Martin Casado on Frontier Model Economics, AI's Slowdown, and the Cursor Deal - [Invest Like the Best, EP.498]

0:00 Speaker 1 Ramp is the only platform built to make your finance team leaner, faster and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp just open for business in the United Kingdom. So if you're running a business in the UK, you can now use Ramps AI powered finance platform to manage cards, expenses, bills, approvals and accounting all in one place. 0:21 I run my business on Ramp and so should you. Learn more at ramp.com/invest. Rogo is the AI platform purpose built for financial institutions, serving 10s of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. 0:40 They can screen deals, draft Sims, run buyer outreach and due diligence on data rooms, all with full security and compliance handled. Every action feeds Rogo Intelligence the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. 0:59 To learn more, visit rogo.com/invest. Open AI Cursor, Anthropic, Perplexity, and Purcell all have something in common. They all use Work OS, and here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, skim RBAC, and audit logs. 1:19 That's where Work OS comes in. Instead of spending months building these mission critical capabilities yourself, you can just use Work OSAPIS to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on Work OS Work OS is the fastest way to become enterprise ready and stay focused on what matters most, your product. 1:39 Visit workos.com to get started. 1:41 Speaker 2 I mean, let's be honest, software has sucked. Technology has been hard to use. Like it's most often broken. And now we're actually in a point where the 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. 1:59 And I just think that like the biggest constraint is no longer the tech. It's literally in our creativity. 2:15 Speaker 3 You recently wrote about sort of four buckets of styles of investing that might be applied in, call it venture, I don't know, big private market tech, let's call it. Can you enumerate those and just like, say a sentence on yeah perspective on each? 2:27 Speaker 2 Yeah, yeah, yeah. Again, I feel like I'm just in this point in my career. I've been there this for a decade. I've been hundreds of deals and that I sit in these discussions and I just realized people aren't communicating very well. So I kind of did this exercise where I'm like, OK, So what are the mindsets people bring to investing? 2:43 And I think there's basically four types of mindsets. So the one that 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. Spreadsheet stuff. Yeah. It's like, it's like, what are the SAS metrics? 3:00 What 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 did 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. 3:22 And if you believe in that, as it creates value, it will create market. And as it creates value, you're going to have to find ways for the business to catch up to that value. And so the type of diligence you do if you're a productive investors is more like what is the technical approach? What are the adjacent markets? 3:37 You know, how fast is this likely to evolve? How close is it to a capturing value like that sort of stuff? Is this the right team to build it? 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 kind of Yolo, man. 3:54 It's like founders know better than VCs and they've seen something that I don't see and they're going to go out there and do it and like, who am I to second guess them? And it's about if they almost look like talent networks, right? Like I would say Chris Dixon, remember when he he was investing in nootropics, he invested in Soylent, he invested in Oculus, he invested in Coinbase and in crypto. 4:12 I mean, it's like every science fiction trope from the 70s and 80s you kind of invest in and then 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 is always so surprising how prevalent it is, is speculative investors, which they don't even care about the asset. 4:31 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. It's clearly going to go up in value. 4:47 Oh, this is the top of the market. You know, like I'm going to get out. And so they like they're trying, they're trying to like time markets. They're 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 art types, but you can justify any deal or not justify any deal. 5:05 So if you don't know what your beliefs are, you'll always basically just kind of justify whatever personal bias you. 5:10 Speaker 3 Have if you think about yourself as a productive investor focused on I love this idea that disruptive technologies, 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, 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? 5:35 Speaker 2 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 behaviours. The technology looks a certain way and the industry acts a certain way. 5:52 It's 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? 6:08 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 while they use this. 6:30 You can see if there's like adjacent budgets that, you know, you could that are growing, that you could kind of arguable 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 I'm you know, I've been in the industry for 30 ways every tech wave. 6:50 People like hand wring about merchants, everyone that can remember for the cloud. I remember from mobile. I remember for the Internet, you know, clearly now people worry about margins. O 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 in the actual viable business model. 7:07 So like you're using partial indicators, not full indicators in order to do this. And so my team, I mean, my team has 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't ask the question, how does this tech fit into the broader platforms? 7:24 How does it fit into spending? How does this 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 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. 7:45 So there's a lot of diligence you can do where with Frontier investing, I don't think you do any of that. It's. 7:49 Speaker 3 Like people? 7:50 Speaker 2 It's like it's it's people. Is this the best team to do this crazy thing and then you just Yolo? 7:55 Speaker 3 You were saying before we hit start that nobody understands. Can you can you explain what you're meant? 8:00 Speaker 2 I've realized that like like even the most common terms of 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. 8:11 Speaker 3 I messed it up. 8:12 Speaker 2 No, you didn't mess it up. You said you said what like like 1/3 of people say so I said So what is a network effect and what did you say I. 8:17 Speaker 3 Said a product that gets better as it adds A marginal user. 8:20 Speaker 2 So to me, this is almost a tautological definition because most products will get better as you add a user, right? Like you, 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. 8:46 That grows super linearly and that's why you get like natural monopolies and other people use the term network effect more to be like an an actual flywheel. So like a self reinforcing flight wheel. So not just like things get better, but like it's self reinforces in some sort of a way. 9:01 This 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 is sitting in front to hit pitch and nobody has consensus on what it. 9:21 Speaker 3 Means, but how would you define it? 9:22 Speaker 2 So I think the root of the confusion is not a 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 there's many things that could create a Moat, right, like a brand scale A2 sided marketplace network effect. 9:45 But that Moat to reason about that is only really useful 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 Metcalf swan network effect would, that's really defensible. 10:05 If it grows sub linearly 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 sub linear with scale. 10:24 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 CAF goes down because of brand, what's A2 sided marketplace That's very different to me than a network effect. 10:45 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 is something that actually provides long term defensibility. 11:01 And the last thing I'll say is it's, it's almost impossible to have these conversations in isolation because 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 mode, right? 11:19 Like you may go from, you know, like 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 a very, very basic concepts. We need to not conflate the notion of of the actual shape with the actual mechanism. 11:35 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. 11:42 Speaker 3 Can you give examples of super linear, linear, sub linear just just to bring it to life? 11:46 Speaker 2 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? 12:06 And so as the 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 an adding the same thing with like destinations on a highway or destinations on on a train. 12:23 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? 12:41 So like the growth actually increases over time and that's why you get, that's where you get 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 clearly this is very important when it comes to modes because if you have two competing companies and there's a a sublinear mode, the challenger will always catch up to the leader because the leader has to spend ever more money to grow, you know, all things being equal. 13:11 Speaker 3 Do you have like favorite company, recent company, examples of these different types? 13:14 Speaker 2 So I would so actually I would so, So how many actual network effects in the Metcalf's law of super linear do you think they've actually been here? 13:21 Speaker 3 Very few I don't know. 13:23 Speaker 2 I I, I feel like this almost never happens. Like, I think we can all agree the telcos, yeah, that's why we had to break them up. We could probably all agree LinkedIn like, you know, like they haven't had to like change very much. And they seem to, you know, you know, basically have a, a, a, a permanent status social networking and but all of these examples, it's like the interconnections of people. 13:44 I would say GitHub was another one that had which. 13:47 Speaker 3 Was effectively interconnections. 13:48 Speaker 2 It's interconnections of users, right? 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? 14:05 Like for example, people will say Uber, I'm not sure that that would follow the definition of a network effect. Maybe A2 sided marketplace because you have the drivers and you have the passengers, maybe you could use that. But even then you don't have a lock on either of them. Like all drivers drive for everybody and all passengers because you'd send anybody. 14:22 So that seems like that was more like about 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 affecting was more of like a personal annoyance and me being a, a pedantic, but I actually think defensibility most is kind of it's not really even worth talking about most of the time. 14:44 Let me explain why. So here's my experience for what it's worth. If a market you're investing in is growing, then companies are not going to want to compete. There's like a lot of new white space that gets created. And So what they'll try and do is they'll try and just move into other white space. 15:00 And so talk about defence ability 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 consolidated anyways, independent of most or not. And so I do think we spent a lot of time as investors hand wringing about defence ability when in practice, it doesn't really matter, especially in growing markets which we invested in. 15:23 And we've seen this with AI. Like I remember early discussions on models. I mean, 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 markets growing in a large market, it's going to accrue a lot of value. I remember this about the Neo clouds. 15:39 They're never dependable. 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 kind of not even worth considering. 15:54 Speaker 1 Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant, up to date proof of trust without all the manual work so you can close deals faster and with less friction. 16:10 Customers report a 526% return on investment and more than 16,000 companies use Vanta, including Ramp, RV and Snowflake. Get started with $1000 off at vanta.com/invest Ridgeline. 16:24 Speaker 4 Is the only AI native investment management platform that brings the front, middle and back office together in one system. It gives firms one place for their data and uses AI agents to automate repetitive work like onboarding, reconciliation, post trade compliance, and reporting so teams can spend more time on investing, serving clients, and growing the business. 16:42 Ridgeline is built to give you time back that can change the trajectory of your firm and be the inflection point that takes your growth from incremental to accelerate it. Ridgeline also wants to help asset and wealth managers understand and improve their firm's growth trajectory. So they built an interactive dashboard that lets you see how your firm compares to 16,000 peers. 16:59 If you file a form ATV, your firm is already in it. Visit ridgeline dot AI slash trajectory to see where your firm stands. Then talk to Ridgeline about how you can do more with the team you already have. 17:10 Speaker 3 Do you think that's a unique privilege of your stage of investor and then you know, once you get later stage public, I click someone that's buying the Anthropic S1 needs to probably think about defensibility. 17:24 Speaker 2 I mean, it depends on on your thesis on the size of the market. I mean, if you believe we're .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 of values accrues. I mean, I think that would, you know, I think the nature of venture has really changed actually in in general, you know, if you read back, kind of, 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. 17:54 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. 18:10 And then the size of the private companies has grown like a like a like a private company today rivals public companies have just 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 background. 18:27 So I think a lot of the mindset comes from that world, but I think it gets applied too early. 18:34 Speaker 3 The financial mindset gets applied too early. 18:36 Speaker 2 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, like we didn't even know how to monetize the Internet. 18:51 Like business models always lag, disruptive technologies 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. 19:07 So why would you worry about market size? Because it's creating market size 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, that was like years of like whatever and hand wringing etcetera. 19:22 And so, you know, I mean, I think my caution 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 tech. You're literally looking at the quality of revenue, which which by the way, is going to naturally move you towards non disruptive technology. 19:45 Because if it's disruptive, you probably don't know what the business model is. You probably don't know what the Tam is. On the other hand, if you really believe, you're like you, you believe I invest in tech and tech creates value. And that's the type of investor. And you do that early, you, you should not consider these other things. 20:02 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 don't be. Don't be confused in your basic kind of approach because otherwise you become incoherent. And so then like, you're like, oh, I'm a disruptive tech investor, but I'm looking for like, whatever good NRR that doesn't work that way. 20:22 Speaker 1 How would you describe the? 20:23 Speaker 3 State of the Union today in your world in infrastructure, like obviously it's changing really, really fast. There's new kinds of companies, but there do seem to be like some, we could like name some layers or some categories. How? How would you describe like what it looks like today? 20:37 Speaker 2 So for me, so the definition of infrastructure is like the the the differentiation is actually technical where I don't view SAS or apps is that way. Like do you view like if you invest in like like a SAS and apps company, it's like a technical investment. No, right. 20:53 Because it's like a business, especially now. Yeah, especially 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 and it has to sit kind of when the traditional technical stack. So the traditional technical stack for infrastructure is compute, network, storage and databases, right. 21:10 And each one of those has a sub components and they all have buyers and they all, you know, now we're seeing that being kind of reformed with AIA bit. And so all of them are being disrupted. But also now you have models which I would consider infrastructure. They look to me more like ASICS then they look like, you know, traditional software. 21:29 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 we don't even like, I mean, maybe you could argue with Jeb, They do kind of, you know, you can kind of put them in software, but traditionally they're out almost like, you know, their own separate entity. 21:48 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 major components. And so I think we do have a bit of a traditional mindset of compute, network storage, databases and then models. 22:09 Yeah. And then and then and then, but we also throw up our hands. We're like, this is all changing so fast because, for example, when you're worried about dev tooling in the past, you're worried about ergonomics for a human, 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 going to be using this, so it can't be too complicated. 22:27 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 because 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. 22:37 Speaker 3 Forever. Would the shorthand for this be that all the each of those five portions of infrastructure are being refactored for agents? Is that like overly simplifying? 22:47 Speaker 2 I split it in two pieces for me. There's like people are vibe coding a lot of stuff. There's most software being written ever before and that stuff has to run somewhere. And that's nothing to do with like that's not agents, That's just normal software. 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? 23:04 So that's one thing. And so I just view it's 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. 23:24 Speaker 3 It seems like each new wave of technology, like there's a new kind of database that's needed. Are you seeing that across all the areas that you care about that there's sort of because of AI whole new entrance? 23:37 Speaker 2 I think a lot of infrastructure is 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 OLTPOLTP is a transaction database. I 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. 23:54 But now we're seeing really interesting companies that are like, you know what the hard thing about using a database from a programmer standpoint is databases always have 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. 24:13 But now because of AI and, and, and one of the reasons is like if you, if you change that language, like anybody that knows his databases would know how, wouldn't know how to use it. And you'd have to like train a whole version of of programmers to use this new language. But now we're actually seeing databases come out, which are an entirely new way of thinking about databases. 24:32 You have way better properties. You don't really have to train anybody because the agents already know how to do it. And so, you know, I think these things are path dependent. Like some some sectors, a new entrant is going to come and change it like databases, OLTP may look entirely different in five years. 24:48 And some the incumbent may figure it out and be like, hey, listen, we're just going to 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, I guess. It'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, you know, rigid performance constraints, we're seeing, you know, net new networking companies come out for the first time since Arista. 25:13 You know, we're actually having to change. You seem like actually, you know, changes in in the materials used to build them. You were 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? 25:30 Is it like a container? Is it a VM? Is it a durable workflow like something like temporal like that is changing. I do think we're at a point of maximum disruption where many points of the infrastructure stack can be rebuilt. 25:43 Speaker 3 Can we spend a few minutes on models? Yeah, of course, Tropic S1 came out this week, which obviously 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 mentioned all the debates closed versus open, the public frontier versus the private frontier that the model that companies keep for themselves, you know, movement of talent, opportunities, things that don't exist that should like whatever whatever's on your mind about these things and how you see them evolving, I'm I'm fascinated to hear. 26:15 Speaker 2 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 very big fast growing market. And like what happens there is the frontier. 26:32 Being on the frontier means you've got tremendous pricing power. Even if you're just an Epsilon better than the next guy, you got tremendous 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. 26:48 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 gonna want the smartest thing. And so then under that view, there's nothing structural that gonna change as as as as long as the market's growing. 27:04 And you could argue this is gonna go on for quite a while because I mean, what is it penetration .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, OK, like what happens if the market starts to saturate in pockets, right? 27:24 So if that happens, you know, we'll then things shift to open source. Will you lose pricing power on the frontier? You know, one thing that we don't really like do a good job of accounting 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? 27:47 Let's assume that like anthropic stumbles and they don't release a model for six months. What is the lifespan do you think of the previous model? 27:54 Speaker 3 A very short period of time it's. 27:55 Speaker 2 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 the soda 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. 28:12 And then you slip 3 months, right? It could be the case that you lose the entire market. That in my experience, and I see a lot of these model graphs from apps, companies and from, you know, pitches, I would say that the lifespan of a model is 3 to 6 months. So that means you have to release the next model. So again, for this conversation, I think it's a very interesting conversation. 28:29 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. 28:35 Speaker 3 Three years of. 28:35 Speaker 2 Data. 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 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 back down into open source and, you know, we'll see kind of like a shift that we've never seen because software's always had a long tail of retention. 29:00 Speaker 3 On this trip I've been asking a lot of people what percent of software coding tasks are already saturated, and the typical answer is very, very high percentage. 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 seemed a little at odds, like if I could do it with a six month old model or something that's vastly cheaper. 29:23 Speaker 2 Well, these are two different statements. OK, I think we need to be very This is a great thing to dig in on, but we need to be very careful. 29:29 Speaker 3 Yeah, please tease it. Yeah, yeah. 29:30 Speaker 2 No, this is great. Great. So there's two very different statements. So statement #1 is I'm not using more tokens, right? Because I, you know, I have enough. I, I, I have enough tokens, I've automated 95% already. So I can't automate anywhere. That's, and I think that we're getting pretty close to that. Combat #2 is therefore I can use a cheaper model does not follow from combat #1 right? 29:51 So if you and I are, are like, I'm you and I are competing start-ups. We've got Martine start up and we've got Patrick start up, right? So we've got competing start-ups, and because we're in competitive equilibria, are you going to choose a Dumber model to compete with me in competitive equilibria? 30:09 You wouldn't. You would always pay 10 times more for the smartest model. Yeah, always, always, always. And so, you know, there's this very open question of 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 equilibrium like, you know, and when when you're actually competing. 30:31 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 it better? And can you compete in the 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. 30:49 And so either you have to increase prices or you're going to have to go broad. But in my experience, the pricing is maintained for sure. And because of the competitive equilibrium, like you always want the smartest thing. And then we're just seeing like, like a broadening use of these outside of code, right, to general knowledge work. 31:07 Like I'm a huge grok by user. It's like my favorite thing ever. And so I think we're starting to see, yeah, take over more and more of these. 31:14 Speaker 3 Say more about your personal experience with that. What you're learning, what you're doing with it, what you're learning from that experience. 31:19 Speaker 2 Like my my new grok. This is so funny. 31:21 Speaker 3 And they have like a home automation thing going on. 31:23 Speaker 2 I know so good. I just feel like I've weighted my entire entire life for this power, this technology to show off 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 I want the whole thing. 31:40 And like, it's never, we've never been able to do it. And now it's kind of trivial. So I use Grokblot for a lot of stuff. I use it for personal finances. My big hobby is modular synthesis. Like I like synthesizers, like making music, but well, like the 1970s, like patching, tweaking type stuff. It helps me with a lot of that stuff. So I use it for a lot of like standard things. 31:57 But my new kind of passion project is I want Grokbot to embody itself. So when I tell it I'm like I say, 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. 32:13 And it's literally. 32:14 Speaker 3 Buy stuff it's. 32:16 Speaker 2 Literally buying stuff. So I was like, oh, let's try this. I was like yesterday I bought a few $100 worth of gear that's coming to the house and like I'm going to have it embody itself so that. 32:24 Speaker 3 So what gear is it buying? 32:26 Speaker 2 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. Like, I know some home automation stuff. Like I know like Zigby Life, I know some home, but it's like really obscure stuff that it's decided to to buy. 32:42 But like, like I just have in my head this vision of like, 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 walked into the house. Yeah. Like, yeah. Like 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. 33:02 And so I'll let you know how that goes. 33:04 Speaker 3 What is this and other experiences using this technology? Since you're probably in the .0001% of you know creativity using it, what does it tell you about what's coming? 33:15 Speaker 2 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 what, you just didn't have the technology to implement. 33:39 And 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 Safian World Labs, great company. 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 a few months ago and you know, that would like like generate basically anything, but it also supported dynamics that would move. 34:09 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, you know, and like you're there and like, did you ever see sleep? 34:26 No, more like the of. 34:27 Speaker 3 Course, yeah, I went to it. 34:28 Speaker 2 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 3 hours. It was in this kind of three story hotel or something like that building in New York and you're like running around trying to figure figure out and like, like, like Hamlet is being played out or whatever. 34:48 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. 35:05 It's literally our creativity. And it's just because we've been beaten down by 30 years of software being shit. I mean, 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 in a point where the stuff could actually start working. 35:22 And so for me, it's like, I'm so excited for what this brings. 35:25 Speaker 3 It's tempting to say you could just freeze the models and their current capability and still get all of what you described. So there's like a diffusion question I'd like to explore. But before 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? 35:39 Speaker 2 Continue to get better. So I think again, 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. 36:04 So you know, 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 kit 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. 36:19 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's and you can train on it. We can solve. 36:35 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 sure, sure, you could set up a lab to like make speakers or whatever, you know, to do that. 36:52 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. 37:07 Speaker 3 Current digital side. 37:08 Speaker 2 The digital side is done. I mean, it's done how? 37:09 Speaker 3 Have you processed all this interesting security stuff? Breaches. I've heard it called Breach Apocalypse it. It's obvious that we're like entering an interest a very interesting era in cyber. What? What do you make of all this? 37:22 Speaker 2 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 economic damage. 37:41 We'd taken out critical infrastructure. I, I remember when it was remember Windows 95. You're probably. 37:47 Speaker 3 Taking no, I remember. 37:48 Speaker 2 OK, remember Windows 95? Barely. 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. Like take it around like not a joke, right? Like, and so like, you didn't even have time to patch it, right? 38:06 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. 38:21 And so I think we can look to what happened with the 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? 38:38 If the answer is yes, which is typically the case, you will come up with a tactical solution. And I think that will be the case here. And you're already starting to see it. I mean like these vulnerabilities that you see showing up, and this is good. That means that somebody used the model to like make things safer by pointing out the vulnerabilities. 38:54 I think there's going exactly like you would expect it to go, but I think the net impact will be it software will be much, much, much more secure because AI is also securing it. 39:02 Speaker 3 You talk about capital like what your sense of there's very interesting things happening in capital markets Rate, you know, 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. 39:18 There's clearly many companies that five years ago we would have there. 39:21 Speaker 2 Does that mean demand? I don't know what demand destruction. 39:22 Speaker 3 Meaning like we're not funding things that we would have 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. I was looking at this company this morning where if you and I had seen the deck five years ago, you'd be like, this is amazing, like software business and we should fund it. 39:41 And now if it seems to pale in comparison to other things that we could put our capital to, the capital itself feels like this interesting variable in this whole equation as a constraint. And I'm curious how that feels to you. What would comment? 39:54 Speaker 2 So I sit on the boards of a lot of great companies that have the problem that you have profitable growing forty 5060% at scale, but like they're like say 304 hundred 500 million in AR. What are those companies do? You can't really IPO because they're subscale, but they're great companies. 40:11 And I think about my own personal portfolio, I'm like, whenever I'm in muni bonds, 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'd be happy with your personal money being in that if you can guarantee it. 40:29 And so I still think these are very good companies. And so I feel it's more but a question, again, this is my naivety of not understanding this, of like, are we going to see a shift in the market so that people can get exposure to this asset class? 40:46 Because I think the risk profile is very, very different than AI. Nobody knows what's going to happen to AI when the market slows down. Nobody knows, 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 a basket, a basket that includes these slower growing but like these assets that don't like disappear if they like miss. 41:11 Speaker 3 Are, are most of the ones though, that, that are in your mind extremely subjected to this disruption risk? Meaning like I, I won't name specific companies, but there's lots of companies with that kind of rough profile, which seem terrifying in the face of like how easy it's going to be to create. 41:28 Speaker 2 So I'm an infra guy, so for me more software means you need more infra like like like we haven't seen a big like we don't vibe code databases yet like you do have your. 41:40 Speaker 3 Data so like snowflake be a well known example of kind of what you're talking about. 41:43 Speaker 2 Suffix A great. Yeah, it's a great example. I mean, like this is like, it's really hard. It's not just building. It's really hard to have the long tail of features, the long tail of performance tricks 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. 41:59 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 is going to disrupt these. 42:16 I think they are good businesses. And so I think more and more the industry's going to 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 SPAC's come back. I get a lot of calls now for companies that sit on the board, which are good companies where these SPAC's like, hey, listen, like we can, you know, we can make this stuff go public. 42:33 So 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. 42:43 Speaker 3 Most meetings that you have around here in the last for at least for me in the last, let's say three months, some half the meeting is about compute and that the product across categories like just needs more compute that is available to accomplish its goals. So there's another way of asking about the capital thing. 43:00 OK. Interesting. Where you guys manage a ton of money, Everyone's deploying their funds at very fast paces. 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, that would be a good thing. 43:23 Speaker 2 Great, great, great. I think this is great. OK, so I, I really think and I, I said this before, but I'm going to reiterate because it's so important. I really think the big disruption of AI is the fact that you can throw capital at problems. 43:38 In the history of like tech, you haven't been able to do that because there's always an engineering problem, right, which just doesn't scale. The mythical man month is a mythical man month for the reason. And so in 10 years ago, if I had, you know, 5 founders show up and they asked for a billion dollars and I gave it to them, what would happen? 43:54 Speaker 3 Sits there. 43:55 Speaker 2 Or they would detonate, right? They'd like, hire a bunch of engineers. 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. 44:05 Speaker 3 I'm sorry, can you explain the mythical man month? That's a very interesting I guess. 44:09 Speaker 2 The Mythical The Mythical Man Month basically says that you can't increase the speed of engineering. It's like a natural. 44:17 Speaker 3 Sub linear it's. 44:19 Speaker 2 Very sub linear and it has to come with like basically like organizational communication, which is one of the conclusions. But the take away 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. 44:34 And so they would throw more people at and then they would go slower, right? And so like engineering has always been this bottleneck and in my experiences, and I had my own startup. I, you know, 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. 45:01 Now, do they make 500 million from that billion or 2 billion? I have no idea. But the fact that you can productively use that money is so new and it's 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. 45:18 And if you look at Anthropic and Opening Eye, 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. 45:41 But here's here's what I'm seeing on the ground is we're running out of available debt. And if you're like, whatever the Norway pension fund, are you going to like give debt to a 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. 46:03 And so it's going to prefer the investment grade companies and then those that are not investment grade will have a harder time. 46:10 Speaker 3 So scale wins again. 46:12 Speaker 2 So, yeah, scale will win and then then it'll be a hard 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 capitalism, like, like, like general capital to fund. And I think that, you know, you're going to see a ton of liquidity. 46:28 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. And I think what this will mean was like if we want to have smaller players around that are not IG that get debt, they're going to need to prepay, which means their customers are going to need to prepay. 46:46 So I think over time this all kind of flows back to like the availability of cash and capital because you do have limited amounts of debt. 46:52 Speaker 1 Your finance team isn't losing money on big mistakes, it's leaking through 1000 tiny decisions nobody's watching. Ramp puts guardrails on spending before it happens. Real time limits, automatic rules, 0 firefighting. Try it at ramp.com/invest. 47:09 As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk. Learn more at Vantacom Invest Every investment firm is unique and generic. AI chat bots don't understand your process. 47:24 Rogo is built by finance professionals. It's connected to your data, learns your process, and quickly produces client ready outputs. Check them out at rogo.com/invest. The best AI and software companies, from Open AI to Cursor to Perplexity, use work OS to become enterprise ready overnight, not in months. 47:42 Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline offers one unified platform that automates away the complexity across portfolio, accounting, reconciliation, reporting, trading, compliance, and more, all at scale. Schedule a demo at Ridgeline dot AI. 48:00 Speaker 3 This reminds me to ask about sort of Neo Labs, Neo clouds, inference providers and companies. This I don't know if you call these infrastructure or what part of the stack you call. 48:11 Speaker 2 Those all those infrastructure. 48:12 Speaker 3 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, are sort of at scale. And I'm curious how you think this plays out, like what roles they should and will play, what the market feels like. 48:30 These things are growing so unbelievably fast in most cases. I'm very interested by these companies and I'm curious what you what you think of these categories. 48:37 Speaker 2 Well, let's put a neolabs aside, because I think that's the least well defined of all of them. I. 48:42 Speaker 3 Included them, to be clear, because so many of them seem to be building their own. 48:45 Speaker 2 They're becoming, they're becoming neoclass. 48:47 Speaker 3 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 and some Neo Labs are going all the way down to like buying land and power and all the way up through the token, you know what I mean? So there's this weird like. 49:02 Speaker 2 Yeah, I think we really needed to. So in my world, that wouldn't be considered a NEO lab, That would be it. Like all NEO clouds want to be token factories. So I would view if like your output is like a token of an existing model. 49:13 Speaker 3 Architecture or something else? 49:14 Speaker 2 Yeah. And then you're a Neo Cloud. Like all of them want to do that, and they'll all verticalize. I really think that the actual real limitation right now is hardware supply. That's it. It's not capital, it's not debt. That's the actual real limitation and power and power. So NEO Cloud to me is one that actually provides like the physical infrastructure that ends at tokens, right? 49:33 And so they can provide GPUs, they can provide, you know, fine tuning services or they can provide tokens. But it's, I 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. 49:49 I love a lot of these business plans are like, if we solve ASI, that's our business model, like literally like that. 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 open AI's original thesis was. And, and to some extent anthropics, which is like, if you solve the intelligence problem, the business model part is easy. 50:09 So that's to me. And Neo lab is new architecture model training. And they're, you know, and sometimes they'll buy their own infrastructure and buy their own GPU's, but like that's not what they're building towards their selling, right. So I think that the entire, like the real constraint right now is power. It is, it is chips, it is materials. 50:26 Like that's the sort of thing. And I, I think that with that sort of a limitation, 80% of these things are going to go to the scale providers and you know, 20% are just going to be kind of hard to reach places, niche type places. 50:42 You know, like I have the relationship with a random government that has the power. That's kind of funny. I've met this company that it does basically a GPU marketplace. That's what it does. And but it's like the most low level version of it and many of their GPU's converted chicken shit, you know, like like little like chicken coops where like people like in farms are like, you know, just like finding ways to provide it. 51:08 So there I think there will always, the market is so large. There will always be a significant portion that just doesn't make sense for the Spacex's to do. Like SpaceX is not going to go and like, you know, find out how to create a marketplace of like chicken coop GPU's, right. And so I, I think this is what is end up happening is, is the 20% is large enough to have a very wide fragmented array of folks. 51:28 But then the 80% of the market are going to be these kind of homage, you know, things like SpaceX. 51:33 Speaker 3 I heard this story recently that someone asked Larry Page how much of compute will be orbital and he said 5 nines and the reason was it's just physics. Like eventually it's just like all gonna be in space. 51:44 Speaker 2 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. 51:50 Speaker 3 Just because it skirts it just. 51:52 Speaker 2 Yeah, it's just basically, I think there's 500 proposed legislations to stop data center building the United States alone, including Texas, right? 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, and I've heard from many of the top data center people like it's very tough to break ground right now. 52:12 Then either you go international or you go into space. 52:15 Speaker 3 You're coming on, you know, these kind of software companies growing 60%. It reminds me to ask about prevailing valuations in the market and as a productive investor that's focused on disruptive assets being created. Even if the disruptive asset is truly incredibly valuable and great, every one of those has a price that will ruin the return of investing in it if it's too high. 52:37 Speaker 2 So this is another one of my bugaboos, which is I think I listen, I don't know how you can be AVC and not believe tech creates value, which is the first conversation we had. Now I'm going to have a second conversation, which is like, I don't know how you can be AVC and not believe markets are efficient. Like what are we doing? 52:52 Like for capitalists, 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. 53:08 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. 53:26 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, I, I, I believe that my diligence reduces that risk for me, but I don't, I don't do it on market value. 53:43 I'm not trying to like find a better price. I'm just trying to find a less riskier asset. 53:47 Speaker 3 Doesn't it follow 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. 53:56 Speaker 2 Oh, I would love to, but you can't. I mean, you can't pragmatic. 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 this is kind of like how we think about investing, which is, of course if I have an index of every competitive, like literally my dream would be I get 5. 54:13 Speaker 3 Percent cognition when you buy cursor. 54:17 Speaker 2 I've got 5% in every competitive and the only thing like I really believe private markets are super efficient. I do. And I think that like the consensus is actually smart because 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 and I'd be happy. 54:33 But 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 into the competitive bus hub, but even more importantly, like, how do I not like run out of our ability to keep track and help these companies? 54:50 Because, listen, I, I do take the boards. I do get very involved, very, very involved, right? I mean, I, you know, 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. 55:09 If they're risking their futures and the opportunity cost of their families 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, potentially look at all of that stuff. 55:27 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. 55:41 Speaker 3 Yeah, there's this thing in indexing called stratified samplings. From my old quantitative days, you can buy a small subset of the index and have your tracking error to the index be very low because you let actually literally what you're describing is effectively stratified sampling that you like get exposure to, you know, best, biggest, whatever per category and that will perform like similar to. 56:03 Speaker 2 Yeah, OK. So that makes it, I mean, I just, you know, 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 I didn't overwrote. I mean, often there was no, there's no business to evaluate. 56:19 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, to me, there's always this huge dissonance 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. 56:36 Like like by definition of everybody's calling it a bubble. It's probably not a bubble. Like it's almost like a bubble. Like when, when everybody's investing because they think there's still upsides and then they don't somehow think private markets are efficient. Like remember I got like, I got like VC cancelled because I'm like, non consistent investing is dangerous. 56:52 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 and they're like, you know, all like all of these deals that we've done were non consensus. 57:10 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 that certainly if you look at our portfolio, the ones that return money, we're actually very, very competitive in general. 57:28 So I just, I just think these markets are actually quite efficient. 57:30 Speaker 3 In addition to this, like I love the idea of market efficiency in private markets being 1 area that you disagree maybe with lots of well known. 57:37 Speaker 2 Yeah, apparently. 57:39 Speaker 3 Apparently all of them, yeah. Are there any other areas like that where you feel especially alone? Yes, there's. 57:45 Speaker 2 Another one that drives me nuts. This is my last one. 57:47 Speaker 3 I promise. 57:49 Speaker 2 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 tab of private markets. I love Roloff at Sequoia. I think he's fantastic. He's an incredibly intelligent guy. He's a great board member. But he gave this talk, do you remember, at TechCrunch Disruptor. 58:07 He's like, and many, many investors believe this. So this isn't to call off Roloff because I was actually a huge fan of his, but he was like venture capital is not an asset class. There's only 20 start-ups that matter and that's it. And so if there's too much money going in, you've over funded private markets. 58:24 And to me this is ludicrous. I mean like capital creates 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. 58:41 You've gotten massively large private companies, tons of opportunities, very large exits. And so this 0 sum thinking about private markets being like some amount of fixed against. 58:54 Speaker 3 Supply of great things. 58:55 Speaker 2 It's 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 how common this mindset is. I'm very often I'll talk to a pretty senior GP who's been investing for 20 years. 59:12 They'll be like, oh, there's too much money. I'm like, what are you talking about? There'd be more money in there. We need more, more ecosystem, more value, more investment. So I, I do don't, 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 ever has before. 59:31 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 there's, again, we need to all step away from zero sum thinking. I think we all need to believe that like technology creates value. 59:48 I think now especially they have, we need to believe that capital enables technology to create value. And if that's the case, more money in private markets, we should deploy the money and we should create the value. 1:00:00 Speaker 3 You've been on, if you put this in NBAGM terms, you're like the the player that's like on fire. It can like jump really high with with between like cursor and Fifa's company and open router. You know, all of these in a very short period of time. So well, well done. But I'm, I'm more curious about the lessons you learned about M&A and kind of what's what's going on in this market having been a part of three of the maybe the three biggest. 1:00:25 Major scale M and as in the last couple of months. 1:00:27 Speaker 2 Yeah. So, so actually I kind of have a history with strategic M&A going back to my company. So when I sold my company to VM Ware, it was a $1.26 billion exit, which at the time was the high. I think I beat David Sacks's Yammer. It was the highest acquisitions I'm. 1:00:43 Speaker 3 Counting. Yeah, it. 1:00:47 Speaker 2 Was kind of funny because Catalyst showed me this chart, you know, it was just kind of like multiples of revenue and like the total exit and there's like 2 outliers and it was like, yeah, you're in another company and they're like really close to each other. I was like marginally better just a small bit at the time. I think that was the highest acquisition on multiples of revenue enterprise software. 1:01:04 And then actually I was part of a deal that beat that, which was the Tabular acquisition to Databricks. And then of course Curse, I think it was the largest price fit acquisition ever. And of course, you know, Fayfy's acquisition is 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. 1:01:19 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, like deep, deep personal alignment because you know, it's, it's, it's a real risk for both sides. 1:01:35 I think like Lisa Sue and Faye Faye or think Michael Truel and Elon or think like Alex and Patrick. I mean, you know, 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. 1:01:54 I mean, that's almost always present is like there's some existing trust relation, like existing alignment. The second one is you have to take it from the mind of the buyer. So what the buyer's doing is they're basically buying an option on a large market, right? So in the case of like VM, we're buying the sera, they're buying a $1 billion option on a $40 billion market. 1:02:16 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 in the networking. We can get that budget. And then you have to do the inductive step and you guys have virtual machines. 1:02:34 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. 1:02:45 Speaker 3 The company's job is to show the inductive step. 1:02:47 Speaker 2 The the company being acquired or both of them? The the like that, like like like the discussion. 1:02:52 Speaker 3 Clearing has to. 1:02:53 Speaker 2 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, right. 1:03:15 But those conversations are very, it's almost like like really high stakes enterprise sales. I've been a part of a lot of also like more auctiony type M&A. 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 etc. 1:03:31 And so I just think they've got a very different flavor. But I think that especially with the timelines going down on tech, I think we're going to see more and more strategic access. And so I think it's probably important for people to get pretty smart of how these things look. And it's very different than they assume. And most people assume you should hire a banker and you should have an auction. 1:03:49 You get the highest price and it's like hiring a Baker is fine. You can do that if you want. But like the idea that you're going to like go to like the Apex Predator or something like whatever Mark Zuckerberg and play poker. Like you're kidding yourself, right? Like these things are built on trust and, and telling out a story, not playing games or playing poker. 1:04:07 And so I just think that there's kind of a mindset that you need to have if you're going to engage in these things that's quite different than the intuition. 1:04:13 Speaker 3 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. 1:04:22 Speaker 2 We'd actually just, we're just about ready to get a raise done and we'd, you know, raise a few billion dollars and like that was was getting done. But this is about the time that Mythos came out. 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 methos. 1:04:41 And so we're like, you know, if, if cursor is going to, you know, build models, we need to raise more money and get access to those computes. And, 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 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. 1:05:05 And so again, this is one of these great examples where the, you know, in the end, you know, SpaceX had the compute and cursor did have the data and had a team that had very successfully built, you know, models and built this practice and code was very important. So there's just a lot of alignment and what, and it always helps to have a, a raise can because that helps actually set the value and set the price of these things. 1:05:28 So that was actually a fairly straightforward deal. 1:05:32 Speaker 3 I'm curious if there's anything similar lesson wise from Open Router or from Fit Face Company, World Labs and anything else that is shareable from those two experiences that might be indicative of or bear some lessons from this kind of strategic M&A? 1:05:47 Speaker 2 So I'm going to go back to what I said, which is for these strategic M and 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. 1:06:03 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 going to beat you in poker, dude. That guy will fucking take out your sucker and pat you on the head. I'm kidding, like I'm not kidding, right? 1:06:20 It's like like these are the like these. And so in every one of these deals, the companies 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's a lot of trust. 1:06:44 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. 1:07:03 You got to leak it to the I mean, I would think like, I mean, God, the open router deal drove me nuts. Like I got leaked. Did you notice, by the way, that World Labs did not get? 1:07:10 Speaker 3 Leaked. Yeah. Interesting. 1:07:11 Speaker 2 Didn't get leaked because we didn't tell anybody. It was like me and there's a few people not leaked, open router was leaking constantly, right. And I think that like a, a number of VCs think all this is how you drive up the price or this is how you get it done. And you, you see these things leak all the time that destabilizes deals. 1:07:28 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 do it with very professionally. 1:07:46 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 and realize that the people that you're are that are counterparties to this are the most sophisticated people in the industry. And, and I I would say that in the founders in every one of these cases treated it that way. 1:08:03 Michael Trude treated it that way and his team, you know, Alex and Chris treated that way for open router and for sure Fei Fei treated. 1:08:10 Speaker 3 How are you seeing the role of investors evolve? But 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. 1:08:25 It seems like because the stakes are so high, growth is so big capital, the capital dollar amounts are so big that all these investors are overseeing. It's kind of crazy. Maybe some commentary on like the role of the investor? 1:08:37 Speaker 2 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, you know, 20 years ago, I think like Stripe or like data bricks or like SpaceX before an IPO or Android or like these companies are massive. 1:08:56 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 stage startups. 1:09:16 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. 1:09:35 Like for example, you wouldn't put me on a mining company board, right? I mean, like, I mean, so somebody's like, I mean, remember 2021, So remember 2021, we'd have like these like PE investors, they would invest in these serious 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. 1:09:55 I have no idea. Like trying to like reason about the company through like burn. Like, dude, you're going to be miserable here because 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. 1:10:15 You know, I think this, I think a lot of this is just growing pains. Like how much larger do you think the market is now than it was 30 years ago, 1000 times. So of course, of course, you know, the industry is going to have to mature around that. And of course you're going to have to like view it like a kind of later stage investor, of course. 1:10:31 But I, I just think that there's like a, 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. 1:10:48 Like I don't think it goes together. Like what would I do? I go there and, you know, tell you how to like run a finance opera. It's just not, it's just not me. So I think that's 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. 1:11:07 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 right about burn, you know, or like they're over rotating on metrics for disruptive technology. 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. 1:11:29 Speaker 3 Has anything changed about how when you meet somebody for the first time, if it, like if I came and watched 10 meetings in a week with you 10 years ago when you started and then I came today and watched 10 meetings with you? Have you changed how you conduct your interest? Your early interactions with founders? 1:11:46 Speaker 2 Yeah, a lot. I mean, 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 and then you also have to learn how to ask the questions that you really care about so that you can kind of answer the questions to yourself. 1:12:09 It's a very delicate balance, right? And 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. And so you'll be like, who's your competition? And then a lot of it is just asking questions for asking questions sake. 1:12:26 But like it doesn't actually inform your decision. And then especially if you're an operator, you'll hear something you disagree with and you kind of want to 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 see, 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? 1:12:51 And so like, if you disagree, you disagree, but like, that's what they believe. I mean, it's 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 a truly an art. The second one that I mentioned is truly an art. Like you have to make a decision based on that meeting. 1:13:06 There's a lot of silly questions you could ask that are not really going to inform that decision. What if Amazon does that? What is open AI? Like whatever. You can answer that yourself. Like like like maybe you can like figure out how they think. But there are key questions that you really want to 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. 1:13:25 And I think I've really kind of refined my ability to do. 1:13:27 Speaker 3 That 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. And I know all of that excites you. But if you had to just step all the way back and describe why this job is so incredibly fun right now and why you're most excited, how would you frame? 1:13:49 Speaker 2 It, I have more and more of the belief that this is an industrial revolution level shift. And for me, the industrial revolution went from basically a 0 sum world to like a, you know, non 0 sum or value is being created, That 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. 1:14:13 And then you'd 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 you, 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. 1:14:28 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 because I think this is a scale up capital game that causes that to happen. I don't believe private markets have limited Tam. 1:14:44 I think they have unlimited tab 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, very, very key to the innovation chain and we're both very lucky to be here. 1:14:58 Speaker 3 I love the every two year schedule that we're on. I hope we get to do it again in another two years. I can only imagine what the world is going to look like 2 years from now. Yeah. 1:15:06 Speaker 2 We'll have our rock box talk. Thank you. So. 1:15:08 Speaker 3 Much for your time. 1:15:09 Speaker 2 Thank you. 1:15:13 Speaker 5 You know how small advantages compound over time? That's true in investing and just as true and how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default. Better data, better decisions, better economics over time. See how at ramp.com/invest. 1:15:28 As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for [email protected]/invest. The best AI and software companies, from Open AI to Cursor to Perplexity, use Work OS to become enterprise ready overnight, not in months. 1:15:44 Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline is redefining asset management technology as a true partner, not just a software vendor. They've helped firms 5X and scale, enabling faster growth, smarter operations, and a competitive edge. 1:16:00 Visit ridgelineapps.com to see what they can unlock for your firm.