30 Years of Lessons From Silicon Valley’s Top AI Investor

Martin Casado 0:00 I mean, [laughs] let's be honest, software has sucked. Technology's been hard to use. Like, it's most often broken, and now we're actually at a point where this stuff could actually start working. People keep thinking of these things as software, and it's not. And I think we just keep mapping it to things like search, and it's not. Like, we're starting to realize how powerful this stuff is, and I just think that, like, the biggest constraint is no longer the tech. It's literally our creativity. [upbeat music]

Patrick O'Shaughnessy 0:33 You recently wrote about sort of four buckets of styles of investing that might be applied in-

Martin Casado 0:38 Yeah

Patrick O'Shaughnessy 0:38 ... call it venture, I don't know, bit- private market tech, let's call it. Um, can you enumerate those and just, like, say a, a sentence on, on-

Martin Casado 0:45 Yeah, yeah

Patrick O'Shaughnessy 0:45 ... your perspective on each?

Martin Casado 0:46 Yeah, yeah, yeah. Again, again, I feel like I'm just in this point in my career. I've been doing this for a decade. I've done hundreds of deals, um, and that I sit in these discussions, and I just realize people aren't communicating very well. So I kinda did this exercise where I'm like, "Okay, so what are the mindsets people bring to investing?" And I think there's basically four types of mindsets. So the one that, um, I- we were talking about before is, like, like, this traditional finance mindset, which is the correct proxy for the health of a business is the market it's going into and its finances. That's what it is.

Patrick O'Shaughnessy 1:15 Spreadsheet stuff.

Martin Casado 1:16 Yeah, it's like, it's like, what are the SAS metrics? What [laughs] you know, what is the size of the market, you know? And that is a proxy for the entire business. So that's one. And then, you know, clearly growth investors do this. Clearly, you know... But, like, more and more you see, like, early stage series A people do this, right? Which to me is, is a little bit discordant. Then there's what I call productive investors, which you believe you're investing in a productive asset, and the productive asset is what's creating value. And if you believe in that, as it creates value, it will create market. And as it creates value, you're gonna have to find ways for the business to catch up to that value. Um, and so the type of diligence you do if you're a productive investor is, is more like, what is the technical approach? What are the adjacent markets? You know, how fast is this likely to evolve? How close is it to capturing value? Like, that sort of stuff. Is this the right team to build it? Um, then there's what I call frontier investing, which is... I think this is the romantic, idealized version of VC from, like, the '60s and '70s, which is kinda YOLO, man. It's like, founders know better than VCs, and they've seen something that I don't see, and they're gonna go out there and do it, and, like, who am I to second-guess them? And it's about... You know, they almost look like talent networks, right? Like, I always take Chris Dixon as... Remember when he, he was investing in nootropics, he invested in Soylent, he invested in Oculus, he invested in Coinbase and in crypto. I mean, it's like every science fiction trope from the '70s and '80s you kind of invest in, and you just don't overthink any of these things. So it's almost like the strong form of the productive. And then the last one, and it, it is always so surprising how prevalent it is, is speculative investors. Which they don't even care about the asset. They just want to time a market, right? And so, like, you'll see this, like, in crypto and in secondary markets, where they don't know about the underlying asset. They probably don't even know about, very much about the actual, like, business metrics. But they're like, "Well, this looks cheap. [laughs] It's clearly gonna go up in value," or, "This is the top of the market," you know. Like, "I'm gonna get out." And so they, like... They're try- they're trying to, like, time markets. They're, they're, they're actually just doing speculative. They're not, not really interested in, like, will the business grow. And again, the problem is, is if you have all of these archetypes, but you can justify any deal or not justify any deal. So if you don't know what your beliefs are, you'll always basically just kind of justify whatever personal bias you have.

Patrick O'Shaughnessy 3:28 If you think about yourself as a productive investor-

Martin Casado 3:32 Yeah

Patrick O'Shaughnessy 3:32 ... focused on disrupt... I love this idea that disruptive technologies, y- you should not look at these, some of these traditional things that so many investors talk about. Talk about what you do look at. Like, when you're looking at, you know, you look at so many companies.

Martin Casado 3:45 Yeah.

Patrick O'Shaughnessy 3:45 What is your method by which to assess whether or not someone might be creating a truly disruptive piece of technology, a really, a valuable asset?

Martin Casado 3:53 So I only do computer science investing, and I only do infrastructure investing. And I think infrastructure as an industry has a certain shape, right? And so it's not like... You know, it's got certain buying behaviors. The technology looks a certain way, and the industry acts a certain way. And so what you're trying to do is you're trying to take a technical approach, and you're trying to map it into that techno strategic framework, right? Like, this is kind of what we try and do. And, and the way you do that is, is, you know, there's a number of approaches. One of them, you're like, what are the leading companies doing internally? And this will kind of give you a glimpse on, like, maybe this is how platforms evolve. Or perhaps the team that is doing this had, had did it in another context, and now they're actually bringing it to this context. I think it's one of these things where, like, how, how much can you diligence without actually just using the numbers for a proxy? As far as, you know, you can talk to customers, will they use this? You can see if there's, like, adjacent budgets that, you know, you could, that are growing that you could, kind of argue will fragment to become this. Or maybe you have early signs of willingness to pay, but they actually haven't figured out the business model. Like, I always, I always think, like, every tech wave I've, um, you know, I've been in the industry for 30 years. Every tech wave, people like hand-wring about margins. [laughs] Everyone. Like, I remember for the cloud, and I remember for mobile, I remember for the internet. You know, clearly now people worry about margins. So maybe you're like, there's a willingness to pay, and there's a lot of interest, but, like, we haven't figured out the cogs yet and the actual viable business model. So, like, you're using partial indicators, not full indicators in order to do this. And so my team, I mean, my team is a lot of, you know, technical people, product people. Many have PhDs. And, like, listen, we don't second-guess the tech. Like, the founder will know the tech better than us, but, but we can ask the question, how does this tech fit into the broader platforms? How does the tech fit into spending? How does this, um, uh, tech fit in with our understanding of stickiness and defensibility? And then we look at early market numbers And we'll say, "Hey, listen," like, "no," like, "this is..." [laughs] Like, a financial investor would puke at this, but, like, it actually shows that the customers are really interested to figure something out, and will they be able to figure something out? So there's a lot of diligence you can do. Where with frontier investing, I don't think you do any of that. You're like-

Patrick O'Shaughnessy 6:08 It's just people is the idea

Martin Casado 6:08 ... Is this a bad- [laughs] It's like, it's, it's, it's people. Is this the best team to do this crazy thing? And then you just YOLO.

Patrick O'Shaughnessy 6:13 You were

Patrick O'Shaughnessy 6:14 saying before we hit start that nobody understands network effects. Can you, can you explain what you meant?

Martin Casado 6:19 I've realized that, like, like even the most common terms we use, there's no consensus on. And so for example, network effects is the favorite one that I pick on. Like, I asked you before-

Patrick O'Shaughnessy 6:30 I messed it up. [laughs]

Martin Casado 6:30 No, you didn't mess it up. You said, you said what like a, like a third of people say. So I said, "So what is a network effect?" And what did you say?

Patrick O'Shaughnessy 6:35 I said a product that gets better as it adds a marginal user.

Martin Casado 6:38 So to me this is a almost a tautological definition because most products will get better as you add a user, right? Like you can... They will find a bug. They will provide feedback. They will tell other people, you know? And so that doesn't say anything about defensibility. But other people when they use the term network effects, they actually mean the original meaning of the term network effect from the '70s, which is a telephone network where when users join, they interact with each other, and that grows super linearly, and that's where you get, like, natural monopolies. And other people use the term network effect more to mean like an a- an actual flywheel, so like a self-reinforcing flywheel. So not just like things get better, but like it self-reinforces in some sort of a way. This is where you hear the term like data network effects. But even in that sense, like you don't know if, like, that self-reinforcement is super linear or not, so you don't know if it makes it defensible or not. So you have this very common word that's on 70% of pitch decks where a founder's sitting in front [laughs] doing a pitch and nobody has consensus on what it means.

Patrick O'Shaughnessy 7:39 So how would you define it?

Martin Casado 7:41 So I think the root of the confusion is not a, is, is not a definitional problem. I think the root of the confusion is conceptually we conflate the mechanism with the shape of the moat. And I'll... Let me explain. So, um, there's many things that could create a moat, right? Like a brand, a scale, a two-sided marketplace, a network effect. But that moat, to reason about that, is only really useful, um, if it has some properties that, that, that makes the company defensible against somebody else, right? So if that moat grows super linearly with scale, like a classic Metcalfe's law network effect would, that's really defensible. If it grows sublinearly with scale, it may not be useful at all. And so I think to you and I have a conversation of defensibility, I think the first thing we need to do is not conflate the shape of the moat with the net mechanism that caused it. So first, I think we should say the moat that we have is super linear, it's linear, or it's sublinear with scale. And then the second thing, a lot of things actually contribute to a moat, right? To that. Like for example, like we mentioned, network effects will, or scale effect. What's a scale effect? A scale effect to me is as the company scale, it can reduce unit economics normally because it has pricing power, for example, or maybe CAC goes down because of brand. Um, what's a two-sided marketplace? That's very different to me than a network effect. That's where basically you've got, you know, self-reinforcing supply and demand. So I think that there are many mechanisms, and then there's the shape, and then, then to have a discussion I think you want to make sure that the shape, um, is something that actually provides [laughs] long-term defensibility. Then the last thing I'll say is it, it's, it's almost impossible to have these conversations in isolation 'cause we're actually talking about competitive equilibria. Because if you have a network effect, then maybe the, the competitor has a network effect too, and maybe if they have more capital than you and grow faster, that probably, like, impacts the shape of your moat, right? Like you may go from, you know, like, uh, you may not grow as fast marginally because they're stealing your customers or something like that. And so I just think that we need like very, very basic concepts. We need to not conflate the notion of, uh, of the actual shape with the actual mechanism. And then I think we should actually, like, enumerate the mechanisms because something like the definition that you had just doesn't really help anybody.

Patrick O'Shaughnessy 10:00 Hm. Can you give examples of super linear, linear, or sublinear, just, just to bring it to life?

Martin Casado 10:05 Yeah, for sure. A common view of where network effects came from was from the '70s. I forgot the name of the, the, the economist that did it, but he worked at Bell Labs, and he noticed for like train networks and communications networks, the interactions of users grows with the square of the number of people that join, right? And so as a user joins, they can communicate to many other people, so the actual number of connections grows more than the number of users. And then, you know, therefore the value grows more than just linearly with them adding. The same thing with like destinations on a highway or destinations on a, on a train. So, so that would mean that if you have two competing companies and one takes, you know, $5 million of funding and the other takes $5 million of funding, but this one is marginally better, marginally larger, this one's a little bit larger, for the same amount of investment it will grow faster than a challenger, right? So like the growth actually increases over time, and that's why you get, that's where you get a m- a, a, a monopoly. Whereas sublinear means if I'm a leader and I invest the same amount as a challenger, I grow marginally slower than the challenger. That would be sublinear. And, uh, uh, clearly this is very important when it comes to moats because if you have two competing companies and there's a, a sublinear moat, the challenger will always catch up to the leader 'cause the leader has to spend ever more money to grow and, and, you know, all things being equal.

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Martin Casado 13:15 So I would, l- so actually, I would, so, so how many actual network effects in the Metcalfe's Law of super linear do you think there have actually been? Can-

Patrick O'Shaughnessy 13:22 Very, very few? I don't know.

Martin Casado 13:23 I, I f- I feel like this almost never happens. Like, I think we can all agree the telcos.

Patrick O'Shaughnessy 13:27 Yeah.

Martin Casado 13:28 That's why we had to break them up. We can probably all agree LinkedIn, [laughs] like, you know, like they haven't had to, like, change very much, and they seem to, you know-

Patrick O'Shaughnessy 13:35 Yeah

Martin Casado 13:35 ... you know, basically have a, a, a, a, a permanent status. Social networking. And, but all of these examples, it's, like, the interconnections of people. I would say GitHub was another one that had a-

Patrick O'Shaughnessy 13:47 Which was effectively interconnections

Martin Casado 13:48 ... it's interconnections of users, right?

Patrick O'Shaughnessy 13:50 Yeah.

Martin Casado 13:50 Um, so I think there are not very many examples, but when you have them, these are these very durable high-value companies, right? So that, to me, would be like a classic network effect. I would say other companies probably are more scale effects, right? Like for example, people will say Uber. I'm not sure that that would fall the definition of a network effect. Maybe a two-sided marketplace because you have the drivers and you have, uh, the passengers. Maybe you could use that, but even then, you don't have a lock on either of them. Like, a- all drivers drive for everybody, and all passengers could use anybody. So that seemed like that was more like of a funding scale effect, just because they could raise more money, and therefore, the unit economics went down. And so, you know, I, my, my meta observation, so first off, I actually think the network effect thing was more of, like, a personal annoyance and me being, um, pedantic. [laughs] But I actually think defensibility and moats is kind of, um... It's not really even worth talking about

Martin Casado 14:44 most of the time. Let me explain why. So, um, here's my experience, for what it's worth. If a market you're investing in is growing, then companies are not gonna wanna compete. There's, like, an, a lot of new white space that gets created, and so what they'll try and do is [laughs] they'll try and just move into other white space. And so talking about defensibility is kind of a second or third order thing, if the market is growing. And if the market is slowing down or shrinking, normally it consolidates anyways independent of moats or not. And so I do think we spend a lot of time as investors hand-wringing about defensibility when, in practice, uh, it doesn't really matter, especially in growing markets which we invest into. And we've seen this with AI. Like, I remember early discussions on models. I mean, [laughs] like, I can't tell you how many investors are like, "I will never invest in a model. It'll never accrue value. It's not defensible." That's fine, but as long as the market's growing in a large market, it's gonna accrue a lot of value. I remember this about the Neoclouds. They're never defensible. The quality of revenue is bad, you know? And so, you know, and so I think that we kind of trick ourselves as investors by thinking in terms of defensibility when if there's a growing market it's kinda not even worth considering.

Patrick O'Shaughnessy 15:53 Do you think that's a unique, uh, privilege of your stage of investor? And then, you know, once you get later stage public, like, someone that's buying the Anthropic S1 needs to probably think about defensibility.

Martin Casado 16:07 I mean, it depends on, [laughs] on your thesis on the size of the market. I mean, if you believe we're 0.1% penetrated, like, you know, this has got a long way to go and, and value will accrue. And if you're liquid, you can go in and out of the market if values accrue. I mean, I think that would, you know... I think the nature of venture has really changed actually in, in general. Um, you know, if you read back kinda, you know, the time of like, you know, Arthur Rock, it was really frontier-like investing, and people didn't think about quality of revenue. [laughs] You know, they didn't think about defensibility so much. It was, like, people, and it was tech, and it was new markets. And, you know, over the last 30 years, like, the tech market has grown so large, and the amount of money that's available to investors has grown so much. And then the size of the private companies has grown. Like a, like a, like a private company today rivals public companies of just, you know, 10 years ago. And so I say in some level VCs have actually become public investors, right? And then a lot of the people that have entered VC have come from, like, more business school and traditional finance backgrounds. So I think a lot of the mindset comes from that world, but I think it gets applied too early.

Patrick O'Shaughnessy 17:17 The financial mindset gets applied too early.

Martin Casado 17:19 Like, a traditional investing financial mindset gets applied too early, and I think it's kind of confused. I mean, I, I, it's very often, for example, I talk to, like, a junior investor, and they're like, "Oh, the quality of revenue is bad." I'm like, "Homie, [laughs] like, we didn't even know how to monetize the internet." [laughs] Like, like business models always lag disruptive technologies.

Patrick O'Shaughnessy 17:39 Mm.

Martin Casado 17:40 Always, right? And so, like, you can't, if you, if, if, if you truly believe you're investing in a disruptive technology, if that's what you truly believe in, A, you should believe it creates TAM, so why would you worry about market size? 'Cause it's creating market size. Um, and B, it's disruptive, so we don't even know what the pricing model is. Like, remember, like, how long it took for us to go from, like, perpetual to, like, recurring? [laughs] That was, like, years of, like, whatever and hand-wringing, et cetera. And so, you know, I mean, I think my caution is is, like, maybe- Most investors should know what kind of investor they are. Like, maybe you're a financial investor and, like, that's how you view the world, but then you're not actually evaluating tech. You're not evaluating the, the, the disruptive power of tech. You're literally looking at the quality of revenue, which, which by the way, is going to n- naturally move you towards non-disruptive technology because if it's disruptive [laughs] , you probably don't know what the business model is. You probably don't know what the TAM is.

Patrick O'Shaughnessy 18:32 Mm.

Martin Casado 18:33 On the other hand, if you really believe you're an ve- like, if you believe, "I invest in tech and tech creates value," and that's the type of investor are and you do that early, you, you should not consider these other things. Um, and so I, I think it's less about is there a right way to invest? No, these are all legitimate, coherent, right ways to invest. It's more about-

Patrick O'Shaughnessy 18:52 Of who you are

Martin Casado 18:52 ... don't be, don't be confused in your basic kind of approach because otherwise you become incoherent.

Patrick O'Shaughnessy 18:59 Mm.

Martin Casado 18:59 And so then, like, you're like, "Oh, I'm a, a disruptive tech investor, but I'm looking for, like, whatever, good NRR." Like [laughs] it, it doesn't

Martin Casado 19:05 work that way.

Patrick O'Shaughnessy 19:05 How would you describe the state of the union today in your world in infrastructure? Like, um, obviously it's changing really, really fast. There's new-

Martin Casado 19:12 Yeah

Patrick O'Shaughnessy 19:12 ... kinds of companies, but there do seem to be, like, some, uh, we could, like, name some layers or some categories.

Martin Casado 19:18 Yeah.

Patrick O'Shaughnessy 19:18 How, how would you describe, like, what it looks like today?

Martin Casado 19:20 So for me, the, the, so the definition of infrastructure is, like, the, the, the differentiation is actually technical, where I don't view SaaS or apps as that way. Like, do you view, like, if you in- invest in, like, um, like a, a SaaS or an apps company as, like, a technical investment?

Patrick O'Shaughnessy 19:35 No.

Martin Casado 19:36 Right. 'Cause it's like a business-

Patrick O'Shaughnessy 19:37 Especially now.

Martin Casado 19:38 Yeah, [laughs] especially now.

Patrick O'Shaughnessy 19:39 [laughs]

Martin Casado 19:39 I mean, it's like a business model innovation or, or, or something like that. And so, so because for us as a technical innovation, it has to sit kind of in the traditional technical stack. So the traditional technical stack for infrastructure is compute, network, storage, and databases, right? And each one of those has a subcomponent, and they all have buyers, and they all, you know. Now, we're seeing that being kind of reformed with AI a bit, and so all of them are being disrupted, but also now you have, um, models which I would consider infrastructure. They look to me more like ASICs than they look like, um, you know, traditional software. I mean, like, the capital needs, the, you know, the competitive pressures on them. And then I think they're disrupting the entire application stack above them. Like, we don't even know if they will layer. Like [laughs] we don't even, like... I mean, maybe you could argue with JEFF they do kind of, you know, you can kind of put them in software. But traditionally, they're a- almost like, you know, th- their own separate entity that's, that's, that you can't marry to software very well. And so I would say we're in kind of a point of maximum disruption. But all of the infrastructure supporting software, whether it's vibe coded or not, or whether it's to build models or not, I mean, that doesn't change, like, the, the, the, the, the major components. And so I think we do have a bit of a traditional mindset of compute, network, storage, databases, and then-

Patrick O'Shaughnessy 20:51 And models

Martin Casado 20:52 ... yeah. And then, and then, and then, but we also throw up our hands and we're like, "This is all changing so fast," because for example, when you worried about dev tooling in the past, you're worried about ergonomics for a human. [laughs] And now you don't really worry about that. And then actually a lot of the design decisions that we made as technologists were like, "Well, a human's gonna be using this, so it can't be too complicated." And then you would trade off things like safety, or you trade off things like reliability. We don't really have to do that anymore 'cause the models are doing it. So I think this is kind of the biggest refresh of computer science infrastructure we've seen maybe since the internet, maybe forever.

Patrick O'Shaughnessy 21:21 Would the shorthand for this be that all the, the, each of those five portions of infrastructure are being refactored for agents? Is, is that, like, overly simplifying?

Martin Casado 21:30 I split it in two pieces. For me, there's like, people are vibe coding a lot of stuff. There's more software being written than ever before, and that stuff has to run somewhere. And that has nothing to do with, like, that's not agents. That's just normal software.

Patrick O'Shaughnessy 21:43 Mm.

Martin Casado 21:43 Maybe it's an agent that created it, maybe it's a human being created it, but, like, it needs databases and it needs compute, right? So that's one thing. And so I just view it as, like, the, it's, like, the product of agents. And then the other one is, like, we're building these new systems that are kind of, that have AI at the heart of them, and then how does that change it? Those are the kind of two. So just assume way more software being built and has to be run and deployed somewhere, and then think that now we also have systems that, like, actually create agents.

Patrick O'Shaughnessy 22:07 It seems like each new wave of technology, like there's a new kind of database that's needed.

Martin Casado 22:13 Yeah, yeah.

Patrick O'Shaughnessy 22:13 Are, are you seeing that across all the areas that you care about, that there's sort of because of AI whole new entrants?

Martin Casado 22:20 I think a lot of infrastructure's getting a bit sleepy, and now because agents are very sophisticated, that's no longer the case. Like, so for example, let's take OLTP. What is OLTP? OLTP is a transaction database, so like think the Oracles of the world. That's a pretty mature industry. You didn't see a lot of movement. Maybe you saw, like, movement to the cloud or something like that. But now we're seeing really interesting companies that are like, you know what? Um, the hard thing about using a database from a programmer's standpoint is databases have always had their own languages, and they've had their own languages for technical reasons, and it's really, really hard to, to have them work with normal programming languages. But now because of AI, a- and, and, and one of the reasons is, like, if you, if you change that language, like anybody that knows databases would know how, wouldn't know how to use it, and you'd have to, like, train a whole version of, um, of programmers to use this new language. But now we're actually seeing databases come out which are entirely new way of thinking about databases. They have way better properties. They don't really have to train anybody because the agents already know how to do it.

Patrick O'Shaughnessy 23:20 Mm.

Martin Casado 23:20 And so, you know, I think these things are path dependent. Like, some com- some sectors, a new entrant r- is gonna come and change it. Like, databases, OLTP may look entirely different in five years. In some, the incumbent may figure it out and be like, "Hey, listen, we're just gonna add a few things." Networking, of course, which is this, I did my PhD in networking, so it's an area of, like, special interest. Like, that's been so sleepy and boring for the last 15 years, and now because these clusters that we're building are so large and they've got, like, such, um, you know, rigid performance constraints, we're seeing, you know, net- new networking companies come out for the first time since Arista. Um, you know, we're actually having to change, you're seeing like actually, you know, changes in, in the materials used to build them. We're seeing optical come up, and so there's a lot of interesting stuff in networking. Compute, I mean, you know, the whole notion of, like, what are the abstractions- To, to run an agent on. Is it like a container? Is it a VM? Is it a durable workflow, like something like Temporal? Like, that is changing. But I do think we're at a point of maximum disruption where many points of the infrastructure stack can be rebuilt.

Patrick O'Shaughnessy 24:25 Hmm.

Patrick O'Shaughnessy 24:26 Can we spend a few minutes on models?

Martin Casado 24:27 Yeah, yeah. Of course.

Patrick O'Shaughnessy 24:28 The, the Anthropic S-1 came out this week-

Martin Casado 24:30 Yeah

Patrick O'Shaughnessy 24:30 ... which, you know, obviously a b- a beautiful view into the business itself, but I'm actually more interested, because the infrastructure stuff, in what you think about just the state of these things today. I, I mean, just in all the debates.

Martin Casado 24:42 Yeah.

Patrick O'Shaughnessy 24:42 Closed versus open, um, the public frontier versus the private frontier that the model, the companies keep for themselves, you know, movement of talent, opportunities, things that don't exist that should. Like, whatever- what- whatever's on your mind about these things and how you see them evolving, I'm, I'm fascinated to hear.

Martin Casado 24:58 So I think the biggest question is, is what happens if and when the market slows down? Because I think we've got three years of data of what happens in the existing, [laughs] very big, fast-growing market, and like, what happens there is the frontier, being on the frontier means you've got tremendous pricing power. Even if you're just an epsilon better than the next guy, you've got tremendous pri- pricing power. There's no demand problem. You've got basically infinite demand. And so, like, these kind of frontier models that are the best tend to get 80%-plus of the market dollar weighted, and that's happened for three years, right? But you could argue the reason that's happening is because we've not saturated the market at all, and people always are gonna want the smartest thing. And so then under that view, there's nothing structural that are gonna change as, as, as, as long as the market's growing. And you could argue this is gonna go on for quite a while, because, I mean, what is the pu- penetration, 0.1% or something like that? They say 3% of the enterprise, but actually from budget-wise it's like 1% or less, right? And so there's just a lot to grow given how useful the technology is. The more interesting question to me is, okay, like, what happens if the market starts to saturate in pockets, right? So if that happens, you know, will then things shift to open source? Will you lose pricing power on the frontier? You know, one thing that we don't really e- like, do a good job of accounting, um, for when we model out the numbers for these companies is, you know, what happens if you invest in a model and then, like, you don't get the next model out really quickly, right? Let's assume that, like, Anthropic stumbles-

Patrick O'Shaughnessy 26:32 Mm-hmm

Martin Casado 26:32 ... and they don't release a model for six months. What is the lifespan, do you think, of the previous model?

Patrick O'Shaughnessy 26:37 A very short period of time.

Martin Casado 26:38 It's very, very short, which is entirely different than we've ever had in software. In software, we've always assumed some long tail, but it turns out, like, what, what happens if, like, you know, like, you put out this sort of model and everybody loves it, then something internal happens. I don't know if it's regulatory, I don't know if it's technical, I don't know if it's a breach, whatever, and then you slip three months, right? It could be the case that you lose the entire market. Uh, I mean, my experience, and I see a lot of these model graphs from apps companies and from, you know, pitches, I would say the lifespan of a model is three to six months. So that means you, you have to release the next model. So again, for this conversation, I think it's a very interesting conversation. We should tease it apart. Like, what happens if the market continues to grow and doesn't saturate? I think we already know, because you have th-

Patrick O'Shaughnessy 27:18 Three years of data

Martin Casado 27:18 ... you have three years of data, and it just means the frontier wins, and they do a great job, and they create great models. Then what happens is if either the use cases saturate, meaning you don't need more intelligence, or the market sl- starts slowing down for another reason, nobody knows the answer to that. I think there's a very real world where this, the value comes crashing [laughs] back down into open source and, you know, we'll see kind of like a, a shift that we've never seen, because software's always had a long tail of retention.

Patrick O'Shaughnessy 27:43 Uh, on this trip I've been asking a lot of people what percent of software coding tasks are already saturated, and the, the typical answer is very, very high percentage.

Martin Casado 27:51 Yeah.

Patrick O'Shaughnessy 27:52 That's been the biggest market, and yet it still seems like people are spending like crazy on frontier tokens to do coding, which it, it seems a little at odds. Like, if I could do it with a six-month-old model or something that's vastly cheaper.

Martin Casado 28:06 Well, these are two different statements. We, we, like, I think we need to be very... This is a great thing to dig in on, but we need to be very careful when we talk about it.

Patrick O'Shaughnessy 28:12 Yeah, please tease it apart.

Martin Casado 28:13 Yeah, yeah. No, this is great. Great. So there's two very different statements. So statement number one is, "I am not using more tokens," right? Because I've, I, you know, I, I have enough, I, I, I have enough tokens. I've automated 95% already, so I can't automate anymore. That's... And I think that we're getting pretty close to that. Comment number two is, "Therefore, I can use a cheaper model," does not follow from comment number one, right? So if you and I are, are, like, I'm, uh, uh, you and I are competing startups. We've got Martin's startup and we've got Patrick's startup, right? So we've got competing startups, and because we're in competitive equilibria, are you gonna choose a dumber model to compete with me?

Patrick O'Shaughnessy 28:49 Hmm.

Martin Casado 28:51 In competitive equilibria, you wouldn't. You would always pay 10 times more for the smartest model.

Patrick O'Shaughnessy 28:56 Right. Yeah.

Martin Casado 28:56 Yeah. Always. Always. Always. And so, you know, there's this very open question of, um, will you saturate tasks so that you don't need a smarter model? And I think it's very hard to do that in a Darwinistic economic equilibria, like, you know, and, and when, when you're actually competing. And so some of them may, but I think at the end of the day, it's about can you get software out faster, can you make [laughs] it better, and can you compete in a marketplace? And that will necessarily push you the, the more intelligent thing. Now, that said, clearly things will slow down because you're actually not spending more tokens, and so either you have to increase prices or you're gonna have to go broad. But in my experience, the pricing is maintained, for sure, and because of the competitive equilibria, like, you'll always want the smartest thing. And then we're just seeing, like, like, a broadening use of these outside of code, right? So general knowledge

Martin Casado 29:50 work, like I'm a huge Grok bot user. It's like my favorite thing ever. And so I think we're starting to see AI take over more and more of these.

Patrick O'Shaughnessy 29:57 Sa- say more about your personal experience with that, what, what you're learning, what you're doing with it-

Martin Casado 30:00 [laughs]

Patrick O'Shaughnessy 30:00 ... what you're learning from that experience. Like-

Martin Casado 30:02 My, my new Grok is [laughs] is so funny.

Patrick O'Shaughnessy 30:04 I know you have like a whole-

Martin Casado 30:05 I don't know

Patrick O'Shaughnessy 30:05 ... automation thing going on. [laughs]

Martin Casado 30:06 Oh, I know. It's so good. Like, I just feel like I've waited my entire life for this tech-

Patrick O'Shaughnessy 30:10 For this power [laughs]

Martin Casado 30:11 ... this technology to show up, right? Like, I want Star Trek. I want to go home and I want my house to greet me, and I want to talk to it, and I want to have the face show up on the monitor and- And, and yeah, I want the whole thing. And like it's never, we've never been able to do it, and now it's kind of trivial. So I use GrokBot for a lot of stuff. I use it for personal finances. My big hobby is modular synthesi- like, I like synthesizers, like making music.

Patrick O'Shaughnessy 30:33 Oh, cool.

Martin Casado 30:33 But more like the 1970s, like patching-

Patrick O'Shaughnessy 30:35 Yeah

Martin Casado 30:35 ... tweaking type stuff. It helps me with a lot of that stuff. So I use it for a lot of like standard things. But my new kind of passion project is, um, I want GrokBot to embody itself. [laughs] So like when I, when I tell it, I'm like, I say, um, I say, "You know, I want you to be the living AI in my house. That's what I want, and I want it to be just like Star Trek. Help me make this happen." And it's literally-

Patrick O'Shaughnessy 30:57 Buy stuff [laughs]

Martin Casado 30:58 [laughs] It's literally buying stuff. So I was like, "Oh, let's try this." So like yesterday I bought a few hundred dollars worth of gear [laughs] that's coming to the house, and like I'm gonna have it embody itself so that-

Patrick O'Shaughnessy 31:07 So what gear is it buying?

Martin Casado 31:09 I don't even [laughs]

Patrick O'Shaughnessy 31:10 [laughs]

Martin Casado 31:11 I don't even know. It's like these weird, like these weird open source projects and like, I mean, it's like this really obscure s- like I know some home automation stuff. Like I know like Zigbee light, I know some home... But it's like some really obscure stuff that it's decided to, to buy. But like, like I, I, I just have in my head this vision of like, you know, you know like that kind of like vintage, that kind of vintage view of what the future looked like, like the '70s view, like you watch The House.

Patrick O'Shaughnessy 31:36 The Jetsons kind of thing.

Martin Casado 31:36 Yeah, like yeah, like I mean, I just want that. And so like I've basically just been kind of feeding it pictures and descriptions, and I want it to create that by itself. And so I'll, I'll let you know how that goes.

Patrick O'Shaughnessy 31:47 What is this and other experiences using this technology, since you're probably in the .0001% of, you know, creativity using it-

Martin Casado 31:56 Yeah

Patrick O'Shaughnessy 31:56 ... what, what does it tell you about what's coming?

Martin Casado 31:58 Yeah, no, this, this is it. I just feel people keep thinking of these things as software, and it's not. And I think we just keep mapping it to things like search, and it's not. And we really have created general flexible technology that can bring us like a many of these future things that we've always dreamed about, you know? But like, you know, w- you just didn't have the technology to implement it. I just feel like we're so constrained from our presuppositions of what software can do that people aren't doing as many of these things. You know, I'll give you another example. You know, I worked with Faethm World Labs, great company. Um, and one thing I, I realized as part of that, and what they did is they created, you know, they created a model that would generate, you know, 3D scenes. And, you know, you know, they released a model called Atlas, um, a few months, um, ago, and, you know, that would like, like generate basically any scene, but it also supported dynamics, it would move. And I realized, you know, we're this close, we're this close to being able to step into a movie. I mean, like imagine like, you know, Miyazaki probably wouldn't be happy with it, but imagine like having Spirited Away running, and you throw on your VR glasses and you walk in. [laughs] You know? And like you're there. And like, um, did you ever see Sleep No More? Like the immersive-

Patrick O'Shaughnessy 33:10 Of course, yeah, I went to it.

Martin Casado 33:11 Like so like why, why isn't there a Sleep No More in VR that, right, that you're walking in, like the whole thing is going on, and like... Just for those that are listening, Sleep No More is an immersive play which is three hours. It was in this kind of three-story hotel or something like that-

Patrick O'Shaughnessy 33:25 Yeah, in New York, right

Martin Casado 33:25 ... building in New York, and you're like running around trying to figure out, and like, like, like Hamlet is being played out or whatever. And so I just feel like, like, like we're starting to realize how powerful this stuff is, and that, that a lot of like these tropes and visions of the future from like the '50s and '60s we can actually make. And I just think that like the biggest constraint is no longer the tech. It's literally our creativity, and it's just because we've been beaten down by 30 years of software being shit. I mean, [laughs] let's be honest, software has sucked. Technology's been hard to use. Like old people hate it. Like it's most often broken, and now we're actually at a point where this stuff could actually start working. And so for me it's like I'm so excited for what this brings.

Patrick O'Shaughnessy 34:08 It's tempting to say you could just freeze the models in their current capability and still get all of what you described, so there's like a diffusion question I'd like to explore.

Martin Casado 34:15 Yeah.

Patrick O'Shaughnessy 34:15 But before we do that, what, if anything, do you think is missing from the models? Like how do you hope or wish or dream that the models themselves continue to get better?

Martin Casado 34:23 So I think, so, so I think, again, and I think if you step back and you look at like what we know as an industry, I feel the current tech stack, anything that's digital, I think with enough capital you can solve, right? And the reason is, is because anything you can simulate on a computer, you can create RL environments for, whether that's 3D using Blender, whether that's general application use of the computer, whether it's code. Um, so you know, y- a- and you and I have talked about this before. I think when it's all said and done, the most interesting thing about AI, the meta thing, is that like you can take a money kid and then you can point it at a problem and it will solve that problem. Like maybe it's expensive, but at least you can solve it. In the past, you know, you'd be like blocked in engineering and you die, but like now you can actually just direct money at problems and solve these problems. So I feel very confident saying anything in the digital world, because you can, because you can create an environment, and you can RL it, and you can train on it, we can solve. So I just feel very comfortable. Anything you do with a computer, AI will be able to do. So maybe it doesn't do it now, but like you can just see that path happening. What I don't know is does the same thing apply to like the physical universe, right? You can't like sh- sure, sure you could set up a lab to like nick speakers or whatever, [laughs] you know, to do that, but like are the scaling curves the same? You know, is the distribution the same? So anything to do with like robotics and the physical world and science and medicine, I don't, I have no idea if it follows the same laws, and so I think that's still an unlock. But I feel very comfortable that the current, the current train-

Patrick O'Shaughnessy 35:50 The digital side

Martin Casado 35:51 ... the digital side is do- I mean, is done.

Patrick O'Shaughnessy 35:52 How have you processed all this interesting security stuff, breaches, uh, I've heard it called breachpocalypse.

Martin Casado 35:58 [laughs]

Patrick O'Shaughnessy 35:59 Uh, it, it's obvious that we're like entering an inter- a very interesting era in cyber.

Martin Casado 36:03 Yeah.

Patrick O'Shaughnessy 36:04 Uh, what, what do you make of all this?

Martin Casado 36:05 I lived through, on the ground, you know, with formal studies in security, I lived through the internet, and it was way worse than this. Like if you, like by this time with the internet, you know, we had worms that had cost, you know, billions of dollars of, uh, economic damage. We'd taken out critical infrastructure. I, I remember when it was [laughs] remember Windows 95? You're probably too young.

Patrick O'Shaughnessy 36:30 No, I remember, yeah.

Martin Casado 36:31 Okay. Remember Windows 95?

Patrick O'Shaughnessy 36:32 Just barely, but [laughs]

Martin Casado 36:33 [laughs] So it used to be that if you took Windows 95- And you installed it, and it was connected to the internet. Before the installer was done, it'd be infected [laughs] .

Patrick O'Shaughnessy 36:43 [laughs]

Martin Casado 36:43 Like, they'd even take it out. Like, not a joke, right? Like, and so like, you didn't even have time to patch it, right? And so like, it was just like so chaotic, and there was so much mayhem. And like, this is what happens every time you've got a new technology that, that changes the nature of how we run computers in the security posture, and we're definitely dealing with another one of those moments. And so I think we can look to what happened with the, um, uh, with the internet, which is like, there's a few years of mayhem. You do have breaches that cause real issues, and then, you know, the industry evolves. And, and normally the way you think about security is like, will the good guys be able to spend more money than the bad guys? If the answer is yes, which is typically the case, you will come up with a technical solution, and I think that will be the case here.

Patrick O'Shaughnessy 37:27 Yeah.

Martin Casado 37:27 And you're already starting to see it. I mean, like, the, these vulnerabilities that you see showing up, this is good. That means that somebody used the model to like, make things safer by pointing out the vulnerabilities. I think this is going exactly like you would expect it to go. But I think the net impact will be that software will be much, much, much more secure because AI's also securing it.

Patrick O'Shaughnessy 37:45 Can you talk about capital? Like, what your sense of... Th- there's very interesting things happening in capital markets.

Martin Casado 37:51 [laughs]

Patrick O'Shaughnessy 37:52 Rate, you know, the, the, the, the rate line for our whole lives looked like this, and now it looks like this. And there's clearly lots of demand destruction happening. There's clearly many companies that five years ago we would've said-

Martin Casado 38:04 What does that mean, demand? I don't know what demand destruction means.

Patrick O'Shaughnessy 38:05 Meaning like we're not funding things that we would've in a different environment because the cost of capital to fund them is too high because, and the cost is rising because the returns are so high on anything AI. So you've got these com- I was looking at this company this morning where if you and I had seen the deck five years ago, we'd be like, "This is amazing, like, software business, and we should fund it."

Martin Casado 38:24 Yeah.

Patrick O'Shaughnessy 38:24 And now, um, it seems to pale in comparison to other things that we could put our capital to.

Martin Casado 38:29 Yeah.

Patrick O'Shaughnessy 38:30 The capital itself feels like this interesting variable in this whole equation-

Martin Casado 38:33 Yeah

Patrick O'Shaughnessy 38:34 ... as a constraint, and I'm curious how that feels to you. What, what comment?

Martin Casado 38:37 So, um, I sit on the boards of a lot of great companies that have the problem that you have. Profitable, growing 40, 50, 60% at scale, but like, they're like, say, 300, 400, 500 million in ARR. What do those companies do, right? You can't really IPO 'cause they're subscale, but they're great companies. And I think about my own personal portfolio. I'm like, whatever, I'm in muni bonds [laughs] , man. I'd rather be in these companies, right? And so does it, does it... Like, I think that everybody would be happy with an asset that grows 40%. Like, you would be happy with your personal money being in that if you could guarantee it. And so I still think these are very good companies.

Patrick O'Shaughnessy 39:17 Mm-hmm.

Martin Casado 39:17 And so I feel it's more a question, again, this is my naivety of not understanding this, of like, are we gonna see a shift in the market so that people can get exposure to this asset class? 'Cause I think the risk profile is very, very different than AI. Nobody knows what's gonna happen to AI when the market slows down. Nobody knows.

Patrick O'Shaughnessy 39:35 Yeah.

Martin Casado 39:35 [laughs] You know? Like, it could be easy come, easy go. It could keep going forever. Like, nobody really knows. And so the only free lunch I've ever seen in investing is diversification, and I would love to personally have like-

Patrick O'Shaughnessy 39:46 A basket [laughs]

Martin Casado 39:47 ... a basket that includes these slower growing, but like, these assets that don't like disappear if they, like, miss a, a, those-

Patrick O'Shaughnessy 39:54 Are, are most of the ones, though, that, that are in your mind extremely subjected to this disruption risk? Meaning like, I, I, I won't name specific companies, but there's lots of companies with that kind of rough profile which seem, uh, terrifying in the face of like how easy it's gonna be to create software.

Martin Casado 40:11 So I'm an infra guy, so for me, more software means you need more infra. Like, it, like, like, we haven't seen a big dis- Like, we don't vibe code databases yet. Like, you do if you're a database company.

Patrick O'Shaughnessy 40:23 Would like Snowflake be a well-known example of kinda what you're talking about?

Martin Casado 40:26 Snowflake's a g- Yeah, it's a great example. I mean, like, this is like, it's really hard. And it's not just building it. It's really hard to have the long tail of features, the long tail of performance streaks. To actually operate these things is really, really tough. And so let's say that Snowflake is growing 30 to 40%, and the economics look good. Like, if I just think about my own personal... Like, I don't think that they're at any risk of being disrupted by AI. Like, maybe by Databricks, but that is like pre-AI type of thing. And so again, again, I'm very naive when it comes to these things. I'm a very simplistic thinker. My view is like, I don't think AI's gonna disrupt these. I think they are good businesses. And so I think more and more the industry's gonna want to like wrap these up and get exposure, and I don't know what that looks like. It's kind of interesting. Like, we're even seeing SPACs come back. I get a lot of calls now for companies that I sit on the board at, which are good companies, where these SPACs are like, "Hey, listen, like, we can, you know, we can make this stuff go public." So I, I think that the, the financial markets have to figure that out, but I personally... Like, there's a lot of companies that will be disrupted by AI, but there's a whole bunch that will not, and I just think the market doesn't know what to do with them right now.

Patrick O'Shaughnessy 41:26 Most meetings that you have around here in the la- for, at least for me, in the last, let's say, three months, some half the meeting is about compute, and that the product, uh, across categories, like, just needs more compute that isn't available to accomplish its goals. So this is another way of asking about the capital thing. Okay, uh, Andreessen Horowitz, you guys manage a ton of money.

Martin Casado 41:46 Yeah.

Patrick O'Shaughnessy 41:46 Um, everyone's deploying, uh, their funds at very fast paces.

Martin Casado 41:51 Yeah.

Patrick O'Shaughnessy 41:51 Each company that's working requires lots of capital. I'm trying to get a sense of the feel of how much capital itself is the constraint that, like, if we just airdropped a trillion dollars more in all the funds like yours-

Martin Casado 42:04 Great, great, great

Patrick O'Shaughnessy 42:04 ... that would be a good thing.

Martin Casado 42:06 Great, great, great. This, I think this is great. Um, [smacks lips] uh, okay. So I, I really think, and I, I said this before, but I'm gonna reiterate 'cause it's so important. I really think the big disruption of AI is the fact that you can throw capital at problems. In the history of, like, tech, you haven't been able to do that 'cause it was always an engineering problem-

Patrick O'Shaughnessy 42:26 Mm

Martin Casado 42:26 ... right? Which just doesn't scale. The mythical man-month is the mythical man-month for a reason. And so in, 10 years ago, if I had, you know, five founders show up, and they asked for a billion dollars, and I gave it to them, what would happen?

Patrick O'Shaughnessy 42:37 Sits there.

Martin Casado 42:38 Or they would detonate, right? They would like hire [laughs] a bunch of engineers, and then you're really fucked, right? Like, I mean, like it just, you can't, you can't, you can't throw capital in it. But, but, but now I'm-

Patrick O'Shaughnessy 42:48 Sorry, can you explain the Mythical Man-Month? It's a very interesting idea.

Martin Casado 42:51 Yeah, so the, the mythical, the Mythical Man-Month basically says that you can't increase the speed of engineering. There's like a natural law of physics.

Patrick O'Shaughnessy 43:00 It's sublinear. [laughs]

Martin Casado 43:01 [laughs] It's very sublinear, and it has to come with, like, basically, like, organizational communication. It's one of the conclusions, but the takeaway for those that are listening is that, this comes from, I think, Fred Brooks, right, in the, like, the '70s. Which is basically, like, you had these software projects and you wanted to get them out faster, and so they would throw more people at, and then it would go slower, right? And so, like, engineering has always been this bottleneck. And in my experience, this, and I had my own startup. I, you know, I, I did it for 10 years, right? It ended up being a very large business. This is totally true, right? It's just you can't scale engineering. And to me, the biggest shift, like, if you step back, like, industrial revolution level shift with AI is I can take 20 people and I can give them a billion dollars, and they can use that.

Patrick O'Shaughnessy 43:43 Mm.

Martin Casado 43:44 Now, do they make 500 million from that billion or two billion? I have no idea, but the fact that you can productively use that money is so new and is so interesting. And so at some level, the capital markets need to figure this out because, like, they are, you know, taking this money and using it. And if, if you look at Anthropic and OpenAI, they've probably raised more money in aggregate than the entire downstream ecosystem combined by a factor of whatever. So, like, I mean, they are these kind of mechanisms of turning money into growth. So that's one side. But there's another side, which is the debt side, which is, which is quite a bit different than the equity side, which is I do think that currently... I'm not a, I'm not a, a growth investor. I'm not a public markets guy. I'm not a debt guy. But here's, here's what I'm seeing on the ground is, um, we're running out of available debt, and if you're, like, whatever, the Norway pension fund, are you gonna, like, [laughs] give debt to random NeoCloud or, like, buy T-bills, you know? And I think that we're just at a point where, like, you just don't have, you don't have enough money to go around, and so it's gonna prefer the investment grade companies, and then those that are not investment grade will have a harder time.

Patrick O'Shaughnessy 44:53 So scale wins again.

Martin Casado 44:55 So s- yes, scale will win, and then, then it'll be a harder time raising debt. So if, like, if your business model requires you to raise debt, there's just not enough debt to go out. So I think, like, I know you're talking about capital as in, like-

Patrick O'Shaughnessy 45:06 Yeah

Martin Casado 45:06 ... like, like general capital to fund, and I think that, you know, you're gonna see a ton of liquidity. I think that that feels fine to me. The debt side, I, I, I don't know these markets, but I definitely know it's harder to get debt now.

Patrick O'Shaughnessy 45:17 Mm.

Martin Casado 45:18 And I think what this will mean with, like, um, if we want to have smaller players around that are not IG, that get debt, they're gonna need to prepay, which means their customers are gonna need to prepay. So I think over time, this all kind of flows back to, like, the availability of cash and capital, 'cause you do have limited amounts of debt.

Patrick O'Shaughnessy 45:35 Uh, th- this reminds me to ask about sort of Neo Labs, Neo Clouds, inference, uh, providers and companies. This l- I don't know if you call these infrastructure or what part of the stack you call this.

Martin Casado 45:44 Yeah, yeah. Of, those all as infrastructure. Yeah.

Patrick O'Shaughnessy 45:46 So, so if you take the, you know, those kind of categories in turn, there's, there's lots of them. Lots of them have been funded. Very few of them are l- are sort of at scale.

Martin Casado 45:56 Yeah.

Patrick O'Shaughnessy 45:56 And I'm curious how you think this plays out, like, what roles they should and will play, uh, what the market feels like. These things are growing so unbelievably fast in most cases.

Martin Casado 46:08 Yeah.

Patrick O'Shaughnessy 46:08 I'm very interested by these companies-

Martin Casado 46:09 Yeah

Patrick O'Shaughnessy 46:09 ... and I'm curious what y- what you think of these categories.

Martin Casado 46:12 Well, let's put it Neo Labs aside-

Patrick O'Shaughnessy 46:14 Sure

Martin Casado 46:14 ... 'cause I think that's the least well-defined of all of them.

Patrick O'Shaughnessy 46:16 I included them, to be clear, because so many of them seem to be building their own, like their-

Martin Casado 46:20 [laughs] They're becoming Neo- they're becoming NeoGlass. [laughs]

Patrick O'Shaughnessy 46:22 Well, like, every pitch, so many pitches turn into some version of, like, we're building a token factory with some degree of vertical integration, you know? And, and some Neo Labs are going all the way down to, like, buying land and power and, and all the way up through the token, you know what I mean? So there's this weird, like-

Martin Casado 46:36 Yeah, I think we, yeah, I think we really need to... So in my world, that wouldn't be considered a Neo lab. That would be a... Like, all Neo Clouds wanna be token factories, so I would view if, like, your output is, like, a token of an existing model architecture-

Patrick O'Shaughnessy 46:48 Communication or something? Yeah

Martin Casado 46:49 ... yeah, then you're a, you're a, you're a Neo Cloud. Like, all of them want to do that, and they will all verticalize. Um, I mean, listen, I really think that the actual real limitation right now is, is hardware supply. That's it. It's not capital. It's not debt. That's the actual real limitation.

Patrick O'Shaughnessy 47:02 And power.

Martin Casado 47:03 And power. So Neo Cloud to me is one that actually provides, like, the physical infrastructure that ends at tokens, right? And so they could provide GPUs. They could provide, um, you know, you know, fine-tuning services, or they can provide tokens. But it's, like, think of, like, a data center that actually has the GPUs in it. That's what a Neo Cloud is to me. A Neo lab to me is give me a billion dollars. We're gonna come up with a new model architecture.

Patrick O'Shaughnessy 47:23 Train model.

Martin Casado 47:24 [laughs] And I love a lot of these business plans are like, "If we solve ASI, that's our business model." Like, literally, like, that's, [laughs] that's, like, literally, like, you know. And so, like, these are, like, these kind of, like... And by the way, to be fair, that's what OpenAI's [laughs] original thesis was and, and to some extent Anthropic's, which is like, if you solve the intelligence problem, the business model part is easy. So that's to me a Neo lab is new architecture, model training, and they're, you know, and sometimes they'll buy their own infrastructure and buy their own GPUs, but, like, that's not what they're building towards or selling, right? So the back- I, I think that the entire, like, the real constraint right now is power. It is, it is chips. It is materials. Like, that's the sort of thing. And I, I think that with that sort of a limitation, 80% of these things are gonna go to the scale providers, and, you know, 20% are just gonna be kinda hard to reach places, niche-y type places. You know, like, I have the relationship with a random government that has the power. That's kind of funny. [laughs] I met this company that it does basically in- you know, a GPU marketplace. That's what it does. And, like, it's, but it's, like, the most, like, low-level version of it, and, like, many of, like, their GPUs, like, converted, like, chicken sheds, [laughs] you know? Like, like, like, little, like, chicken coops where, like, people, like, in farms are, like, you know, just, like, finding ways to provide it. So there, I think there will always, the market is so large, there will always be a significant portion that it just doesn't make sense for the SpaceXs to do. Like, SpaceX is not gonna go and, like, you know, f- find out how to create a marketplace of, like, chicken coop GPUs, right? And so I, and I think this is what is e- end up happening is, is the 20% is large enough to have a very wide fragmented array of folks, but then the 80% of the market are gonna be these kinda homogenous, you know, things like SpaceX.

Patrick O'Shaughnessy 49:07 Hmm. I heard this story recently that someone asked Larry Page how much of compute will be orbital, and he said, "Five nines." And, and the reason was it's just physics. Like, e- eventually it's just, like, all gonna be in space.

Martin Casado 49:19 You know what's so funny is, like, the actual space thing actually pencils out, but the reason it makes the most sense is just regulatory.

Patrick O'Shaughnessy 49:25 Just 'cause it skirts it? Just-

Martin Casado 49:26 Yeah, yeah. It's just ba- basically regulatory. I think there's 500 proposed le- legislations to stop data center building in the United States alone, including Texas, right? [laughs] Like, if you can't build a data center in Texas, I don't know what we're doing anymore. And so I think, like, if that regulatory pressure continues and you actually can't break ground, uh, and I've heard from many of the top data center people, like, it's very tough to break ground right now, then either you go international or you go into space. [upbeat music]

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Martin Casado 51:24 Yeah

Patrick O'Shaughnessy 51:24 ... every one of those has a price that will ruin the return of investing in it if it's too high.

Martin Casado 51:29 So this is another one of my bugaboos, which is, um, I thi- I... Listen, I don't know how you can be a VC and not believe tech creates value, which is the first conversation we had. Now I'm gonna have a second conversation, which is like, I don't know how you can be a VC and not believe markets are efficient. [laughs] Like, what are we doing? Like, for a capitalist, you have to believe markets are efficient. And so I think private markets are very efficient, and private markets figure out the price. And so I just don't try and bet against the market. I think a lot of people think that whatever, like, the private markets aren't efficient, and so you can find the deal and find the non-consensus deal. And all of those deals were incredibly competitive. And I didn't win by, you know, looking for a deal or whatever. I, I, you know, I mean, I, I pay the market price, and maybe I'll get a little discount because, you know, for negotiating or whatever, but I, I never really worry about that. And so as a productive investor, I just trust that the markets are actually pretty smart, and I trust that the markets will value it at, you know, given risk adjusted. And I've, I, I, I believe that my diligence reduces that risk for me. But I d- I don't, I don't do it on market value. I'm not trying to, like, find a better price. I'm just trying to find a less riskier asset.

Patrick O'Shaughnessy 52:39 Doesn't it follow though that then the approach would be to just actually try to index the private markets? Like, you just try to own literally the market weights of your category.

Martin Casado 52:47 Oh, I would love... Oh, I would love to, but you can't. I mean, you can't pragmat- I would love to, of course, of course. Yeah, so this is right. This is right. So this is where this all lends to, and, and this is kind of, like, how we think about investing, which is, um, um, of course, if I could [laughs] have an index of every competitive deal... Like, literally, my, my, my dream would be I get 5%-

Patrick O'Shaughnessy 53:04 You have to own Cognition when you buy Cursor. [laughs]

Martin Casado 53:06 I, I [laughs] have every, have every comp. I've gotten 5% in every competitive... And the only th- Like, I really believe private markets are super efficient. I do. And I think that, like, the consensus is actually smart, 'cause I'm a capitalist and I believe in markets. And so if I could have 5% of every competitive deal where you've got, like, legit investors in it, I'd be happy. But you, you, you simply cannot do that. You can't do that for competitive reasons. You can't do that for capacity reasons. So as an investor, you have to come up with, you know, how, how, how do I, like, you know, not run to the competitive bus stop, but even more importantly, like, how do I not, like, run out of our ability to keep track and help these companies? Because listen, I, I do take the boards. I do get very involved. Very, very involved, right? I mean, I, you know, um, and so, like, that's just impractical. So the way that we think about it, which you've heard before, but I'll, I'll restate it, is for every legitimate sector, and by that legitimate sector, we define it as there's good founders in it, because we don't second-guess founders. If they're risking their futures and the opportunity costs and their family's wealth, then it's a good sector. We do the work to try and decide for every good sector, which is the top company. And that means you look at the team, you look at the tech, and you look at the monetization potential. You look at the market growth potential. You look at all of that stuff, and then we choose the best one, and then we assume that the market is efficient. Like, that's kind of the approach. So it's kind of an index, but, like, we're actually segmenting it by market. You know, and sometimes we get it right and sometimes we get it wrong, but that way you're kind of indexing, you know, like, the interesting markets.

Patrick O'Shaughnessy 54:32 Yeah, there's this thing in indexing called stratified sampling. It's from my old quantitative days. You can, uh, buy a small subset of the index and have your tracking error to the index be very low because you, uh, l- actually, literally what you're describing is effectively stratified sampling, that you, like, get-

Martin Casado 54:47 Oh, yeah

Patrick O'Shaughnessy 54:47 ... exposure to, you know, best, biggest, whatever per category.

Martin Casado 54:52 Yeah.

Patrick O'Shaughnessy 54:52 And that will perform, like, similar [laughs] to the end.

Martin Casado 54:54 Yeah. Okay, so that makes it... And, and I mean, I just, you know, I, I just think it's funny. Like, I think venture capital early on, and I wasn't there, I just read the books, I mean, I, I think it believed that tech created value, so it didn't overrode... I mean, often there was no, [laughs] there was no business to evaluate. And I just don't see so much hand-wringing about bubbles or this or that or the other thing, right? I mean, like, the market price is the market price. And so, like- You know, I, to me there's always this huge dissonance in, in when I'm talking with other investors where they're, you know, they, they're always decrying the bubbles, which nobody can ever predict. [laughs] Like, like by definition if everybody's calling it a bubble, it's probably not a bubble. Like, it's almost like a bubble, like when no- when everybody's investing because they think there's still upsides. Um, and then they don't somehow think private markets are efficient. Like remember I got like, I got like VC canceled because I'm like, "Non-consensus investing is dangerous." So like I once on Twitter was like, "Hey, listen, I think it's, it's, it's dangerous to, to, to invest against consensus because like basically there's follow-on capital risk." And from saying that, many prominent VCs came out [laughs] and they were like, you know, "Oh, like all of these deals that we've done were non-consensus." And I think there's something very kind of tied to the, I, I, like the ego and the identity of, of investors that they, they have to believe that they, they can truffle hunt and find the non-consensus thing, they're smarter than everybody else. But I think the reality is, is certainly if you look at our portfolio, the ones that return money were actually very, very competitive in general. So I just, I just think these markets are actually quite efficient.

Patrick O'Shaughnessy 56:22 In addition to this, like I love the idea of market efficiency and private markets being one area that you disagree maybe with lots of, uh, well-known private investors.

Martin Casado 56:29 Yeah, ap- apparently a [laughs] lot. Yeah.

Patrick O'Shaughnessy 56:30 Apparently all of them. Um-

Martin Casado 56:32 Yeah

Patrick O'Shaughnessy 56:32 ... are there any other areas like that where you feel-

Martin Casado 56:34 Yes

Patrick O'Shaughnessy 56:34 ... especially alone?

Martin Casado 56:35 Yes. [laughs]

Patrick O'Shaughnessy 56:36 [laughs]

Martin Casado 56:36 There's another one that drives me nuts. This is my last one, I promise.

Patrick O'Shaughnessy 56:39 Okay.

Martin Casado 56:39 So [laughs] so another thing that drives me nuts is like this belief that if you put money in private markets, it doesn't somehow grow the TAM of private markets. I love Roloff at Sequoia. He, I, I think he's fantastic. He's incredibly intelligent guy, he's a great board member. But he gave this talk, do you remember at TechCrunch, uh, um, Disrupt, where he's like, a- and ma- many, many investors believe this, so this isn't to, to call out Roloff 'cause I'm actually a huge fan of his. But he was like, "Venture capital is not an asset class. There's only 20 startups that matter, and that's it. And so if there's too much money going in, you've overfunded private markets." And to me this is ludicrous. I mean, like capital creates [laughs] opportunities and markets and value. And even though there is so much more money that has gone into the private markets in the last, you know, 20 years, the value in the private markets has, has kept pace, right? You've gotten massively large private companies, tons of opportunities, very large exits. And so this zero sum thinking about private markets being like some s- amount of fixed, again, to me it's-

Patrick O'Shaughnessy 57:45 Fixed supply of great things, yeah

Martin Casado 57:46 ... is so bizarre. Like we're venture investors, we create value, like tech creates value. If you put more money in it, it'll grow. And you will be surprised, and maybe you won't be surprised, but I've been very surprised at how common this mindset is. I'm very often I'll talk to a pretty senior GP who's been investing for 20 years and they'll be like, "Oh, there's too much money." [laughs] I'm like, "What are you talking about? There should be more money in there. We need more, more ecosystem, more value, more investment." So I, I do don- I do believe money in private markets creates TAM. It creates the investable TAM. I think it creates value. I think there's more opportunity to deploy money now in private markets than there ever has before. Even very simple examples of that are like companies not going public, right? Like if there's more private money available, why would you even go public, right? And so, you know, I, I think this, again, we need to all step away from zero sum thinking. [laughs] I think we all need to believe that like technology creates value. I think now especially with AI, we need to believe that capital enables technology to create value. And if that's the case, more money in private markets, [laughs] we should deploy the money and we should create the value.

Patrick O'Shaughnessy 58:51 Uh, you've been on, if you put this in NBA jam terms, you're like the, uh, the player that's like on fire, can like jump really high with, with between like Cursor, um, and Fei-Fei's company and OpenRouter, you know, all of these in a very short period of time. So well, well done. But I, I'm, I'm more curious about the lessons you learned about M&A and kind of what's g- what's going on in this market, having been a part of three of the, maybe the three biggest major scale M&As in the last couple months.

Martin Casado 59:19 Yeah. So, um, so actually I, I kind of have a history with strategic M&A going back to my company. So when I sold my company to VMware, it was a $1.26 billion exit, which at the time was the high- I think I beat David Sax's Yammer. [laughs]

Patrick O'Shaughnessy 59:32 [laughs]

Martin Casado 59:33 It was the highest acquisitions on multiple-

Patrick O'Shaughnessy 59:34 Who's counting? Yeah. [laughs]

Martin Casado 59:36 [laughs] It was, it was, it was kind of funny 'cause Catalyst, Catalyst showed me this chart, you know, it was just kind of like multiples of revenue and like the total exit and there was like two outliers and it was like Yammer-

Patrick O'Shaughnessy 59:47 You and Yammer

Martin Casado 59:47 ... and my company, and they're like really close to each other.

Patrick O'Shaughnessy 59:49 Yeah.

Martin Casado 59:49 I was like marginally better. [laughs]

Patrick O'Shaughnessy 59:50 [laughs]

Martin Casado 59:51 Just a small bit. At the time, I think that was the highest acquisition on multiples of revenue at Enterprise Software. And then actually I was part of a deal that beat that, which was the Tabular acquisition to Databricks. And then of course Cursor I think was the largest private fit acquisition ever. Um, and of course, you know, Fei-Fei's acquisition was a very large acquisition. So I've done, I've just, I've just been a part of a lot of these kind of strategic M&A. And so I think my comment only goes to strategic M&A. And I'll say I've, I've, I've noticed a few trends. One of them is there's almost always like a, like deep, deep personal alignment. Um, because, you know, it's, it's, it's a real risk for both sides. I think like Lisa Su and Fei-Fei, or think Michael Truel and Elon, or think like Alex and Patrick. I mean, you nev- almost always have people that have same visions of the future and they think about it the same way, right? Whether it's you think in terms of markets or you thinks in terms of infrastructure or you thinks in terms of data. I mean, that's almost always present, is like there's some trust, existing trust relation, ex- like existing alignment. The second one is you have to take it from the mind of the buyer. So what the buyer is doing is they're basically buying an option on a large market, right? So in the case of like VMware buying Nicira, they're buying a $1 billion option on a $40 billion market. And so in order for that to make sense, you kind of have to, to solve the inductive proof for them. You have to show them the base case, which is like, you know, we have this business that sells into networking and we can get that budget. And then you have to do the inductive step And you guys have virtual machines, so if we attach this to a virtual machine, we can sell more. And then therefore, for part of you, we can sell a whole bunch of these things. So you almost, because they're buying a future option on a market, you have to show

Patrick O'Shaughnessy 1:01:36 The company's job is to show the inductive step.

Martin Casado 1:01:38 The, the company being acquired, or both of them, the, the, like, the, like, like, like the discussion for-

Patrick O'Shaughnessy 1:01:43 The clearing has to-

Martin Casado 1:01:44 Yeah, the discussion, the discussion for the M&A has to, to be that you have the base case, you have the inductive step, therefore you can get this to this market, which is like the large markets that happens. And, and that conversation in my experience is one kind of only founders can do, and maybe investors that really, really understand strategically what is going on.

Patrick O'Shaughnessy 1:02:06 Yeah.

Martin Casado 1:02:06 Right? But those conversations are very ki- it's almost like, like really high stakes enterprise sales. I've been a part of a lot of also like more auctionee type M&A. You, you never have those conversations. It's sort of like, you know, like you're looking at the financial metrics and the spreadsheets and kind of they're buying an asset, et cetera. And so I just think they've got a very different flavor. But I, I think that especially with the timelines going down on tech, I think we're gonna see more and more strategic exits. And so I think it's probably important for people to get pretty smart of how these things look. And, and it's very different than they assume. And most people assume you should hire a banker, and you should have an auction, and get the highest price. And it's, it's, it's like hiring a banker's fine. You can do that if you want. But like the idea that you're gonna like go to like the apex predator, someone like whatever, Mark Zuckerberg, and play poker, like you're kidding yourself, [laughs] right? Like these things are built on trust and, and telling out a story, not playing games or playing poker. And so I just think that there's kind of a mindset that you need to have if you're gonna engage in these things that's quite different than the intuition.

Patrick O'Shaughnessy 1:03:05 So can you tell from your perspective the story of, let's say, the Cursor, since that's the biggest one, of that acquisition, since you're such an involved investor? Just like give us the story.

Martin Casado 1:03:13 We'd actually just... We were just about ready to get a, a raise done.

Patrick O'Shaughnessy 1:03:16 Yep.

Martin Casado 1:03:16 And, uh, we'd r- you know, we raised a few billion dollars, and like that was, was, um, getting done. But this is about the time that Mythos came out. Um, and if you kind of worked backwards, you realize it probably cost, I, I don't know how much it cost, but it probably cost say $3 billion to make Mythos. And so we're like, "You know, um, if, if Cursor is gonna, you know, build models, we need to raise more money and get access to those computes." And, um, and so then of course, that kicks off strategic discussions. Like there's not a lot of people that have access to that much compute. And so then you have those conversations. Um, and then of course, there's quite a bit of interest in the company as a result, just because the company had like by far the most users and by far the most data. Um, and so again, this is one of these great examples where the, you know, in the end, you know, SpaceX had the compute, um, and Cursor did have the data, and it had a team that had very successfully built, you know, models and built this practice, and code was very important. So there was just a lot of alignment. And what... And it always helps to have a, a raise, Ken, because that helps actually set the value and set the price of these things. So that was actually a fairly straightforward-

Patrick O'Shaughnessy 1:04:22 Mm

Martin Casado 1:04:23 ... um, deal.

Patrick O'Shaughnessy 1:04:24 I'm curious if there's anything similar lesson-wise from OpenRouter or from Fei-Fei's company, World Labs, and anything else that is shareable from those two experiences that might be indicative of or, or bear some lessons from this kind of strategic M&A.

Martin Casado 1:04:38 [lip smack] So I'm gonna go back to what I said, which is, um, for these strategic M... The most important thing is the, the intuition, especially of a lot of investors is there's a deal going down. We need to like play games. We need to leak it to the press. Drives me fucking nuts.

Patrick O'Shaughnessy 1:04:53 [laughs]

Martin Casado 1:04:54 Um, you know, we need to get other, we need to get other companies in. And you don't realize, like you're literally dealing with the most sophisticated people on the planet. Like I just don't think you walk up to Zuckerberg and you're like, "Oh, like I'm gonna beat you in poker, dude." That guy will fucking take out your sucker-

Patrick O'Shaughnessy 1:05:08 [laughs]

Martin Casado 1:05:08 ... and pat you on the head. I'm kidding. Uh, like I'm not kidding, right? It's like, like these are the, like these...

Patrick O'Shaughnessy 1:05:13 [laughs]

Martin Casado 1:05:13 And so in every one of these deals, the company's in very good faith, and these are world record deals, in very good faith engaged with the buyers. And they didn't play any games, and they knew that they were worth a certain amount of money because, you know, they had done raises or there were market comps, and there was a lot of trust. And having now done so many of these deals, I've just found that that approach for strategic M&A is the right approach. And I've been part of deals that are that size that have fallen apart because, you know, somehow a board member got into the head of the founder that, "Oh, you need to like get competitors. You gotta leak it to the pr-" I mean, I would think like, I mean, God, that OpenRouter deal drove me nuts. Like it got leaked. Did you notice by the way that World Labs did not get leaked?

Patrick O'Shaughnessy 1:06:01 Yeah, interesting.

Martin Casado 1:06:03 Didn't get leaked-

Patrick O'Shaughnessy 1:06:03 Nothing

Martin Casado 1:06:04 ... 'cause we didn't tell anybody. It was like me [laughs] and Fei, and it was a few people. Not leaked. OpenRouter was leaking constantly, right? And I think that like a, a number of VCs think, "Oh, this is how you drive up the price, or this is how you get it done." And you c- you see these things leak all the time. That destabilizes deals.

Patrick O'Shaughnessy 1:06:19 Mm.

Martin Casado 1:06:19 Having done all these deals, it destabilizes deals. And so again, I think the most important thing is a structural thing, like your intuition as an investor in particular, that this is like some commodity asset and it's about playing poker. It's just wrong. If it's strategic deal for a lot of money, I think you need to view it with very professionally. I think you need to develop a trust relationship. I think you need to do it in good faith. You should not leak that, you know, leak these deals, um, and realize that the people that you're, are, that are counterparties to this are the most sophisticated people in the industry.

Patrick O'Shaughnessy 1:06:50 Mm.

Martin Casado 1:06:50 And, and I, I would say that in, in the founders in every one of these cases treated it that way. Michael Truog treated it that way and the team. You know, Alex and Chris treated that way for OpenRouter. Um, and, uh, for sure, Fei-Fei treated it that way.

Patrick O'Shaughnessy 1:07:02 How are you seeing the role of investors evolve? Um, b- both literally how it's evolving, you know, across these many, many boards that you're on, but also some commentary on if you think investors generally of your type are doing a good job or, or actually helping companies. Um, it seems like because the stakes are h- so high, growth is so big, capital, the capital dollar amounts are so big that all these investors are overseeing, it's kinda crazy Yeah, maybe some commentary on like the role of the investor

Martin Casado 1:07:28 One thing I will say is like venture is not what it used to be and, and the startup ecosystem is not what it used to be. I mean, if you look at the largest private companies right now, they would rival any public companies, um, you know, 20 years ago. I think like Stripe or like Databricks or like SpaceX before it IPO'd, or Anduril, or... Like, I mean, these companies are massive. And so I think that one of the problems is, is what it means to be a startup has shifted so much that now you have this undefined quagmire of like who should probably be public investors, like, you know, investing in early [laughs] stage startups, and I just don't think those intuitions are, are right. And so I think that's kind of the root of like the, the confusion. I think maybe it just actually makes sense to like start delineating a bit more on the, on the, on the private side. Or if, you know, you have later stage people investing in earlier stage companies, maybe a little bit more self-awareness. Like for example, you wouldn't put me on a mining company board, right? I mean, like, I mean, so some of these, like, I mean, remember 2021?

Patrick O'Shaughnessy 1:08:35 Yeah.

Martin Casado 1:08:35 So remember 2021 and we'd have like these like PE investors. [laughs] They would invest in these Series B companies. I would sit on the board with these people, and it's like, it's literally like putting me on the board of a mining company. [laughs]

Patrick O'Shaughnessy 1:08:45 Yeah. [laughs]

Martin Casado 1:08:46 They have no idea. Like, they're like trying to like reason about the company through like burn. Like, [laughs] dude, you're gonna be miserable here, 'cause all we do is burn cash for a long time. And so it's more kind of the blending of the lines because private companies are a real asset class for late stage investors. You know, I think this, I think a lot of this is just growing pains. Like how, how much larger do you think the market is now than it was 30 years ago?

Patrick O'Shaughnessy 1:09:13 A thousand times. [laughs]

Martin Casado 1:09:14 So of course, of course, you know, the industry's gonna have to mature around that, and of course you're gonna have to like view it like a kinda later stage investor, of course. But I, I, I just think that, um, there's like an i- this funny identity crisis with a lot of like later stage investors, you know, who, who view the world that way just because they're in private markets and their VCs think that, you know, all of a sudden now... You know, like for example, I would never invest in a late stage company. Like, I don't think it goes... [laughs] Like I'm gonna have... [laughs] Like what would I do? I'd go there and, you know, tell you how to like run a finance op. It's just not, it's just not me. So I think that's it. It, it's just like, it's basically the encourage of traditional banking and traditional finance and venture, which absolutely belongs there. I just think sometimes it kind of reaches a little bit too low, and then you would kind of have these issues, you have these issues in M&A where they're like, "Whatever, let's like run an auction and let's like do all this other stuff." Or, you know, you have it in the operations of a company where like, you know, you're 20 people and they're worried about burn, you know? Or like they're over-rotating on metrics for disruptive technology.

Patrick O'Shaughnessy 1:10:13 Yeah.

Martin Casado 1:10:13 I just feel like we need to kind of bring back traditional productive VC for some subset of this, and there's enough market for everyone.

Patrick O'Shaughnessy 1:10:20 Has anything changed about how when you meet somebody for the first time, if like, if I came and watched 10 meetings in a week with you 10 years ago when you started-

Martin Casado 1:10:29 Yeah

Patrick O'Shaughnessy 1:10:29 ... and then I came today and watched 10 meetings with you.

Martin Casado 1:10:31 Yeah.

Patrick O'Shaughnessy 1:10:31 Have you changed how you conduct your inter- your early interactions with founders?

Martin Casado 1:10:38 Yeah, a lot. I mean, uh, I think most investors go through this, especially when you come from the operating world. Like, you have to learn how to listen as an investor. Um, and then you also have to learn how to ask the questions that you really care about so that you can kinda answer the questions to yourself. And it's a very delicate balance, right? And, um, I do feel like a lot of like meetings with founders tend to be pretty performative on both sides. Like you don't know what to ask [laughs] and so you'll be like, "Who's your competition?" And then a lot of it is just asking questions for asking questions' sake, but like it doesn't actually inform your decision. Um, and then especially if you're an operator, you'll hear something you disagree with and you kind of wanna fix that problem, but that's kind of not your job. And so I think I, I, I have learned, and I'm not perfect, but... And I, I see there's a lot of junior like investors that come in that, you know, the primary job is just to listen. And, and then like a founder will tell you what they believe, right? And so like if you disagree, you disagree, but like that's what they believe. I mean, so often they'll say something like, "Well, but I would do it better, and therefore like it's a good investment." And then the, the, the... And it's, it's, it's truly an art, the second one that I mentioned, it's truly an art like you have to make a decision based on that meeting. There's a lot of silly questions you could ask that are not really gonna inform that decision. What if Amazon does that? What does OpenAI... Like whatever, you can answer that yourself. Like, [laughs] like it, like maybe you can like figure out how they think. But there are key questions that you really wanna know, and kind of knowing which ones those are during the time of the meeting and getting those in is a kind of a hard thing to do. And I think I've really kind of refined my ability to do that.

Patrick O'Shaughnessy 1:12:18 Maybe a fun place to close is just to ask what has you most excited. We've talked about lots of the magic of this new era of technology and things that weren't possible before, bottlenecks that are now gone. I, I know all of that excites you. But if you had to just step all the way back and, uh, describe why this job is so incredibly fun right now and why you're most excited, how would you frame it?

Martin Casado 1:12:41 Well, I'm more and more of the belief that this is an industrial revolution level shift. And for me, the industrial revolution went from basically a zero sum world to like a, you know, um, non-zero sum where value is being created, net new value is being created. And I think this is a very similar shift where before to solve problems you had to solve very complex engineering problems, which basically didn't scale and were very tough, and then you would have to maintain them, and that's why all software has always sucked, to like basically being a capital problem. Like I think that for... Again, I don't know about the real world yet. I think we'll get there, but I think like we've turned this into a capital resourcing problem, and we're capital allocators. And so I just feel like we're just in this very, very strategic position to help drive the future in a much, much more direct way than we ever have been before.

Patrick O'Shaughnessy 1:13:29 Mm.

Martin Casado 1:13:29 Because I think this is a scale of capital game that causes that to happen. I don't believe private markets have limited TAM. I think they have [laughs] unlimited TAM, and the more money that goes into them, the more value that would be created. So I just feel like we're in this very privileged position, you know, very, very key to the innovation chain. And, uh, we're both very lucky to be here.

Patrick O'Shaughnessy 1:13:49 I love the every two-year schedule that we're on. I hope we get to do it again-

Martin Casado 1:13:52 [laughs] I know. Me too

Patrick O'Shaughnessy 1:13:53 ... in another two years.

Martin Casado 1:13:54 I know.

Patrick O'Shaughnessy 1:13:54 I can only imagine what the world's gonna look like two years from now.

Martin Casado 1:13:56 Right. Yeah, yeah.

Patrick O'Shaughnessy 1:13:57 Thank you so-

Martin Casado 1:13:57 We'll, we'll have, we'll have our Grok bots talk. [laughs]

Patrick O'Shaughnessy 1:13:59 [laughs] Thank you so much for your time.

Martin Casado 1:14:00 Great. Thank you. [upbeat music]

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