--:-- Host (Harry Stebbings): There's only been one sin, and that one sin is zero-sum thinking. We always worry about, "Oh, is this defensible? Will this layer get margin? Will this layer get value?" And the answer has kind of been unilaterally, yes. The answer has been every layer has gotten value. Every layer has winners. These markets are so large, and they're growing so fast, we're actually seeing brand effects take place. In this phase of model scaling, a lot of the approaches to scaling don't generalize. This gives a ton of room for the application developers to build their own models. I think that right now, open source is most dangerous because China is better at it than we are.
--:-- [Intro music fades in and out]
--:-- Host (Harry Stebbings): Today on the podcast, we're sharing a conversation from our friends at 20VC with A16Z general partner, Martin Casado. They cover the state of AI investing, why the real sin is zero-sum thinking, how value is being created at every layer of the stack, and the risks of monopolies versus the reality of concentrated markets. Let's get into it.
--:-- Host (Harry Stebbings): Martin, man, I love our conversations. I was so excited when you said you'd join me again. Thank you so much for doing this, man.
--:-- Guest (Martin Casado): So excited to be here. It's great to see you.
--:-- Host (Harry Stebbings): Dude, I freaking hate these "how did you get into venture" intro questions. So I just want to dive right in. It is a freaking nuts time. Starting off, how do you evaluate where we're at today in the AI investing landscape? Peak hype cycle, great, super excited, both. How do you evaluate it?
--:-- Guest (Martin Casado): I'm kind of of two minds. Of one mind, I do feel like my intuition doesn't really work like it has the last 20 years. The future is very uncertain. One reason is because this is really the first time software development and software creation is being disrupted. So on one hand, I was like, I don't really know what to think.
--:-- Guest (Martin Casado): On the other hand, observationally, there's only been one sin, and that one sin is zero-sum thinking. We always worry about, "Oh, is this defensible? Will this layer get margin? Will this layer get value?" And the answer has kind of been unilaterally, yes. The answer has been every layer has gotten value. Every layer has winners. Things that we thought were silly are making money. It's been solved. There are profitable companies. The business case is there. So I think the one sin is not playing the game.
--:-- Host (Harry Stebbings): Do you agree with the "playing the game on the field" sentiment? When we look back at 2021, I remember everyone saying "playing the game on the field." I wish I hadn't played the game on the field. To be transparent, Martin, do you agree that you have to play the game on the field in AI?
--:-- Guest (Martin Casado): I think behavior should follow business. It shouldn't follow marks. In 2021, behavior was following marks. The public markets just decided these companies were valued a whole bunch. Tiger came in with a ton of money and deployed it. So behavior following investment in marks is a bad idea. But in this case, you have some of the fastest growing companies we've ever seen by users, by revenue. The amount of value that's shifted to this is so significant. Investors' behavior should follow that. If not, what are we doing?
--:-- Host (Harry Stebbings): When you think about shifting value, a lot of people have fun with the disruption of software development. There is a ton of players in the vibe coding space. They are predominantly all sitting on top of Anthropic. Claude Code is gaining more and more dominance. How do you think about these providers' reliance on a tool that could eventually shut them off?
--:-- Guest (Martin Casado): There are two futures for code. In one future, you've got Anthropic as a monopoly. In another future, you have an oligopoly, or maybe even a bit more of a market of these coding models. They are very different futures. When you answer this question, you have to consider both.
--:-- Guest (Martin Casado): I will say the timing of this conversation is pretty soon after Claude 4 launched. That's a major model launch. These models are so episodic. Every time one launches, everybody's like, "It's the future, everything's going to happen." Remember the whole Ghibli OpenAI launch? We were like, "Image is going to change forever." Then it comes, we're excited, and then it passes. Maybe that'll happen here. Maybe it won't. Our perception is colored by that launch.
--:-- Guest (Martin Casado): Let's consider both. For the first one: historically, models don't really keep much of an advantage because they're so easy to distill. Even in the last week, we've seen launches of models from Qwen and Kimi. They're great. People like them and adopt them. In that world where you continue to have new models from different providers—I would never count out Google; their coding models are fantastic, and the rumor is that GPT-5 coding is going to be great—you need to have a consumption layer that's independent. All of these companies will add that consumption layer value, for non-technical users, Python users, professional coders, whatever. That's going to be a very healthy layer.
--:-- Guest (Martin Casado): The other future: let's assume Anthropic is just a monopoly on coding models. In that case, they will decide where it's not profitable for them to enter, or they'll change their business model. Maybe they say, "We want to have the consumption layer, but we're never going to be an app dev tool company." It's a different sales motion, a different sales team. Nobody knows where that stops, but they will put pressure on anybody they view in their core focus. They will do whatever they can to capture that margin or market share.
--:-- Guest (Martin Casado): I just think it's the wrong time to have this conversation right after a major model launch. These models are so episodic, and we always assume every time a model launches, it's going to be a monopoly. It just really hasn't been the case.
--:-- Host (Harry Stebbings): Going to your zero-sum thinking, if you were to put a bet on which future is more likely, which future do you think is more likely?
--:-- Guest (Martin Casado): Oligopoly. This is how the cloud played. I think the best analog we have is the cloud. The other companies behind models can subsidize these things arbitrarily. I think about Gemini. They don't have to do this in a way where they have the same economics as an independent company.
--:-- Guest (Martin Casado): Look at the cloud. AWS was like 70 or 80% market share early on. Nobody thought they could ever catch up. They were the massive market leaders that created the category. They had way more dominance than Anthropic has now. Microsoft and Google said, "That's an important big market. We have to be in it." They just spun their way into it. You ended up with an oligopoly on the clouds.
--:-- Guest (Martin Casado): I see no reason it would be different. Gemini 2.5 is a great model. On price performance, in many use cases, it's the one I actually use as my standard model. It's better than Anthropic for some use cases if you take into account price performance. And Google can arbitrarily subsidize that. Never count out OpenAI. They started the party. They haven't had a major model release in a while, certainly around code, so that's going to show up. The players, the money behind the players, the fact that these models distill—this will end up in an oligopoly. That's just my guess.
--:-- Host (Harry Stebbings): To what extent do you think the large model providers in 10 years' time have already been created, or are they yet to be founded?
--:-- Guest (Martin Casado): I think you end up with models with different flavors. There are going to be a lot of new flavor models that come out. Mira and Ilya are out there creating models. You've got these very legit teams that were some of the pioneers, just starting up models for the sciences. As you get more into RL territory, these models really get a certain flavor. They don't generalize nearly as much. That's going to naturally fragment the models from a technical perspective.
--:-- Guest (Martin Casado): For the core base model for language, search, and code—even code is still so early. It's very, very early in the super cycle. In previous super cycles, it took two or three generations for the winners to emerge. Google was third generation search. Facebook was third generation social networking. There was MySpace, Friendster, and then MySpace before that. There's a lot of change to come. But I do think both Anthropic and OpenAI have done a remarkable job with brand independence and market share. I suspect they'll continue to be stalwarts in the industry.
--:-- Host (Harry Stebbings): Are you in either of them?
--:-- Guest (Martin Casado): We're investors in OpenAI, yeah.
--:-- Host (Harry Stebbings): Got you. My question to you is, fundamentally, do you think models are good investments for venture firms? When you look at employee stock compensation, the dilution that comes from it, and the dilutive nature of the businesses, it's a hard sell.
--:-- Guest (Martin Casado): If there's one thing I've learned—for anybody listening, this will be worth your time—there is no one way to think of AI and no one way to think about models. The models themselves are entirely different businesses depending on how you talk about them. To answer that question, we have to tease apart what you mean by "model."
--:-- Guest (Martin Casado): For example, look at the diffusion models: Eleven Labs, Midjourney, Black Forest Labs, Ideogram. These are wonderful businesses with great economics because the models are smaller. The ecosystem isn't subsidized in the same way. Google subsidizes language, code, and video, but not speech. From an investor perspective, these are clearly great investments if you look at the metrics alone.
--:-- Guest (Martin Casado): On the other hand, the frontier language space is much more complicated because there's so much subsidization. You have Meta, Google, and a bunch of Chinese players entering. For a subset of the players—and this is why it's a tricky question—clearly these are the fastest growing companies we've ever seen. There's tons of value. These are very valuable entities: Anthropic, OpenAI. But at the same time, even three years in, there have already been a number of companies that have had to exit early. It's a high stakes game where the winners really win, but it requires a lot of capital to enter. If you're not one of the leaders, that capital is forfeit.
--:-- Host (Harry Stebbings): We do a show every week with Rory O'Driscoll and Jason Lemkin. Rory very aptly said, "With the transition to AI, every investor has just accepted a willingness to go massively up the risk curve on investing." Do you agree with that?
--:-- Guest (Martin Casado): I think it's the requirement of the game. These are very capital-intensive companies to build. They have to get the capital from somewhere. They're also the fastest growing companies. For the winners, it's justified. It's not that investors are willing to go up the risk curve. We'd be very happy not to. It would be great to have great returns with low risk. But the nature of the system and the game we're playing requires it.
--:-- Guest (Martin Casado): This is the dissonance in all of this, and it's so important to call out. On one hand, you have these great businesses that are very fast growing. Zero-sum thinking has been tremendously wrong. Nvidia is continuing to grow in value. The hosting providers, which everybody wrote off as non-defensible, continue to grow in value. The model companies—I can't tell you how many investors wrote off the models; this question has been around for three years—they continue to grow in value. Every layer of the stack continues to grow in value. On one hand, it's all working. You should be in the leaders on every layer of the stack.
--:-- Guest (Martin Casado): On the other hand, we've seen tons of wipeouts already for the non-leaders. It's almost this bipolar or paradoxical situation. You kind of have to play, but it's very, very high risk. If you don't play, you're missing one of the fastest growths in value we've seen in 20 years.
--:-- Host (Harry Stebbings): Do you think you see the concentration of value to one or two players across markets in every market? Whether you look at voice with Eleven Labs, or Replit and Lovable, or OpenAI and Anthropic, or Cursor.
--:-- Guest (Martin Casado): This is such a great question. It's so early, we don't know. Maybe in a month all this gets proven wrong. But we actually talk about this a lot internally. Here's one thesis, and this is the one I'm attached to: these markets are so large and they're growing so fast, we're actually seeing brand effects take place. We haven't seen that since the internet.
--:-- Guest (Martin Casado): By brand effects, I mean if you become the household name, you will get the adoption because it doesn't require a lot of education. It doesn't require a lot of competitive discussion or positioning in the field. For many of these models, is one better than the other? Maybe, but they're pretty close. But people know ChatGPT. It's a household name. My mom knows ChatGPT.
--:-- Host (Harry Stebbings): Honestly, why did I do Lovable? For the exact same reason that ChatGPT wins. I thought it was the consumer brand that would win. A hundred percent.
--:-- Guest (Martin Casado): I just think these markets are so large, brand effects work. Let's talk about Midjourney. Midjourney was the first that got above the quality bar. It's taken zero investment from institutions. It's still the market leader. It continues to do great. Meanwhile, a bunch of other people have entered the market. It's not unreasonable to assume that these markets are very large. Leaders are going to have brand monopolies and brand moats. They'll be able to maintain them until things slow down.
--:-- Guest (Martin Casado): In general, markets tend to expand and then contract. Think about cloud. It was this funny thing and became very massive. Then it slowed down. When it slows down, you have consolidation and competitive dynamics. We're clearly in a massive market expansion phase. In which case, the leaders are going to continue to have a distribution advantage just through brand recognition.
--:-- Host (Harry Stebbings): When does that tail off? When does the importance of brand and brand recognition dwindle and product prioritization or product quality trump it?
--:-- Guest (Martin Casado): As soon as the market growth slows down. Let's take cloud as an example.
--:-- Host (Harry Stebbings): Do you think these are actually tools of market growth, or just consumer intrigue? There are a lot of people who want to try building a website on Replit, Lovable, or Bolt. A lot of people who want to try voice with Eleven Labs. To what extent is it market intrigue versus expansion of market?
--:-- Guest (Martin Casado): The expansion of market provides the dynamic so that you don't saturate the user with competing messages. The idea of market expansion is the frontier continues to expand. The first thing the frontier hears is the household names. So the household names win. That's a natural artifact of expansion. As soon as the expansion slows, that frontier is going to hear both names. Then you're in a discussion of which one to use.
--:-- Guest (Martin Casado): For the longest time when the cloud market was expanding, everybody knew AWS. It was the leader at 70-80% market share. As soon as that growth slowed down, market share started to shift dramatically. It wasn't obvious: "Do you do GCP? Do you do Azure?" That's less an artifact of Google and Microsoft deciding to enter the game and much more that the market growth itself started to slow down.
--:-- Host (Harry Stebbings): So we see market growth slow down, and then we see the dispersion of value across players more so?
--:-- Guest (Martin Casado): That's right. The market slows down. Once that happens, the frontier becomes more saturated because we're not adding people as much. They will get more of the educated message. They'll start making more decisions. You can have more of a conversation. Of course, Anthropic would love to have the same brand as ChatGPT as a household name. But how do you reach that frontier if it's growing that fast? It's operationally tough. The only way is through brand recognition, word-of-mouth, being on every podcast. We're seeing brand effects happen now. We saw these in the early internet. The brand leader tends to get 80% of the market. It tends to break out Pareto for a while. Over time, it'll slow down and even out more based on product differentiation.
--:-- Host (Harry Stebbings): How do you factor that into your thinking when investing today?
--:-- Guest (Martin Casado): You just try to invest in the leader. It's worth paying up for the leader. I ask two questions. Number one: for the area it's focused on, is it the leader? If it is, it's definitely worth paying up. Number two: the story has been that in a competitive space, almost everybody just found a new niche, a white space.
--:-- Guest (Martin Casado): Take OpenAI. OpenAI was the first to code with GitHub Copilot. They provided the weights, as far as I know, and they lost that. They were first to image with DALL-E, and they lost that. They were first to video with Sora, and as far as I can tell, they lost that. Yet they're still the massively dominant player in language and continue to be. Arguably, that was the right thing for them because that's by far the largest market. OpenAI acted totally rationally and has the largest market. But that gave Midjourney the ability to take image, BFL to take image. Google seems to have grabbed video with Veo 3. On the model side, Anthropic has turned code into this wonderful business.
--:-- Guest (Martin Casado): When markets expand, not only do you have these brand effects, they also tend to fracture. What seemed like a sub-market will emerge as a leading market. You see this on the image side. You've got viable image players that focus on different things. Ideogram is great for designers, a professional design community. BFL is the open source community, especially for developers that use these things in products. Midjourney is for more of the fantasy, also professional designers, but it's a very stylized, opinionated view. All of these are independent, viable companies. I think we're going to see fragmentation for quite a while before we see consolidation.
--:-- Host (Harry Stebbings): I need your advice. The show is successful because I'm very open with my troubles. Abridge in the US—I'm not sure if you're in it, but I'm sure you know it. Very simple: as a European player that does medical transcription for nurses, they went from one to eight million in a year. We're looking at leading their A, and I'm thinking exactly the same: you're going up against Abridge because you're going to need to compete in the US. Is this going to be a big business? Is that a losing game where you are a European competitor?
--:-- Guest (Martin Casado): This is a great question. Another very interesting thing we haven't seen in a very long time is geographic biases showing up with AI. The regulatory environments are quite balkanized. There are language and cultural biases that are also balkanized. We're actually seeing a lot of regional players show up. I think it's very legitimate. The thesis cannot be "European company X wins the American market." But I promise, when it comes to AI, the European market is large enough. A very legit thesis is this becomes a regional player in Europe and then maybe a portion of the US market.
--:-- Host (Harry Stebbings): A lot of people denigrate these businesses we've discussed because of their margins. They simply pass through funnels to the large language models. Do you think that is something that changes over time?
--:-- Guest (Martin Casado): It's the same for all great businesses. Uber started off with shit margins. Now they have better margins. I just don't buy that these are endemic to the business model. This is certainly not my experience at all.
--:-- Guest (Martin Casado): If you're a founder and you get access to relatively cheap private capital, you can do a trade-off between margins and distribution. It's land grab time. What would you do? The argument is the incremental user is someone you can monetize forever down the road. If you don't get that user during the land grab, you can never monetize them. The rational business decision is to sacrifice margin for distribution. We've seen this forever. Hell, the web wasn't even monetized. This time, we can actually monetize these things. Forget break-even or negative margins. It was massively negative because we didn't even have a business model until advertisements came up. This is the most rational thing markets have been doing, at least tech markets, forever. It's no different this time with AI.
--:-- Guest (Martin Casado): There is a question of how you turn on margins. You'll either have to build a traditional moat—a two-sided marketplace, a brand moat, the long tail kind of integration and domain understanding. For example, if your healthcare company really cracks the European market and understands all the regulation, Anthropic is not going to take the time to do that. There's clearly pricing power on that side. Or you have to do actual technical differentiation.
--:-- Guest (Martin Casado): One thing we're learning is in this phase of model scaling, a lot of the approaches to scaling don't generalize. If I want to be much better at coding, I may not be so good at something else. This gives a ton of room for application developers to build their own models that service certain areas the large models just aren't focused on. There's even a ton of technical level to differentiate.
--:-- Guest (Martin Casado): In my experience, most of these companies that have break-even margins, it's a board-level specific choice to prioritize distribution. It's not just because this is systemically something they have to do.
--:-- Host (Harry Stebbings): We mentioned data sovereignty. I am intrigued how you think about safety and safety around AI and models. You had Vinod Khosla saying, "We have to lock this down. If this was not locked down, it would be like nuclear secrets being handed out." Then Mark came and was like, "Fuck that. No way." How do you feel about the future of safety within this landscape?
--:-- Guest (Martin Casado): It's crazy to have VCs talking against open source. Founders Fund did too. For me, it's wild when pro-innovation sectors of the economy and academia have decided that open transparent innovation is somehow an antithesis of safety. I know that's not what you asked, but I just want to make the point. We were in very bizarro land for a while. It seems like we're coming out of that now.
--:-- Host (Harry Stebbings): Do you think we're coming out of that?
--:-- Guest (Martin Casado): I think we're moving more and more into that.
--:-- Host (Harry Stebbings): Great. Meta and Alex are going to turn Llama fully closed.
--:-- Guest (Martin Casado): Let's go back to that in just one second. I'm going to answer the question you actually asked on how I view this, and then we'll go to whether we're coming out or not.
--:-- Guest (Martin Casado): How do I think about safety? I was very, very close to security during the rise of the internet. I worked for the intelligence community. I worked for Livermore National Labs. When I did my PhD, a good 50% of my work was in security. I taught a cybersecurity policy course.
--:-- Guest (Martin Casado): With the internet, you had very specific examples of new types of attacks that impacted nation states. Critical infrastructure would go down. You had things like the Morris worm. These really significant examples kicked off a large discussion on how to handle it. It was so significant at the nation state level that we started thinking we have to change our doctrine. We were in this Cold War era of mutually assured destruction. We had to change it to this notion of defense asymmetry, which meant the more we relied on these things, the more vulnerable we were, as opposed to a country that didn't rely on them because you could be attacked. Then there was the whole terrorist information warfare stuff. The implications were so absolute, and you had so many proof points, and you could articulate them incredibly well.
--:-- Guest (Martin Casado): If you look at the AI stuff, for every computer system, you have security considerations. But we've got this 30-40 year, very robust discourse around this that we can draw from. The thing I don't understand is how all of a sudden we've decided that these are not computer systems. They don't obey the same laws. We have to throw out everything we've learned and revisit the discourse, even though we don't have the same proof points. Nobody can make a strong argument on asymmetry or a need to shift doctrine. If they can, let's have that discussion. I still have yet to see the dramatic new attack. It's going to come for sure, but we haven't seen it yet. The discourse around this is not in line with the reality. It's not in line with historical precedence. We should absolutely take these things seriously, but we should draw on the information we've learned from the past and the approaches we've taken in the past.
--:-- Guest (Martin Casado): The last thing I'll say: the biggest difference this time is in the past, the people creating the technology were pro-tech, and the people selling security solutions were the fear mongers. You'd have somebody create the internet and say, "This is safe and great for everybody." Then you'd have somebody create a firewall and say, "The internet is dangerous. Every sociopath is your next door neighbor." You had both voices, but in two different bodies based on interests. The interesting thing this time is they're in the same body. The person creating the thing is also saying, "This thing is very dangerous." I don't recall the last time we had something like that. It's created a dynamic that's been very confusing for everyone.
--:-- Host (Harry Stebbings): Do you not think open source increases the opportunity set for hostile actors like China and Russia to harm us?
--:-- Guest (Martin Casado): I think it's tautologically true. You can say, "Do you believe computers and the availability of computers increase their ability to harm us?"
--:-- Guest (Martin Casado): And I would say absolutely computers and the availability of computers do. But very specifically, open source over closed source. So I think that right now open source is most dangerous because China is better at it than we are. And as a result of that, we're seeing a proliferation of Chinese open source models everywhere. Now, unfortunately, we don't have control over Chinese regulation. And so I would say the answer is yes, because of China, not because of us. And the right way for us to respond is to fuel our open source efforts against that.
--:-- So let me just be very specific. I think Chinese open source can be a national security issue, for sure. And any of the software that's produced by a nation state that we view quasi-adversarially, the way that we combat that is we also are incredibly open, and we also do a proliferation of technology.
--:-- Host (Harry Stebbings): What do you think we can learn from China regulatory-wise that would enable us to have the same or better open source ecosystems?
--:-- Guest (Martin Casado): I mean, to me, this is, the United States has a long history of being pro-innovation, pro-innovation for national security, pro-innovation for national defense. I think we should be funding this stuff like crazy. I think we should get the national labs involved. We should get academia involved. We should make this a national priority, just like China does. And we should just have a full-throated endorsement of all of this stuff. I think we should do closed stuff. I think we should do open stuff. And we've done this forever.
--:-- My first job out of college, this is 1999, was working at Lawrence Livermore National Labs on the ASCII program. And what were we doing then? The broad program was stipulating nuclear weapons. I mean, this is what it was. And a lot of the concerns we have today were concerns we had then around compute. We actually stopped Saddam Hussein from importing PlayStations because we were worried about using them for simulation. We'd put export controls on the hardware and we'd say the same things like, "Computers out there, they're going to enable the enemies and all sorts of stuff." And this is nuclear weapons. This isn't some abstract AI thing. This is actual on the ground weapons.
--:-- The posture that we took at the time, the conclusion is we're just going to be the leaders in all of this stuff. And we funded academia and we funded the labs and we won. And we were able to control the technical discourse of the planet going forward. And this time, instead, we want to put our head in the sand and let somebody else do it. So they're going to learn from our success and somehow we're not.
--:-- Host (Harry Stebbings): Do Trump's cuts to universities' research labs not impact your ability to do what you just said? Are you not actively going against what you should be doing?
--:-- Guest (Martin Casado): I am very pro-investing in academia and in the national labs. I think there's always a political shift in money depending on what they view is in line with administration politics. I did my PhD at Stanford. I've done a bunch of NSF grants. I don't remember ever somebody saying we like indirect costs. Every researcher, every professor, every single one was like, indirect costs are terrible. Obama tried to get rid of indirect costs. He said, "Universities have a tax-exempt status. So why don't we just have them spend 5% of their endowments like any other tax-exempt organization? That will cover a lot of indirect costs." And he couldn't get it through. So this is a bipartisan issue that is longstanding.
--:-- I would say that a change is needed. Now, to the extent that these things are very hard to implement, I would say concretely, yes, we should invest in these things. Yes, we need a shift in how funding happens. I do think that indirect costs have gotten way out of hand. And until it was Trump doing it, everybody that I know in academia totally agreed. But yes, of course, change and shifts in funding will be disruptive. So I think all things are true. I just don't want to reduce this to a simple "Trump does bad things" because I don't think that is the case. And then "funding science is arbitrarily good" because I don't think that's the case. I definitely think we should fund as much or more. I definitely think that a shift in funding and change to the system is needed. And the right path through that is complex. I don't quite know it.
--:-- Host (Harry Stebbings): You very kindly said that I asked a good question on the reversion back to closed source when we mentioned Alex joining Meta, what it meant for Llama. I said quite zero-sum-wise, to your point, we're clearly seeing a movement back towards closed and away from open. How do you see that? And do you disagree with my statement on the transition?
--:-- Guest (Martin Casado): No, I think that's... So I agree on the ground 100% that I think we're seeing a movement away from open source, but the rhetoric around open source has shifted. We just had the AI – what was the name of the bill that just came out? The American AI policy and recommendations is a full-throated endorsement for open source. So I think discourse-wise, there's more support for open source than ever before. I think ecosystem-wise, I think you're right. I do think it's quite likely that we're going to see less open source.
--:-- Now, listen, OpenAI has said that they're going to open source. That would be wonderful. And if they do that, I think that would be very, very positive.
--:-- Host (Harry Stebbings): Do you think they will?
--:-- Guest (Martin Casado): I just have no idea. I hope so. It would be very rational. But here's the thing – we say open source, but it's such a misnomer when it comes to AI. The standard model of open sourcing AI is you open source the smaller model and you keep the more capable model closed source. It's a way that you get distribution and brand recognition, but you don't actually erode your business. This has been very, very successful as a business model. And unlike actual software open source, just because you release your model doesn't mean somebody can replicate it. To replicate it, you'd have to recreate the data pipeline and the training pipeline. So I think that there's just a lot of concern of investing hundreds of millions of dollars or billions of dollars to train something and then just giving all of that away.
--:-- But I feel very confident that the business justification is there and behavior will always follow business. We're going to continue to see open source be a large part of the ecosystem. And remember, historically open source has only been about 20% of the total market value. I would say it's much higher than that for AI. So in a way, we're doing better than software has historically.
--:-- Host (Harry Stebbings): What did you believe about the AI landscape that you now no longer believe? We've touched on so many different elements.
--:-- Guest (Martin Casado): My mindsets have changed around so many. The one for me that I've just consistently got wrong is just how fast these coding models advance. And this has probably just sunk cost fallacy. My entire life, I've just been this nerdy programmer. I've been programming since the 90s. It's my happy place. And I just never thought that they would advance to the level that they have. I still develop most evenings. Instead of watching a sitcom, I just goof off writing old video games or whatever, just for fun. It's silly stuff. And I'm already at the point that I couldn't work back to working without them. And I've spent 30 years without them.
--:-- Their ability to offload all of the shit I didn't want to learn is remarkable. The thing that kept me away from code for a while was that I would dabble with it, I would drop it. You have to learn all these weird frameworks and none of the knowledge is foundational. It's just some random dev came up with some weird way to do something and you've got to learn some poor design decision to do it. None of it made any sense. It felt like you're wasting your brain space on poor decisions made by random open source developers. That was programming in the past.
--:-- Let me put it in context. In the late 90s, programming was you download your IDE, you sit down at your computer, you program something, and then it would turn into a binary and you'd run that binary. You could really get a lot done just by sitting down and writing code. By 2015 or so, writing something meant you'd have to download 50 million packages. To run it, you got to run some stupid dev server. To have anybody else use it, you got to learn how to host it. It was a bunch of libraries dealing with incompatibilities. It was a weird platform. 90% of your time had nothing to do with code. 90% of your time was just dealing with all the environment platform bullshit.
--:-- So what's so nice now is you can just focus on your code. I use Cursor and I just have the AI tell me how to host the thing and tell me what package to use. I just strictly focus on what I want and the logic. It's almost like it's brought coding back. You can see this across the industry. I grew up in the industry. I know a bunch of very strong developers that have been developing for a very long time that have basically stopped. They're running companies now, and they're all back to programming at night.
--:-- You know how there's the adage of the old man that goes into the garage and makes the train set for nostalgic reasons? I think the modern version of it is these old systems programmers vibe coding at night just because it's become pleasant again. I know you asked about the thing that's surprised me the most, but I really think it's such a marvel what these coding models are able to do. And they add very real value.
--:-- Host (Harry Stebbings): Do you think they make 1X engineers 10X or 10X engineers 100X?
--:-- Guest (Martin Casado): 10X engineers 100X would be what I said. But I don't actually think it's that. I think they make 10X engineers 2X. Every company I work with uses Cursor. If I actually look at whether that has increased the velocity of the products coming out, I don't think that much.
--:-- Host (Harry Stebbings): So what's changing then? Because dev productivity is going up. So is the quality of product going up if the product release cadence isn't?
--:-- Guest (Martin Casado): I just think the things that are hard remain really hard. Let's talk about creating a model. Let's say I'm creating a new frontier model. To create that new frontier model, I've got to collect data and I've got to run a pipeline and I've got to sit with my Jupyter notebook and look at the loss curves and rerun it. That's a lot of experimentation. There's no coding model that's going to do that for you. But if I wanted to create tests or a test suite or visualization or documentation, it's actually really good at that.
--:-- I would say that in the long run, having more robust, maintainable code bases with less bugs is just as likely to be the impact as feature velocity. Because in startups – I'm an infra guy. This is probably different from apps. I've always thought apps had no technology to begin with. Every time I look at vertical SaaS, I'm like, why do we even care about the technical team? It's CRUD. Create, read, update, delete. They all do the same thing. They all look like a web app. Who cares about the technology? The technology is simple. These are all go-to-market things.
--:-- But infrastructure is different. Infrastructure has very real trade-offs in the design space that only somebody who understands computer science would know. For infrastructure companies, I think it's quite unlikely that AI will really help speed that up because it comes down to something that the developer has to decide on, has to articulate the trade-offs. But I do think it could really help with the development process so you have less bugs. I view it more as a more robust development methodology that necessarily speeds up the core product.
--:-- Host (Harry Stebbings): Given the kind of dev productivity changes that occur because of these tools, how does that impact defensibility within companies today? Misha at Fiverr said on the show that time to copy has basically been reduced to nothing. To what extent does that change defensibility for companies?
--:-- Guest (Martin Casado): I still think we should just go back to the split between apps and infrastructure. For apps, how long does it take to copy it anyways? There are entire companies whose stated purpose is just to copy another copy in the app space. It's so easy to do. There is no core technology for a random app. There's no differentiated technology for a random app. Let's say you're creating some healthcare vertical SaaS. You could contract, and you have been forever. The business is actually the long tail of understanding that domain. I just don't think it changes that paradigm at all.
--:-- When it comes to core infrastructure, which is what I focus on – things like databases, foundation models – there's no way that right now models can just copy. The reason is not that the models aren't capable of doing the technology. It's that there is a long tail of understanding of the trade-offs for the particular use case and domain. And because it's a new market often, you understand that through market exploration. So I just don't feel that – I think these models really help with the software development process for non-deeply technical areas like apps. Sure, they can help speed it up. But over time, all of these reduce to a long-tail understanding of the market.
--:-- Aaron Levy said it so beautifully. Do you know what the average pull request is for a production code base? How many lines of code is the average change that gets accepted? What would you guess for some production enterprise app?
--:-- Host (Harry Stebbings): I have no idea.
--:-- Guest (Martin Casado): It's two. It's two. Very, very small. But let's say it's 12. What does that two or 12 line signify? It signifies probably some learning in the field or some understanding of what is needed. The long tail, the thing that's the hard thing, is to understand the specific deployment environment and market you're going to. That's the hard thing. The hard thing isn't the two lines of code. That's actually quite easy.
--:-- In many ways, the AI is getting rid of the middle. Very new computer science, models don't know how to do just because nobody's done it before. That's pushing the state of the art. In the app space, all of the hard stuff is the business anyways. This is why the changes are very small and you learn everything through go-to-market, which the models don't know just because you're exploring a new market. It's all the bullshit in the middle that they're helping us with. For me, it's just kind of net accretive.
--:-- Host (Harry Stebbings): Do you think that CS holds the same weight as a study in education discipline that it always did and you would always recommend it? Or does that change in a world that's fundamentally more democratized in terms of creation like we discussed?
--:-- Guest (Martin Casado): I feel very strongly that if you care about building systems out of computers, you have to understand how they work.
--:-- Host (Harry Stebbings): What do you think we do today, Martin, that we will look back on in five or ten years' time and go, "I can't believe we did that?"
--:-- Guest (Martin Casado): It could be prompting. It could be choosing the model that we're working on. I find it ridiculous that we are supposed to choose which model. Grok 3, Grok 4, Grok 5, Grok Shopping, Grok Weather. What the fuck? Just figure it out.
--:-- From a programmer's view, I think hopefully we'll just stop worrying about frameworks altogether and maybe even languages. Maybe even a proto language evolves and we can just focus on logic and fundamental trade-offs. We've gotten in this very backwards world where these days programmers think about all the non-fundamental stuff and they don't think about the fundamental stuff.
--:-- Let me give you an example. This is going to be a weird philosophical rant. When I was doing grad school and research, I felt like we entered a space where there's so much research that has been done over the years that you never know if you're doing something new. You just couldn't do the literature search. There's so much. The entire industry just spent all of its time redoing research. It's like you're cleaning a room and you're trying to sweep out the dust, but rather than sweep it out the door, you're just moving it to the bed or to the wall. That's all you do is just sweep the dust around, but you never actually get it out of the house. That's what research felt to me.
--:-- On top of that, many of the most important problems were kind of between disciplines. In order to even solve them, you just have to know too many things and we couldn't do that. I felt like the entire scientific industrial establishment was just redoing the same stuff. In a way, I think AI has the ability to pull us out of this mass craziness, this mass ineffectiveness. A, it's very good at telling you if you've done it before. It actually knows all the literature, knows all the history. And it's also very good at tying different disciplines. It is an expert in all of these things.
--:-- I think we've been stuck in this morass. And it's a bit of a liberator. We can actually focus on the new problems and know we're doing new things. I've got this very optimistic view of where it's pulling us. I know it's more of a philosophical answer to the question you asked. But in a way, I think it needed to happen to get to the next level of problems that we need to solve.
--:-- Host (Harry Stebbings): In terms of societal implications there, the worst question ever is the job displacement question. But I am intrigued. On the one hand, I see intense job displacement happening faster than ever. And then I'm also very aware of Brad Feld who wrote a brilliant post where he basically said every single cycle, every time we've always said, "What are we going to do?" Calculators, what are we going to do? Computers, what are we going to do? AI, now what are we going to do? To what extent does this actually require the "what are we going to do" versus another "for fuck's sake, don't we see the pattern?"
--:-- Guest (Martin Casado): Yeah. So I'm very sympathetic to concerns around job displacement. I think we should take them very seriously as a society. I'm in no way libertarian. I think this is where governments do step in and we do help out. But first we have to understand, and it's actually very unclear.
--:-- Let me tell you a quick anecdote. My cousins are all pretty established translators. They have been for a long time, multiple languages. They visited recently, a husband and wife pair. They said, "Listen, we have to change jobs because translation is all going to AI." I asked, "So the jobs are going away?" And they said, "Well, no, they're shifting. Now instead we've got to spot check these AIs. And the only way we can hold it up to our standards is if we rewrite the entire thing, but they won't pay for that." By the way, these are Italian, so they speak this way, but they said, "I can't work on something without a soul."
--:-- I think their dilemma is a good microcosm for the broader dilemma. One thing that's very unique about AI is that it actually requires today a human handler. They're just so unpredictable. Most of the use cases that we know, all the monetized use cases, have a human on the other side of it. Coding, you've got a professional coder. All the creative stuff, you've got somebody doing all of the creation. It's kind of an enabler and a tool, but the nature of what you do does shift. That's very different from electricity, where it doesn't require a human. Either you light the fire or there's no fire to light.
--:-- I think we as a society need to understand the level of displacement. We have to understand. I think it's very important that we do. I think these are things that governments should get involved in.
--:-- Host (Harry Stebbings): I just have to turn to your venture investing before we do a quick fire. Do you enjoy it as much as you did before? It is a much faster landscape. The money is much bigger. Do you enjoy it as much as you did before? I suppose many of your founders said that they didn't think you enjoyed the administrative work that you now have to do with the size and scale of Andreessen?
--:-- Guest (Martin Casado): Oh, well, those are two different questions. I love the investing. The investing is great. It's just the most exciting time in the industry since the late 90s. It's great to be part of a super cycle. I love it. I actually really like the firm building side. Frankly, I could do without endless meetings, but I've actually been pretty good at limiting those too. No, I think this is actually the most exciting time to be in the industry and venture. Candidly, I'm not trying to bullshit.
--:-- Host (Harry Stebbings): No, I'm a venture investor too. I'm with you and I say the same to our LPs. Is your price elasticity more on deals because of the super cycle entry point that we're in or less because of the risk or uncertainty level that we're in?
--:-- Guest (Martin Casado): Philosophically, I just think the market sets the price. I just don't have the hubris to think I can somehow outsmart the market or that a single deal is going to bend to my will. How we think about investing in general, price often doesn't matter. Ownership does. What is the ownership we need? It all depends on the fund, the market, the size of the market, the understanding. Everything comes down to ownership for us, not price. You just can't make the fund mechanics work if you don't get the ownership.
--:-- Now, for very, very, very large markets that are obviously very large, for very large checks, then we don't care as much. But that tends to be growth territory anyways. For early stage investments, you kind of need to understand what the median outcome is and you have to be able to size the median outcome in a way that at least returns a fifth of the fund or half of the fund.
--:-- Host (Harry Stebbings): Is that not the joy of being at Andreessen? You can take a 5% ownership on first check because you can size up into the next and size up into the next. Is it not my challenge that I have to get as much as possible on the seed or the A?
--:-- Guest (Martin Casado): The way that I view it is a bit different. I think there's two legit ways of investing now that have emerged. One of them is you're very much a specialist and you've got a special network, special value. You understand a special size of the market. You're very, very much a specialist. That is how you win deals, get the ownership, keep the ownership, and then make your company successful.
--:-- The other one is – and I wouldn't say it's an a16z thing – but it's like you have all of the products so that you could be adaptive in the market. I've been doing this for 10 years. The strategy that works has shifted this entire time. Sometimes it's early, sometimes it's mid-stage, sometimes it's collaborating with growth. Sometimes it's credit, which we don't have a credit fund, but I can understand why people do it. The market is competitive and everybody's scrambling for deals. If you don't have the different funds or products to offer, then often that's where people are going to squeeze you out or get alpha.
--:-- I think that for the game that we play, it's very, very important that you have all of these funds and the ability to enter at all stages for exactly that reason. Again, I don't think it's a you-me thing. I think you play a very different game than we do. On one side, you have to go very specialized, very focused, very early. For us, we're trying to find out what is the right time to enter to get the ownership that we need.
--:-- Host (Harry Stebbings): What's the size of fund that you primarily invest out of day to day? I know you have flexibility.
--:-- Guest (Martin Casado): 1.2 billion. I run the infrastructure fund, which is a $1.2 billion fund.
--:-- Host (Harry Stebbings): So my challenge here is your cost of capital is just so much less than mine. Your ability to put a larger check in, bluntly, with much more confidence is there because I'm investing out of a $275 million Series A fund and $125 million. It's just much more meaningful dollars for me than it is for you, which will affect my willingness.
--:-- Guest (Martin Casado): Yeah. Well, my challenge is we have to live with these investments forever and conflicts are very, very, very difficult for us to do. So we don't enter very often at the stage that you do for this reason.
--:-- Host (Harry Stebbings): I mean this respectfully. Everyone chastises Andreessen for their conflicts and for investing in many conflicting companies. Do you think that's unfair?
--:-- Guest (Martin Casado): It's so hard to keep your nose clean on this one because especially with a shift towards AI, companies pivot all the time after you invest. I don't recall intentionally investing in a company. In fact, one of the top reasons we don't invest in companies is because of conflicts. I mean, we do it. I just did it. Recently, I can't say the name of the company. We didn't invest because it was a hard conflict. Even though the portfolio company was not doing the thing, but it was on the roadmap. And the founder called me. He said, "Martin, you just can't invest in this company." I said, "Okay." So I think we try our best to keep it clean.
--:-- Host (Harry Stebbings): Can I push back on you there? If it's not on the roadmap, I'm really sorry, founder. I have as much faith and conviction in you as possible. But if it's not on the roadmap, I'm not having you tell me how to do my job.
--:-- Guest (Martin Casado): Here's my talk track. And it's evolved over the years. I stole this from Chris Dixon. I say, "Listen, you have one mortal enemy. You choose whoever that mortal enemy is. Whoever it is, I'm with you, we're going to go kill that mortal enemy together. But you get one. You don't get an arbitrary number of mortal enemies." In this case, I said, "Is this it? Is this your one mortal enemy?" And the founder said, "Yes, this is the one mortal enemy." I said, "All right, fuck them, let's go kill them." That's it.
--:-- Now, listen, we have a number of companies where they pivot midstream and they start competing after we've invested. It happens all the time. We also have the venture and the growth fund, and we try to minimize conflicts there, but sometimes they happen. Very different stage companies, very different teams working on it. But I would say that we try very, very hard to steer away from conflicts.
--:-- Host (Harry Stebbings): Given the nature of, as you said there, the volume of pivots that occur today, given your entry point, I always advocate wholeheartedly for being 98% founder. And then you have wonderfully smart people like Elad Gil who wholeheartedly advocate for being market first. How does the pivot frequency and experiences you've had impact your prioritization mechanism around where you spend time?
--:-- Guest (Martin Casado): I don't want to speak for Elad, but that's not my experience working with Elad. I've done many deals with him. Elad is very, very focused on the founder. I think the one thing I would say is he's very good with founder market fit. Maybe the best in the industry. I have a huge respect for how Elad invests.
--:-- Host (Harry Stebbings): Unpack that. Why and how does he do founder market fit that's the best?
--:-- Guest (Martin Casado): He will find a market that he really likes. And sometimes it's like even a fast follow market.
--:-- Guest (Martin Casado): Like, you know. And then he will find who he thinks is a great founder for that market. So he's very good at this kind of boy band construction based on the market. The primary point I want to make is that in his investment cycle, the founders have always mattered. I've followed on deals he's done, he's followed on deals I've done, we've done a bunch of deals together. I've never gotten the impression that the founder isn't the primary decision once he's chosen the market. So I would say it's a primary concern for him.
--:-- Host (Harry Stebbings): When you have misjudged a founder, what did you not see that you should have seen? And can I ask you your previous question? Because you're like, okay, so how do we think about it?
--:-- Guest (Martin Casado): So we think about it very simply. The only sin in investing—and I've sinned so much—is missing the winner. There's no other sin. It's fine to invest in a category that doesn't work. It's fine to lose money. But if you choose the wrong company, that's not okay. And listen, it's just so hard to get it right all of the time. So the way we view it is we just look for viable spaces. And it's determined viable because...
--:-- Host (Harry Stebbings): Someone said to me the other day—I'm so sorry to interrupt you—that at Andreessen you get killed for choosing the wrong company but being right about the space. You won't get killed if you were just wrong about a space.
--:-- Guest (Martin Casado): Correct. That's exactly right. Yeah. So the view is that there's basically no amount of work you can do to determine if a space is going to work or not. That's like weather prediction. But given a set of companies, you can actually do the work to understand which one of those are the best. Now, we've got it wrong constantly.
--:-- Host (Harry Stebbings): Do you think you can beat the market with that strategy?
--:-- Guest (Martin Casado): Yes, I think you can beat the market. No, I do not think that you can unequivocally tell the best. Can you beat the expectation of the market by running this strategy? I would say yes. Can you specifically pick the winner every time? Absolutely not. Clearly not.
--:-- Host (Harry Stebbings): When did you most poignantly pick the market but pick the wrong horse?
--:-- Guest (Martin Casado): I just don't want to call out any specific company.
--:-- Host (Harry Stebbings): Fair enough. When you think about sins there, what's a big sin that comes to your mind?
--:-- Guest (Martin Casado): Well, I can answer the opposite question. There are a bunch of markets that just haven't really worked. The entire streaming market has been very tough. The data streaming market has turned out to be a subset of the analytics batch market. Maybe ClickHouse—Aaron Kess is doing phenomenal with that, and I'm not an investor—but that may be the one breakout since Confluent. That's just been a very tough space historically, whether you're at the dashboard layer, the transformation layer, or the feature store layer. There have been entire spaces where we played multiple bets and it just didn't work out. So many times we'll invest in a space where none of them work. I will tell you, there have definitely been companies where at the time they were the very clear leader, and then something happened—some macro shifts, something else—and that's just how the game goes.
--:-- Host (Harry Stebbings): You've probably heard this. The thing with having a strategy like that is if you're trying to scale a venture firm, you just need something you can articulate and teach other people. I just find it hard: if you pick the right market and the wrong horse, bad, Martin. But if you don't pick the right market, fine. To me, some points need to be given for the insightfulness to pick the right market, and some forgiveness for the fact that it's fucking hard to pick the horse. Almost I'd fire the one who picked the wrong market entirely. Where was your insight, at least?
--:-- Guest (Martin Casado): And this is why you run your own venture firm and you can have whatever strategy you want.
--:-- Host (Harry Stebbings): Is that moronic?
--:-- Guest (Martin Casado): No, I learned from thinking. No, it's not. I just think it's philosophically different on the approach. I actually don't believe you can predict the future of technology adoption. It's a very tough thing. You don't know what a big company is going to do—it can wipe out an entire market. You don't know what an innovation will wipe out entire markets. This happens all the time. You could argue that AI is really invalidating tons of markets, and I don't think anybody could have seen that happen. But if you have 10 companies that have some traction and you can talk to the founders, you can diligence the teams, the market, the project, the technical approach—I think you can say something a lot more concrete than whether some future innovation is going to wipe out this entire market.
--:-- Host (Harry Stebbings): Do you think it's paradoxical or opposing to believe that both AGI will be dominant and present in a set time period and to at the same time be investing in enterprise SaaS?
--:-- Guest (Martin Casado): I don't know. I would say humans are AGI and we still invest in enterprise SaaS. This is the problem: everybody somehow thinks that AGI just means unlimited, powerful, and anything I want to disappear in the future disappears. Come on, you're AGI. I'm AGI. We invested in the process. To be honest, Sam Altman defines what AGI is. So whatever him and Microsoft decide is AGI will be AGI.
--:-- Host (Harry Stebbings): Dude, I want to do a quick fire round. I say a short statement, you give me your immediate thoughts.
--:-- Guest (Martin Casado): Yeah.
--:-- Host (Harry Stebbings): What's one of the most overhyped AI categories today?
--:-- Guest (Martin Casado): ASI.
--:-- Host (Harry Stebbings): What's one of the worst VC takes on AI you've heard recently?
--:-- Guest (Martin Casado): Open source is bad for national security.
--:-- Host (Harry Stebbings): What one founder would you back in any category? Whatever they did, you just want to wire them the money.
--:-- Guest (Martin Casado): Michael Trull.
--:-- Host (Harry Stebbings): Why? Specifically?
--:-- Guest (Martin Casado): I've worked with him for a year. He's just remarkable. It's so rare that I've found a founder who knows. He has three things. He knows what he wants. He's got an intuition that's impeccable. And he listens incredibly well and gathers information. It's a very potent combination. And of course, he's incredibly smart and he's got great product taste.
--:-- Host (Harry Stebbings): What's your favorite trait in yourself that has been most impactful to your own success?
--:-- Guest (Martin Casado): Deep-seated anxiety from being poor.
--:-- Host (Harry Stebbings): Seriously, I agree.
--:-- Guest (Martin Casado): Listen, I grew up—you name it, food stamps, dirt road. I come from Montana. It's so funny, people hear the name Martin and they think, "Oh, he must be some sophisticated European." I was actually born in Spain, so I'm a Spanish citizen. They think, "He must be some sophisticated European." I'm like, motherfucker, I grew up on a dirt road in Montana. When there was hunting season, my school shut down. I'm a Western country boy. I had a great family, I didn't have any of those hardships. I had a wonderful family and an educated family. We kind of muddled our way through. But you go through that and you see how hard your parents work, and you just don't take anything for granted. I sold a very successful outcome from a company and I could have retired on that day. And I still have not taken a day off. I haven't worked since basically forever. I'll take a week off while I have a job, but I've never not had a job in 20 years. It's just...
--:-- Host (Harry Stebbings): Did that day feel fucking awesome? Coming from a dirt track and funny food stamps, as you said, you can retire today. I know you didn't, but did it feel as good as you thought it would?
--:-- Guest (Martin Casado): You know, it's kind of an interesting thing. No. It was very bittersweet. I think selling companies is very bittersweet for any founder. It's a death in a way. You spend so much time with something and then it shifts. But here's the interesting thing, and maybe this is advice to other founders: you always think about that thing you'll do when you make the $100 million or whatever. You think, "I'm going to go do that thing." But you only think about that thing in the most stressful times. So my thing was—my cousin's a movie director, his name is Vincenzo Natale, pretty legit guy—and I was like, "You know what I'm gonna do as soon as the money hits the bank? I'm gonna drive down to Hollywood and I'm gonna help him make movies and be an actor and just be one of those people." So it happened, the wire hit, and I was driving down the 5 and I'm like, "What the fuck am I doing? I love technology. I love my job. I hate Hollywood. I have nothing in common with these people." I probably got two hours out of town and I just turned my car around and came right back. Because you only have those visions at the most stressful time. When you're not stressed, you realize that there's something that brought you to this place, and it's genuine interest and genuine love of it. My only advice to other people going through this is: don't use those dreams that you concocted when you were really in the pressure cooker—not sleeping, your relationships are falling apart. That's not the steady state you're going to want to do. You're probably where you are because of the love of it, and letting that go tends to be pretty disastrous for some people.
--:-- Host (Harry Stebbings): Was making money or having money what you thought it would be?
--:-- Guest (Martin Casado): I had to play all of these tricks. I actually borrowed one, which was very helpful. I just had a hard time spending money. When I got into the Stanford PhD program—this is so embarrassing—but we always thought $20 was a lot of money growing up. We'd call it the yuppie food stamp because it was $20. I remember I was going to go to Byte's Cafe and I was going to pay with a $20 bill because that was like some stamp of having money. I was just so naive to all of these things. So it was very hard for me once I made generational wealth. I talked to a friend of mine who went through something similar. He said, "You know what I did? I came up with a Brad coin. Let's call him Brad. The Brad coin—let's say I'm worth 10 times more than an average rich person. So the Brad coin is worth 10 times more. I buy things in Brad coins. If it's a business class flight, that's $10,000, but in Brad coins it's only $1,000, and $1,000 sounds a lot better than $10,000. So I feel good." I actually had to adopt a lot of these mechanisms where I'll make a Martine coin and it's worth this much money.
--:-- Host (Harry Stebbings): What got worse with money?
--:-- Guest (Martin Casado): This is something I have to deal with all the time. My wife forces me to keep it real. She just won't abide by any of the shit. I got three fucking dogs that are crazy. She doesn't like helping the house. I drive a fucking Volkswagen. We have three chickens in the back. I'm fucking schlepping the kid all the time. If it were me, I would be living your life. I'd be 100% in New York in the penthouse with a private jet. Instead, I'm in a fucking Volkswagen with three dogs in a messy house and no help.
--:-- Host (Harry Stebbings): You're so whipped.
--:-- Guest (Martin Casado): It's not even that. This is what marriage is.
--:-- Host (Harry Stebbings): What's your biggest lesson on marriage? I'm 29, I got a great relationship, but not quite there yet. What would you tell me about greatness in marriage that I should know?
--:-- Guest (Martin Casado): Well, listen, I got it wrong once. I'm not sure I'm the right guy to ask. My startup was really tough. It was really tough. I think that burned through my first marriage, and she was great. Fuck, dude, I'm the wrong guy to ask. I'm really the wrong guy to ask. I will say something, which is a different question than you asked, but I think it's important. I have found that men in particular who have stable relationships just do a much better job in work. They're just much more stable. The best founders I have tend to have families. I don't want to make it a gender thing—it may just be my observation. I work with a lot of men, and families are really, really good for men, even though they can be a pain in the ass. The only high-level view is that these things are super important. Whoever you have and you're working with, it's an important thing that keeps you grounded. In my case, I got chickens, baby. What does Zorba the Greek say? It's the full catastrophe. It's the only way I can do what I do. There's no other way. The level of pressure and the amount of work I do—I probably work 80 to 100 hours a week, I've been doing it for 10 years. The amount of demands is very hard to do without support and grounding. So again, I'm not the right person to answer how do you treat your wife. I'm a fucking autistic nerd. I have no idea. But I do know that these things are incredibly important for us, and you should value them and treat them as such.
--:-- Host (Harry Stebbings): If you think about Andreessen in 10 years' time, where do you think Andreessen will be then? 10 years ago when you remember it, it was a different firm. Amazing and innovative in its own time, but from where it is now, night and day. Where is the 10-year Andreessen in 2035?
--:-- Guest (Martin Casado): The most remarkable thing about the firm, in my opinion, is that it's able to evolve and adapt very aggressively because of the way it's structured. Mark and Ben really are the top of the firm. They really are. I think it's a feature, not a bug. It's kind of a historical quirk that VC was created around a partnership model. That's the same thing you use for a dentist office or a law firm. There are positives in that there are a bunch of different agendas that sit at the same level. But for decision velocity and disruptive change, it's death. So I think that's a massive benefit to the firm. I'm just delighted that this is the way it is because they can make these big aggressive moves. I don't know what it's going to look like in 10 years. I guarantee it's going to look different as it evolves with the landscape.
--:-- Host (Harry Stebbings): Martin, I so appreciate you, dude. You are fantastic. You're open. You're honest. I love the last 15 minutes there, but I really appreciate you, man.
--:-- Guest (Martin Casado): Likewise, Harry. Always a pleasure. You're the best.
--:-- [End of interview. Podcast outro begins.]
--:-- Host (Harry Stebbings): Thanks for listening to the A16Z podcast. If you enjoyed the episode, let us know by leaving a review at ratethispodcast.com slash A16Z. We've got more great conversations coming your way. See you next time.
--:-- [Disclaimer: This podcast is produced by Andreessen Horowitz. The views expressed are those of the speakers and do not necessarily reflect the views of Andreessen Horowitz, A16Z Capital Management LLC, or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.]
