Nvidia Part III: The Dawn of the AI Era (2022-2023)

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 at 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 Bucks. Totally fit right in. All right. Let's do it, let's do it.

Who got the truth? is it? you? is it you? is it you? who got the truth now?

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.

Including Nvidia.

Including Nvidia, who had that massive inventory write-off for what they thought was overordering. 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.

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. Slash 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 Acquired..fm. Slash Slack.

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 $1 trillion TAM.

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 1 of that. Because surely their compute will amount to 1 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 think about starting a startup, you know? Oh, if we can just get 1 of this big market, blah, blah, blah.

It's the toppiest down way I can think of to size a market.

So you Ben rightly so called this out at the end of Nvidia part 2, 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.

Yeah, sure, we shrugged it off and we were 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?

And David, you were telling me it's the largest ever. Use of mechanical Turk up to that point was to label the Imagenet dataset.

Yeah, it's wild. I mean, until this competition and until Alexnet, 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 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 what 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 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 these supercomputer 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 Knapp moment and these three researchers from Toronto kicked off, 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 on, like 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.

Yes.

So the three people who made up the Alexnet team were, of course, Alex Krzyzewski, who was a P.h.D. 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 Boole, 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.

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 it made sense to start one. And whatever they did, it was going to get acquired real fast by Google within six months.

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 DeepMind famously, and then, right around the same time, Facebook scoops up computer science professor Jan Lekun, 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.

I think that is literally what Instagram is worth to meta now. By the way, I have 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.

That feels like an interesting question. It's probably a couple steps too far in the conclusion, but still sort of a fun straw man to think about, a fun straw man.

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 Open.AI 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 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 Buck's and Woodside or something like that.

But it does actually just show like where the seeds of Openai come from. It is very different than this sort of organic, scrappy way that the Nvidia's 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 pointed at 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.

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 Openai.

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, Crusoe's data centers are nothing but racks and racks of A100s and H100s. And because Crusoe'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's 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 Crusoe 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 customers. On cost, 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.

Yep, 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 crusoecloud..com slash acquired that's c-r-u-s-o-e cloud dot com slash acquired or click the link in. The show notes Okay, so David Open.ai is formed. It's 2015..

So as we were talking about a little bit, AI at this point in time, super good for narrow use cases, looks nothing like GPT-4 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 Alexnet 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.

Labeling this is so cool. So at Spring GTC this year, Jensen did a fireside chat with Ilya and it's amazing. You should go watch the whole thing. But in it, this question comes up.

Oh 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.

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. While 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 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.

Oh, that's giving 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 a.k.a. 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 nine times the cost. It gets real, gnarly.

Yeah, this is huge, and 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 slight 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.

I don't know.

Maybe it did, maybe I mean, this is Google here, like, the capabilities are there, the product sense not as much. This is when they really start investing in Waymo.

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.

Yeah, it was scattershot, 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.

You made this leap to 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 word should come next to the sequence.

As a parent of a two-year-old can confirm.

And then a second 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, very large amounts of data here is crazy expensive. GPT-1 had roughly 120 million parameters that it was trained on, GPT-2 had 1.5 billion, GPT-3 had 175 billion, and GPT-4. Open.AI hasn't announced, but it's rumored that it has about 1.7 trillion parameters that it was trained on.

It's scaling like Nvidia's market cap.

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.

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 in time. This is when they start building their own chips TPUS, because they're still buying tons of hardware from Nvidia, but they're also starting to source their own here.

Yeah, 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.

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.

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 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.

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 Statsig.

Yeah, if you listened 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-AI 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.

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.

Right, there's these little workloads here and there that we'll 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, supercomputers. 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?

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.

I thought you were going. And this is why the data center and what Nvidia has 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 H100 on chip RAM is like 80 gigabytes.

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 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 for it, yeah.

And it was already a public company, right? It was, yep, yeah, but it was definitely quirky now.

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, 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 the data center?

Yeah, you want really fast data interconnects between them, right? People thought, Oh, sure, for supercomputers for these academic purposes, but what the enterprise market needs in my shared cloud computing data center is Ethernet. 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.

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 making a CPU. This is like, heretical, but Jensen?

Computing was going to be accelerated. What are we doing here on these arms 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.

Welcome to the show, one more piece to talk about the third leg of the stool there. 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.

COOs When you start talking to the real semi nerds, that's when they start busting out the Coos conversation.

This is when a certain segment of our listeners are going to 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 COOS technology. This allows them to monopolize, like a huge amount of TSMC's capacity to make the COOS 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 COOS 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 2.5D chip, which is basically how you assemble this COOS stuff to get the memory really close to the processor and, of course, 2.5D.

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 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.

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 Ben 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.

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 H100s. You're like, I don't care about the Grace CPU, I don't care about this Melanox stuff.

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 Cruso.

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? And you've got a data center of your own? Well, Nvidia has a DGX for you.

Yes, they do. Full GPU based supercomputer 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 supercomputer because it all runs Cuda amazing More on Cuda in a minute. But as we said, you say solution, I hear gross margin.

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 billing this as the first turnkey AI data center that you can just buy and can train a trillion parameter GPT4 class model. The pricing on that is call us. Of course it is, but I'm imagining, like, hundreds of millions of dollars, like.

Well, let's talk about the H100.

That's what they're selling to Amazon and Google and Facebook, right?

And you mentioned that five hundred thousand dollar price point, the five hundred thousand dollars 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 hundred and eighty thousand dollars of margin that Nvidia is getting out of selling the solution.

It's an arm 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.

This is one H100.

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 chips 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 to 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 or something you're using it in your business? If you were going to 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.

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 to 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.

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.

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 P5.48 X 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.

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 you're McDonald's or UPS or whoever, and you're like, you know, I really like Jensen.

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 provider 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.

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. Whizzy wig stuff. There's an integration with Hugging Face where you can easily deploy models right off of Hugging Face. You can upload your data. Like, everything is just really whizzy. wig is probably the way to describe it.

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 thirty seven thousand dollars 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 A100 DGX system would be today something like 120K. Remember, this is the previous generation, this is not H100, and you can rent it for 37k a month.

Which is crucially important because at this point, the CFO, Colette Kress 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 the developer relationship through CuDa. You know, they've got this unbelievable ecosystem right now of Nvidia developers that's stronger than ever.

All of this brings us to 2023. In May of this year, Nvidia reported their Q1 Fiscal 24 earnings. Nvidia is on this weird January fiscal year-end thing, so Q1 24 is essentially Q1 23. But anyway, in which? Revenue was up 19 percent quarter over quarter to 7.2 billion. Which is great because, remember, they had a terrible end of 2022 with the write-offs, crypto falling off a cliff and all that.

Yes, it's amazing that in that stratechery interview, when was that? In, uh, March of 2023? Jensen said Last year was unquestionably a disappointing year.

The 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 forecasts Q2 revenue of 11 billion dollars, which would be up another 53 percent quarter over 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 percent 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 on acquired. Because, like in the long arc of time, who cares?

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.

So, here are the full numbers for the quarter total company revenue of 13.

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 going to 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.

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 One.

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, Blinkist has made a special collection for us. Amazingly, there are not really books about the 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 Slash Nvidia. You can find books by Gary Kasparov, Kaifu Li and Cade 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 Blinkist 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 6500 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. Our thanks to both of them and links in the show notes Okay, so David analysis We got to talk about Cuda before we even start analyzing anything else.

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. 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.

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.

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.

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.

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 then 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.

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, Maybe we'll see. And of course, then they launched DGX 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. 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. It's basically been a linear climb quarter over quarter. As they've deepened their moat and as they've deepened their differentiation in the industry.

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.

Baidu, Alibaba Tencent others, and, by the way, Baidu has potentially the largest model of anyone. Their GPT competitor is over a trillion parameters and may actually be larger than GPT4.

Wow, I didn't know that. Yep, that'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 bans.

I mean, yes, that's a fine line, and this is pretty close to bans, 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 chips 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 and saying, Oh, export controls might get even more severe. So I should get them while I still can these A800s.

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.

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 ray traced 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.

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.

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, as 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 at Nvidia. It's like, No, he's 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.

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 Laurie 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 them, 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.

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.

Text him 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 2.5D 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 Tranium and Inferentia.

Hardware providers that are their direct competitors now, too, yep, 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, fair point. They're all trying to put GPUS in the data center, too.

Everyone's just going to 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?

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.

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.

I think in the current state of things, it's even more favorable to Nvidia 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?

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 gonna need a bigger moat. And you need to figure out a, uh, you know how to defend the castle harder? I'm mixing so many metaphors here, but you get the idea.

Part of this, as I was alluding to, is covet 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 a annual shipping cycle. There were annual GTC's. Since Covid, 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 WWDC's a year. Yeah, that's what's happening at Nvidia.

It's crazy.

So, on the one hand, that's a culture thing, on the other hand, that is an acknowledgement 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 purchase and rollout 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 cap ex is some of the stickiest.

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 some lock-in. 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 were 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 2.5D architecture, so.

Should we talk cornered 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 cornered resource a little bit. They reserved all that wafer supply for a different purpose, partially crypto mining. But AMD doesn't have it, AMD does have a ton of capacity.

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.

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.

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-existing 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.

Yeah, I mean, we've talked about this many times on the show, including with Hamilton, Helmer and Chenyi 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.

This is what you're getting at. Yep, they do 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.

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 that 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 and their six-month shipping cycle. That clearly they had in the past, and they didn't have for a while, and now they have again. I don't know, I think you can make an argument here.

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.

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.

Yep, 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.

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. While 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.

Yep, I think there's maybe 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.

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.

Yeah, but I think for a lot of their 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. We're wildly inventive and innovative in creating these like true breakthrough innovations, and we're really, really right about huge markets. Yep, 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.

Microsoft is the exclusive cloud infrastructure provider for Open AI, 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.

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, I bet the way it plays out is that where you landed in Cloud 1?

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 their 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 gonna 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.

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 dumber? Well, they need to train em and inferentia a little bit harder. Ah, Jesus, okay, rant over.

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 your Amazon's not that profitable, AWS's, 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.

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 got to write a lot of new stuff underneath and ship a lot of hardware.

Yep, for sure, no doubt that that's going to happen, all right. 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 chat interfaces. And that crisis of confidence, that mini 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 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.

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 and 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 yet. The transformative change that we believe is coming.

You can't even imagine. The most interesting thing about the overhype 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.

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 are 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.

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 Copilot, so.

Is that valuable? Yeah, 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.

Yep, 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.

Yeah, I think for me, at least again. Just speaking personally to, I had a very strong element 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's 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.

Oh, 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.

Yep, 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.

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 going to be a closed off market. So what's going to come out of there, what's going to happen? Yep.

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.

Handle that when it happens. Oh, I love, that's a great turn of phrase. There you, uh, up to your training model.

You should see the number of parameters, haha, I love it all right. Just to list the bulk cases. One, Jensen is right about accelerated computing.

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 on this episode, I don't think Jensen and Nvidia are saying that traditional compute is going away or getting get smaller.

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 a latent, accelerated, addressable computing out there that just hasn't been accelerated yet, right?

It's like, 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 bulk case one Jensen is right about accelerated computing.

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.

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 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 percent per year for the next five years.

I mean, it was like, I mean, it took the PC wave to disrupt IBM, which was a personal computer. In today's parlance, 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 gonna be a lot of machine learning done on phones that are gonna like call up to cloud-based models for the hard stuff.

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 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'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.

Yeah, well, when you put it that way, I think the conclusion that we can come to is that Mark Andreason was right.

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 should have just said, no, thank you to all of them and put it all 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. Yep.

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 an insane 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.

Or an itzioni from AI2, Luis from Octo ML. 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.

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.

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. My car about related for my stage of life.

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.

Yep, 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.

All right, great adding it to my list and it's got the rock. How can you complain? There you go? Well, listeners are a huge thank you to our good friends at Blinkist and Go One at Stat Sig and at Cruso.

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 teaser and I was like, Oh, everybody's gonna know exactly what this is.

Uh, we have a hat, you should buy it, and this is not a thing that we make a lot of margin on. We just are excited about more people sporting the ACQ around, so participate in the movement, show it to your friends.

Is it you? is it you? is it you who got the truth now, huh?