acquired nvidia part iii transcript

do you like my bucks t-shirt I love your bucks t-shirt I went for the first time what two weeks ago when I was down for meeting a benchmark and the Nostalgia in there is just unbelievable I can’t believe you hadn’t been before I know Jensen is a Denny’s guy but I feel like he would meet us at bucks if we asked him or at the very least we should figure out some Nvidia memorabilia to get on the wall at books totally fit right in all right let’s do it let’s do it who got the truth [Music] welcome to season 13 episode 3 of acquired the podcast about great technology companies and the stories and playbooks behind them I’m Ben Gilbert I’m David Rosenthal and we are your hosts today we tell a story that we thought we had already finished Nvidia but the last 18 months have been so insane listeners that it warranted an entire episode on its own so today is a part three for us with Nvidia telling the story of the AI Revolution how we got here and why it’s happening now starting all the way down at the level of Adams and silicon so here’s something crazy that I did a transcript search on to see if it was true in our April 2022 episodes we never once said the word generative that is how fast things have changed unbelievable totally crazy and the timing of all of this AI stuff in the world is unbelievably coincidental and uh very favorable so recall back to 18 months ago throughout 2022 we all watched financial markets from public equities to early stage startups to real estate just fall off a cliff due to Rapid rise in interest rates the crypto and web 3 Bubble Burst Banks fail it seemed like the whole Tech economy and potentially a lot with it was heading into a Long Winter including Nvidia including Nvidia who had that massive inventory write-off for what they thought was over ordering yep wow how things have changed yeah but by the fall of 2022 right when everything looked the absolute bleakest a breakthrough technology finally became useful after years in research Labs large language models or llms built on the Innovative Transformer machine learning mechanism burst onto the scene first with open ai’s chat GPT which became the fastest app in history to a hundred million active users and then quickly followed by Microsoft Google and seemingly every other company in November of 2022 AI definitely had its Netscape moment and time will tell but it may have even been its iPhone moment that is definitely what Jensen believes yep well today we will explore exactly how this breakthrough came to be the individuals behind it and of course why the entire thing has happened on top of nvidia’s hardware and software if you want to make sure you know every time there’s a new episode go sign up at acquired.fm email you’ll also get access to two things that we aren’t putting anywhere else one a clue as to what the next episode will be and two follow-ups from previous episodes from things that we learned after release you can come talk about this episode with us after listening at acquire.fm slack if you want more of David and I check out our interview show acq2 our next few episodes are about AI with CEOs leading the way in this world we are talking about today and a great interview with Doug demiro where uh we wanted to talk about a lot more than just Porsche with him but uh you know we only had 11 hours or whatever we had in Doug’s garage so a lot of the car industry chat and learning about Doug and his journey and his business we saved for acq2 so go check it out one final announcement many of you have been wondering and we’ve been getting a lot of emails when will those hats be back in stock well they’re back for a limited time you can get an AC EQ embroidered hat at acquired.fm store go put your order in before they uh go back into the Disney Vault forever this is great I can finally get Jenny one of her own so she stops stealing mine yes well without further Ado this show is not investment advice David and I may have investments in the companies we discuss and this show is for informational and entertainment purposes only David history and facts oh man well on the one hand we only have 18 months to talk about except that I know you’re not going to start 18 months ago on the other hand we have decades and Decades of foundational research to cover so when I was starting my research I went to the Natural first place which was our old episodes from April 2022 and I was listening to them and I got to the end of the second one and uh man I had forgotten about this I think Jensen maybe wishes we all had forgotten about this in one of nvidia’s earning slides in 2021 they put up their total addressable market and they said they had a one trillion dollar Tam and the way that they calculated this was that they were going to serve customers Who provided a hundred trillion dollars worth of industry and they were going to capture just one percent of it and there was some stuff on the slide that was fairly speculative you know like autonomous vehicles and the Omniverse and I think robotics were a big part of it and the argument is basically like well Cars Plus factories plus all these things added together is a hundred trillion and we can just take one percent of that because surely their compute will amount to one percent of that which I’m not arguing is wrong but it is a very blunt way to analyze that market yeah it’s usually not the right way to um think about starting a startup you know oh if we can just get one percent of this big Market blah blah blah it’s the topiest downway I can think of to size a market so you Ben rightly so called this out at the end of Nvidia part two and you’re like you know I think to justify where Nvidia is trading at the moment you kind of actually got to believe that all of this is gonna happen and happen soon autonomous cars robotics everything yeah importantly I felt like the way for them to become worth what they were worth at that time literally had to be to power all of this Hardware in the physical world yep I kind of can’t believe that I said this because it was unintentional and uninformed but I was kind of grasping at straws trying to play devil’s advocate for you and we just spent most of that whole episode talking about how machine learning powered by Nvidia ended up having this incredibly valuable use case which was powering social media feed recommenders and that Facebook and Google had grown bigger than anyone ever imagined on the internet with those feed recommendations and Nvidia was powering all of it and so I just sort of idly proposed well maybe but what if you don’t actually need to believe any of that to still think that Nvidia could be worth a trillion dollars what if maybe just maybe the internet and software and the digital world are gonna keep growing and there will be a new foundational layer that Nvidia can power is that possible and I think we were both like yeah I don’t know let’s end the episode yeah sure we Shrugged it off and we’re like all right carve outs but the crazy thing is that of course at least in this time frame most things on Jensen’s trillion dollar Tam slide have not come to pass but that crazy question just might have come to pass and from nvidia’s revenue and earnings standpoint definitely has it’s just wild all right so how did we get here let’s rewind and tell the story so back in 2012 there was the Big Bang moment of artificial intelligence or as it was more humbly referred to back then machine learning and that was alexnet we talked a lot about this on the last episode it was three researchers from the University of Toronto who submitted the alexnet algorithm to the imagenet computer science competition now imagenet was a competition where you would look at a set of 14 million images that had been hand labeled with what the pictures were of like of a strawberry or a cat or a dog or whatever and David you were telling me it’s the largest ever use of Mechanical Turk up to that point was to label the imagenet data set yeah it’s wild I mean until this competition and until Alex net there was no machine learning algorithm that could accurately label images so thousands of people on Mechanical Turk got paid however much two bucks an hour to label these images yeah and if I’m remembering from our episode basically what happened is the alexnet team did way better than anybody else had ever done the complete step changed better I think the error rate went from mislabeling images 25 of the time to suddenly only mislabeling them 15 of the time and that was like a huge leap over the tiny incremental progress that have been made along the way you were spot on and the way that they did it and what completely changed the fortunes of the internet of Google of Facebook and certainly of Nvidia was they actually used old algorithms a branch of computer science and artificial intelligence called neural networks specifically convolutional neural networks which had been around since the 60s but they were really computationally intensive to train and so nobody thought it would be practical to actually train and use these things at least not anytime soon or in our lifetimes and with these guys from Toronto did is they went out probably to their local Best Buy or equivalent in Canada they bought two GeForce GTX 580s which were the top of the line cards at the time and they wrote their algorithm their convolutional neural network in Cuda in nvidia’s software development platform for gpus and by God they trained this thing on like a thousand dollars worth of consumer grade hardware and basically the algorithm that other people had been trying over the years just wasn’t massively parallel the way that a graphics card sort of enables so if you actually can consume the full compute of a graphics card then perhaps you could run some unique novel algorithm and do it on you know a fraction of the time and expense that it would take in the super computer Laboratories yeah everybody before was trying to run these things on CPUs CPUs are awesome but they only execute one instruction at a time gpus on the other hand execute hundreds or thousands of instructions at a time so gpus Nvidia graphics cards accelerated Computing what Jensen and the company likes to call this you can really think of it like a giant Archimedes lever whatever advances are happening in Moore’s law and the number of transistors on a chip if you have an algorithm that can run in parallel which is not all problem spaces but many can then you can basically lever up Moore’s law by hundreds of times or thousands of times or today tens of thousands of times and execute something a lot faster than you otherwise could and it’s so interesting that there was this first Market called Graphics that was obviously parallel where every pixel on a screen is not sequentially dependent on the pixel next to it it literally can be computed independently and output to the screen so you have however many tens of thousands or now hundreds of thousands of pixels on a screen that can all actually be done in parallel and little did Nvidia realize of course that Ai and crypto and all this other linear algebra Matrix math based things that turned into accelerated Computing pulling things off the CPU and putting them on GPU and other parallel processors was an entire New Frontier of other applications that could use the very same technology they had pioneered for graphics yeah it was pretty useful stuff and this Alex net moment and these three researchers from Toronto kicked off you know Jensen calls it and he’s absolutely right the Big Bang moment for AI so David the last time we told this story in full we talked about this team from Toronto we did not follow what this team of three went on to do afterwards Yeah so basically what we said was it turned out that a natural consequence of what these guys were doing was oh actually you can use this to surface the next Post in a social media feed unlike an Instagram feed or the YouTube feed or something like that and that unlocked billions and billions of value and those guys and everybody else working in the field they all got scooped up by Google and Facebook well that’s true and then as a consequence of that Google and Facebook started buying a lot of Nvidia gpus but turns out there’s also another chapter to that story that we completely skipped over and it starts with the question you asked Ben who are these people yes so the three people who made up the alexnet team were of course Alex koschevsky who was a PhD student under his faculty advisor the legendary computer science Professor Jeff Hinton I have an amazing piece of trivia about Jeff Hinton do you know who his great great grandparents were no I have no idea he is the great great grandson of George and Mary boole you know like Boolean algebra and Boolean logic this guy was born to be a computer science researcher oh my God right foundational stuff for computation and computer science I also didn’t know there were people named bull that that’s where that came from that’s hilarious yeah you know the and or xor nor operators that comes from George and Mary wild so he’s the faculty advisor and then there was a third person on the team Alex’s fellow PhD student in this lab when Ilya satskiver and if you know where we’re going with this you are probably jumping up and down right now in your seat Ilya is the co-founder and current chief scientist of open AI yes so after alexnet Alex Jeff and Ilya do the very natural thing they start a company I don’t know what they were doing in the company but uh it made sense to start one and whatever they did it was gonna get acquired real fast by Google within six months so they get scooped up by Google they join a bunch of other academics and researchers that Google has been monopolizing really in the field three specifically Greg Corrado Jeff Dean and Andrew ing the famous Stanford Professor the three of them had just formed the Google brain team within Google to Turbo Charge all of this AI work that has been Unleashed by alexnet and of course to turn it into huge amounts of profit for Google turns out individually serving advertising that’s perfectly targeted on the internet through Facebook or Google or YouTube is an enormously profitable business and one that consumes a whole lot of Nvidia gpus yes so about a year later Google also acquires Deep Mind famously and then right around the same time Facebook Scoops up computer science Professor Jan lacun who also is a legend in the field and the two of them basically establish a duopoly on leading AI researchers now at this point nobody is mistaking what these companies and these people are doing for True human level intelligence or anything close to it this is AI that is very good at narrow tasks like we talked about social media feed recommendations so the Google brain team and Jeff and Alex and Ilya one of the big projects they work on is redoing the YouTube algorithm and this is when YouTube goes from like money losing you know crazy thing that Google acquired to the just absolute Juggernaut that it is today I mean back then in like 2013 2014 we did our YouTube episode not that long after the majority of views of YouTube videos were embeds on other web pages this is when they build it into a social media site they start the feed they start auto play all this stuff is coming out of AI research some of the other stuff that happens at Google famously after they acquired Deep Mind Deep Mind built a bunch of algorithms to save on cooling costs and Facebook of course they probably had the last laugh in this generation because they’re using all this work and John lacoon is doing his thing and hiring lots of researchers there this is just a couple years after they acquired Instagram man we need to like go back and redo that episode because Instagram would have been a great acquisition anyway but it was AI powered recommendations in the feed that made that into a 100 200 500 billion dollar asset for Facebook and I don’t think you’re exaggerating I think that is literally what Instagram is worth to meta now by the way I bought a lot of things on Instagram ads so that the targeting works it absolutely does there’s this amazing quote from Astro Teller who ran Google X at the time and still does in a New York Times piece where he says that the gains from Google brain during this period I don’t think this even includes deepmind just the gains from the Google brain team alone in terms of profits to Google more than funded everything they were doing in Google X which has there ever been anything profitable out of Google X Google brain yeah I mean yeah we’ll leave it at that so this takes us to 2015. when a few people in Silicon Valley start to realize that this Google Facebook AI duopoly is actually a really really big problem and most people had no idea about this this is really Visionary of these two people and not just a problem for like the other big tech companies because you could make the argument it’s a problem because like Siri’s terrible all the other companies that have lots of consumer touch points have pretty bad AI at the time but the concern is for a much greater reason I think there are three levels of concern here one obviously is the other tech companies then there’s the problem of startups this is terrible for startups how are you gonna compete with Google and Facebook when this is the primary value driver of this generation of technology I mean there really is another lens to view what happened with SNAP what happened with musically and having to sell themselves to bite dance and becoming Tick-Tock and going to the Chinese maybe it was business decisions maybe it was execution or whatever that prevented those platforms from getting to Independent scale snaps a public company now but like it’s no Facebook maybe it was that they didn’t have access to the same AI researches that Facebook and Google had hmm that feels like an interesting question it’s probably a couple steps too far on the conclusion but still sort of a fun straw man to think about a fun strawman nonetheless this is definitely a problem the third layer of the problem is just like this sucks for the world that all these people are locked up in Google and Facebook this is probably a good time to mention this founding of openai was motivated by the desire to find AGI or artificial general intelligence first before the big tech companies did and deepmind was the same thing it was going to be this winding and circuitous path at the time since really nobody knew then or knows now the best path to get to AGI but the big idea at open ai’s founding was whoever figures out and finds AGI first will be so big and so powerful so quickly they’ll have an immense amount of control and that is best in the uh open so these two people who are quite concerned about this convene a very fateful dinner in 2015 at of all places is it the Rosewood the Rosewood Hotel on Sand Hill Road naturally it would have been way better if it were a Denny’s or bucks and Woodside or something like that but it does does actually just show like where the seeds of open AI come from it is very different than this sort of organic Scrappy way that the nvidias of the world got started you know this is powers on high and existing money saying no we need to will something into existence yep so of course those two shadowy figures are Elon Musk and Sam Altman who at the time was president of Y combinator so they get this dinner together and they invite basically all of the top AI researchers at Google and Facebook and they’re like yo what is it gonna take for you to leave and to break this duopoly and the answer from almost all of them is nothing you can’t why would we ever leave we’re happy as clams here we’ve gotten to hire the people that we want we’ve built these great teams there’s a money spigot point in our face right not only are we getting paid just ungodly amounts of money but we get to work directly with the best AI researchers in the field if we were still at academic institutions you know say you’re at the University of Washington amazing academic Institution for computer science one of the top in the world or the University of Toronto where these guys came from you’re still at a fragmented Market if you go to Google or you go to Facebook you’re with everybody yep so the answer is no from basically everybody except there’s one person who’s intrigued by Elon and Sam’s pitch and to quote an amazing wired article from the time by Cade Metz that we will link to in our sources quote the trouble was so many of the people most qualified to solve all these AI problems were already working for Google and Facebook and no one at the dinner was quite sure that these thinkers could be lured to a new startup even if musk and Altman were behind it but one key player was at least open to the idea of jumping ship and then they have a quote from that key player I felt there were risks involved but I also felt it would be a very interesting thing to try and that key player was Ilya satskiver yep so after the dinner Ilya leaves Google and signs up to become as we said co-founder and chief scientist of a new independent AI non-profit research lab backed by Elon and Sam open AI yes and before we talk about what openai would go on to do in its first chapter which is quite different than today this is a great time to tell you about one of our very favorite companies and actually the perfect fit for this episode Crusoe so Crusoe as listeners know by now is a clean compute cloud provider specifically built for AI workloads Nvidia is one of their major partners and literally cruso’s data centers are nothing but racks and racks of a100s and h-100s and because cruso’s cloud is purpose built for AI and run on wasted stranded or clean energy they can provide significantly better performance per dollar than traditional Cloud providers just as one example Crusoe was one of the first clouds to deploy the 3200 gigabit infiniband which we will be talking about a lot more later on the episode to dramatically accelerate the performance of large AI training clusters in their data centers yes we talked about that on our acq2 episode with Crusoe CEO Chase lockmiller which David that actually ended up being very helpful for my research for this episode infiniband is wild and it’s just one example of how Nvidia has built such a dominant position in AI that we’ll talk about later in this episode we’ll link to that interview in the show notes so you can hear what it’s like for Crusoe actually deploying that technology in their data centers and the lengths that Crusoe goes to to maximize performance the other element that makes crew so special is the Environmental angle Crusoe of course locates their data centers at stranded energy sites so think oil flares wind farms that can’t use all the energy they generate Etc and uses that power that would otherwise be wasted to run your AI workloads instead yep obviously it’s a huge benefit for the environment and for for customers on costs since Crusoe doesn’t rely on the energy grid energy is the second largest cost of running AI after of course the price you pay Nvidia for the chips and these lower energy costs get passed on to customers it’s super cool that they can put their data centers out there in these remote locations where quote unquote energy happens as opposed to the other hyperscalers such as AWS and Google and Azure who need to build their data centers close to major traffic hubs where the internet happens because they are doing everything in their clouds yup if you your company or your portfolio companies would like to use the lower cost and more performant infrastructure for your AI workloads go to crusocloud.com acquired that’s Cru soe cloud.com acquired or click the link in the show notes okay so David openai is formed it’s 2015. here we are eight years later and we have chat GPT super linear path from there to here right turns out uh no so as we were talking about a little bit AI at this point in time super good for narrow use cases looks nothing like gpt4 today the capabilities that it had were pretty limited and one of the big reasons was that the amount of data that you could practically train these models on was pretty limited so the Alex net example you’re talking about 14 million images in the grand scheme of the internet 14 million images is a drop in the bucket and this was both a hardware and a software constraint on the software side we just didn’t actually have the algorithms to sort of suppose that we could be so bold to train one single foundational model on the whole internet like it wasn’t a thing yeah that was a crazy idea right people were excited about the concept of language models but we actually didn’t know how we could algorithmically get it done so in 2015 Andre carpathy who was then at open Ai and went on to lead AI for Tesla and is actually now back at open AI writes this seminal blog post called the unreasonable effectiveness of neural networks and David I don’t think we’re gonna go into it on this episode but note that recurrent neural networks are a little bit of a different thing than convolutional neural networks which was the 2012 paper the state of the art had evolved yes and and right around that same time there is also a video that hits YouTube a little bit later in 2016 that is actually on nvidia’s channel and it has two people in this very short one minute and 45 second video one is a young Elia setskiver and two is Andre carpathy and here is a quote from Andre from that YouTube video one algorithm I’m excited about is a language model the idea that you can take a large amount of data and you feed it into the network and it figures out the pattern in how words follow each other in sentences so for example you could take a large amount of data on how people talk to each other on the internet you can train basically a chat bot but you can do it in a way that the computer learns how language works and how people interact eventually we’ll use that to talk to computers just like we talk to each other wow this is 2015. this is two years before the Transformer well carpathy is at open AI he both comes up with the idea or espouses the idea of a chat bot so that sort of had already been discussed but even before we had the Transformer the method to actually pull this off he sort of had the idea that there’s an important part here it figures out the pattern in how words follow each other in sentences so there’s this idea that the very structure of language and the way to interpret knowledge is actually embedded in the training data itself rather than requiring labeling this is so cool so at Spring GTC this year Jensen did a fireside chat with Ilia and it’s amazing you should go watch the whole thing but in it this question comes up Jensen kind of poses as a straw man like hey some people say that GPT 3 4 chat CPT everything going on all these llms they’re just probabilistically predicting the next word in a sentence they don’t actually have knowledge and Ilya has this amazing response to that he says okay well consider a detective novel yes at the end of the novel the detective gathers everyone together in a room and says I am now going to tell you all the name of the person who committed the crime and that person’s name is blank the more accurately an llm predicts that next word I.E the name of the Criminal ipso facto the greater it’s understanding not only of the novel but of all General human level knowledge and intelligence because you need all of your experience in the world and as a human to be able to guess who the criminal is and the llms that are out there today gbt3 gpt4 llama Bard these others they can guess who the criminal is ooh yeah put a pin in that understanding versus predicting it’s a Hot Topic du jour so David is now a good time to fast forward two years to 2017 to the Transformer paper absolutely Ben tell us about the Transformer okay so Google 2017 Transformer paper comes out it’s called attention is all you need and it’s from the Google brain team right yes that Ilya just left just left two years before to start open AI so machine learning on natural language just to set the table here had long been used for things like autocorrect or foreign language translation but in 2017 Google came out with this paper and discovered a new model that would change everything for these fields and unlock another one so here is the scenario you’re translating a sentence from English to French you could imagine that a way to do this would be one word at a time in order but for anyone who’s ever traveled abroad and tried to do this you know that words are sometimes rearranged in different languages so that’s a terrible way to do it you know United States in Spanish is Estados unidos so failure on the very first word in that example so enter this concept of attention which is a key part of this research paper so this attention this fairly magical component of the Transformer paper it literally is what it sounds like it is a way for the model to attend to different areas of the input text at different times you can look at a large amount of context while considering what word to pick next in your translation so for every single word that you’re about to Output in French you can look over the entire set of inputted words to figure out what words you should wait heavily in your decision for what to do next this is why Ai and machine learning was so narrowly applicable before if you anthropomorphize it and you think of it like a human it was like a human with a very very short attention span yes now here’s the magical part well it does look at the whole input text to consider what the next word should be it doesn’t mean that it throws away the no notion of position entirely it uses a technique called positional encoding so it doesn’t forget the position of the words altogether so it’s got this cool thing where it weights the important part relevant to your particular word and it still understands position so remember I said the attention mechanism looks over the entire input every time it’s picking what word to Output that sounds very computationally hard yes in computer science terms this means that the attention mechanism is O of N squared oh that’s given me the heebie-jeebies back to my intro CS classes in college oh just wait till we get through this episode it gets deeper so obviously yes traditionally you’d say this is very very inefficient and it actually means that the larger your context window AKA token limit AKA prompt length gets the more computationally expensive it gets on a quadratic basis so doubling your input means quadrupling the cost to compute an output or tripling your input means 9 times the cost it gets real gnarly yeah it gets real expensive real fast but gpus to the rescue the amazing news for us here is that these Transformer comparisons can be done in parallel so even though there are lots of them to do if you have big GPU chips with tons of cores you can do them all at exactly the same time and previous Technologies to accomplish this like recurrent neural networks or lstms long short-term memory networks which is a type of recurrent neural network etc those required knowing the output of each step before beginning the next one before you picked the next word so in other words they were sequential since they depended on the previous word now with Transformers even if your string of text that you’re inputting is a thousand words long it can happen just as quickly in humid measurable time as if it were 10 words long supposing that there were enough cores in that big GPU so the big innovation here is you could Now train sequence based models in a parallel way you couldn’t train models of this size at all before let alone cost effectively yeah this is Houston probably for all listeners out there starting to sound very familiar to the world that we live in today yeah I sort of did a sleight of hand there morphing translation to using words like context window and token length you can kind of see where this is going yep so this Transformer paper comes out in 2017. the significance is huge but for whatever reason there’s a window of time where the rest of the world doesn’t quite realize it so Google obviously knows how important this is and there’s like a year where Google’s AI work even though Ilya has laughed and open AI is a thing now accelerates again Beyond anybody else in the field so this is when Google comes out with smart compose and Gmail and they do that thing where they have an AI bot that’ll call local businesses for you remember that uh demo from IO that they did did that ever ship I don’t know it did baby I mean this is Google here like the capabilities are there the product Sensei about as much this is when they really start investing in waymo but again where it really manifests is just back to serving ads in Search and recommending YouTube videos like they’re just crushing it in this period of time openai and everyone else though they haven’t adopted Transformers yet they’re kind of stuck in the past and they’re still doing these really researchy computer vision projects so like this is when they build a bot to play Dota 2 defense of the agents 2 the video game and super impressive stuff like they beat the best DOTA players in the world at DOTA by literally just consuming computer vision like consuming screenshots and inferring from there and that’s a really hard problem because Dota 2 is not a game where you get to see the whole board at once so it has to do a lot of like really intelligent construction of the rest of the game based on just a single player’s worth of input so it’s unbelievably Cutting Edge research for the past generation it’s a faster horse basically maybe yeah I mean they were also doing stuff like uh Universe which was the 3D modeled world to train self-driving cars you don’t really hear anything about that anymore but they built this whole thing I think it was using Grand Theft Auto as the environment and then it was doing computer vision training for cars using the GTA world I mean it was crazy stuff but it was kind of scatter shot you yeah it was scatter shot and I guess what I’m saying is it was still in this narrow use case World they weren’t doing anything approaching GPT at this point in time meanwhile Google had kind of moved on yep now one thing I do want to say in defense of open Ai and everybody else in the field at the time they didn’t just have their heads in the sand to do what Transformers enabled you to do which Ben you’re going to talk about in a sec cost a lot in computing power gpus and Nvidia and the Transformer made it possible but to work with the size of models you’re talking about you’re talking about spending an amount of money that’s certainly for a non-profit and anybody really except Google was untenable right it’s funny David you made this leap too expensive and large models all we were doing before was merely talking about translating one sentence to another the application of a transformer does not necessarily require you to go and consume the whole internet and create a foundational model but let’s talk about this Transformers lend themselves quite well as we now know to a different type of task so for a given input sentence instead of translating to a target language they can also be used as next word predictors to figure out what words should come next in a sequence you could even do this idea of pre-training with some Corpus of text to help the model understand how it should go about predicting that next word so backing up a little bit let’s go back to the recurrent neural networks the state of the art before Transformers well they had this problem in addition to the fact that they were sequential rather than parallel they also had a very short context window so you could do a next word predictor but it wasn’t that useful because it didn’t know what you were saying more than a few words Ago by the time you’d get to the end of the paragraph it would forget what was happening at the beginning it couldn’t sort of hold on to all that information at the same time so this idea of a next word predictor that was pre-trained with a Transformer could really start to do something pretty powerful which is consume large amounts of text and then complete the next word based on a huge amount of context yep we’re starting to come up this idea of a large language model and we’re going to Flash Forward here just for a moment to do some illustration and then we’ll come back to the story in GPT one the first open AI model this generative pre-trained Transformer model GPT it used unsupervised pre-training which basically meant that as it was consuming this Corpus of language it was unlabeled data the model was inferring the structure and meaning of language merely by reading it which is a very New Concept in machine learning the canonical wisdom is that you needed extremely structured data to train your smallish model on because how else are you going to learn what the data actually means this was a new thing you can learn what the data means from the data itself it’s like how a child consumes the world where only occasionally does their parents say no no you have that wrong that’s actually the color red but most of the time they’re just self-teaching by observing the world as a parent of a two-year-old can confirm one thing happens after this unsupervised pre-training step where you then have supervised fine-tuning the unsupervised pre-training used a large Corpus of text to learn the sort of General language and then it was fine-tuned on labeled data sets for specific tasks that you sort of really want the model to be actually useful for so to give people a sense of why we’re saying that the idea of training on very very large amounts of data here is crazy expensive dpt1 had roughly 120 million parameters that it was trained on gpt2 had 1.5 billion gpt3 had 175 billion and gpt4 openai hasn’t announced but it’s rumored that it has about 1.72 trillion parameters that it was trained on this is a long way from alexnet here it’s scaling like nvidia’s market cap there is this interesting Discovery basically that the more parameters you have the more correctly you can predict the next word these models were basically bad sub 10 billion parameters I mean maybe even sub 100 billion parameters they would just hallucinate or they would be nonsensical it’s funny when you look at some of the like 1 billion parameter models you’re like there is no chance that turns into anything useful ever but by merely adding more training data and more parameters it just gets way way better there’s this weirdly emergent property where Transformer based models scale really well due to the parallelism so as you throw huge amounts of data at training them you can also throw huge amounts of Nvidia gpus at processing that exactly and the output sort of unexpectedly gets magically better I mean I know I keep saying that but it is like wait so we don’t change anything about the structure we just give it way more data and let it run these models for a long time and make the parameters of the model way bigger and like no researchers expected them to reason about the world as well as they do but it just kind of happened as they were exploring larger and larger models so in defense of open AI they knew all this but the amount of money that you would have to spend to buy gpus or to rent gpus in the cloud to train these models is prohibitively expensive and you know even Google at this point time this is when they start building their own chips tpus because you know they’re still buying tons of Hardware from Nvidia but they’re also starting to Source their own here yep and importantly they’ve at this point are getting ready to release tensorflow to the public so they have a framework where people can develop for stuff and they’re like look if people are developing using our software then maybe it should run on our Hardware that’s optimized to work with that software so they actually do have this very plausible story around why their Hardware why their software framework it was kind of a surprising move when they open sourced it because people were like gasp you know why is Google giving away the farm for free here but this was three four years early and a very prescient move to really get a lot of people using Google architecture compute at scale yep all within Google Cloud yep so with this it starts to look like maybe this whole open AI boondoggle didn’t actually accomplish anything and the world’s AI resources are more than ever just locked back into Google so in 2018 Elon gets super frustrated by all this basically throws a hissy fit and quits and pieces out of open AI there’s a lot of drama around this that we’re not going to cover now he may or may not have given an ultimatum to the rest of the team that he would either take over and run things or leave who knows it’s Elon but whatever happened this turns out to be a major Catalyst for the rest of the open AI team and truly a history turning on a knife Point moment it was also a probably super bad decision by Elon but again story for another day so there’s this great explanation of what happened in the semaphore piece that we’ll link to in our sources the author says that fall it became even more apparent to some people at open AI that the costs of becoming a Cutting Edge AI company were going to go up Google brain’s Transformer had blown open a new frontier where AI could improve endlessly but that meant feeding endless data to train it a costly endeavor openai made a big decision to Pivot toward these Transformer models on March 11 2019 openai announced it was creating a for-profit entity so it could raise enough money to pay for all the compute power necessary to pursue the most ambitious AI models we want to increase our ability to raise Capital while still serving our mission and no pre-existing legal structure that we know of strikes the right balance the company wrote at the time openai said it was capping profits for investors with any excess going back to the original non-profit less than six months later openai took a one billion dollar investment from Microsoft yeah and I believe this is mostly if not all due to Sam Altman’s influence and taking over here so you know on the one hand you can look at this sort of skeptically and say okay Sam you took your non-profit and you converted it into an entity worth 30 billion dollars today on the other hand knowing this history now this was kind of the only path they had they had to raise money to get the Computing resources to compete with Google and Sam goes out and does these Landmark deals with Microsoft yeah truly amazing and their opinion at the time of why they’re doing this is basically this is going to be super expensive we still have the same mission to ensure that artificial general intelligence benefits all of humanity but it’s going to be ludicrously expensive to get there and so we need to basically be a for-profit Enterprise and a going concern and have a business that funds our research eventually to pursue that mission yep so 2019 they do the conversion to a for-profit company Microsoft invests a billion dollars as you say and becomes the exclusive cloud provider for open AI which is going to become highly relevant here for NVIDIA more on that in a minute June of 2020 GPT 3 comes out in September of 2020 Microsoft licenses exclusive commercial use of the underlying model for Microsoft products 2021 GitHub co-pilot comes out Microsoft invests another two billion dollars in open Ai and then of course this all leads to November 30th 2022 in Jensen’s words the AI heard around the world open AI comes out with chat DPT as you said Ben the fastest product in history to reach 100 million users in January 2023 this year Microsoft invests another 10 billion dollars in open AI announces they’re integrating GPT into all of their products and then in May of this year gpt4 comes out and that basically Catches Us up to today we eventually need to go do a whole nother episode about all the details here of openai and Microsoft but for today the Salient points are one thanks to all this generative AI as a user-facing product emerges as this enormous opportunity two to facilitate that happening you needed enormous amounts of GPU compute obviously benefiting in video but just as important three it becomes obvious now that the predominant way that companies are going to access and provide that compute is through the cloud and the combination of those three things turns out to be basically the single greatest moment that could ever happen for NVIDIA yes so you’re teeing all of this up and so far I’m thinking so this is like the open Ai and Microsoft episode like what does this have to do with Nvidia and God there’s a great Nvidia story here to be told so let’s get to the Nvidia side of it but first we want to thank our friends at statzig so we’ve been talking this episode about names and AI that you know Nvidia open AI Google Etc but there’s a name you probably don’t know that’s powering a lot of the AI wave behind the scenes statsic a ton of big AI companies open AI anthropic character AI rely on statzig to test deploy and improve their models and applications and how this happened is crazy because statsig did not start as an AI company yeah if you listen to our acq2 interview with founder and CEO Vijay Raji you know that statsig is a platform that combines feature flagging experimentation and product analytics they help teams run experiments in their products automate the analysis launch new features and analyze product performance their focus was on taking these pretty traditional product workflows and making them Easier by giving teams one connected tool to move fast to make data-driven product decisions so how does that relate to AI well if you’ve ever built anything with these AI apis you know there are a ton of things to test like the model version The Prompt or the temperature and adjusting these can have huge impact on the AI application’s performance so these AI companies have started using statsig to measure the impact of changes to their models and the customer facing applications using real user data even non-i companies like notion and figma have been using statsig to launch their AI features ensuring that these new features Drive successful outcomes for their businesses in today’s generative AI World product decisions aren’t just the product features anymore they’re literally like the weights and temperatures of the models underlying the products yep so whether you’re building with AI or not statsig can help your team ship faster and make data driven product decisions if you’re a startup they have a super generous free tier and a special program for venture-backed companies if you’re a large Enterprise they have clear transparent pricing with no seat based fees acquired community members can take advantage of a special offer to including 5 million free events a month and white glove onboarding support just go visit statcig.com acquired that’s s-t-a-t-s-i-g.com acquired to get started on your data driven Journey okay so Nvidia okay so we just said these three things that we’ve painted the picture of on the first part of the episode here that a generative AI is like possible a thing and it’s now getting traction B it requires an unbelievably massive amount of GPU compute to train and three it looks like the predominant way that companies are going to use that compute is going to be in the cloud the combination of these three things is I think the most perfect example we’ve ever covered on this show of the old saying about luck being what happens when preparation meets opportunity for NVIDIA here so obviously the opportunity is generative AI but the preparation front Nvidia has literally just spent the past five years working insanely hard to build a new Computing platform for the data center a GPU accelerated Computing platform to in their minds replace the old cpu-led Intel dominated x86 architecture in the data center and for many years I mean they were getting some traction right in the data center segment was growing for NVIDIA but people were like okay you want this to happen but like why is it going to happen right there’s these little workloads here and there that will toss you Jensen that we think can be accelerated by your cool gpus and then you know crazy things like crypto happened and there was like AI researchers in academic Labs that are using it as you know super computers but for the longest time the data center segment of Nvidia it just wasn’t clear that organizations had enormous parts of their software stack that they were going to shift to gpus like why what’s driving this and now we know what could be driving it and that is AI not only could be but if you look at their most recent quarter absolutely freaking is okay so now it begs the question why is it driving it and David are you open to me giving a little computer science lecture on computer architecture oh please do all right I need to do my best Professor impression here dude I love involved computer science in college they were my favorite classes I will say doing these episodes this tsmc it really does bring back the thrill of being in a CS lecture and being like Oh that’s how that works like it’s just really fun so let’s take a step back and consider the classic computer architecture the Von Neumann architecture now the Von Neumann architecture is what most computers most CPUs are based on today where they can store a program in the computer’s memory and run that program you can imagine why this is the dominant architecture otherwise we’d need a computer that is specialized for every single task the key thing to know is that the memory of the computer can store two different things the data that the program uses and the instructions of the program itself the literal lines of code and in this example we’re about to paint all of this is wildly simplified because I don’t want to get into caching and speeds of memory and you know where memory is located not located so let’s just keep it simple so the processor in the Yvonne Neumann architecture executes this program written in Assembly Language which is the language that compiles down to the byte code that the processor itself can speak so it’s written in an instruction set architecture an Isa from arm for example or Intel before that yes and each line of the program is very simplistic so we’re going to consider this example where I’m going to use some Assembly Language pseudo code to add the numbers 2 and 3 to equal five Ben are you about to program live on acquired well it’s pseudo Assembly Language code so the first line is we’re going to load the number two from memory we’re going to fetch it out of memory and we’re going to load it into a register on the processor so now we’ve got the number two actually sitting right there on our CPU ready to do something with that’s line of code number one two we’re going to load the number three in exactly the same fashion into a second register so we’ve got two CPU registers with two different numbers the third line we’re going to perform an add operation which performs the arithmetic to add the two registers together on the CPU and store the value in some either third register or into one of those registers so that’s a more complex instruction since it’s arithmetic that we actually have to perform but these are the things that CPUs are very good at doing math operations on data fetched from memory and then the fourth and final line of code in our example is we are going to take that 5 that has just been computed and is currently held temporarily in a register on the CPU and we’re going to write that back to an address in memory so the four lines of code are load load add store this all sounds familiar to me so you can see each of those four steps is capable of Performing one and only one operation at a time and each of these happens with one cycle of the CPU so if you’ve heard of gigahertz that’s the number of cycles per second so a one gigahertz computer could handle the simple program that we just wrote 250 million times in a single second but you can see something going on here three of our four clock Cycles are taken up by loading and storing data to memory now this is known as the Von Neumann bottleneck and it is one of the central constraints of AI or at least it has been historically each step must happen in order and only one at a time so in this simple example it actually would not be helpful for us to add a bunch more memory to this computer I can’t do anything with it it’s also only incrementally helpful to increase the clock speed if I double the clock speed I can only execute the program twice as fast if I need like a million x speed up for some AI work that I’m doing I’m not going to get it there with just a faster clock speed that’s not going to do it and it would of course be helpful to increase the speed at which I can read and write to memory but I’m kind of bound by the laws of physics there there’s only so fast fast that I can transmit data over a wire now the great irony of all of this is that the bottleneck actually gets worse over time not better because the CPUs get faster and the memory size increases but the architecture is still limited so this one pesky single Channel known as a bus I don’t actually get to enjoy the performance gains nearly as much as I should because I’m jamming everything through that one channel and it only gets to sort of be used one time per every clock cycle so the magical unlock of course is to make a computer that is not a Von Neumann architecture to make programs executable in parallel and massively increase the number of processors or cores and that is exactly what Nvidia did on the hardware side and all these AI researchers figured out how to leverage on the software side but interestingly now that we’ve done that David the constraint is not the clock speed or the number of cores anymore for these absolutely enormous language models it’s actually the amount of on-chip memory that concerns us I thought you were going and this is why the data center and what nvidia’s been doing is so important yes there’s this amazing video that we’ll link to on the asianometry YouTube channel that we link to also on the tsmc episode but the constraint today is actually in how much high performance memory is available on the chip these models need to be in memory all at the same time and they take up hundreds of gigabytes so while memory has scaled up I mean we’re going to get flashing all the way forward the h100s on chip Ram is like 80 gigabytes the memory hasn’t scaled up nearly as fast as the models have actually scaled in size the memory requirements for training AI are just obscene so it becomes imperative to network multiple chips and multiple servers of chips and multiple racks of servers of chips together into one single little computer and I’m putting computer and air quotes there in order to actually train these models it’s also worth noting we can’t make the memory chips any bigger due to a quirk of the extreme ultraviolet photo lithography that we talked about the euv on the tsmc episode chips are already the full size of the reticle it’s a physics and wavelength constraint you really can’t etch chips larger without some new invention that we don’t have commercially viable yet so what it ends up meaning is you need huge amounts of memory very close to the processors all running in parallel with the fastest possible data transfer and again this is a vast oversimplification but you kind of get the idea of why all of this becomes so important okay so back to the data center and here’s what Nvidia is doing that I don’t think anybody else out there is doing and why it’s so important for them that all of this new generative AI world this new to Computing era as Jensen dubs it runs in the data center so Nvidia has done three things over the last five years one and probably most importantly related to what you’re talking about Ben they made one of the best Acquisitions of all time back in 2020 and nobody had any idea they bought a quirky little networking company out of Israel called melanox well it wasn’t little they paid seven billion dollars okay yeah and it was already a public company right it was yep yep but it was definitely quirky now what was melanox melanox’s primary product was something called infiniband which we talked about a lot with Chase lockmiller on our acq2 episode with him from Crusoe and actually infiniband was an open source standard or managed by a Consortium there were a bunch of players in it but the traditional wisdom was well infiniband is way faster way higher bandwidth a much more efficient way to transfer data around a data center at the end of the day a ethernet is the lowest common denominator and so everyone had to implement ethernet anyway and so most companies actually exited the market and melanox was kind of the only infiniband spec provider left yeah so you said wait what is infiniband it is a competing standard to ethernet it is a way to move data between racks in a data center and back in 2020 everybody was like ethernet’s fine why do you need more bandwidth than ethernet between racks in a data center what could ever require 3200 gigabits a second of bandwidth running down a wire in a data center well it turns out if you’re trying to address hundreds maybe more than hundreds of gpus as one single compute cluster to train a massive AI model yeah you want really fast data interconnects between them right people thought oh sure for super computers for these academic purposes but what the Enterprise Market needs in my shared cloud computing data center is ethernet and that’s fine and most workloads are going to happen right there on one rack and maybe maybe maybe things will expand to multiple computers on that rack but certainly they won’t need to network multiple racks together and Nvidia steps in and you got Jensen saying hey dummies the data center is the computer listen to me when I tell you the whole data center needs to be one computer and when you start thinking that way you start thinking geez we’re really going to be cramming huge amounts of data through wires that are going between these rally how can we sort of think about them as if it’s all sort of on-ship memory or as close as we can make it to on-chip memory even though that’s in a box located three feet away yep so that’s piece number one of nvidia’s grand data center plan over the last five years piece number two is in September 2022 Nvidia makes a quite surprising announcement of a new chip not just a new chip an entirely new class of chips that they are making called The Grace CPU processor Nvidia is baking a CPU this is like heretical but Jensen I thought all Computing was going to be accelerated what are we doing here on these armed CPUs yeah these Grace CPUs are not for putting in your laptop they are for being the CPU component of your entire data center solution that is specifically from the ground up design to orchestrate with these massive GPU clusters This is the End Game of a ballet that has been in motion for 30 years remember when the graphics card was subservient to the pcie slot in Intel’s motherboard and then eventually you know we fast forward to the future Nvidia makes these gpus that are these beautiful Standalone boxes in your data center or perhaps these little workstations that sit next to you while you’re doing Graphics programming while you’re directly programming your GPU and then of course they need some CPU to put in that so they’re using AMD or Intel or they’re licensing some CPU and now they’re saying you know what we’re actually just going to do the CPU too so now we make a box and it’s a fully integrated Nvidia solution with our gpus rcpus rnv link between them are infinite band to network it to other boxes and you know welcome to the show one more piece to talk about the Third Leg of this tool their strategy before we get to what it all means that I think you’re about to go to spoiler alert you say solution I hear gross margin the third part of it is the gpus up until nvidia’s current GPU generation the hopper generation of gpus for the data center there was only one GPU architecture at Nvidia and that same architecture and those same chips from the same Wafers made at tsmc some of them went to Consumer gaming graphics cards and some of those dies went to a100 gpus in the data center it was all the same architecture starting in September of 2022 they broke out the two business lines into different architectures so there’s the hopper architecture named after great computer scientist Grace Hopper I think rear Admiral in the US Navy Grace Hopper get it Grace CPU Hopper GPU Grace Hopper the h100s that was for the data centers and then on the consumer side they start a whole new architecture called Lovelace after Ada Lovelace and that is the RTX 40xx so you buy uh you know top of the line RTX 40 what have you gaming card right now that is no longer the same architecture as the h100s that are powering chat GPT it’s got its own architecture this is a really big deal because what they do with the hopper architecture is they start using what’s called chip on wafer on substrate c-o-w-oos when you start talking to the real semi-nurds that’s when they start busting out the co-ops conversation this is when a certain segment of our listeners are gonna get really excited so essentially what this is back to this whole concept of memory being so important for gpus and for AI workloads this is a way to stack more memory on the GPU chips themselves essentially by going vertical in how you build the chips this is the absolute bleeding edge technology that is coming out of tsmc and by Nvidia bifurcating their chip architectures into a gaming segment that does not have this latest co-was technology this allows them to monopolize like a huge amount of tsmc’s capacity to make the co-was chips specifically for these h100s which allows them to have way more memory than other gpus on the market yes so this gets to the point of why can’t they seem to make enough chips right now well it’s literally a tsmc capacity problem so there’s these two components that are extremely related that you’re talking about the co-ops chip on wafer on substrate and the high bandwidth memory so there’s this great post from semi-analysis where the author points out a 2.5 D chip which is basically how you assemble this co-auth stuff to get the memory really close to the processor and of course 2.5 D it is literally 3D but 3D means something else it’s even more 3D so they came up with this 2.5 D denominator anyway the 2.5 D chip packaging technology from tsmc is where you take multiple active silicon dies like the logic chips and the stack of high bandwidth memory and they stack them on one piece of silicon and there’s more complexity here but the important thing is coas is the most popular technology for gpus and AI accelerators for packaging these chips and it’s the primary method to co-package high bandwidth memory again remember think back to the thing that’s most important right now is get as much high bandwidth memory as you can closest to the CPU next to the logic to get the most performance for trading in inference so coas represents right now about 10 to 15 percent of tsmc’s capacities and many of the facilities are custom built for exactly these types of chips that they’re producing so when Nvidia needs to reserve more capacity there’s a pretty good chance that they’ve already reserved some large part of the 10 to 15 of tsmc’s total footprint and Tia SMC needs to like go make more Fabs in order for NVIDIA to have access to more coas capable capacity yeah which as we know it takes years for tsmc to do this yep there are more experimental things that are happening like I would be uh remiss not to mention there are actually experiments of doing compute in memory like as we shift away from Von Neumann and sort of all bets are off now that we’re open to new Computing architectures there are people exploring well what if we just process the data where it is in memory instead of doing the very lossy expensive energy intensive thing of moving data over the copper wire to get it to the CPU all sorts of trade-offs in there but it is very fun to sort of dive into the academic computer science world right now where they really are rethinking like what is a computer so these three things that Nvidia has been building the dedicated Hopper data center GPU architecture the grace CPU platform the melanox powered networking stack they now have a full sweet solution for generative AI data centers and then when I say solution I hear margins but let’s be clear you don’t need to offer some sort of solution to get high margins if you’re Nvidia price is set where Supply meets demand and they’re adding as much Supply as they possibly can right now like believe me for all sorts of reasons Nvidia wants everyone who wants h100s to have h100s but for now the price is kind of like a I’ll write you a blank check and Nvidia you write whatever you want on the check so their margins are crazy right now just literally because there’s way more demand than supply for these things yes okay so let’s break down what they’re actually selling so like you were saying Ben of course you can and lots of people do just go buy h100 so you’re like I don’t care about the grace CPU don’t care about this melanax stuff I’m running my own data center I’m really good at it and the people who are most likely to do this are the hyperscalers or as Nvidia refers to them the csps the cloud service providers this is AWS this is azure this is Google this is Facebook for their internal use like Nvidia don’t give me one of these dgx servers that you assemble just give me the chip and I will integrate it the way that I want to integrate it I am a world-class data center architect and operator I don’t want your solution I just want your chips so they sell a lot of those now Nvidia of course has also been seeding new Cloud providers out there in the ecosystem like our friends at Crusoe also core weave and Lambda Labs if you’ve heard of them these are all new GPU dedicated clouds that Nvidia is working closely with so they’re selling h100s and a100s before that too all these Cloud providers but let’s say you are an arbitrary company in the Fortune 500 that is not a technology company and my god do you not want to miss the boat on generative AI Ai and you’ve got a data center of your own well Nvidia has a dgx for you yes they do full GPU based super Computer Solution in a box that you can just plug right into your data center and it just works there’s nothing else on the market like this and it all runs Cuda it is all speaking the exact language of the entire ecosystem of developers that know exactly how to write software for this thing which means that whatever developers you already had who were working on AI or anything else everything they were working on is just going to come right over and run within your brand new shiny AI secret computer because it all runs Cuda amazing more on Cuda in a minute but as we said you say solution I hear gross margin Nvidia sells these DTX systems for like 150 to 300 000 a box that’s wild and now with all these three new legs of the stool Hopper Grace and melanox these systems are just getting way more integrated way more proprietary and way better so if you want to buy a new top of the line dgx h100 system the price starts at five hundred thousand dollars for one box and if you want to buy the dgx gh200 super pod this is the AI wall that Jensen recently unveiled the huge like room full of AI and it’s like 20 racks wide imagine an entire row at a data center yes this is 256 Grace Hopper dgx racks all connected together in one wall they’re building this as the first TurnKey AI data center that you can just buy and can train a trillion parameter DPT for a class model the pricing on that is call us of course it is but I’m imagining like hundreds of millions of dollars like I doubt it’s a billion but hundreds of millions easily wild well let’s talk about the h100 of the baseball card right here on this uh insane thing that they’ve built so they launched it in September 2022 it’s the successor to the a100 one GPU one h100 costs forty thousand dollars so that’s how you get to that price point you’re talking about that’s what they’re selling to Amazon and Google and Facebook right and you mentioned that 500 000 price point the 500 000 is the eight forty thousand dollar h100s in a box with the gray CPU and you know the nice bow around it yep which do the math on that so eight times forty thousand that’s three hundred and twenty thousand dollars so that’s essentially an extra 180 000 of margin that Nvidia is getting out of selling the solution it’s an armed CPU it doesn’t cost them anything to make that and these forty thousand dollar h100s have margin of their own so like every time they bundle more there’s more margin in the fully assembled I mean that’s literally bundle economics you are entitled to margin when you bundle more things together and provide more value for customers but just to like illustrate the way that this pricing works so the reason you want an h100 is they’re 30 times faster than an a100 which might do is only like two and a half years older it is nine times faster for AI training the h100 is literally purpose built for training LMS like the full self driving video stuff it’s super easy to scale up it’s got 18 and a half thousand Cuda cores remember when we were talking about the Von Neumann example earlier like that is one Computing core that is able to handle you know those four Assembly Language instructions this one each 100 which they’re calling a GPU has 18 and a half thousand cores that are capable of running Cuda software it’s got 640 tensor cores which are highly specialized for matrix multiplication they have 80 streaming multi-processors so what are we up to here close to 20 000 unique cores on this thing it’s got meaningfully higher energy usage than the a100 I mean a big takeaway here is that Nvidia is massively increasing the power requirement every time they come out with the Next Generation they’re both figuring out how to push the edge of physics but they’re also constrained by physics some of this stuff is only possible with way more energy this thing weighs 70 pounds this is one h100 since it makes a big deal about this every keynote that he gives it like oh I can’t lift it it’s got a quarter trillion transistors across 35 000 Parts it requires robots to assemble it not only does it require physical robots to assemble it it requires AI to design it they’re actually using AI to design the ships themselves now I mean they have completely reinvented the notion of what a computer is totally and this is all part of Jensen’s pitch here too customers yes our Solutions are very expensive however he uses the line that he loves the more you buy the more you save if you could get your hands on some right but what he means by that is like okay say you’re McDonald’s and you’re trying to build a generative AI so that I don’t know customers can order something you’re using it in your business if you were gonna try and build and run that in your existing data center infrastructure it would take so much time and cost you so much more over the long run and compute than if you just went and bought my super pod here you can plug and play and have it up and running in a month yep and by the fact that this is all accelerated Computing the things you’re doing on it you literally wouldn’t be able to do otherwise or might take you a lot more energy A lot more time a lot more cost there is a very valid story too buying and running your workloads here or renting from any of the cloud service providers and running your workloads here is more performant because the results just happen much faster much cheaper or at all yep you mentioned energy here like this is also Jensen’s argument he’s like yes these things take a ton of energy but the alternative takes even more energy so we are actually saving energy if you assume this stuff is going to happen now there’s a bit of caveat here in that it can’t happen except on these types of machines so he enabled this whole thing but he has a point oh I totally buy it though I mean I think there’s a very real case around look you only have to train a model once and then you can do inference on it over and over and over again I mean the analogy I think makes a lot of sense for model training is to think about it as a form of compression llms are turning the entire internet of text into a much smaller set of model weights this has the benefit of storing a huge amount of usefulness in a small footprint but also enabling a very inexpensive amount of compute again relatively speaking in the inference step for every time that you need to prompt that model for an answer of course the trade-off you’re making there is once you encode all the training data into the model it is very expensive to redo it so you better do it right the first time or figure out little ways to modify it later which a lot of ml researchers are working on but I always think a reasonable comparison here is to compress a zillion layer photoshop file for anybody that’s ever dealt with oh I’ve got a three gigabyte Photoshop file well that’s not a thing you’re going to send to a client you’re going to compress it into a JPEG and you’re going to send that and the jpeg is in many ways more useful as a compressed facsimile of the original layers comprising the Photoshop file but the trade-off is you can never get from that compressed little jpeg back to the original thing so I think the analogy here is like you’re saving everyone from needing to make the full PSD every time because you can just use the jpeg the vast vast majority of the time so hopefully we’ve now painted a relatively coherent picture of both the advances that made the generative AI opportunity possible that it has truly become a real opportunity and why Nvidia even above the obvious reasons was just so well positioned here particularly because of the data center-centric nature of these workloads and that they had been working so hard for the past five years to fundamentally re-architect the data center yep so on top of all this Nvidia recently announced yet another pretty incredible piece of their Cloud strategy here so today like we’ve been saying if you want to use h100s and a100s say you’re an AI startup the way you’re probably going to do that is you’re going to go to a cloud either a hyperscaler or a dedicated GPU cloud like cruso or core Weaver Lambda Labs or the like and you’re going to rent your gpus and Ben you did some research on this so like what does that cost oh I just looked at the pricing pages on public clouds today I think Azure and AWS were where I looked you can get access to a dgx server that’s eight a100s for about 30 bucks an hour or you can go over to AWS and get a p5.48x large instance which is eight h100s which I believe is an hgx server for about a hundred dollars an hour so about three times as much and again what I say you can get access I don’t actually mean you can get access I mean that’s the price right if you could get access that’s what you would pay for it correct okay that’s just getting the gpus but if you buy everything we were talking about a minute ago say your McDonald’s or UPS or whoever and you’re like you know I really like Jensen I buy what you’re selling I want this whole integrated package I want an AI super computer in a box that I can plug into my wall and have it run but I’m all in on the cloud I don’t run my own data centers anymore Nvidia has now introduced dgx Cloud yeah and of course you could rent these instances from Amazon Microsoft Google Oracle but like you’re not getting that full integrated solution right and you’re getting some integration the way that the cloud service a rider wants to create the integration using their proprietary services and to be honest you might not have the right people on staff to be able to deal with this stuff in a pseudo bare metal way even if it’s not in your data center and you’re renting it from the cloud you might actually need based on your Workforce to just use a web browser and just use a real nice easy web interface to load some models in from a trusted source that you can easily pair with your data and just click run and not have to worry about any of the complexity of managing a cloud application that’s in Amazon or Microsoft or something a little bit scarier and closer to the metal you yep so Nvidia has introduced dgx Cloud which is a virtualized dgx system that is provided to you right now via other clouds so Azure and Oracle and Google right the boxes are sitting in the data centers of these other csps right they’re sitting in the other cloud service providers but as a customer it looks like you have your own box that you’re renting you log into the dgx cloud website through Nvidia and it’s all nice wysiwyg stuff there’s an integration with hugging face where you could easily deploy models right off of hugging face you can upload your data like everything is just really wysiwyg it’s probably the way to describe it this is unbelievable Nvidia launched their own cloud service through other clouds and Nvidia does have I think six data centers but that I don’t believe is what they’re actually using to back dgx Cloud no so starting price for dgx cloud is 37 000 a month which will get you an a100 based system not an h100 based system so the margins on this are insane for NVIDIA and their Partners a listener helped us out and estimated that the cost to actually build an equivalent a 100 dgx system would be today something like 120k remember this is the previous generation this is not h100s and you can rent it for 37k a month so that’s three month payback on the capex for this stuff for NVIDIA and their Cloud Partners together and even more for NVIDIA more important longer term for Enterprises that buy this Nvidia now has a direct sales relationship with those companies not necessarily intermediated by sales through Azure or Google or AWS even though the compute is sitting in their clouds which is crucially important because at this point the CFO collect crass said on their last earnings call that about half of the revenue from the data center business unit is csps and then I believe after that is the consumer internet companies and after that is Enterprises so there’s a few interesting things in there one of which is oh my God their revenue for this is concentrated among like five to eight companies with these csps two they don’t necessarily own the customer relationship they own in the developer relationship through Cuda you know they’ve got this unbelievable ecosystem right now of Nvidia developers that’s stronger than ever but in terms of the actual customer half of their revenue is intermediated by Cloud providers the second interesting thing about this is even today in this AI explosion the second biggest segment of data centers is still the consumer internet companies it it’s still all that stuff we were talking about before of the uses of machine learning to figure out what should show up in your social media algorithms and match ads to you that’s actually bigger than all of the direct Enterprises who are buying from Nvidia so the dgx cloud play is a way to sort of shift some of that CSP Revenue into direct relationship Revenue so all of this brings us to 2023. in May of this year Nvidia reported their q1 fiscal 24 earnings and videos on this weird January fiscal year end thing so q124 is essentially q123 but anyway in which Revenue was up 19 quarter over a quarter to 7.2 billion which is great because remember they had a terrible end of 2022 with the write-offs and crypto falling off a cliff and all that yes it’s amazing that in that strategory interview when was that in uh March of 2023 Jensen said last year was unquestionably a disappointing year this is the year chat GPT was released it is wild the roller coaster this company has been on time frame is so compressed here and part of that of course is ethereum moving to proof of stay the end of the crypto thing for NVIDIA which I’m sure they’re actually thrilled about but part of it was they also put in a ton of pre-orders for capacity with tsmc that then they thought they weren’t going to need so they had to write down so from an accounting perspective it looks like a big loss like a really big blemish on their finances last year but now oh my God are they glad that they reserved all that capacity yep it’s actually going to be quite valuable So speaking of you know this q1 earnings is like great up 19 quarter over quarter but then they dropped the bombshell due to unprecedented demand for generative AI compute in data centers Nvidia forecast Q2 revenue of 11 billion dollars which would be up another 53 quarter of a quarter over q1 and 65 year over year the stock goes nuts 25 in after hours training Yep this is a trillion dollar company or at least this made them a trillion dollar company but like a company that was previously valued at around 800 billion dollars popped 25 after earnings well and it’s even crazier than that back when we did our episodes last April Nvidia was the eighth largest company in the world by market cap had about a 660 billion dollar market cap that was down slightly off the highs but that was kind of the order of magnitude back then it crashed down below 300 billion and then within a matter of months it’s now back up over a trillion just a while and then all of this culminates last week at the time of this recording When Nvidia reports Q2 fiscal 24 earnings and this earnings release we usually don’t talk about like individual earnings releases unacquired because like in the long Arc of time who cares this was a historic event I think this was one of if not the most incredible earnings release by any scaled public company ever seriously no matter what happens going forward last week was a historic moment the thing that blows my mind the most is that their data center segment alone did 10 billion dollars in the quarter that’s more than doubling off of the previous quarter in three months they grew from four-ish billion to 10 billion of Revenue in that segment and revenue only happens when they deliver products to customers this isn’t pre-orders this isn’t clicks this isn’t wave your hands around stuff this is we delivered stuff to customers and they paid us an additional six billion dollars this quarter than they did last quarter so here are the full numbers for the quarter total company revenue of 13.5 billion up 88 from the previous quarter and over a hundred percent from a year ago and then Ben like you said in the data center or segment revenue of 10.3 billion so 10.3 out of 13.5 for a segment that basically didn’t exist five years ago for the company that’s up 141 from q1 and 171 from a year ago this is 10 billion dollars that kind of growth at this scale I’ve never seen anything like it no neither has the market that’s right and so this this is the first time I noticed it Jensen had talked about this in q1 earnings so it wasn’t the first time but he brings back the trillion dollar Tam not in a slide I think this time he just talks about it no but in a new way that I think is a better way to slice it this time it’s different you know look we’ll spend a while here now talking about what we think about this but this is very different this time he frames nvidia’s trillion dollar opportunity as the data center and this is what he says there is one trillion dollars worth of hard assets sitting in data centers around the world right now growing at 250 billion a year annual spend on data centers to update and add to that capex is 250 billion dollars a year and Nvidia has certainly the most cohesive fulsome and coherent platform to be the future of what those data centers are going to look like for a large amount of compute workloads this is a very different story than like oh we’re gonna get one percent of this hundred trillion dollars of Industry out there and the thing you have to believe now because whenever someone paints a picture you say okay what do I have to believe the thing you have to believe is there is real user value being created by these Ai workloads and the applications that they are creating and there’s pretty good evidence I mean chat GPT made it so open AI is rumored to be doing over a billion dollar run rate now maybe multiple single digit billions and still growing meaningfully and so that is like the shining example again that’s the Netscape Navigator here of this whole boom but the BET especially with all these Fortune 500s is that there are going to be gpt-like experiences in everyone’s private applications in a zillion other public interfaces I mean Jensen frames it as in the future every application will have a GPT front end it will be a way that you decide that you want to interact with computers that is more natural and I don’t think he means like versus clicking buttons I think he means everyone can kind of become a programmer but the programming language is English and so when you’re sort of like well why is everyone spending all of this money it is that the world’s Executives with the purchasing power to go write a 10 billion dollar check last quarter to Nvidia for all this stuff wholeheartedly believes from the data they’ve seen so far that this technology is going to change the world enough for them to make these huge bets and the thing that we don’t know yet is is that true is the gpt-like experiences going to be an enduring thing for the far future or not there’s pretty good evidence so far are that people like this stuff and that it’s quite useful in transforming the way that you know everyone lives their lives and goes about day to day and does their jobs and goes through school and you know on and on and on but that is the thing you have to believe so we have a lot to talk about with regard to that in analysis but before we move to analysis I think we should talk about another one of our very favorite companies here at acquired blinkist from go on yes absolutely and listeners you know we’re doing something very cool with them this season as you know blinkist takes books and condenses them into the most important points so you can read or listen to the summaries it’s almost like a large language model compression for books there you go so a couple cool things we’re doing one of which is David and I have made a blinkist page that represents our bookshelf so if you want to read the books that influence us you can go to blinkist.com Acquired and for this particular Nvidia episode blinkus has made a special collection for us amazingly there are not really books about the hist history of Nvidia itself at least not yet which is unbelievable yeah but there are plenty on AI Through The Years so if you go to blinkist.com Nvidia you can find books by Gary Kasparov kaifu Lee and Kade Metz who we already mentioned earlier on the show who wrote The Amazing wired article yep you’ll of course get free access to that Nvidia blinkus collection and anyone who signs up through that link or uses the coupon code Nvidia will then get a 50 off premium subscription to all 6 500 titles in their library and for those of you who are leaders at companies check out blinkist for business this gives your whole team the power to tap into world-class knowledge right from their phones anytime they need it available at blinkist.com business blinkist is a great way for your team to master soft skills which if you believe that this new AI world is coming is going to be even more important for the humans in your Workforce yes our huge thanks to blinkist and their parent company go one where David and I are both huge fans and Angel Investors go one and blinkist are both amazing ways for your company to get access to the most engaging and compelling content in the world are thanks to both of them and links in the show notes okay so David analysis we gotta talk about Cuda before we even start analyzing anything else here we talked about a lot of Hardware so far on this episode but there’s this huge piece of the Nvidia puzzle that we haven’t talked about since part two and Cuda as folks know was the initiative started in 2006 by Jensen and Ian buck and a bunch of other folks on the Nvidia team to really make a bet on scientific Computing that people could use graphics cards for more than just graphics and they would need great software tools to help them do that it also was the glimmer in Jensen’s eye of oh maybe I can build my own relationship with developers and you know there can be this notion not of a Microsoft or an Intel developer who happens to be able to you know have a standard interface to my chip but I can have my own developer ecosystem which has been huge for the company so Cuda has become the foundation that everything that we’ve talked about all the AI applications are written on top of today so you know you hear Jensen in these Keynotes reference KU to the platform who to the language and I spent some time trying to figure out like when I was watching developer sessions and like literally learning some Cuda programs what is the right way to characterize it and what is the right way to characterize it today because it has evolved a lot yes so today Cuda is starting from the bottom and going up a compiler a runtime a set of development tools like a debugger and a profiler it is its own programming language Cuda C plus plus it has industry specific libraries it works on every card that they ship and have shipped since 2006 which is a really important thing to know and if you’re a Cuda developer your stuff works on everything anything Nvidia all this unified interface it has many layers of abstractions at existing libraries that are optimized so these libraries of code that you can call to keep your development work short and simple instead of Reinventing the wheel so you know there are things that you can decide slide that you want to write in C plus and just rely on their compiler to make it run well on Nvidia hardware for you or you can write stuff in their native language and try to implement things yourself in Cuda C plus plus the answer is it’s incredibly flexible it is very well supported and there’s this huge community of people that are developing with you and building stuff for you to build on top of if you look at the number of Cuda developers over time it was released in 2006 it took four years to get the first hundred thousand people then by 2016 13 years in they got to a million developers then just two years later they got to 2 million so 13 years to add their first 13 million then two years to add their second 2022 they hit 3 million developers and then just one year later in May of 2023 Cuda has 4 million registered developers so at this point there’s a huge moat for NVIDIA and I think when you talk to folks there and frankly when we did talk to folks there they don’t describe it this way they don’t think about it like well Cuda is our moat versus competitors it’s more like well look we envisioned a world of accelerated Computing in the future and we thought there are way more workloads that should be parallelized and made more efficient that we want people to run on our hardware and we need to make it as easy as possible for them to do that and we’re going to go to Great Lengths and have one two thousand people that work at our company that are going to be full-time software Engineers building this programming language and compiler and foundation and framework and everything on top of it to let the maximum number of people build on our stuff that is how you build a developer ecosystem it’s different language but the bottom line is they have a huge reference for the power that it gives them at the company this is something we touched on on our last episode but has really crystallized for me in doing this one Nvidia thinks of themselves as and I believe is a platform company especially this week after the blowout earnings and everything that happened this quarter and the stock and whatnot sort of a popular take out there that you’ve been seeing a lot is oh we’ve seen this movie before this happened with Cisco you could say over a longer time scale this happened with Intel yeah these Hardware providers these semiconductor companies they’re hot when they’re hot and people want to you know spend capex and then when they’re not hot they’re not hot but I don’t think that’s quite the right way to characterize Nvidia they do make semiconductors and they do make data center gear but really they are a platform company the right analogy for NVIDIA also is Microsoft they make the operating system they make the programming environment they make many of the applications right Cisco doesn’t really have developers Intel never had developers Microsoft had developers and Intel had Microsoft but Intel didn’t have developers Nvidia has developers I mean they’ve built a new architecture that is not a Von Neumann computer they’ve bucked 50 years of progress and instead every GPU has a stream processor unit and as you’d imagine you need a whole new type of programming language and compiler and everything to deal with this new Computing model and that’s Cuda and it freaking works and there’s all these people that develop their livelihood in it you talk to Jensen and you talk to other people at the company and they will tell you we are a foundational computer science company we’re not just slinging Hardware here yeah I mean it’s interesting they’re a platform company for sure they’re also a systems company they’re effectively selling mainframes I mean it’s not that different than IBM way back when they’re trying to sell you uh you know 100 million dollar wall that goes in your data center and it’s all fully integrated and it all just works yeah and maybe IBM actually is a really good analogy like old school IBM here they make the underlying technology they make the hardware they make the Silicon they make the operating system for the Silicon they make the solutions for customers they make everything and they sell it as a solution yep okay so a couple other things to catch us up here as we’re starting analysis one big point I want to make is let’s look at a timeline because I didn’t discover this until like two hours before we started recording in March of 2019 Nvidia announced they were acquiring melanox for seven billion dollars in cash and I think intel was considering the purchase and that Nvidia came in and kind of blew him out of the water and it is fair to say nobody really understood what Nvidia was going to do there and why it was so important but the question is why well Nvidia knew that these new models coming out would need to run across multiple servers multiple racks and they put a huge level of importance on the bandwidth between the the machines and of course how did they know that well in August of 2019 Nvidia released what was at the time the largest Transformer based language model called Megatron 8.3 billion parameters trained on 512 gpus for nine days which at the time at retail would have cost something like half a million dollars to train which at the time was a huge amount of money to spend on model training which is what only four years ago but today that’s quaint Nvidia did that because they do a huge amount of research at the company and they work with every other company doing AI research and they were like oh yes this stuff is going to work and this stuff is going to require the fastest networking available and I think that has to do with why no one else saw how valuable the melanox technology could be yep another thing that I want to talk about for nvidia’s business today is this notion of the data center is the computer and Jensen did a great interview with Ben Thompson last year where he talks about the idea that they build their systems full stack like their dream is that you own and operate a dgx super pod and he says we build our systems full stack but we go to market in a disaggregated way integrating into the compute fabric of the industry so I think that’s his sort of way of saying look customers need to use us in a bunch of different ways so we need to be flexible on that but we don’t want to build each of our components such that if you do assemble them all together it’s this unbelievable experience and we’ll figure out how to provide the right experience to you if you only want to use them in piecemeal ways or you want to use us in the cloud or the cloud providers want to use us again it’s build the product as a system build the system full stack but go to market in a design aggregated way and I think if I remember right in that interview Ben picked up on this and was like wait are you building your own cloud and Jensen was like well maybe we’ll see and of course then they launched djx cloud in a well maybe we’ll see sort of way yeah you could imagine there are more Nvidia data centers likely on the way that are uh fully owned and operated speaking of all of this we got to talk some numbers on margin this last quarter they had a gross margin of 70 percent and they forecasted for next quarter to have a gross margin of 72 percent I mean if you go back pre-cuda when they were a commoditized graphics card manufacturer it was 24 so they’ve gone 24 to 70 on gross margin and with the exception of a few quarters along the way for these strange one-time events that’s basically been a linear climb quarter over quarter as they’ve deepened their mode and as they’ve deepened their differentiation in the industry we’re definitely at a place right now that I think is temporary due to the supply shortage of the world’s Enterprises and in some cases even governments you look at the UK or some of the Middle Eastern countries like blank check I just need access to Nvidia Hardware that’s going to go away but I don’t think this very high you know 65 percent plus margin is going to erode too much yes I mean I think two things here one I really do believe what we were talking about a minute ago that Nvidia is not just a hardware company they’re not just a chips company they are a platform company and there is a lot of differentiation baked into what they do if you want to train GPT or a GPT class model there’s one option you’re doing it on Nvidia there’s one option and yes we should talk about there’s lots of less than GPT class stuff out there that you can do and especially inference is more of a wide open market versus training but you can do on other platforms but they’re the best and they’re not just the best because of their Hardware they’re not just the best because of their data center Solutions they’re not just the best because of Cuda they’re the best because of all of those so the other sort of illustrative thing for me that shows how wide their lead is we haven’t talked about China yet the land of a800s yes so what’s going on last year China was 25 or sales to Mainland China was 25 of nvidia’s Revenue and a lot of that is they were selling to the hyperscalers to the cloud providers in China Baidu Alibaba tencent other and by the way Baidu has potentially the largest model of anyone there our GPT competitor is over a trillion parameters and may actually be larger than gpt4 wow I didn’t know that yep ah it’s wild so then I believe also in September of 2022 last year the Biden Administration announced pretty sweeping regulations and bans on sales of advanced Computing infrastructure David their export controls don’t say bands I mean yes that’s a fine line and this is pretty close to bands what the administration introduced as part of that Nvidia can no longer sell their top of the line h100s or a100s to anybody in China so they created a nerfed skew essentially that meets the regulations the performance regulations the a800 and h800s which I think they basically just crank down the NV links data transfer speeds so it’s like buying a top of the line a100 but not with as fast of data connections as you need which basically makes it so you can’t train large models right or you can’t train them as well or as fast as you could with the latest stuff the incredibly telling thing is that those tips and those machines are still selling like hotcakes in China they’re still the best hardware and platform that you can get in China even a crippled version and I think that’s true anywhere in the world and there’s been a even a more recent Spike of them because a lot of Chinese companies are reading the tea leaves since saying ooh export controls might get even more severe so I should get them while I still can these 800s yep so I mean I can’t think of a better illustration of just how wide their lead is yeah that’s a great point talking about the rest of Nvidia just for a moment I mean this episode is about the data center segment but Oh you mean they still make gaming cards too it is worth talking about this idea that Omniverse is starting to look really interesting as of their conference six months ago they had 700 Enterprises who had signed up as customers and the reason this is interesting is it could be where their two Different Worlds Collide 3D graphics with Ray tracing which is new and amazing and the demos are mind-blowing and AI they have been playing in both of these markets since the workloads are both massively parallelizable that is the sort of original reason for them to be in the AI Market if you recall back to way back our part one episode the original Mission of Nvidia was to make graphics a storytelling medium and then their mission has expanded as they’ve realized my God our Hardware is really good at other stuff that needs to be parallelized too but fascinatingly with Omniverse the future could actually look like applications where you need both amazing graphical capability and AI capability for the same application and I mean for all the other amazing uniqueness about Nvidia that we’ve been talking about and how well positioned they are adding this on top where they’re the number one provider for graphics hardware and software and AI hardware and software oh and by the way there’s this huge application emerging where you actually do need both they’re just going to knock it out of the park if that comes true there was a super cool demo at a recent keynote it might have been at siggraph where Nvidia created a game environment you know fully retraced game environment looks like a triple A game it looks amazing you know basically distinguishable from reality but like you really gotta look hard to tell that this isn’t real and this isn’t a real human you’re talking to so there’s a non-playable character that you’re talking to an NPC who’s giving you like a mission they show this demo it looks amazing then they’re like the script the words that that character was saying to you were not scripted that was all generated with AI dynamically so you’re like holy crap you know you think about you play a video game the characters are scripted but in this world that you’re talking about you can have generative AI controlled avatars that are unscripted that have their own intelligences and that drives the story totally or you know an airplane that’s in a simulation of not just a wind tunnel but simulating millions of hours of flying time using real-time weather that’s actually going on in the world and using AI to project the weather in the future so you can sort of know the real world potential things that your aircraft could encounter all in a generated graphical AI simulation I mean there’s gonna be a lot more of this stuff to come yep totally another thing to know about Nvidia that we really didn’t talk about on the last episode they’re pretty employee efficient they have 26 000 employees and that sounds like a big number but for comparison Microsoft whose market cap is only twice as big has 220 000 so that is 5X the number of employees per dollar of market cap going on over at Microsoft and this is a little bit farcical since you know Nvidia only recently has had such a massive market cap but the scale of the platform that Nvidia is building is on the order of magnitude of Microsoft scale right they have 46 million dollars of market cap per employee wild crazy which I think translates into the culture there is we’ve gotten to know some folks there it really is a very Unique Kind of culture like it is a big Tech Scale company but you never hear about the same kind of silly big Tech stuff that you hear at other companies at Nvidia as far as I know I could be wrong on this there is no like you know oh work from home or return to the office policy it in video it’s like no you just like you do the job and you know nobody’s forcing anybody to come into the office here and like they’ve accelerated their ship Cycles well I also get the sense that it’s a little bit of a do your life’s work or don’t be here situation like Jensen is rumored to have 40 direct reports and his office is basically just an empty conference room because he’s just bouncing around so much and he’s on his phone and he’s talking to this person and that person and like you can’t manage 40 people directly if you’re worrying about someone’s career Ambitions yep he’s talked about this he’s like I have 40 direct reports they are the best in the world at what they do this is their life’s work I don’t talk to them about their career Ambitions like I don’t need to like you know yeah for like recent College grads we do mentoring but if you’re a senior employee you’ve been here for 20 years you’re the best in the world of what you do and we’re hyper efficient and I start my day at 5am 7 days a week and you do too it’s crazy yeah there’s actually this amazing quote from Jensen that I heard on an interview with him that I was listening to or towards the end of the conversation in the interviewer asks him Jensen you in Nvidia do these just amazing things what do you do to relax and Jensen’s answer is I’m reading this is a quote direct quote I’ve relaxed all the time I enjoy relaxing at work because work is relaxing for me solving problems is relaxing for me achieving something is relaxing for me and he’s a hundred percent serious like a thousand percent serious how old is Jensen the dude is 60 years old it kind of feels like all of his peers have either decided to retire and relax or are uh you know relaxing while running their companies I think there’s another crop of people that are doing that and that is just not at all interesting to him or what he’s doing and I kind of get the sense like he’s got another 30 years in him and he’s architected the company in such a way that that’s the plan I don’t think there’s anyone else there where they’re like getting ready for that person to take over I think the company is a extension of Jensen’s thoughts and will and drive and belief about the future and that’s kind of what happens I don’t know if there is or isn’t a Jensen and Lori Huang Foundation but if there is he’s not spending his time on it he’s not buying sports franchises he’s not buying Mega Yachts or if he is he isn’t talking about him and he’s working from them yeah he’s not buying social media platforms and newspapers yeah totally I mean it is quite telling that when you watch one of their Keynotes it’s Jensen on stage and it’s some customer demos but it’s not like the Apple Keynotes where Tim Cook’s calling up another Apple employee it’s the Jensen show right nobody would accuse Tim Cook of not working hard I don’t think but you go to those Keynotes and it’s like Tim does the welcome and then the handoff and you know a parade of other Executives talk about stuff good morning all right power let’s talk power all right so for listeners who are new to the show this is the section where we talk about what it is about the company that enables them to achieve persistent differential returns or in other words to be more profitable than their closest competitor and do so sustainably and Nvidia is fascinating because they sort of have a direct competitor but that’s not the most interesting form of competition for them disintermediation is sure ostensibly there’s Nvidia versus AMD but like AMD doesn’t have all this capacity reserved from tsmc at least not for the 2.5 D packaging process for the high-end gpus AMD doesn’t have the developer ecosystem from Cuda they’re the closest direct comp but it’s Amazon building trainium and inferentia it’s if Microsoft decides to go and build their own ship as they’re rumored to with AMD it’s Google and the TPU Facebook developing Pi torch and then leveraging their foothold with pytorch with the developer Community to figure out how to extend underneath of pytorch there’s a lot of competitive vectors coming at Nvidia but not directly not to mention all the data center Hardware providers that are their direct competitors now too yup Intel Etc on Down the Line yep now all that said they’ve got a lot of powers so as we move through these one by one I think let’s just say them all and we can decide if there’s something to talk about here counter positioning is the one where I actually don’t think there’s anything here I don’t think there’s anything that Nvidia does where there’s another company that’s actively choosing not to do that because any company would want to be Nvidia right now I would have agreed with you but I actually think there is strong counter positioning in the data center world right now Nvidia and Jensen put a flag in the ground several years ago where they said we are going to re-architect the data center and all the existing data center hardware and compute providers had strong incentives not to do that but like right now what do you think other data center Hardware providers what are they not doing yeah Fairborn they’re all trying to put gpus in the data center too everyone’s just gonna chase exactly what Nvidia is doing years behind them that’s the market right now yep okay fair enough and the question is will Nvidia be able to stay ahead in ways that matter that I think is the entire analysis on the company right now is in what ways that matter to customers at large scale and large markets will they be able to sustainably be ahead of people that are just chasing them and trying to copy what they’re doing because the margin profile is so fat and juicy that people don’t want to pay it yep so the second one scale economies this has Cuda written all over it you can make massive fixed cost Investments when you have the scale to amortize that cost across and when you have four million developers who want to develop on your platform you can justify whatever it is 1600 people who actively on LinkedIn at Nvidia today have the word Cuda in their job title I mean I’m sure it’s actually even more than that who just aren’t you know they’re saying software or something like that but thousands of people of an investment that they don’t make any money on software they man they make a de minimis amount on software but that is amortized across the entire developer base I think it’s worth saying a bit more here on this too which we also talked about in our last episode to me the Dynamics here are a lot like apple and iOS yes versus Android Apple has thousands and thousands and thousands of developers working on iOS Android also has thousands and thousands of developers working on it across a widespread ecosystem but at Apple it’s all tightly controlled and it’s coupled with hardware at Android it’s not and like as a user maybe you’ll get the latest operating system update maybe you won’t I think this is exactly the right framing here that Nvidia is the apple of AI and pytorch is sort of Android because it’s open source and it’s got a bunch of different companies that care about it opencl is the Android as it pertains to Graphics but it’s pretty bad and pretty far behind Rock m is the Cuda competitor made by AMD for their Hardware but again new not a lot of adoption they’re working on it but they’ve open sourced that because they realize they can’t go directly head to head with Nvidia they need some different strategy but yes they are absolutely running the Apple Playbook here yep and I think in the current state of things it’s even more favorable to Infinity area than iOS versus Android because Nvidia has had first dozens and then hundreds and now thousands of Engineers working on Cuda for 16 years meanwhile the Android equivalent out there in the open source ecosystem has only just been getting going you know if you think about the Delta of the timeline between IOS and Android it was a year and a half two years there’s a probably at least 10 probably closer to 15-year lead than Nvidia has and so we talked to a few people about this and we’re like oh what’s going on in the open source ecosystem is there an Android equivalent and even the most bullish people we talked to were like oh yeah you know now that Facebook has really moved pytorch into a foundation and outside of Facebook that means that other companies can now contribute you know couple dozen Engineers to work on it and you’re like cool so AMD is going to contribute a couple dozen maybe 100 Engineers to work on pytorch and so we’ll Google and so will Facebook and so will everybody else Nvidia has thousands of Engineers working on Cuda 10 years ahead I sent you this graph David of my estimated number of employees working on Cuda per year since Inception in 2006 and then if you look at the area under the curve and just take the integral it’s approximately 10 000 person years that have gone into Cuda like good luck now again open source is a very powerful thing the market incentives are absolutely there for this to happen right that is the interesting point is every moat only works if the castle is sufficiently small if the prize at the end of the Finish Line becomes sufficiently large you’re going to need a bigger moat and you need to figure out a uh you know how to defend the castle harder I’m mixing so metaphors here but you get the idea yeah I love it this was a perfectly fine moat when the addressable Market was 100 billion dollars is it at a trillion dollar market opportunity probably not basically it means margins come down and competition gets more Fierce over time and I think Nvidia totally gets this because part of this as I was alluding to is covid related but we talked way back in part one about how Nvidia ended up to save the company moving to a six-month shipping cycle for their graphics cards when their competitors were on a one to two year shipping cycle that persisted for several years and then they relaxed back to an annual shipping cycle there were annual gtcs since kovid Nvidia has re-accelerated to a six-month shipping cycle they’ve been doing two gtc’s a year most years since covid which is insane for the level of Technology complexity that they’re doing yep imagine Apple doing two wwdczy here yeah that’s what’s happening in Nvidia it’s crazy so on the one hand that’s a culture thing on the other hand that is an acknowledgment of like we need to be pedal to the floor right now to outrun competition we’ve built some structural ways to defend the business but we need to continue running as fast as we’ve ever run to stay ahead because it’s such an attractive race that we’re in yep all right so that’s scale economies let’s move to switching costs now so far everything of consequence especially model training especially on llms has been built on Nvidia and that alone is just a big pile of code and a big amount of organizational momentum so switching away from that even from the software perspective is going to be hard but there are companies today in 2023 both at the hyperscalers and Fortune 500 companies that own their own data centers making data center or purchase and roll out decisions that will last at least the next five years because these data center re-architectures don’t happen very often and so you better believe that Nvidia is trying as hard as they can to ship as much product as they can while they have the lead in order to lock in that data center architecture for the next n years yeah we talked to many people in preparation for this episode but one of the most interesting conversations was with some of our favorite Public Market investors out there the NCS Capital guys who I stole many insights from for this episode oh they’re just so great and obviously I’ve been following Nvidia in the space for a long time they made the point that data center revenue and data center capex is some of the stickiest Revenue that is known to humankind just the organizational switching costs involved in data center procurement and data center architecture standardization decisions God that’s a mouthful even to say at Fortune 500 companies and the like is like they’re not changing that more than once a decade at most so even if we’re sort of in this bubbly moment around the excitement of generative AI before we necessarily know the full set of applications Nvidia is leveraging this excitement to go get to block it I’ve seen some people on the internet being like they love how Supply constrained they are I don’t think so I think they’re looking for capacity in every way they can get it to exploit this opportunity while it exists I completely agree with that yeah I think you know again we didn’t talk to Colette Nvidia CFO about this but I strongly suspect if I with them I would be happy to trade some of this gross margin right now for increased throughput on sales yep but there’s only one tsmc and there’s only so many Fabs that they have that can do the what do they call it the 2.5 D architecture so should we talk coordinate resource yeah this is probably the textbook cornered resource Nvidia has access to a huge amount of capacity at tsmc that none of their competitors can get their hands on I mean they did luck into this quartered resource a little bit they reserved all that way for supply for a different purpose partially crypto mining but AMD doesn’t have it AMD does have a ton of capacity it’s worth saying at tsmc for their other products data center CPUs which they’ve actually been doing very well in but Nvidia did end up with this wide open Lane all to themselves on co-opas capacity at tsmc and they gotta make the most of that for as long as they have it yep and I guess to say a little more though it’s not like this is not a commodity as we talked about on our tsmc episode although tsmc is a contract manufacturer it is the opposite of a commodity especially at the highest end Leading Edge it’s like a invention delivered by aliens that very few humans know how to actually do yes it is worth acknowledging it’s kind of a two-horse race for llm training I know we’ve been harping on Nvidia but Google tpus are also manufactured at volume you can just only get them through Google cloud and I think I don’t know if you have to use the tensorflow framework which has been waning in popularity relative to pytorch but it’s certainly not an industry standard to use tpus the way that it is to use nvidia’s Hardware I suspect a lot of the volume of the tpus is being used internally by Google for Bard for doing stuff in Google search like I know they’ve added a lot of the generative AI capability to search yep totally two points on this just sticking to the scope of this business and Market discussion this is a major casualty of a strategy Conflict at Google obviously the way you want to do this is the way Nvidia is doing this of like your customers want to buy through the cloud you want to be in every cloud but obviously Google is not going to be in AWS and Azure and Oracle and all the new Cloud providers they’re only going to be in gcp maybe David but I was going to say though through the expanded lens though I think this makes sense for Google because their primary business is their own products right and they run among the most profitable businesses the world has ever seen so anything they can do to further advantage and extend that Runway they probably should do nothing has changed through all of this with respect to the fact that what the previous generation of AI enabled with machine learning with regard to social media and internet applications being the most profitable cash flow geysers known to man none of that has changed that is still true in this current world and still true for Google yep the last one that I had highlighted is Network economies they have a large number of developers out there and a large number of customers that they can amortize these technology Investments across and who all benefit from each other I mean remember there are people building libraries on top of Cuda and you can use the building blocks that other people built to build your code you can write amazing Cuda programs that just don’t have that many lines of code because it’s calling other pre-exist stuff and Nvidia made a decision in 2006 that at the time was very costly like big investment decision but it looks genius in hindsight to make sure that every GPU that went out the door was fully Cuda capable and today there are 500 million Cuda capable gpus for developers to Target it’s just very attractive I’m putting this in the network economies I think it’s probably more a scale economy than a network economy but you could imagine a lot of people hoe humming around Nvidia in 2006 to 2012 saying why do I have to make it so that my software fits on this tiny little footprint and we can include Cuda taking up a huge amount of space on this thing and make all these trade-offs and our Hardware so that we can are people going to use Cuda and today it just looks so genius yeah I mean we’ve talked about this many times on the show including with Hamilton Helmer and chenyu themselves but for platform companies like Nvidia clearly is there is this special brand of power that is a combination of scale economies and network economies and this is what you’re getting at yep they do you have branding power for sure yeah I actually think it’s worth talking about this a little bit this is the nobody gets fired for buying IBM I mean Nvidia is the modern IBM in the AI era yep look I don’t feel confident enough to like pound the table on this but given the nature of how the company started how long they’ve been around and the fact that they also have the market leading product in a totally different business in graphics you know which is both consumers but also professional graphics I think that probably does lend some brand power to them especially when the CIO and the c-suite at McDonald’s is making a buying decision here like everybody knows Nvidia hmm you’re saying that They Carried their consumer brand into their Enterprise posture this is way way way down the stack and power but I don’t think it’s hurt them they’ve always been known as a technology leader and the whole world has known for decades at this point the the stuff that they can enable is magical yeah there’s a big strength leads to strength thing here too where I bet the revenue results from last quarter massively dwarf any brand benefit that they ever got from the consumer side I think it’s just the fact that like hey look everyone else is buying Nvidia I’d be an idiot not to nobody is getting fired for buying Nvidia anytime soon yep right or taking a big dependency on them or targeting that development platform it’s just the like if you’re innovating in your business you don’t want to take risk on the platform you’re building on top of you want to be the only risk in the value chain all right then the last one right is process power yeah and this is probably the weakest one even though I’m sure you could make some argument that they have process power it’s just that all the other powers are so much more valuable it’s always so tricky to tease out yep you know I think the argument here would just be like nvidia’s culture in their six-month shipping cycle that clearly they had in the past than they didn’t have for a while and now they have again I don’t know I think you can make an argument here Is it feasible let’s do a thought exercise could any of their competitors really in any domain move to a six-month ship cycle that’d be really hard yeah you know could a apple sized company due to wwdc’s a year like no the question is does that actually matter there are so many people that are using a100s right now and in fact most workloads can be run on a100s unless you’re doing model training of gpt4 I just don’t know that it actually matters that much or as much as other factors and I’ll give you an example AMD does have 3D packaging on one of their latest gpus it’s a more sophisticated way of doing real coppered or real copper direct connection without a silicon inner poser I’m getting into a little bit of the details but basically it’s more sophisticated than the process that the h100 2.5 D is using to make sure that memory is extremely close to compute and does that matter not really what matters is everything else that we’ve been talking about and nobody’s going to make a purchase decision on this thing because it’s you know a little bit of a better mousetrap yeah thinking about this more I think actually brand is a really important power for NVIDIA right now yeah and in a strength leads to strength way so you can see why they’re trying to sort of seize this moment yeah Playbook all right let’s move on to Playbook so one thing that I want to point out is Jensen keeps referring to this as the iPhone moment for AI and when he says it the common understanding is that he means a new mainstream method for interacting with computers but there’s another way to interpret it does this sound familiar David when I say a hardware company differentiated by software that then expanded into services yes yes it does it’s quite tongue-in-cheek to be referring to the iPhone moment of AI when referring to oneself Nvidia as the Apple because I really think that the parallels are uncanny that they have this vertically integrated hardware and software stack provided by Nvidia you use their tools to develop for it they’ve shipped the most units so developers have a big incentive to Target that market it’s the best individual buyers to Target because they’re the least cost sensitive and they appreciate you building the best experiences for them I mean it’s the iPhone but in many ways it’s better because the target is a B2B Target instead of consumers yeah the only way in which it’s different is Apple has always had a market cap that sort of lagged its proven value to users whereas Nvidia right now is uh exactly over their skis well let’s save that for Bull and Bear at the end great the second one is that they’ve moved on from becoming a hardware company to truly being a systems company well nvidia’s chips are typically ahead it really doesn’t matter on a chip to chip comparison that is not the playing field it is all about how well multiple gpus and multiple racks of gpus work together as one system with all the hardware and networking and software that enables that they have just entirely changed the vector of competition which I think lots of companies can learn from and my third one here is this quote that Jensen had again from the same strategory interview which is you build a great company by doing things that other people can’t do you don’t build a company by fighting other people to do things that everyone can do and I think it’s so Salient it comes out in all these interesting ways one of which is NVIDIA never dedicated resources to building a CPU until there was a differentiated way and a real reason for them to build their own CPU which is now and the way that they’re doing it by the way is not terribly differentiated it’s an off-the-shelf arm architecture that they’re putting some of their own secret sauce on but it’s not like they’re doing apple style M3 creation of a chip from scratch it’s not the hero product right there are many ways that Nvidia sort of applies this where I think we talked about in the last episode if they think it’s going to be a low margin opportunity they don’t go after it but the nicer way to say that is we don’t want to compete for things that anybody can do we want to do things it only we can do oh and by the way we will fully realize the value of those things when we do them yeah I think this may be a related Playbook theme Here for NVIDIA of strike when the timing is right I suspect that a lot of the inner competitive drive and motivation for Jensen and the company over the past 10 15 years here has been to really fight against Intel Intel tried to kill them as we talked about many times in the previous episodes we talked to somebody who framed it as intel was the country club and Nvidia is the fight club and back in the days the Intel Country Club didn’t want to let Nvidia in Intel controlled the motherboard Intel control the most important chip was the CPU Intel would integrate and commoditize all other chips into the motherboard eventually and if they couldn’t do that well then they’d try and make the tips themselves and they tried to run all these playbooks on Nvidia and Nvidia just barely survived and then in the data center Intel control the data center for so long PCI Express you know that was the interconnect in the data center for so long and Nvidia had to live in there and I’m sure they hated every single minute of it but they didn’t turn around 10 years ago and just be like guess what we’re making a CPU too they waited until the time was right it is crazy they used to have to plug into other people’s servers and then they started making servers that plugged into other people’s racks and rows and architectures and then they started making their own entire rows and walls and at some point here they’re going to start running their own buildings full of servers too and they’re going to say we don’t have to plug into anything yep but I think for a lot of other leaders it would have been hard to have the patience that they’ve had totally you only get to do the stuff they’re doing if you invested 10 years ahead of the industry were wildly inventive and Innovative in creating these like true breakthrough Innovations and were really really right about huge markets none of this stuff applies unless you’re doing those three things yeah Fortune 500 cios aren’t making buying decisions if none of what you just said isn’t true right so there’s this interesting conversation I wanted to have with you ahead of winding it up with the Bull and Bear case so think back to our AWS episode we talked a lot about how AWS is just locked in the databases are a ridiculously durable Advantage once your data has been shipped to a particular Cloud often literally in semi trucks full of hard drives snowball yeah it’s hard to move off of it there’s this sort of interesting question of will winning Cloud 1.0 for all these Google Microsoft Amazon will that Toe Hold actually enable them to win in the cloud AI era on the one hand you’d think yes absolutely because I want to train my AI models right next to where my data is it’s really expensive to move my data somewhere else to do that case in point Microsoft is the exclusive Cloud infrastructure provider for openai which runs as far as we know solely on Nvidia infrastructure but they buy it all through Microsoft right on the other hand the experience that customers are demanding is the full stack Nvidia experience not this oh you found the cheapest possible cost of goods sold way to offer me something that’s like the experience that I want and sometimes the cloud providers have to offer me an a100 or an h100 because my code is way too complicated to ever re-architect for whatever accelerated Computing devices they’re offering me that’s first party and cheaper for them I don’t know I just think for the first time in the last five years or so I’ve sort of cocked my head a little bit at the moat of these existing Cloud providers and said huh maybe there really is a vector to compete with them and cloud is not a settled Frontier yeah well this is pejorative here cloud is a euphemism for data centers right there’s so much more to the hyperscalers and public clouds than just data centers right but physically they’re data centers yeah there is a mile of distance metaphorically between like an equinix and AWS yep but their data centers and there is a fundamental shift at least according to Jensen a fundamental shift that is happening in data centers so I think that probably does create some shifting Sands that the cloud Market is going to have to navigate yep I bet the way it plays out is that where you landed in Cloud 1.0 strongly dictates where you will land in this AI Cloud era because at the end of the day if customers are demanding Nvidia stuff then the cloud providers have every incentive in the world to make it so that you can run your applications great in their Cloud but also like there’s more to this too Crusoe exists core weave exists Lambda Labs exist these are well funded startups with billions of dollars that a lot of smart people think there’s a major cloud-sized opportunity for yep that would not have happened a few years ago super true all right let’s do the bull case in bear case and bring this one home oh boy we’ve been trying to delay this as long as possible this is the Crux of the question right now yeah I mean part of it is is there existing moat big enough if gpus actually become a hundred billion dollar a year Market I mean right now gpus in the data center are like a 30 billion dollar a year Market going to like a 50 billion dollar next year and like if this actually goes the way that everyone seems to think it’s going to go there’s just too many margin dollars out there for these big companies to not invest heavily meta through tens of billions of dollars making the metaverse I mean Apple’s put 15 billion dollars into rumored into their headset Amazon’s put tens of billions of dollars into devices which by all means was a terrible investment how is Echo paying anything back oh man total sidebar I’m so disappointed I have standardized my house on the echo ecosystem and it keeps getting Dumber how in this world of incredibly accelerating AI capabilities are my Echoes getting summer well they need to train him and inferentia a little bit harder ah Jesus okay rant over yeah yeah never doubt big Tech’s ability to throw tens of billions of dollars into something if the payoff could be big enough these are ludicrously profitable monopolies except for Amazon’s not that profitable AWS is yeah but Google Facebook Apple at some point here there’s a game of chicken that ends and some of these companies go all in and say yeah we have Smart Engineers too like we’re gonna figure this out yeah but also never underestimate the inability of big Tech to execute on stuff that it thinks it can especially with major strategy shifts yeah yeah all right so let’s actually do this bear case let’s start with the bear case so you just Illustrated I think bear case number one which is literally everybody else in the technology ecosystem is now aligned and incentivized to say I want to take a piece of nvidia’s pie and these companies have Untold resources yep and to put a finer point on that let’s look at pytorch for a minute now that all the developers or lots of developers are using pytorch it does enable pytorch to aggregate customers which gives them the opportunity to disintermediate maybe you gotta write a lot of new stuff underneath and ship a lot of Hardware I mean the cloud service providers have taken some steps here it was originally developed by meta and while it’s open source it’s still hard for all these companies to invest in it if it’s really sort of owned and controlled by meta so now pytorch has been moved out into a foundation that a lot of companies are contributing to and again it is a absolute false equivalence to be like Pi Torch versus Nvidia but in real Ben Thompson aggregation theory parlance if you aggregate the customers you have the opportunity then to take more margin to disintermediate to direct where that attention is going and Pie torch has that opportunity that feels like the vector that a lot of these csps will try and compete on and say look if you’re building for pi torch it runs really well on our thing too yep for sure no doubt that that’s gonna happen alright so that’s bear case number two kind of as part of bear case number one the next one is like literally the market isn’t as big as the market cap reflects I think there’s a pretty reasonable chance that there’s some falter in the next 12 to 18 months where there’s a crisis of confidence among investors where at some point something will come out where we all observe oh maybe gpts aren’t as useful as we thought maybe people don’t want chatterfaces and that crisis of confidence that many Bubble Burst will trickle out to America’s cios and CEOs make it harder to advocate in the boardroom to make this big fundamental purchase and re-architecture of our whole budget from this year that we agreed on that I’m trying to propose US changing there’s a crypto like element to to a excitement bubble bursting that will for some companies slow their spend and the question is sort of like when that happens because it’s not an if it’s a win I have a hard time believing that given all the hype around everything right now AI will be even more useful than everyone believes and it will continue in a linear fashion where without any drawdowns everyone’s excitement only gets bigger from here it may end up being way more useful than anyone thought but there at some point will be some Valley or trough and it’s sort of about how does Nvidia fare during that crisis of confidence it’s funny you know again we talked to a lot of people for this episode including a set of some of the foremost AI researchers and practitioners out there and Founders in c-suites of companies that are doing all this and pretty much to a t they all said the same thing when we asked them about this question they all said yeah this is overhyped right now of course obviously but on a 10-year time scale you haven’t seen anything yep the transformative change that we believe is coming you can’t even imagine the most interesting thing about the over hype is that it’s actually showing up in Revenue it’s everyone who is buying access to all this compute believes something and for NVIDIA because it’s showing up in the form of Revenue the belief is real then so they just need to make sure that they smooth the Gap to customers actually realizing as much value as the cios of the world are currently investing ahead of yep so I think the sub point to that that’s worth the discussion right now is like okay generative AI yeah is it all it’s cracked up to be well David I haven’t asked you about this in like a month or so but a month ago you were pounding the table insisting to me like I have no need for I’ve never used chat gbt I can’t find it to be useful it’s hallucinating all the time I never think to use it it’s not a part of my workflow but like where are you at still basically there including forcing myself to try to use it a bunch in preparation for this episode but also as we talk to more people I think I’ve realized that like David rosenthal’s use case doesn’t really matter here at all right a because as a business we are such a hyper specialized unique little unicorn thing where accuracy and the depth of the work and thought that we ourselves put into episodes is the Paramount thing well and we have no co-workers there’s so many things about our business that is weird like we never have to prepare a brief for a meeting right all this stuff anything external that we prepare is a labor of love for us and there is nothing we prepare internal I know people who use chat gbt to set their okrs and I’m like okay what’s an okr and they’re like I wish my life were like that too that’s why I have chat gbt do it right honestly like I think through doing this and talking to some folks and reading I think there’s a very compelling use case for it for writing code right now no matter what level of software developer you are from zero all the way up through Elite software developer you can get a lot more leverage out of this thing and GitHub co-pilot so is that valuable like for sure that’s valuable yeah the llms are unbelievably good at writing and helping you write code I’m a huge believer in that use case yeah and then I think you know there’s the slightly more speculative stuff but you can actually sort of see it now of like that gaming demo that I mentioned recently from Nvidia of like oh you’re talking to a non-playable character that wasn’t scripted we did an acq2 episode recently with Chris Valenzuela from the CEO of Runway that was used in everything everywhere all at once and he said that’s just the tip of the iceberg like the stuff that you can do that is happening that’s out there today with generative AI in these domains is astounding yeah I think what you’re saying is one could be a bear on your own experience every time you try to use a generative AI application it doesn’t fit into your workflow you don’t find it useful you’re not sticky but on the other hand actually what AI will be is a sum of a whole bunch of niches there’s a video game Market there’s a writing Market there’s a creative writing Market there’s a software developer Market there’s a marketing copy Market you know there’s a million of these things and you just may happen to not fall into one of the first few niches of it yeah I think for me at least again just speaking personally too at a very strong women of skepticism initially because the timing was just too perfect you know it’s like all uvcs out there you just told everybody about how crypto is the future and whatever you’re talking about and then interest rates went to you know five percent and your world fell off a cliff uh the number of people who were like out raising a fund and they’re like the future is AI yeah right this is the best time ever to be investing and so there was a large part of me that I was just like come on guys yeah it’s too perfect you’re right it’s too perfect but this most recent couple months in this quarter for NVIDIA has shown that put all that aside Fortune 500s are adopting this stuff cios are adopting this stuff Nvidia is selling real dollars and learning also about what it takes to train these models and the step scale function of knowledge and utility going from a billion parameters to 10 billion parameters to 200 to a trillion parameter models yeah like something’s going on there for sure so this leads me to my next bear case which is the models will get good enough and then they’ll all be trained and then we’ll shift to inference and most of the compute load will be on inference where Nvidia is less differentiated there’s a bunch of reasons I don’t believe that that is a popular narrative though one of the big reasons I don’t believe that is the Transformer is not the end of the road in a bunch of the research that we did David it’s very clear that there are things beyond the Transformer that are in the research phase right now and the experiences are only going to get more magical and only going to get more efficient so there’s sort of a second bear case there which is right now we threw a Brute Force kitchen sink at training these things and all of that Revenue accrued to Nvidia because they’re the ones that make the kitchen sinks and over time like you look at uh Google’s chinchilla or llama too they actually use less parameters than gpt4 and have equivalent quality or you know many other people can be the judge of that but we’re high quality models with less parameters so there is this potential bear case around future models will be more clever and not require as much compute it’s worth saying that even today the vast majority of AI workloads don’t look like llms at least until very recently llms are like the current Maxima in human history of jobs to be done that require a ton of compute and I guess the question is will that continue I mean many other magical recent AI experiences have happened with far less expensive model training like diffusion models and the entire genre of generative AI on images which we really haven’t talked about a lot on this episode because they’re less compute intensive but many tasks don’t require an entire internet of training data and a trillion parameters to pull off yeah that makes sense to me and I think there also is some Merit to workloads are shifting to inference that is happening I agree with you I don’t think training is going anywhere but until recently you know thinking back to the Google days training was what everybody was spending money on that’s what everybody was focused on as usage scales with this stuff than inference and inference of course being the compute that has to happen to get outputs out of the models after they’re already trained that becomes a bigger part of the pie and as you say the infrastructure and ecosystems around doing that is less differentiated than training yep okay those are the bear cases there’s probably also a bear case around China which is a legitimate one because that’s gonna be a problem for lots of people a large Market that they won’t be able to address for the foreseeable future in a meaningful way and just what’s going to happen generally like obviously China is racing to develop their own homegrown ecosystems and competitors and like that’s gonna be a closed off market so what’s going to come out of there what’s going to happen yep that’s definitely one too my last one is a bear case but it ends up not being a bear case for most companies I would say that if they were trading at this very high multiple and they just experienced this tremendous real growth in revenue and operating profit that that sort of Spike to the system when it goes away will irreparably harm the company when things slow down stock compensation is an issue employee morale is an issue customer perception is an issue but this is NVIDIA yeah this is nothing new the number of times that they’ve risen From the Ashes after you know years long terrible sentiment with something mind-blowingly Innovative they’re probably the best positioned company or the company with the best disposition to handle that when it happens oh I thought that’s a great turn of phrase there you uh upped your training model language there you should see the number of parameters ah I love it all right just to list the ball cases one Jensen is right about accelerated Computing the majority of workloads right now are not accelerated they’re bound to CPUs they could be accelerated and that shifts from some crazy low number like five or ten percent of workloads being accelerated today to 50 plus percent in the future and there’s way more compute happening in parallel and that mostly accrues to Nvidia oh I have one Nuance I want to add to that on the surface I think a lot of people look at that and they’re like yeah come on but I think there actually is a lot of Merit to that argument in the generative AI world and everything we’ve talked about in this episode I don’t think Jensen and Nvidia are saying that traditional compute is going away or again gets smaller I think what he’s saying is that AI compute will be added on to everything and the amount of compute required for doing that will dwarf what’s happening in general purpose compute so like it’s not that people are going to stop running SharePoint servers or that whatever products you use are gonna stop using their whatever interfaces that they use it’s that generative AI will be added to all of those things and the use cases will pop up which will also use traditional general purpose cpu-based Computing but the amount of workloads that go into making those things magical is just going to be so much bigger yep also just a general statement on software development writing parallelizable code is really hard unless you have a framework to do it for you even writing code with multiple threads like if anybody remembers a CS College in class where they had a race condition or they needed to write a semaphore these are the hardest things to debug and I would argue that a lot of things that could happen in an accelerated way aren’t just because it’s harder to develop for and so if we live in some future where Nvidia has reinvented the notion of a computer to shift away from Von Neumann architecture into this stream processor architecture that they’ve developed and they have the full stack to make it just as easy to write applications and move existing applications especially once all the hardware has been bought and paid for and sitting in data centers there’s probably a lot of workloads that actually do make sense to accelerate if it’s easy enough to do so yeah that’s great but so your point is that there’s a lot of latent accelerated addressable Computing out there that just hasn’t been accelerated yet right it’s like ah this workload’s not that expensive and I’m not going to pay an engineer to go re-architect the system so it’s fine how it is how about that I think there’s a lot of that so bookcase one Jensen is right about accelerated Computing full case 2 Jensen is right about generative AI I mean combined with accelerated Computing this will massively shift spend in the data center to nvidia’s hardware and as we’ve mentioned openai is rumored to be doing over a billion dollars in recurring revenue on chat GPT so I think there’s let’s call it 3 billion because that’s the most sort of credible estimate that I’ve heard maybe that was a forecast for next year but like they’re not the only one I mean Google with Bard which I’ve found tremendously useful actually preparing for this episode is not directly monetizing that but they’re sort of retaining me as a Google search customer by doing it there is a lot of real economic value even today not nearly the amount that’s sort of baked into the valuation but I suppose the bare case of this is that everything has to go right for NVIDIA but the bull cases indications are things are going right for NVIDIA yep third bold cases Nvidia just moves so fast whatever the developments are it’s hard to believe that they’re not going to find a way to be really well positioned to capture it that’s just a cultural thing four is the point that you brought up earlier that there’s a trillion dollars installed in data centers 250 billion more being spent every year to refresh and expand capacity and that Nvidia could take a meaningful share of that I think today what’s their annual revenue at like 30 billion or something well if you run rate this current quarter then it’s uh like 50 50 plus yeah so right now that puts them at like 20 of the current data center spend you could imagine that being much higher okay wait that includes the Gaming revenue it’s about 40 because the data center revenue is 40 is 10. so 40 annualized all right so 15 18 yeah but you could imagine that creeping up again if the accelerated Computing and generative AI belief comes true like they’ll expand that 250 number and they’ll take a greater percent of it yep an interesting way to do a sort of a check on this math is to look at what other people in the ecosystem are reporting in their numbers tsmc in their last earnings said that AI Hardware currently only represents six percent of their revenue but all indications over there is that they expect AI Revenue to grow 50 per year for the next five years wow so we’re trying to come at it from the customer workload side and say is it useful there but if you come at it from this other side of what do nvidia’s suppliers forecasting and they have to put their money where their mouth is building these new wafer Fabs to be able to facilitate that and packaging and all the other things that go into chip so it’s expensive for tsmc to be wrong yep so that’s another bull case the last one that I have before leaving you with one final thought are you saying you have one more thing yes is that Nvidia isn’t Intel and I think that’s the biggest realization that you helped me have and it’s not Cisco yeah the comparison we were making in the last episode was wrong they are Microsoft they control the whole software stack and they simultaneously can have relationships with the developer and customer ecosystems and I mean it may even be better than Microsoft because they make all the hardware too yeah maybe old school IBM right imagine if IBM operated in a Computing Market of today’s magnitude come Computing this tiny little market back then right I mean it was like that I mean it took the PC wave to disrupt IBM which was a personal computer in today’s cartilage Edge Computing you know device-based Computing IBM dominated the B2B Mainframe cycle of computing and again if you believe everything Jensen is saying and how he steered the company for the last five years we are going back into a centralized data center modern version of a Mainframe dominated Computing cycle yep I suspect a lot of inference will get done on the edge if you think about the insane amount of compute that’s walking around in our pockets that is not fully leveraged right now there’s going to be a lot of machine learning done on phones that are going to like call up to cloud-based models for the hard stuff no no I don’t think training is happening at the edge anytime soon though no I certainly agree with that all right well just like our tsmc episode I wanted to end and leave you with a thought David of what it would take to compete with Nvidia because my big takeaway from the tsmc episode was like wow that’s a lot of things you’d have to believe about a government putting billions of dollars in and hiring all this talent and I was like what’s the equivalent for NVIDIA so here’s what you would need to do to compete let’s say you could design GPU chips that are just as good which arguably AMD Google and Amazon are doing you’d of course then need to build up the chip to chip networking capabilities like nvlink that very few have and you’d of course need to build relationships with Hardware assemblers like Foxconn to actually build these chips into servers like the dgx and even if you did all that you’d need to create server to server in rack to Rack networking capabilities as good as melanox who was the best on the market with infiniband that Nvidia now fully owns and controls which basically nobody has and even if you did all that you’d need to go convince all the customers to buy your thing which means it would need to be either better or cheaper or both not just equal to Nvidia and by a wide margin too to this brand you’re not going to get fired for buying Nvidia anytime soon like this is the canonical you got to be 10x better than Nvidia on this stuff if you’re going to convince a CIO yep and even if you got the customer demand you’d need to contract with tsmc to get the manufacturing capability of their newest Cutting Edge Fabs to do this 2.5 D coas lithography and packaging which there of course isn’t any more of so you know good luck getting that and even if you figured out how to do that you’d need to build software that is as good or better than Cuda and of course that’s going to take 10 000 person years which would of course cost you not only billions and billions of dollars but all that actual time and even if you made all these Investments and lined all of this up you’d of course need to go and convince the developers to actually start using your thing instead of Cuda well Nvidia also wouldn’t be standing still so you’d have to do all of this in record time to catch up to them and surpass whatever additional capabilities they developed since you started this effort so I think the bottom line here is nearly impossible to compete with them head on and if anybody’s going to unseat Nvidia in the future of AI and accelerated Computing it’s either going to be from some unknown flank attack that they don’t see or the future will turn out to just not be accelerated Computing in AI which seems very unlikely yeah well when you put it that way I think the conclusion that we can come to is that Mark Andreessen was right in what years was this that we were talking about on uh it was like 2015 or something yeah like 2015 2016. they should have put every dollar of every fund that a16z raised into nvidia’s market price of the stock every single day yeah because they were seeing all of these startups doing deep learning machine learning at the time early Ai and they were all building on Nvidia and they said it just said no thank you to all of them and put it all in in Nvidia Mark is right once again strength leads to strength there you go there it is well listeners I acknowledge that this episode generalized a lot of the details especially for technical listeners out there but also for the finance folks who are listening our goal was to make this more of a lasting Nvidia part three big picture episode than sort of a how did they do last quarter and what are the implications on that of the next three quarters so hopefully this holds up a little bit longer than just some current Nvidia commentary but thank you so much for going on the journey with us yeah we also as we’ve alluded to throughout the show we owe a bunch of thank yous to lots of people who are so kind to help us out including people who have way better things to do with their time so we’re very very grateful I mean one Ian Buck from Nvidia who leads the data center effort and is one of the original team members that invented Cuda way back when really grateful to him for speaking with us to prep for this absolutely also big shout out to friend and listener of the show Jeremy from ABC data who prepared four PDFs for us completely unprompted like in Saiyan write up for us about a lot of the technical detail behind this private blog posts yeah private blog posts so our acquired Community is just the best like you guys continue to blow us away so thank you Julian the CTO of hugging face or anizioni from ai2 Luis from octoml and of course our friends at nzs capital thank you all for helping us research this indeed all right carve outs let’s shift gears carve outs what you got my wife and I have been on an alias binge oh wow yeah Jennifer Garner yes I never saw it when it came out it is like the perfect early 2000s junk food when you have one more hour at the end of the day and you’re just laying on the couch then I never have one more hour at the end of the day I have a two-year-old but I really appreciate it for uh 16 years from now when she goes to college I’ll keep that on my list oh you play games it’s true but that’s research I’m just checking out the latest Graphics technology so my review of Alias is it’s a little bit campy they repeat themselves pretty often I mean it’s weird to observe how much TV has changed between now and then because they make very similar shows today but they’re just much more subtle they’re much darker they leave much more sort of to the imagination and in the early 2000s everything was just so like explicit and on the nose and restated three times I’m just glad the show doesn’t have a laugh track but it’s well worth the watch sometimes you have to imagine it has a different soundtrack because every episode has like a matrix type song to it yes that’s right this is like the TV version of The Matrix right yes but it’s great I don’t know we’re having a lot of fun watching it Mike I’m about related for my stage of life also something I missed and discovered recently we just watched our first full movie full Disney movie with her whoa milestone and she freaking loved it I think we picked a great one Moana which neither Jenny or I had seen before and in Reading just a little bit about it afterwards you know how super sadly Pixar kind of fell off in recent years like such a bummer I mean they’re still Pixar but like they’re not Pixar it’s not the uh guaranteed hit every time that it used to be yeah so Moana came out in this kind of generation with tangled and some of the other stuff out of actual Disney Animation after the Pixar acquisition that are just like these are return to form Eisner era Disney animated just like fires on all cylinders and we loved it we watched with our brother and sister-in-law who don’t have kids in our 30-somethings living in San Francisco they loved it our daughter loved it highly recommend Moana no matter what live phase you’re in all right great adding it to my list and it’s got the rock how can you come win there you go well listeners are a huge thank you to our good friends at blinkist and go one at statsig and at Crusoe all the links to all of those phenomenal products and offerings are in the show notes if you want to be notified every time we drop a new episode and you want to make sure you don’t miss it and you want little hints to play a guessing game at our next episode or you want follow-ups from our previous episode in case we learn from listeners hey here’s a little piece of information that we wanted to pass along we will exclusively be dropping those in the email acquire.fm slash email it was so fun I think you’re about to talk about our slack it was so fun watching people in slack talk about the hints for this episode we wrote the little t-shirt I was like oh everybody’s gonna know exactly what this is no one got it I was shocked yeah eventually 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