listening to jensen and being like show me the market 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 going to take us and how many dollars and resources it going to take us to actually get to something that's useful for that market when it materializes because while cuda development began in 2006 that was not a useful usable platform for six plus years at nvidia yep 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 we made some apis and worked with microsoft so that you can program for my thing right i'm going to flash way forward just to illustrate the insane undertaking of this i search 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 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 going to get there in time and cost and all that but even just put that aside is there a market for this is the first order question and the answer to that is probably no 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 super computers or access to a super computer to run some calculation that they think is going to take weeks or months and wouldn't it be nice if they could do it cheaper or faster is that kind of the market they're looking at yeah they're

attacking like the cray market like crazy super computers like that kind of 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 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 and not enough to justify the time and the cost of what you're gonna have to build out to go after this to any rational person so you know here we come jensen in a video like they are doing this he is committed he's drunk the kool-aid 2006 2007 2008 they are pouring a lot of resources into building what will become cuda that we'll get to

in a second um i 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 that they can develop hardware to accelerate specific use cases in computing that he finds sort of fanciful and he likes the idea of making it more possible to do more things for humanity with computers but the other part of it is certainly a business model realization where he has spent the last gosh at this point 13 14 years being commoditized in all these different ways and i think he sees a path here to 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 a company that can throw

its weight around in the industry 100 jensen i don't know if he used it at the time because he probably would have gotten pilloried but maybe he did i don't think he cared uh he certainly uh has used it since 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 i'm pretty sure if we build it they will come it's one step removed from that it's like well if we don't build it they can't even possibly come i don't know if they will come but they can't come if we don't build it so wall street is mostly willing to ignore this in 2006 2007 2008. the company still growing really nicely this great market cap run leading up to right before the financial crisis but then you know you mentioned last time i think it gets announced in 2006 maybe and closes in 2007 amd

acquires ati yup an ati was a very legit competitor's the only standing legit competitor to nvidia through its whole life but now amd acquired 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 a lot of resources like they weren't just acquiring this to you know get some talent like they're like no no this is gonna be a big product 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 radeon line which used to be the ati radio online that is how you think about amd as a company is that they make these gpus mostly for the gaming use case yep before the acquisition i think the first pc i built in like end of high school beginning of college i think i had a radeon card in it i think i was probably in the minority i think nvidia was bigger but

for whatever reason i liked ati at that point in time so like they were legit well so here's nvidia now focusing on this whole other thing and you're still in the gaming market which like we said is like massive rising tide your competitor now has all these resources and amd that's fully dedicated to going after it mid 2008 nvidia whiffs on earnings like this is natural they took their eye off the ball of course they did and uh the stock gets just hammered because and anything that cuda empowers is not yet a revenue driver and they've totally taken their eye off of gaming yes so you know we said the high was around a 20 billion dollar market cap it drops 80 percent 8-0 this isn't just the financial crisis it's almost coined i think you know for me thinking back on the financial crisis now and like people freaking out the dow you know the s p dropping five percent in a day like oh that's a thursday these

days it is literally the thursday that we are recording yes for a company stock to drop 80 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 if you're most ceos at this point in time you're probably calling up goldman or you know allen company or frank quatron and you're shopping this thing because how are you going to recover but not jensen but not jensen obviously so instead he goes and builds cuda and continues to build cuda and this is you know just set context like we get excited about a lot of stuff unacquired but i think cuda is like one of the greatest business stories of the last 10 years 20 years more i don't know what do you think ben i mean i'd say it's one of the boldest bets we've ever covered but so were programmable shaders

and so was nvidia's original attempt to make a more efficient quadrilateral focus 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 a choir 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 this will be a generational company yep so what is cuda it is nvidia's compute unified device architecture it is as we've referred to you know thus far throughout the episode a full and i mean full development framework for doing any kind of computation that you would want on gpus yeah and in particular it's interesting because i've heard jensen reference it as a programming language

i've heard him reference it as a computing platform it is all of these things it's an api it is an extension of c or c plus plus so there's a way that it's sort of a language but importantly it's got all these frameworks and libraries that live on top of it and it enables super high level application development you know really high abstraction layer development for hundreds of industries at this point to communicate down to cuda which communicates down to the gpu and everything else that they have done at this point oh 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 weeks ago ben thompson had this amazing interview with jensen on strategy and uh jensen in this interview i think puts what cuda is and and how important it is better than i've seen anywhere else so this is jensen speaking to ben

we've been advancing cuda and the ecosystem for 15 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 150 sdks that serve industries from gaming and design to life and earth sciences quantum computing ai cyber security 5g and robotics and then he talks about what it took to make this this is like the point we were trying to like hammer home here he says you have to internalize that this is a brand new programming model and everything that's associated with being a program processor company or a computing platform company had to be created so we had to create a compiler team we had to think about sdks we had to think about

libraries we had to reach out to developers and evangelize our architecture and help people realize the benefits of it and we even had to help them market this vision so that there would be demand for their software that they write on our platform and on and on and on it's crazy it's amazing and when he says that it's a whole new programming i think he says maybe paradigm or way of programming it is literally true because most programming languages up to this point and most computing platforms primarily contemplated serial execution of programs and what cuda did was it said you know what the way that our gpus work and the way that they're 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 over 10 000 cores on their most recent consumer graphics card so insanely or dare i say embarrassingly parallel and cuda is designed

for parallel execution from the very beginning that's the like catchphrase in the industry of embarrassingly parallel 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 ah it's not that it's parallel that's embarrassing it's embarrassing if you were to do it the old way on cpus serially 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 cuda grasp we're going to spend a few minutes talking about how brilliant this was everything we just described this whole

undertaking like it's like building the pyramids of egypt 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 for cuda but anyone can download it learn it use it you know blah blah blah you know all of this work stand on the shoulders of everything nvidia is done but ben what is the butt 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 there like terms of service that you can't deploy this on you know amd chips or whatever like literally doesn't work nope it's full stack it's like if you were to develop an ios uh app and then try and deploy it on windows like uh it wouldn't work 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 but nothing in cuda is available to run elsewhere it's so great okay so now you can see this is just like apple and it's the apple business model apple gives away all of this amazing platform ecosystem that they 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 2006 even before that when they started and then all through it there was no ios there was no iphone like it wasn't obvious that this was a great model in fact most people thought this was a dumb model that like apple lost and the mac was stupid a niche and like windows and intel is what won the open ecosystem 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 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 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 but i think that's just representative to me of like

everybody thought that like the apple model sucked yeah i mean it sucks unless you're at scale and at the time there was very little to believe that nvidia was going to have the scale required to justify this investment or that there was a market to let them achieve the scale to justify this 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 because they probably still held the stock being like where's the market that's going to enable the scale you need to run this playbook all right so are you going to take us to 2011 12 where are we hopping back in here uh if only the world were uh worked 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 work but they have some misadventures in between so stock's getting hammered it's 2008 and uh i'm just completely speculating on my own but they're in the penalty box they're committed to continuing to invest in cuda and making general purpose computing on gpu a thing i do wonder if they felt like well we got to do something to appease shareholders here yeah we got to show that we're trying to be commercial here so it's 2008. what's going on in 2008 you know in the tech world it's mobile so in 2008 they launched the tegra chip 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 uh that's maybe that's too rough on nvidia but what was tigra uh people might recognize that name it was a full-on system on a chip for smartphones competing directly with qualcomm with samsung like it was a processor like an arm based cpu

plus all of the other stuff you would need for a system on a chip to power android headsets uh i mean this is like a wild departure for it leverages none of nvidia's core skill sets except maybe graphics being part of smartphones but like come on if there's ever a use case for integrated graphics it's smartphones right right low power smaller footprint yep totally do you know this is one of my favorite parts about the whole research do you know what the first product was that shipped using a tegra chip uh no it was the microsoft zune hd media player uh that just tells you pretty much everything you need to know uh it did though the tiger system it is still around sort of to this day it powered the original tesla model s touch screen so like before any of the autopilot autonomous driving stuff they were the processor powering just the infotainment the touchscreen infotainment in the model s and i think

that actually starts to help nvidia get into the automotive market the tegra platform still to this day is the main processor of the nintendo switch oh they repurposed it for that yeah for that and they 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 3 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 because they kind of 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 gpu companies have not been good at 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 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 apple it is qualcomm and today is qualcomm's mobile gpu division and qualcomm's good at mobile and so it's a natural home for it do you know what that line of mobile gpu processors is called no it is the arduino ar deno processors and do you know why it's called the arduino or ardeno no that sounds super familiar but no the letters are rearranged from radeon [Laughter] that's great yeah that's great so you're saying nvidia's mobile graphics efforts didn't quite pan out no we didn't talk about this as much in the sony episode but my impression of the whole android value chain ecosystem is that there's no profits to be made anywhere and google

keeps it that way on purpose ironically they make a lot of money now on the play store uh yeah the play store 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 oh yeah 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 are the scale player but like these things are designed to sell for dirt cheap as end products like there's no margin to be had here yep yep also before we continue you just did the sidebar on the amd mobile graphics chip i see your sidebar i'm gonna raise you one more sidebar that we have to include that you know because the ncs guys told us about this so when nvidia is going after mobile they buy a mobile baseband company called isera a british company called isera in

2011. you know where i'm going with this oh yes this is so good there's a good seed plant to come back to later uh you know because they're investing in mobile integra is going to be a thing blah blah blah and then a few years later when they end up pretty much shutting down the whole thing they shut down what they bought from my sarah they lay everyone off the isera founders who made a lot of money when nvidia bought them they go off and they found a company called graphcore that uh we're going to 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 the nvidia bear cases nvidia killers out there they've now raised about 700 million in venture capital and pick up [Laughter] 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

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 is bumping along through all of this in the early late 2000s early 2010s you know some years growth is like 10 maybe it's flat in others like this company has completely gone sideways in 2011 they whiff on earnings again stock goes through another 50 drawdown it's cliche 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 but obviously not them so what happened 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 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 alex net or see it coming because nobody saw alex neck coming so in 2009 a princeton computer science professor and also undergrad alum of princeton just like yours truly woo a wonderful place named fei-fei li their specialty is artificial intelligence and computer science starts working on an image classifying project that she calls imagenet now the inspiration for this was actually a way old project from i think the 80s at princeton called word net that was

like classifying words this is classifying image imagenet and her 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 something like that and that with that database then artificial intelligence image recognition algorithms could run against that database and see how they do so like oh look at this image of you know you and i were looking to be like that's a strawberry but you don't give the answer to the algorithm and the algorithm figures out if it thinks it's a strawberry or a dog or whatever so sheena collaborators start working on this it's super cool they build the database they use a mechanical turk amazon mechanical turk to build it and then one of them i'm not exactly sure who if it was fifa or somebody else has the idea of like we know we've got this database we want people to use it

well let's make a competition this is like a very standard thing in computer science academia of like let's have a competition an algorithm competition so we'll do this annually and anyone any team can submit their algorithms against the imagenet database and they'll compete like who can get the lowest error rate like the most number of images percentage of the image is correct and uh this is great so it brings her great renown becomes popular in the ai research community she gets poached away by stanford the next year i guess that's okay because i went there too so that's fine and she's still there i know i couldn't resist i couldn't resist she's like a kindred spirit to me do you know i know you do know but i bet most listeners do not know what her endowed tenure chair is at stamford today i do she is the sequoia chair yes the sequoia capital professor of computer science at stanford so cool why did she become the sequoia

capital chair and what does all this have to do with nvidia well in the 2012 competition a team from the university of toronto submits an algorithm that wins the competition and it doesn't just win it by like a little bit it wins it by a lot so the way they measure this is 100 of the images in the database what percentage of them did you get wrong so it wins it by over 10 i think it had a 15 error rate or something in the next like all the best previous ones have been like 25 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 because they just didn't brute force their way all the way there they like tried a completely different approach and then boom showed that we could get way more accurate than anyone else ever thought so what was that approach well they called the team which was composed of

alex krazebski was the primary lead of the team he was a phd student and collaboration with ilya sutscover and jeff hinton jeff hinton was the phd advisor of alex they call it alexnet what is it it is a convolutional neural network which is a branch of artificial intelligence called deep learning now deep learning is new for this use case but ben is you weren't exactly right it had been around for a long time a very long time and deep learning neural networks this was not a new idea the algorithms had existed for many decades i think but they were really really really computationally intensive they required to train the models to do a 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 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 you know more transistors in a given space but because you're able to run programs in parallel now yes so alex net took these old ideas and it implemented them on gpus and to be very specific it implemented them in cuda on nvidia gpus we cannot overstate the importance of this moment not just for nvidia but for like computer science for technology for business for the world for us staring at the screens of our phones all day every day this was the big bang moment for artificial intelligence and nvidia and cuda were right there yep there's funny there's another example

within the next couple of years 2012 2013 where nvidia had been thinking about this notion of general-purpose computing for their architecture for a long time in fact they even thought about should we relaunch our gpus as gp gpus general purpose graphics processing units and of course they decided not to do that but just built kudo 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 lot from this alex net competition and so in 2013 brian catanzaro who's a research scientist at nvidia published a paper with some other researchers at stanford which included andrew ing 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 so totally different model super parallelized lots of compute for a super short period of time in a really high performance computing way or hpc as it would sort of become known and this ends up being the very core of what becomes coup dnn which is the library for deep neural networks that's actually baked into cuda that makes it easy for data scientists and research scientists everywhere who aren't hardware engineers or software engineers to just pretty easily write high performance deep neural networks on nvidia hardware so this alexnet thing plus then brian and andrew ing'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 in the future and specifically to do deep learning so i think at this point like everybody knows that this is pretty important but it's not that much of a leap to say if you can train a computer to recognize images on its own that you can then train a computer to see on its own to drive a car on its own to play chess to play go to make your photos look really awesome when you take them on the latest iphone even if you don't have everything right 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 yep and then most importantly for the market that jensen and nvidia are looking for you can use this same branch of ai to predict what type of content you might like to see next show up in your feed of content and what type

of ad might work 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 fifa league goes to google brian went to baidu and he's back at nvidia now doing applied ai brian went to baidu jeff hinton goes to facebook so you know all the other markets like even throw out say you don't believe in self-driving cars you don't think it's going to happen or any of this other stuff like this it doesn't matter like the market of advertising of digital advertising that this enables is a freaking multi-trillion dollar market and it's funny because like that feels like oh 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 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 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 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 yes in the ben thompson um jensen interview ben actually says this when he's sort of realizing this talking to jensen he says this is ben talking the way value accrues on the internet in a world of zero marginal costs where there's just an explosion and abundance of content that value accrues to those who help you

navigate the content he's talking about aggregation theory duh and then he says what i'm hearing from you jensen is that yes the value accrues to people that help you navigate that content but someone has to make the chips and the software so that they can do that effectively 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 list of other companies on the consumer side and they're all dependent on nvidia 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 realize this we were the class of people working in tech as venture capitalists that should have ooh do you know the market and recent quote

oh no oh this is awesome okay so it's a couple years later so it's like getting more obvious but it's 2016 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 90s or all building on the iphone in the late 2000s and then he says for fun our firm has an internal game of what public companies we'd invest in if we were a hedge fund we'd put in all of our money to nvidia 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 we all should have been doing this so literally nvidia stock in 20 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 220 a share the high in the past year has been well over 300 like if you realized what was going on 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 2017 and 2018 with crypto in a little bit but there was a massive stock run up to like 65 a share in 2018 and even 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 34 bucks a share in 2019 if you zoom out on that graph which is the next tweet here that you can see that like in retrospect that little crash just looks like nothing you don't even pay attention to it in the crazy run-up that they had to 350 or whatever their all-time high was

yeah it's wild and a few more wild things about this it's not until 2016. again alex and that happens in 2012. it's not until 2016 that nvidia gets back to the 20 billion market cap peak that they were in 2007 when they were just a gaming company that's almost 10 years i really hadn't thought about it the way that you're describing it but the breakthrough happened in 2010 2011 2012 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 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 going to go all in and we're not pivoting the business but from the amount of attention that he's giving in earnings calls to this versus gaming i mean everyone's just like uh are you off your rocker well i think people had just lost trust and interest you know after like there were so many years of like they were so early with cuda and early taking again they didn't even know that this like they didn't know alex that was gonna happen right jensen felt like the gpu platform could enable things that the cpu paradigm could not and he would like had this faith that something would happen but 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 well look the gpu has accelerated the graphics workload so

we've taken the graphics workload off of the cpu the cpu is great it's your primary workhorse for all sorts of flexible stuff but we know graphics needs to happen in its own separate environment and have all these fancy fans on it and get super cooled and it needs these matrix transforms the math that needs to be done is matrix multiplication and there was starting to be this belief that like oh well because the you know professor the apocryphal professor told me that he was able to use these program the matrix transforms to work for him you know maybe this matrix math is really useful for other stuff and sure it was for scientific computing and then honestly like it fell so hard into nvidia's lap that the thing that made deep learning work was massively parallelized matrix math and they're like nvidia is just like staring down their gpus like i think we have exactly what you are looking for yes

there's a 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 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 yeah 100 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 graphics is an embarrassingly parallel problem because every pixel on a screen is unique i mean they don't have a core to drive every pixel on the screen

there's only 10 000 cores on the most recent nvidia graphics cards but there's not which is crazy right but there's way more pixels on the screen so you know they're not all doing every single pixel at the same time every clock iteration but it worked out so well that neural networks also can be done entirely in parallel like that where every single computation that is done is independent of all the other computations that need to be done so they also can be done on this super parallel set of course it's just you gotta wonder like when you kind of reduce all this stuff to just math it is interesting that these are two very large applications of the same type of math in the search space of the world of what other problems can we solve with parallel matrix multiplication there may be more there may even be bigger markets out there totally well i think there probably will be 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 you know there's robotics there's autonomous vehicles there's the omniverse it's all coming it's funny we just joked about how like nobody saw this before the run-up in 2016 2017 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 2018 another class of problems that are embarrassingly paralyzable is of course cryptocurrency mining and so a lot of people were going out and buying consumer nvidia you know graphics cards and using them to set up crypto mining rigs in 2016 and 2017 and then when the crypto winter hit in 2018 and the end of the ico craze and all that the mining rig demand it fell off and this have become so big for nvidia that their revenue actually declined right yeah so a couple interesting things here

let's talk about technically why so the way crypto mining works is effectively guess and check you're effectively brute forcing an encryption scheme and when you're mining you know you're trying to discover the answer to something that is hard to discover so you're guessing if that's not the right thing you're incrementing you're guessing again and that's a vast over simplification and not technically exactly right but that's the right way to think about it and if you were going to guess and check at a math problem and you had to do that on the order of a few million times in order to discover the right answer you could very unlikely discover the right answer on the first time but you know that probabilistically is only 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 the problem like this is massively parallelizable because instead of

guessing and checking with one thing you can guess and check with 10 000 at the same time and then 10 000 more and then 10 000 more and the other thing is it is matrix math so yet again there's this third application beyond gaming beyond neural networks there's now this third application in the same decade for the two things that these chips are uniquely good at and so it's interesting that like you could build hardware that's better for crypto mining or better for ai and both of those things have been built by nvidia and their competitors now but the sort of like general purpose gpu happen to be pretty darn good 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 cerebros is the one that i'm thinking of they sort of say well the gpu is a thousand times better or you know much much better than a cpu for doing this

kind of stuff but it's like a thousand times worse than it should be there exists much more optimal solutions for you know doing some of this this ai stuff interesting really begs the question of like how good is good enough in these use cases right and now i mean 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 back to crypto winter just because this is so funny uh hey well crypto itself became like a real industry i don't think that's a controversial statement at this point maybe it is maybe it isn't but certainly it's less controversial than it was in 2018. yes what happens is the nvidia stock gets hammered again it goes through another 50 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 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 going to use these for gaming they're selling to best buy and then people go buy them in best buy 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 drives up for this thing that ua didn't ask for and b had poor visibility into

knowing if people were buying in the first place so the management team just looks terrible to the street at this point because they had just no ability to understand what was going on in their business and i think a lot of street still was still at this hangover of skepticism about is this deep learning thing like what jensen okay and so it was kind of any excuse to sell off it took but anyway that would short live to the 50 dip because uh with the use case and specifically the enterprise use case for gpus for deep learning like it just takes off and so this is really interesting if you look at nvidia's um but they report financials a couple different ways but one of the ways they break it out is a few different segments is the gaming consumer segment and then their data center segment and it's like data center in the data set well all the instances right all of the stuff we're talking about it's all done in the data center

like 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 so like but it's all happening in the data center is uh my point right right i guess what i'm saying my argument is every time i see data center revenue in my mind i sort of make it synonymous with this is their ml segment yes yes that's what i'm saying i agree yeah now the data center this is really interesting again because they used to sell these cards that would get packaged put on a shelf a consumer would buy them yeah they made some specialty cards for the scientific computing market and stuff like that but this data center opportunity like man do you know the prices that you can sell gear to data centers for like it makes the rtx 3090 look like a pins and the rtx 3090 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 a100 it's the h100 uh so the a100 they just announced the h100 and that's what like 20 or 30 grand in order to just get one card yeah and people are buying 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 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 nvidia chips for their training

the current compute cluster that they have that they're still using i want to say i did the math and like assumed some pricing i think they spent between 50 and 100 million dollars that they paid nvidia for all of the compute in that wow one customer that's one customer for one use case at that one customer crazy i mean you see this show up in their earnings so we're at the part of the episode where we're close enough to today that it's best illustrated by the today numbers so i'll just flash forward to what the data center segment looks like now 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 2006 to alexnet all the way you know another decade forward to 2020 gaming is still king it generates almost 6 billion in revenue the data center revenue segment was three billion but had been pretty

flat for a couple years so then insanely over the last two years it 3xed the data center segment 3x it is now doing over 10 and a half billion a year in revenue and it's basically the same size as the gaming segment it's nuts it's amazing how it was like sort of obvious in the mid 2010s but when the enterprises really showed up and said we're buying all this hard work and putting it in our data centers and whether that's the hyperscalers the like cloud folks google microsoft amazon putting it in their data centers or whether it's companies doing it in their own private clouds or whatever they want to call it these days on-prem data centers everyone is now using machine learning hardware in the data center yup and nvidia is selling it for very very very healthy gross margins apple level gross margins yes exactly so speaking of the data center a couple things one in this is so nvidia in 2018 they actually

do change the terms of the user agreements of their consumer cards of geforce cards that you cannot put them in data centers anymore they're like 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 is worth it i mean you buy these crazy data center cards and they have like twice as many transistors and actually they don't even have video outputs like you can't use the data center gpus like the a100 does not have video out so they actually can't be used as graphic cards oh yeah there was a there's a cool um linus tech tips video about this where they get a hold of an a100 somehow and then they run some benchmarks on it but they can't actually like drive a game on it oh fascinating yeah so fun data center stuff is like super high horsepower but of course like useless to run a game on because you can't pipe it to a tv

or a monitor but then it's interesting that they're sort of artificially doing it the other way around and saying for those of you who don't want to spend 30 000 on this and are trying to like make your own little rig at home your own little data center rig at home no you cannot rack these don't think about going to fries and buying a bunch of g-forces ironic because that's how the whole thing started but anyway in 2020 they acquire an israeli data center compute company called melanox that i believe focuses on like uh networking compute within the data center yup for about seven billion integrate that into you know their ambitions and building out the data center and the way to think about what malnox 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 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 melanox 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 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 big boxes with lots of stuff in them you say solutions i hear gross margin that's such a great quote we should like frame that and put it on the wall 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 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 this hardware and now what they're saying is you've got your cpu you've got your gpu now there's a dpu and this data processing unit that's like kind of born out of the melanox stuff is the way that you really efficiently communicate and transform data within data centers so the unit of how you think about like the

black box just went from a box on a rack to now you can kind of think about your data center as the black box and you can write at a really high abstraction layer and then nvidia will help handle how things move around the data center we have one more thing to talk about on data centers but before we do can we tell our audience about one of our favorite things one of our favorite things that will be in person at the acquired arena show yes yes we can it's time for our second sponsor of the episode and all of season 10 huge thank you to vouch the insurance of tech in uh our insurance 101 last time on the first nvidia episode we talked about directors and officers insurance or dino you know this sounds boring like go listen to that like you need this you absolutely need this this insurance 101 stuff is so fun yes it's like it takes something very boring like data centers and makes it very practical

it's the acquired of insurance you might say vouch is the acquired insurance yes that was the greatest if you are a board director or an officer of a company you must have that go to vouch.us acquired pick it up right now take five minutes come back today we are going to talk about employment practices liability insurance or epl so epl is insurance that protects your company from employment related claims anything from harassment and discrimination to improper hiring practices wrongful termination etc etc and not just like you know there's the obvious bad stuff like sexual harassment or discrimination hr laws are super complex and different state by state yes my wife jenny both of her parents are labor and employment attorneys there are whole classes of the law dedicated just to like dealing with all of this with epl i used to think like oh well as long as the company's buttoned up and the

managers are being good people then like this isn't an issue but really it's just a matter of time before you end up with an epl related issue just as you're scaling it's gonna happen if you have started a company been on a board you know this is the most frequent claim that you are going to experience it's not if it is when and indeed actually vouch told us they see this in their own data this is by far the most common claim that comes up and perhaps unsurprisingly over the last two years with everything that's happened epl incidents are up 40 literally 40 in the last two years in terms of volume so the bottom line on this one you could have the best company culture the best managers you could never do anything wrong but if you hire enough employees this is guaranteed to come up so you definitely want to have this what's important to know is that epl coverage protects the company regardless

of whether the claim has merit so if you actually did do something wrong and there's a judgment against you epl coverage of course covers you up to your limit but if it's meritless then a great insurer like vouch and vouch does this they can help you take care of that they can act as an advisor they see this stuff a lot more than you do so when not if but when this comes up in your company pick up the phone call vouch they can help you through it so once again vouch you guys are the best we love you learn more at vouch.us acquired and everybody if you use that link you will get an extra five percent off your coverages great stuff thanks vouch indeed it is 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 were talking about the whole strategy and how excited they are to own arm and how nvidia is going to 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 going to 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 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 softbank acquiring arm episode years and years ago now 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 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 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 in and do that and indeed this year nvidia announced they are making a data center

cpu an arm-based data center cpu called grace to go with the new hopper architecture for their latest gpu so there's grace and hopper of course the rear admiral grace hopper i think i think that's right yeah she was in the navy that's a great computer scientist pioneer 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 because i got the regulatory pressure on this is arms business is simple they make the ip so you can license one of two things from them you can license the instruction set so even apple who designs their own chips is licensing the arm instruction set and so in order to use that i don't know what it actually is 20 keywords or so that that can get compiled to assembly language to run on whatever the chip is you know if you 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 they sell to everyone and so of course a regulatory body is going to step into being like wait wait so nvidia you're a fabulous chip company you're a vertically integrated business model are you going to stop allowing armed licenses to other people and nvidia goes oh 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 is believable beating the drama on that the strategy was going to be is

right now our whole business strategy is that cuda and everything built on top of our whole software services ecosystem is just for our hardware and how cool would it be if you could use that stuff on arm designed ip either just the using the isa or also using the actual designs that people licensed 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 faced too much regulatory pressure to go through with this no but clearly that idea rattled around in jensen's head a bunch and then in videos because um well let's catch us up to today so they just did gtc at the end of march the big developer the big gpu developer conference that they do every year that they started in 2009 as part of building the whole cuda ecosystem i mean it's so freaking impressive now

like there are now three million registered cuda developers 450 separate sdks and models for cuda they announced 60 6-0 new ones at this gtc yeah we talked about the next generation gpu architecture with hopper and then the grace cpu to go along with it i think hopper i could be wrong on this i think hopper is gonna be the world's first four nanometer process chip using tsmc's new four nanometer process which is i think that's right amazing to talk a lot about omniverse we're gonna talk about omniverse in a second but you mentioned this licensing thing they usually do their investor day their analyst day at the same time as gtc and in the analyst day jetson 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

walk it in with a pitch deck we'll put this graphic up on the screen for those watching the video it's a articulation of what the segments are of this trillion dollar addressable opportunity that nvidia has in front of it my view of this is if their stock price wasn't what it was there's no way that they would try to be making this claim that they're going after a trillion dollar market i think it's squishy 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 half a trillion half a trillion dollars they need to sort of justify that unless they are willing to have it go down and so they need to come up with a story about how they're going after this ginormous opportunity which maybe they are but it leads to things like an investor day presentation of let us tell you about our trillion dollar opportunity ahead and the way that they

actually articulate it is we are going to serve customers that represent a 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 that's the thing we're gonna talk about this in narratives in a minute but this is a generational company this is unbelievable this is amazing there's so much to admire here this company did what like 20 something billion in revenue last year and is worth half a trillion dollars they did 27 billion dollars last year in revenue google adwords revenue in the fourth quarter of 2021 was 43 billion google as a whole did 257 billion in revenue so like you got to believe if you're an nvidia shareholder 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 got to believe it's on the

comm yeah you do i mean nvidia has literally three times the price to sales ratio of apple or price to revenue as apple and nearly 2x microsoft and that's on revenue i mean fortunately this nvidia story is not speculative in the way that an early stage startup is speculative like even if you think it's overvalued it is still a very cash generative business yes they generate 8 billion of free cash flow every year so i think they're sitting on 21 billion in cash because 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 an apple or microsoft or these fang companies but it is you know extremely profitable business even on an operating profits perspective well you sell enough of that enterprise data center goodness and uh you can make some money it's crazy they now have a 66 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 because think back we'll put it up on the screen here but back in 99 they had a gross margin of 30 percent on their graphics chips and then in 2014 they broke the 50 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 it's sort of all bundled together and bundle is the right word i think they get great economics because they're bundling so much stuff together it's 66 gross margin business now yep well and you know thinking about increasing that gross margin further and what we were talking about a minute ago with arm and the licensing so at the analyst day around gtc this year they say that they're gonna start

licensing a lot of the software that they make separately it's licensing it separate from the hardware like cuda and there's a quote from jensen here the important thing about our software is that it's built on top of our platform it means that it activates all of nvidia's hardware chips and system platforms and secondarily the software that we do are industry defining software so we've now finally produced a product that an enterprise can license 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 we were joking a few minutes ago about you say solution and i see margin you know yeah like open source software companies have become big for this reason you know data bricks confluent

elastic like these are big companies with big revenue based on open source because enterprises they're like oh i want that software but they're not just going to you know go to give your jpmorgan you're not going to go to github and be like great i got it now you know right you need solutions so to jensen nvidia they see this as an opportunity to i'm sure this isn't going to be cannibalizing hardware customers for them i think this is going to 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 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 going to 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 if we don't think it's going to cannibalize and they're a completely different segment and we can do things in pricing and distribution channel and terms of service that clearly walls off that segment then we can behave in a completely different way to that segment yeah and get further you know returns on our assets that we've generated yep it is a little tim cook though in uh you know tim cook beating the services narrative drum i mean it is kind of you hear public company ceo who has a high market cap and everyone's asking where the next phase of growth is going to come from and saying we're going to sell services and look at this growing business line of licensing that we have oh my goodness but who else is going to

do it wearing a leather jacket at is a great point it's a great frankly elon but well we'll talk about cars 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 20 000 employees we mentioned they did 27 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 fang companies they're growing much faster than apple microsoft google they're growing at 60 percent a year this is a 30 year old company that grew 60 in revenue last year yeah if you're not used to like wrapping your mind around that like startups double and triple b like in the first five years that they exist google has had this amazing run where they're still growing at forty percent microsoft went from ten to

twenty percent over the last decade again amazing they're accelerating but like nvidia's growing is sixty percent right i don't care what your discount rate is having 60 growth in your dcf model versus 20 or 40 will get you a lot more multiple inflation be damned inflation be damned okay a couple other things about specific segments of the business that i think are pretty interesting so they have not slept on gaming like we keep beating this nvidia data center enterprise machine learning argument yeah we haven't even talked about ray tracing and right yeah this rtx set of cards that they came out with the fact that they could do ray tracing in real time holy crap for anyone who's looking for sort of a fun dive on how graphics works go to the wikipedia page for ray tracing it's very cool you model where all the light sources are coming from where all the paths would go in 3d the fact that

nvidia can render that in real time at 60 frames a second or whatever while you're playing a video game is nuts and one of the ways that they do that they invented this new technology that's extremely cool 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 where they basically have faced this problem of well we either could render stuff at low resolution with less frames because we can only render so much per amount of time or we could render really high resolution stuff with less frames and nobody likes less frames but everyone likes high resolution so what if we could cheat death and what if we could get high resolution and high frame rate and they're sitting around thinking how on earth can we do that and they're like you know what maybe this 15-year bet that we've been making on deep learning can help us out

and what they discovered here and invented in dlss and amd does have a competitor to this it's a similar sort of idea but this dlss concept is totally amazing so what they basically do is they say well it's very likely that you can infer what a pixel is going to be based on the pixels around it 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 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 yeah that's awesome it's really cool and when you watch the side by side on all these youtube videos it looks amazing i mean it does involve really tight embedded development with the game developers they have to sort of do stuff to make it dlss enabled but it just looks

phenomenal and it's so cool that when you're looking at this 4k or even 8k output of a game at you know full frame rate you're like whoa in the middle of the graphics pipeline this was not this resolution and then they magically upscaled it it's basically making the like enhanced joke like a real thing that's so awesome i'm remembering back to the riva 128 in the beginning of when they went to game developers and they were like yeah yeah all the blend modes in in directx you know you don't need all 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 the other thing that is funny that's within the gaming segment because they didn't want to create a new segment for it is crypto so because they have poor

visibility into it and before they weren't liking the fact that it was actually reducing the amount of cards that were available to the retail channel for their gamers to go and buy what they did was they artificially crippled the card to make it worse at crypto mining 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 hmm people are just like straight up performing an arbitrage by crypto mining on these cards let's make that more expensive on the cheap cards and let's make dedicated crypto hardware for them to buy to do those 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 have serviced their way to being able to create some segmentation and thus more profitability love it evil evil genius laugh the last thing that you should know about nvidia's gaming segment is this really weird concept of add-in board partners so we've been oversimplifying in this whole episode saying oh you know you go and you buy your rtx 3090 ti at the store and you run your favorite game on it but actually you're not buying that from nvidia the vast majority of the time you are going to some third-party partner asus msi zotac is one there's also like a bunch of really low-end ones as well who nvidia sells the cards to and those people install the cooling and the branding and all this stuff on top of it and you buy it from them and it's really weird to me that nvidia does that i love how consumer gaming graphics cards have become the modern day

equivalent of a hot rod oh dude as you can imagine for this episode i've been hanging a lot on the nvidia subreddit and like it's not actually about nvidia or nvidia the company or nvidia the strategy it's like show off your sick photos of your glowing rig which is pretty funny but like it feels like a remnant of old nvidia that they still do this like they do make something called the founders edition card and it's basically a reference design where you can buy it from nvidia directly but i don't think the vast majority of their sales actually come from that oh it's like um what are the android phones that google makes the pixel yeah it's exactly like that the pixel that's exactly what it is yeah 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 ad and board partner thing but they've built a business on it and so they're not really willing to

cannibalize and alienate but i bet if they had their way and they're becoming a company that can more often have their way they'll find a way to to kind of just go more direct makes sense two other things i want to talk about one is automotive so this segment has been like very small from a revenue perspective for a long time and seems to not have a lot of growth but jensen says in his pitch deck it's going to be a 300 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 doesn't really grow 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 put in there every technology company

has had their fanciful attempt to try to create a meaningfully differentiated experience in the car all have failed you think about microsoft and the ford sync ford sync oh wow you think about car play kind of maybe a little bit works and the only company that's really been successful has been tesla at starting like a completely new car company that's the only way they're able to provide a meaningful differentiated experience nvidia is my perception of what they're doing is they're pivoting this business line this like flat boring undifferentiated business line to say maybe evs electric vehicles and autonomous driving is a way to break in and create a differentiated experience even if we're not going to make our own cars and so i think what's really happening here is when you hear them talk about automotive now and they've got this very fancy name for it it's the something drive platform

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 it kind of feels like they're making the full evav hardware software stack except for the metal and glass and wheels and then going to car companies and saying look you don't know how to do any of this this thing that you need to make is basically a battery and a bunch of gpus and cameras on wheels and like you're issuing these press releases saying you're going that direction but none of this is the core competency of your company except the sales and distribution so like what can we do here and if nvidia is successful in this market it'll basically look like you know an nvidia computer full software hardware with a car chassis around it that is branded by whatever the car company is like the android market yeah and i think we will see if the shift to autonomous vehicles is a

real be near-term and see enough of a dislocation in that market to make it so that someone like nvidia a component supplier actually can get to own a bunch of that value chain versus the auto manufacturer kind of forever stubbornly getting to keep all of it and control the experience yep which to do a mini bowl and bear on this here before we get to the broader on the company you know the bold case for that is we were again a friend of the show jeremy messaging within in slack lotus is one of their partners is lotus gonna go build autonomous driving software like i don't think so ferrari no you know not at all they're gonna be nvidia cars effectively yeah okay last segment thing i want to talk about is how we opened the show talking about the nvidia omniverse and this is not omniverse like metaverse it is similar in that it's kind of a 3d simulation type thing but it's not an open world that you wander around in the

same way that meta is talking about or that you think about in fortnite or something like that what they mean by omniverse is pretty interesting so a good example of it is this uh earth 2 this digital twin of earth that they're creating that has these really sophisticated climate models that they're running that basically is a proof of concept to show enterprises who want to license this platform we can do super realistic simulations of anything that's important to you and what their pitch is to the enterprise is hey you've got something let's say it is a bunch of robots that need to wander around your warehouse to pick and pack if it's a amazon who actually amazon is a customer they showcase amazon and all their fancy videos and they say you're going to 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 going to 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 kind of want to run that in the omniverse first and then when it's working then you want to deploy 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 now that we have the compute the ability to gather the data and the ability to actually you know run these simulations in a way that has a efficient way of running it and a good user interface to understand the data people are going to stop testing in

production with real world assets and everything's going to be modeled in the omniverse first before rolling out this is what an enterprise metaverse is going to be this is not designed for humans humans may interact with this there will be ui you'll be able to be part of it the purpose of this is for simulating applications and most of it i think is gonna run with no humans there yep pretty crazy yeah it's a good idea sounds like a good idea all right you want to talk bear and bull case on the company let's do it analysis so i mean they paint the bowl case for us when they say there's a hundred trillion dollar future we're gonna capture one percent of it there's 300 billion from automotive here's the four or five segments that add up to a trillion dollars of opportunity sure that's like a very neat way with a bow on it and a very wishy-washy hand-wavy way of articulating it so the question

sort of becomes where does amd fall in all this they're a legitimate second place competitor for high-end gaming graphics and i think we'll continue to be that feels like a place where these two are going to keep going head-to-head the bear 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 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 or is there someone else who is coming along with a completely different approach to accelerated computing whether accelerating workloads off the gpu onto something new like a cerebros or like a graph core that is going to eat their lunch in the enterprise ai data center

market that's an open question you know it's interesting like people have been talking about that for a while the other big bear case that people have been talking about again for a while now is you know the big big customers of nvidia that are paying them a lot of money the tesla's the googles the facebooks the amazons the apples and not just paying them a lot of money and getting you know assets of value of that they're paying high gross margin dollars to nvidia for what they're getting that those companies are going to want to say you know it's not that hard to design our own silicon to bring all this stuff in-house we can tune it to exactly our use cases sort of similar to the cerebrus uh graphcore bear case on nvidia i think in both of these cases you know it hasn't happened yet [Laughter] 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's switching hasn't happened yet but switching the to their own for the full self-driving they're they're doing their own stuff on the car and they're switching yep that is switch on the inference side yes on device yes that has happened but look nvidia's probably strong in that but i think the real thing to watch is the data center and google is probably the biggest bear case there yeah it's interesting to talk about these companies and particularly cerebrus because what they're doing is such a gigantic swing and a totally different take than what everyone else has done for folks who hasn't sort of followed the company they're making a chip that's the size of a dinner plate everyone else's chip is like a thumbnail but they're making a dinner plate size chip and you know the yields on these things kind of 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 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's 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 60 times as much power they're way more expensive like if nvidia is gonna sell you a 20 or 30 000 chip cerebrus is going to sell you a two million dollar ship 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 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 i mentioned google that made a bunch of noise about making their own silicon in the data center and then stayed the course and stayed really serious about it with their tpus their business model is different so nobody knows what the bill of materials is to create a tpu nobody knows really what they cost to run they don't retail them they're only available in google cloud and so google is sort of counter-positioned against nvidia here where they're saying we 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 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 mm-hmm interesting so it's kind of the android strategy but run in the data center one thing we haven't mentioned but we should is uh

cloud is also part of the nvidia story too like you can get nvidia gpus in aws and azure and google cloud and that is part of the growth story for nvidia 2. and nvidia starting their own cloud you can get direct from nvidia cloud-based gpu data center gpus interesting yeah it'll be very interesting to see how this all shakes out with uh then video the startups in with google i mean all that said though like i think but look nvidia is 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 60 growing company year over year every year for a while then they're not richly 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 it's not so easy you know like yeah facebook and tesla and google and amazon

and apple are capable of doing a lot but we just told this whole story this is 15 years of cuda and the hardware underneath it and the libraries on top of it that nvidia has built to go recreate that and surpass it on your own is such an enormous enormous bite to bite 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 then your customer customers only yourself so you're constrained by the amount of people you can get to use google cloud well 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 nvidia episode we

covered the first 13 years of the company we talked a lot about what does their power look like up to 2006 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 cerebros in the enterprise or amd and gaming yep and just to enumerate the powers again as we always do counter positioning scale economies switching costs network economies process power branding and cornered resource so there are definitely scale economies the whole cuda investment yes not at first but definitely now is predicated on being able to amortize that 1000 plus employees spend over the base of the 3 million developers and all the people who are buying the hardware to use what those developers create this is the whole reason we spent 20 minutes talking

about if you were going to run this playbook you needed an enormous market to justify the capex you were going to put in right so very few other players have access to the capital and the market that nvidia does to make this type of investment so they're basically just competing against amd for this totally agree scale economies to me is like the biggest one that pops out to the extent that you have locked in to developing on cuda which i think a lot of people really have lock in on cuda then that's major switching costs yup like if you're gonna boot out nvidia that means you're booting out cuda 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 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 the first instantiation of nvidia's power to the extent it had power right yeah i don't know as much today especially because tsmc will work with anybody in fact tsmc 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 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 tsmc because they've been this like great long time partner and stuff but yeah 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 so there was this big cyber security hack a couple of months ago by this group lapsis 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 it's a very public incident and it's clear from the demands of lapses where some of nvidia's power lies because 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 right them trying to look like a bunch of like crypto miner people hey nothing wrong with being a crypto miner but totally not but i think there's a reputation around it and the other thing they demanded is that nvidia open source all of its drivers and make available 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 you know all of cuda and how tightly coupled their hardware and software is nvidia's we just did this our most recent episode with hamilton and chen yi nvidia is a platform in my mind no doubt about it cuda 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 apple microsoft you know and the like go into nvidia yep i think the stew of powers is the right way to phrase that yes anything else here 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 try to think about this in investing

particularly in public markets investing but like man you really really want to invest in whoever is selling the picks and the shovels in a gold rush hmm the ai you know ml deep learning gold rush those years gosh oh my gosh like we should all all be kicking ourselves of 2012 13 maybe not 2012 but certainly 2014 2015 into 2016 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 like maybe we should have bought nvidia like i don't know if any one of those startups any given one is gonna succeed but i'm pretty sure in video it's 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 to enable graphics to be a storytelling medium and of course this led to the invention of the pixel shader and the idea that

everybody can sort of tell their own visual story their own way in a social networked real-time way very cool and now it's much more that wherever there is a cpu there is an opportunity to accelerate that cpu and nvidia will bring accelerated computing to everyone and we will make all the best hardware software and services solutions to make it so that any computing workload runs in the most efficient way possible through accelerated computing that's pretty different than enable graphics as a storytelling medium but also they need 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 not dying yeah they should have died at least four separate times

and they didn't and part of that was brilliant strategy part of that was things going their way but i think a large part of it too was just the company and jensen particularly in this these most recent chapters where they're already a public company just being like yeah i'm willing to just sit here and endure this pain and i have confidence that like we will figure it out the market will come i'm 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 asiometry 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 especially with the way that they sort of overlay different designs on top of

each other on different layers of the chip yeah see more about what floor planning is i bet a lot of listeners won't know so it's funny how they keep appropriating these sort of real world large-scale analogies to chip so floor planning the way that an architect would lay out the 15 rooms in a house or five rooms in a house or two rooms in a house on a chip is laying out all of the circuitry and wires on the actual chip itself except of course there's like 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 wow that is mind bending because 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 going to reach down and pick up my ethernet cable but it's wiring in the

euv etched substrate on chip exposure is probably the term that i'm looking for here photolithography exposure but it is just so tiny i mean you can say four nanometers all you want david but that won't register with me how freaking tiny that is until you're sort of faced with the reality of dozens of miles of quote-unquote wires on this chip 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 but yeah like that's what that means okay here's one that i had that we actually haven't talked about which i think will be fun so i generated a capex graph oh fun we'll show it on screen here for those watching on video obviously there's a very high looking line for amazon because building data centers and fulfillment centers is very expensive especially the last couple 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 and this is relative for people listening on audio relative to a bunch of other you know fang type companies yeah so apple has 10 billion dollars of spend on capital expenditures per year microsoft and google have 25 billion tsmc who makes the chips has 30 billion what a great capital efficient business that nvidia has on their hands only spending a billion dollars a year in capex it's like it's a software business and then basically is well it is right like tsmc does the fabbing nvidia makes software and ip yep 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 grew tsmc 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 37 operating margins so

this is even better than apple and for non-finance folks operating margins so we talked about their 66 gross margin that's like unit economics but that doesn't account for all the head count and the leases and just all the fixed costs in running the business even after you subtract all that out 37 of every dollar that comes in gets to be kept by and via shareholders it's a really really really cash generative business and so if they can continue to scale and can keep these operating margins or even improve them 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 well those are digital monopolies like come on basically zero costs digital monopolies and some of the largest markets in history but uh it's still very good all right well let's do grading and

before we actually grade we want to tell you about another one of our friends for our final sponsor let's talk about the softbank latin america fund so you know this by now these folks created the fund with a simple thesis the region of latin america was overflowing with innovative founders and great opportunities but short on the ingredient of capital softbank has invested 8 billion in 70 plus companies and they have one gigantic takeaway and i can't say this enough because i think it's like you can keep hearing it but i think the important thing is sort of like internalizing it that technology in latin america is not about disruption it's about inclusion so when you're thinking about economic opportunities in this region you don't have to think like ooh how can we overthrow the incumbent it really is like if you're used to living your life or doing business in north america in a lot of the like

ways that you feel are quote unquote modern a lot of these business models and a lot of this technology just has not happened yet to serve the vast populations in latin america you sort of have a case study in some businesses that have worked and now you get to go and bring it to the masses so just amazing uh opportunity for inclusion here the vast majority of the population is underserved by every category from banking to transportation to e-commerce businesses are not served by modern software solutions as i was saying and we want to highlight a great portfolio company v-tex now this is a crazy story speaking of high-growth companies recently they saw 98 growth during the pandemic as companies looked to vtec for their digital commerce native marketplace and order management capabilities today vtex powers over 3 000 online storefronts for global brands like walmart coca-cola nestle and as we

mentioned on our sony episode sony they were recently named the world's fastest growing ecommerce platform and they are just one example of how softbank is partnering with great founders and bringing them the capital and expertise they need to bring the future and build it in latin america now to learn more you can click the link in the show notes or go to latinamericafund.com so cool and shu and palo who run it are just the best they are just the best we love them become such great friends over the years and uh can't say enough good things excited to see shu uh as well at the seattle arena show yes acquired fm slash arena show uh okay grading so i think the way to do this one david is what's the a plus case what's the cks what's the f case i think so and there's sort of an interesting way to do this one because you could do it from a shareholder perspective where you have to evaluate it based on where it's trading

today and sort of like what needs to be true in order to have a a plus investment starting today that sort of thing you mean like a michael mobison expectations investing style yes exactly or you could sort of close your eyes to the price and say let's just look at the company if you're jensen what do you feel would be an a plus scenario for the company regardless of the investment case i kind of think you have to do the first one though like i kind of think it's a cop-out to not think about it like what's the bull and bear investment case from here as we pointed out many times on the episode there's a lot you got to believe to be a bull on nvidia at this share price so what are they well one big one is that they continue their incredible dominance and their 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 i also think with the melanox acquisition there's a very plausible story around this idea of a data processing unit and around being your one-stop shop for ai data center hardware and i think rather than saying like oh the upstart competition will fail i think you kind of have to say that nvidia will find a way to learn from them and then integrate it into their strategy too 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 going to

continue to be able to do their own innovation but also fast follow when it makes sense to 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 this shareholder perspective is do you need to believe that all the real world ai use cases are going to happen do you need to believe that some basket maybe not all of them but that's 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 i'm wrestling with whether that is something you have to believe or whether that's optionality the reason it

would be only optionality only upside is if the digital ai we know that that's a big market there's no question about that at this point is that gonna continue to just get so big are we still only scratching the surface there how much more ai is gonna be baked into all the stuff we do in the digital world and will nvidia continue to be at the center of that i don't know i don't have a great way to assess how much growth is left there that is kind of the right question though yeah they're in 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 you know jensen was really asking you to believe it's like hey we're building this cuda thing just ignore that there's no real use case for it or market now there is a real real use case in market for it which is machine learning deep learning in the

digital world undeniable 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 and if the real world stuff you know these little robots that run around uh factory floors and the autonomous vehicles and if that stuff doesn't materialize then yeah there's no way that it can support the growth that it's been on 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 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 driving in electric vehicles is an internet bet in part if you want to bet on the growth of the internet it'll mean you'll drive less but it also means that you're just going to be on the internet when you're driving yeah yeah or 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 that'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 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 of a peloton or a zoom or

something like that 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 yup all right carve outs ooh carve outs i've got a fun one small one well a collection of small things long time listeners probably know one of my favorite i think my favorite series of books that have been written in the past 10 years is the expanse series amazing sci-fi nine books so great the ninth book came out last fall uh it was just even with like a newborn i made time to read this book that's awesome newborn plus acquired i was like i gotta read this i know that's how you know recently last month so the authors have been writing short stories like companion short stories alongside the main narrative over the

last decade that they've been doing this and they released a compendium of all the short stories plus a few new ones called memories legion and this is really cool like i mean they're great writers great short stories to read even if you don't know anything about the expanse story but if you know the whole nine book saga and then these like just paint little they give you a 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 cool mine is a physical product actually for the episode we did with brad gerstner on altimeter we needed a third camera and so i went out and bought a sony rx100 little point-and-shoot camera and uh recently took it to disneyland and i must say it is so

nice to have a point-and-shoot camera again it's like funny how it's gone full circle i've you know was a dslr person forever and then i got a mirrorless camera and then i became a mirrorless plus big long zoom lens person but it's kind of annoying to lug that around and then once i started downgrading my phone from the massive awesome iphone with the 3x zoom and i now have the iphone 13 mini i think that's what it is with the two cameras and no zoom lens 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 it's not as nice as having a 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 is uh it's slightly more inconvenient to

carry because you kind of need another pocket yeah i was going to ask can you put it in your pocket yeah i put it in a pocket i don't have to have a sort of like a rapid strap around my neck which is nice nice so the sony rx100 great little device it's like the seventh generation of it and they've really refined the industrial design at this point that's awesome that's awesome i actually just bought my first camera cube like a travel camera cube thing for our uh uh alpha seven c's now that we have uh literally it's four required for when after the altimeter episode i was like oh wow we gotta do more in person yeah yeah ben brought his down it's like for sure i'm gonna need to bring this somewhere these cameras are just they are so good they're so good all right listeners thank you so much for listening if you're contemplating coming to seattle on may 4th we would love to see you there it's going to be so fun

it'll be a blast interviewing jim and maybe by the time this comes out we will have announced some of our other little fun surprises too i think we can say now there's going to be an after party there is definitely an after party thank you's to our friends at vouch for renting out a bar basically across the street couple blocks away with huge capacity so that'll be really fun to have everyone wander over from the arena to the vouch after party where they're gonna launch in washington state at the event which is very fun i'm very excited for all my portfolio companies and no matter what whether you are attending that in person or not you should come chat about this episode with us in the slack there's 11 000 other smart members of the acquired community just like you and if you want more acquired content after this and you are all caught up go check out our lp show by searching acquired lp show in any podcast player

hear us interview nick and lauren from trovatrip most recently and we have a job board acquire.fm jobs find your dream job curated just by us the fine folks at the acquired podcast all right with that thank you to vanta vouch and the softbank latin america fund and we will see you next time we'll see you next time who got the truth is it you is it you is it you who got the truth now huh