Marissa Ramirez de Chanlatte
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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, 60,000x 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 sidesteps 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 developing algorithms that let near-term quantum hardware solve real material science problems today. And we have PsiQuantum 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. 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 was 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-gen compute 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.
Okay, thank you, Marissa, for the kind introduction and the overview of Playground, and it's been a fun year. You know, after 45 years waking up in a corporate job, right? The day after Intel, what do I do now? And my wife said, 'You're not done yet.' And I'm grateful the Playground team let me join them to keep the game on. So we have a lot of work and interesting things to get done here, Martin.
Yeah, and Martin, let's start a little bit. You and I have been together for over a decade, right?
We're growing old together.
Yeah, it is, right? You know, and you're 29 now and I'm 32.
Yeah, something like that. So, Martin, go back to the beginning and our first working together and give the story a little bit.
So actually, I think our first days at VMware, our first day was the same day. Is that true?
Almost, almost. I was two days before you, right? But I was the man behind the curtain well before the deal was done here.
Yeah, yeah, yeah. So, when we were doing this, Nicira, 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, we continued to get closer. And then 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 you weren't my 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.
That's right. And some of you in the audience have worked for me before, you still work for me, right? You will work for me forever, right? You know, at your grave, I still work for Pat.
Exactly. So, and you went on to work for me directly.
Right? One of my best employees that I ever had, right?
The best boss I've ever had.
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, like 20 to 30. And then 30 to 40 was building a company, right? So 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 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 again, I do things in 10 years. I feel there's a lot to learn in venture. And so I went to do the 10-year thing.
You know, I'll tell you, I've been at venture now for a year. I just passed my rookie year, right? So anybody doing deals, okay, I'm no longer a rookie by just like a few days now, right? So I'm still bad, but I'm no longer a rookie for it. But don't you miss operating, having your hands on the wheel?
So I think there's different types of venture jobs. It's like this weird historical quirk that most venture funds have the same model as a dentist, right? It's a bunch of partners and they don't really do much together, but they kind of do, and they meet on Monday. And there isn't a lot of operations in those roles, really. And so you kind of 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.
This is the issue with getting an ex-operator on your board, is they kind of want to run the company for you. But what I've learned in my 10 years is that good founders don't listen to you. Like they shouldn't. And I didn't listen to my board, right? So 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 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 very, very like we feel like, you know, I could do such a better job of running this company than person X, and there's a lot of dissonance there. So anyways, it's been a journey. I made all those mistakes, but I think where I've ended up has been the right one for me.
Okay. So what's it like working for Mark and Ben? Right? I mean, I've been on stage, interviewed Mark, so on. We've argued together with someone like that. I am exhausted, right? After 30 minutes on stage with Mark. Man, right? You know, I'm pretty intense, but man, so what's it like working for Ben and Mark?
You know, so I'm very lucky that, you know, it's necessary to turn the first fund, and 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, you know, if you have a thesis and an idea, they'll kind of allow you to be quite successful. They almost treat their investors like they would treat founders. And so for that it's very positive. They're totally different people.
You know, the way that they operate. Like Ben is like the consummate 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 why a16z to begin with. Like 2008 was hard, man. Like 2007, you could raise money just by being a smart Stanford PhD student, right? And then the world ended. And so all of us were in startups. And you know what the VCs were doing at the time?
Like Sequoia wrote like what? Rest in peace, good times. And all the VCs were like, it's over. And you should cut your companies by 60%. Like you couldn't raise money at all. It was horrible. And people like, I remember going to parking lots that were empty. It was like COVID, except it was like a financial issue. It was terrible.
And at that moment, Andreessen Horowitz wrote 'Software Is Eating the World.' And so 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 because we were all so depressed. And we were all so scared. So that's what he's good at. He's very good at saying like, time to build. And I guess if you don't have the context because 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.
So, working for Mark and Ben or working for Pat, which is better?
Oh, okay. Good. Good. We're in the right audience. Okay.
Depends on who's asking. This is what 10 years of operating [teaches you].
So, you've been an investor now for a while. Does having been an engineer, having been an operator, does that make you a better investor?
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, so I'm an infrastructure investor. I invest in computer science infrastructure, right? But if you think about the people I invest in, they're like the Shaolin monks that have spent their entire lives doing technology, right? And I haven't been doing it for a while. So for me to second-guess what the Shaolin monk is doing would be silly. And of course, as my identity being a technologist, I want to do that. I want to be like, oh, 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 you do get a sense for what's real and what's not and where the laws of physics are. And 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. 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 one 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 mean, 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 really, really understanding the game you're playing is important. And it's kind of funny because VC is something that so easily takes to aphorism. Like, you know, we have all these quickie things about 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 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 Sequoia or you're then of course everybody who...
Well, you heard Marissa's description of what we do, right? Deep tech, early tech, hard tech, right? You know, pick winners, right?
And I get that from a thesis standpoint, but 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. 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 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 you kind of need to know, do I have the air force? 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 spent a lot of time with Mike Volpi who's fantastic and Doug Leone who's fantastic and Peter Fenton. I've really, really talked to these people. And if you reverse engineer how they think, 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 we're all clichés because 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 underappreciated.
So, let's, we promised to talk about the future of computing tonight, right? Yeah, so let's fast forward, your current state on the state of AI.
My current state on the state of AI?
Oh boy. I'll tell you what. See, 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, I don't know, Ed Cowan. Ed Cowan comes to me and asks for... 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 Cowan 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 nobody knows what to do, like the capital markets don't know what to do with it. Like there's growth. 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 again, I know you asked for the state of the art, but I think this more than any other type of advancement is going to change how we relate to the industry.
So, are we capital constrained today? Are we infrastructure constrained? Are we power constrained? You know, which is the constraint that the growth...
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, I spend a lot of time trying to figure out what's going to happen to the computer industry. If you talk to, 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 a $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 governments can decide to 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 than China, but you know much more about this than I do.
Yeah. So in this environment then, you know, bubble, no bubble, right? You know, does this continue, just I'll say in the face of those constraints, you know, do we have another decade, two decades in front of this build-out, right? You know,
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 overbuilt 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. Number two thing from Martin, which is... Okay. I think the worst of the worst was dot-com. Like we had overbuilt so much. We had 9/11. Like so, we had so much compute and bandwidth overhang. How long did that last? Does anybody know? The overhang. The supply overhang. I'd say four years. I think the supply overhang of fiber and compute was about four years before we had to start building again, right? So, like the worst worst time ever where we'd overbuilt, even then we were able... So, I think, listen, I think humans have an insatiable capacity for bandwidth, for compute. If it's there, we'll fill it. I think dot-com we were dealing with systemic collapse. I think the fundamentals are still pretty good here. So, now listen, valuations are going to do this. But I don't think we've got a demand-supply issue at all.
Yeah, funny, you know, I've described it, you know, underneath this, you have real companies, real balance sheets, real revenues that are underwriting it. And on top of it, you're fundamentally energy constrained. Right? You're not going to build data centers if you can't get energy lined up for them. You're not going to buy GPUs if you don't. So, you have an upper bound, you have a lower bound, and inside of that, these are real businesses, right? So,
And it's actually real user demand. Like, you know, people like to look at like, oh, there's this kind of circular this and that and the anything, but 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.
