The Future of Compute: Pat Gelsinger & Martin Casado

Welcome everybody. Thank you so much for being here with us this evening. I want to kick tonight off with a number. 60,000. That's how much faster computers got between 1996 and today. That's in pure floating point operations per second, 60,000 times in 20 years. I think it's one of the most remarkable achievements in human history and many don't fully understand that it happened. And here's the thing, that curve is breaking. The old tricks, shrinking transistors, faster clock speeds, are running into the hard limits of physics. And at the same time compute demands of frontier AI models are growing at unprecedented rates. And so that gap between what we need and what conventional hardware can provide is where we live here at Playground. So, Playground is a deep tech venture capital firm. We back scientists and engineers doing the hard, long horizon work that many don't want to touch. And over the past several years we've been building out what we call our next gen compute thesis. So, the basic core of it is this, we are entering a third era of scaling. So, the first was Dennard scaling, shrink the transistor, get the speed up for free. The second was parallelism, GPUs, multi-core, distribute the work. And this third era has no single trick. So, it's requiring reinventing the stack at every layer simultaneously. So here we like to say that Moore's law is not dead, it's just resting. And a big part of what we do at Playground is back the people who are going to wake it up. So, I'm Marissa Ramirez de Chanlatte. I'm an investor here on our next gen compute team. I come from a research background, exascale computing project, Berkeley AI research lab. So, I know firsthand how hard some of these problems are. And I believe our portfolio really reflects that conviction. So, I'm going to tell you a little bit about it. So, at the architectural level, we have Next Silicon breaking free from von Neumann constraints with the data flow accelerator that side steps the memory wall. We have D-Matrix reimagining inference with digital in-memory compute that runs models faster and cheaper than anything else in the market. And at the semiconductor layer, we have ASML working to extend Moore's law itself. Their EUV laser technology could push lithography past the hard limits of what's possible. And we have Snowcap going even further building logic circuits from superconducting materials that are going to deliver orders of magnitude improvements in power efficiency. And for the problems that classical computers can't even touch, we have Phasecraft is developing algorithms that let near-term quantum hardware solve real material science problems today. And we have PsiQuantum is building what we believe will be the world's first utility-scale fault-tolerant quantum computer. So, these are different technologies, different time scales, but the same underlying conviction. The next era of compute won't be won by optimizing the old stack. It is going to be won by the people willing to start from first principles. And that brings us to tonight. We have two of those people here with us. And what I want to say about them before we get into their many, many credentials is that they are genuine engineers. So, these are not people who learned to talk about technology. These are the people who actually built it. And we're really lucky to be hearing from them. So, I will start with Pat. So, Pat wrote his first microcode at Intel at 19 years old, spent three decades there rising to become the company's first chief technology officer, helping architect the processors that powered the PC era. He then built VMware into one of the most important infrastructure companies in the world. And when Intel's at one of its lowest points, he came back as CEO to rebuild American semiconductor manufacturing from the ground up at a moment when geopolitical stakes couldn't have been higher. And we are so lucky that a year ago he joined us here at Playground as a general partner and is helping to lead our next time computer thesis. We also have with us tonight Martin Casado, one of those rare people who changed an entire industry just from a PhD thesis. So his research at Stanford on software-defined networking became the foundation of how modern cloud infrastructure is built. He co-founded Nicira, which VMware acquired for over a billion dollars, and he spent the years since at A16Z backing infrastructure companies that are defining the next generation of the internet. So these are two people who helped build the last era of compute who are now investing in the future, and I really can't think of a better conversation to be having right now. So please help me in warmly welcoming Pat and Martin.

For sure.

We're growing old together.

Exactly.

So actually I think our first days at VMware, our first day was the same day. Is that true?

Yeah, yeah, yeah. So, when we were doing this Sierra, which was now what, 15 years ago or something like that? It was a long circuitous route. You were at EMC at the time.

And it was kind of interesting because at the time VMware was very focused actually more on apps. And like true infrastructure people, a lot of them were at EMC. And so we would spend a lot of time chatting with Pat about what we should do. And as you know, interest in the whole space heated up, you know, we continued to get closer. And then you know, we decided to go into the partnership. And I remember I started the conversation with Paul Maritz and I ended the conversation with you. And then we started VMware together. And we started at the same day, close to the same day. And I wasn't your direct boss. It was me, Raghu, and then you. But what I've learned in the past 20 years is once you're boss, always your boss.

It's okay.

So, and you went on to work for me directly.

Right? One of my best employees that I ever had. Right?

Well, thank you. That works well. And I've just decided I'd do my career in decade chunks, right? Like in my 20s, I was a paper writing, code writing individual contributor. I was an engineer and I was a researcher. I wrote a lot of papers. I feel very happy about that. That was 10 years. It was like 20 to 30. And then 30 to 40 was building a company, right? So like I almost feel like I zoomed out. You zoom out from the paper, you zoom out from the lines of code to now it's like business. It's products, it's product portfolios, it's selling. And I really understood that business for 10 years. But 10 years of that and you, you know, at least in my case, I didn't want to go do it over again. I wanted to zoom out one more time. And so then the primitive goes from like the product of a single company to multiple companies. And you know, again, I do things in 10 years. I feel there's a lot to learn in venture. And so I, you know, I went to do the 10-year thing.

So I think there's different types of venture jobs. Like it's like this weird historical quirk that most venture funds are like have the same models like a dentist, right? It's a bunch of partners and they're like they don't really do much together, but they kind of do and they meet on, you know, Monday. And there isn't a lot of operations in those roles, really. And so like you kind of like vent your operating angst on the companies that you work with. I mean, this is like the standard for most of these things. And what I've learned is like companies are here, so they know the operating angst that I exert on them. Yeah.

But what I've learned in my 10 years is that good founders don't listen to you. Like they should. And I didn't listen to my board, right? So like I think you have to let go of the fact that you can actually provide any guidance. Now, I feel very lucky where I ended up because when I joined Andreessen Horowitz, I was the ninth general partner. I don't know how much we had under management. Let's call it 8 billion. Now we have 70 billion under management. I have my own fund. I have my own team of probably 16 people. I mean, we were probably 70 people when I joined. We're 600 people now. And I was very instrumental in building the firm. And so I find for me it's very satisfying actually doing firm building. It's very satisfying. Like raising the money, coming up with the thesis, operationalizing it, building the team, building the operating functions. And so like I get a lot of my satisfaction for operations within the firm, not outside of the firm. And I honestly think that's one of the biggest mistakes of people like you and I. Like investing in companies, it's like it's very, very like we feel like you know, I could do such a better job of running this company than person X and like there's a lot of dissonance there. So anyways, it's been a journey. I made all those mistakes, but I think like where I've ended up with has been the right one for me.

You know, so I'm very lucky that, you know, it's necessary to turn the first fund and you know, I did a number of pretty good deals early on. And so they give me a lot of latitude. And so I think you know, one thing they're very good about is, if you have a thesis and an idea, they'll kind of allow you to be quite successful. They almost treat like their investors like they would treat founders. And so for that is very positive. They're totally different people, you know, the way that they operate. Like Ben is like the consummate like CEO. He runs the firm and then Mark is Mark, right? He's like this futurist. He's very articulate and he gets the high bit, right? Like he's the guy that says the right thing when everybody's waiting for somebody to say something. Like let me just even take you back to like why would A16Z to begin with? Like 2008 was hard, man. Like 2007, like you could raise money just by being like a smart Stanford PhD student, right? And then the world ended. And so like all of us were in startups. And like you know what the VCs were doing at the time? Like Sequoia wrote like what? Rest in peace, good times. And like all the VCs were like, it's over. And like you should cut your companies by 60%. Like you couldn't raise money at all. It was horrible. And like people like I remember like going to parking lots were empty. It was like COVID, except it was like a financial issue. Like it was terrible. And at that moment, Andreessen Horowitz wrote Software Is Eating the World. And so like I know we look back and like oh, that was some kind of cute aphorism. Oh, that's some smart thing. But it was so perfectly timed cause we were all so depressed. And we were all so scared. So like that's what he's good at. He's very good at like saying like time to build. And I guess if you don't have the context cause you see these things later on, you don't appreciate how meaningful it is. And so, you know, I mean listen, it's with an operator, it's with this futurist. Like, you know, they're great to work with. But mostly I do my own thing.

Oh, Pat. Yeah.

Depends on who's asking. This is what 10 years of operating teaches you.

Well, it's a huge liability. First, it's more of a liability than it is a help, if that makes sense. But if you can get past the liability, it's very positive. And it's a liability in two ways. The first one is I'm an infrastructure investor. I invest in computer science infrastructure, right? But if you think about the people I invested, they're like the Shaolin monks that have spent their entire lives doing technology, right? And like I haven't been doing it for a while. So for me to second-guess what like the Shaolin monk is doing would be silly. And of course as like my identity being a technologist, I want to do that. I want to be like, oh, like, you know, I would implement it very differently or I understand the tech. So I've learned to just not do that. Like if there's one bit of credit I can give to the technologist, one bit of credit is like you actually know what you're doing. But I do think as someone who's written a lot of code and has written a lot of papers, I do think that like you do get a sense for what's real and what's not and where the laws of physics are. And like I don't know how many pitches you see, but my team will see 1,000 pitches a year. Of those, we'll do real work on 300. Of those, we'll probably bring in 80. You know, and so of those 1,000 pitches, 60% of them are just totally bogus, right? And so I do think there is a first-pass filter that you get from being a technologist that's useful. The same thing for the operations. I'll say the wrong way to use an operating background is to go try and run the company. I think that is just like, you know, you just don't have the context. Like if you're not piped into the nervous system of the product market fit, you can't do it. But on the other hand, man, listen, I've been doing this for 10 years. I've done 200 deals. I've seen everything, man. Like I mean, you just have this long list of things that happen. And there as a board member, you need to step in and put Humpty Dumpty back together. It really, really does help. And so again, I think it's more of a liability than a help, but if you get past the liability, I think it's actually very powerful to have. You know, like I said, it's just past my 1 year. So, what's your biggest advice for Pat?

Oh, man, I have so many pieces. Let me just say a few things that I won't monologue, but I've learned there is no VC. Like the game you play is very different than the game I play. And the game that you play as an investor will depend on your fund size, your team, the sector, the geo. And so like really, really understanding like the game you're playing is important. And it's kind of funny because like VC is something that like it so easily takes to aphorism. Like, you know, we have all these quickie things about like, you know, like founders are most important. And all of them are wrong in a way, right? I mean, it's actually like the long tail of long tail disciplines, where real strategy actually works, time on the street works, time in deals works. It's a numbers game. And so, I would say treat it as seriously as you treated every profession. This is intellectually rigorous as anything I've ever done in my life. And really know what your game is because it'll be very different than most other people's games. And I don't know how much that's helpful to you, but like for me it took a long time to realize that there is no one VC. And your firm is constructed in a certain way, and if you don't play to those strengths, it's very tough to differentiate unless you're Pat Gelsinger or then of course everybody who

And I get that from like a thesis standpoint, but like your fund size will dictate what kind of portfolio you construct, how much money goes into pricing competition. There's just so many things that will actually dictate the optimal strategy for you, and I think a lot of times we lose sight of that. Like so, listen, we have 70 billion under management. I invest out of a 1.7 billion dollar fund. We have a growth fund. And so, the types of it's like it's like imagine you've got like some risk board or like whatever, I don't play board games, but it's a very complex adaptive landscape that you're having to battle on, right? It's not just like you choosing a founder, right? And there's all this competition. There's all these founders. And so, like you kind of need to like know, do I have the air force? You know, do I have the infantry? Do I have the boats, and where do you place them? And I will say I've spent a lot of time with some of the best investors on the planet. I mean like I spent a lot of time with Mike Volpi who's fantastic and Doug Leone who's fantastic and Peter Fenton. I mean I've really talked to these people. And if you reverse engineer how they think, I mean they are sitting there in that room, and they understand all of the things at their disposal, and they're using them in very sophisticated ways. And I just felt because VC is such a cliché like you know, we're all clichés cause we're VCs and we used to operate and everybody thinks they understand VC and everybody thinks they understand investing. But the people that do it very well, the nuance in their decisions should not go under appreciated.

My current state on the state of AI? Oh boy. I'll tell you what. When it's all said and done, like say in 10 years and we look back and we're like, what was this all about? This AI stuff? Here's what I think the answer is going to be. I think we've changed what used to be an engineering problem into a capital problem and I don't think we have a clue what to do with that yet, but that more than anything will change how the industry works. And let me give you an example. 10 years ago, let's say Ed Dough. Ed Dough comes to me and asks for, you know, he's doing a software engineer, he's a great founder of ours. He's a phenomenal founder. So, 10 years ago, let's say Ed Dough comes to me and he's like, "Okay, I want to do a software startup, Martin." All right, cool. How much money do you need? A billion dollars. What would he do with that billion dollars? I mean, hire software engineers? He'd blow up, right? I was like, there's nothing. Like we've never been able ever to deploy capital at scale. And we've certainly never been able to take capital and get growth, right? Because I mean, this is why the mythical man-month exists. Like the entire industry has been regulated by engineering complexity. And I'm telling you, if there's one takeaway from this brief meeting tonight, it's this. We're being regulated by something else now because you can like now if Ed came and asked for a billion dollars, he could actually deploy it in a meaningful way and he could actually drive growth out of that pretty directly. And we're seeing this happen. And like nobody knows what to like, the capital markets don't know what to do with it. Like there's growth. Like now listen, is that growth long-term unit economic positive? Nobody knows. Like, you know, should you just keep deploying capital? Should you subsidize? Where do you have economic parity? So then the nature of capital to innovation is very different than it ever has been in the past. And so again, like I know you asked for like the state of the art, but like this more than anything else, any type of advancement, is going to change how we relate to the industry.

Yeah. So, another great takeaway, great learning that we've had is it just turns out we've never been resource constrained, actually. Like we were engineering complexity constrained in the past. We have the resources. And so now it's basically the entire supply chain that we're constrained by. I mean, if you talk to like, you know, I spend a lot of time trying to figure out what's going to happen to the computer industry. If you talk to, you know, like I won't say their names, but you know these people that run these very big clouds, they'll say it's actually regulation. Like that's the constraint. Like we can't break ground. We can't, you know, do whatever. And so I mean, my mental model is so simplistic. My mental model is like you take $10 billion and you train a model. You know, it's not $10 billion in there. And then that $10 billion comes out down the inference path. Like that's basically what's happening. And then maybe that $10 billion turns into 12, and maybe it turns into eight. I don't know, but that's the new capital flow. And that's why all this money is being pumped in these labs. And so basically everything along that path is constrained right now. Like everything like power density is being constrained. I mean, materials, you heard about the Toto story. I mean, like there's everything on that path is constrained. I think at the end of the day if we wanted to speed up, I do think it comes down to regulation. I really think that like governments can decide to like aid that or not and I think you know, by and large it's more complex for example in the United States to do it in China, but you know much more about this than I do.

Yeah, that's the great thing about being an early investor is I'm like, you know, I mean, listen, I'm old enough to remember dot com and it's so different. I mean, let's go through what happened in dot com, right? Well, we had WorldCom which was what? Fraudulent, you know, it had 40 billion dollars of debt. We weren't monetizing the internet. Of the fiber that went into the ground, how many of that was dark? 90% of it was dark. Like so we didn't have a business model, we over built capacity, we had a fraudulent company that was like 60% market share, fraudulent, that was levered on debt, right? And right now what do we have? We have massive companies with hundreds of billions of dollars in the bank that are backstopping this, right? I mean, this is Microsoft and it's Google. There's no dark GPUs. We're monetizing these things. Right? Like the demand as far as I can tell is infinite and so it seems to me that the demand is there, the business model is there. We haven't rationalized the economics yet. Like we don't have economic equilibrium and that will happen, but even if it all goes away, it's not like we're going to have some systemic collapse like we had there. So, I'm actually very bullish on where we are. The one thing that we all forget, I just think it's so important.

And it's actually real user demand. Like, you know, people like to look at like, oh, like there's this kind of circular this and that and the anything, but like just abstract out the entire vendor supply chain. Just abstract it out and ask, are people paying dollars for this? And sitting on all of these the boards of all these companies, like end users are paying dollars for these things. And so, you know that from an actual demand perspective, it's massive.

They're all so different, you know, like I work with Ilya, and he's, you know, he's