The a16z Show - Monopolies vs Oligopolies in AI

Episode Date: August 28, 2025

In this interview from the 20VC podcast, Martin Casado (a16z General Partner) joins Harry Stebbings to unpack the state of AI, the rise of coding models, the future of open vs. closed source, and how value is shifting across the stack.Martin offers a candid view of the opportunities and dangers shaping AI and venture capital today. Resources: Find Martin on X: https://x.com/martin_casadoFind Harry on X: https://x.com/harrystebbingsMore about 20VC:Subscribe on YouTube: https://www.youtube.com/@20VCSubscribe on Spotify:https://open.spotify.com/show/3j2KMcZTtgTNBKwtZBMHvl?si=85bc9196860e4466&nd=1&dlsi=d1dbbc6a0d7c4408Subscribe on Apple Podcasts:https://podcasts.apple.com/us/podcast/the-twenty-minute-vc-20vc-venture-capital-startup/id958230465Visit their Website: https://www.20vc.comSubscribe to their Newsletter: https://www.thetwentyminutevc.com/Follow 20VC on Instagram:  https://www.instagram.com/20vchq/#Follow 20VC on TikTok: https://www.tiktok.com/@20vc_tok Stay Updated: Let us know what you think: https://ratethispodcast.com/a16zFind a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

There's only been one sin, and that one sin is zero-sum thinking. We always worry about, like, oh, is this defensible? Oh, 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. To be on the podcast, we're sharing a conversation from our friends at 20VC with A16Z, general partner, Martine 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. 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. So excited to be here. It's great to see you. Dude, I freaking hate these things.

How did you get into Venture intro questions? So I just want to dive right in. It is a freaking nuts time. So 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? So I'm kind of of two minds. Of one mind is I do feel like my intuition doesn't really work like it has the last 20 years. It's just the future is very uncertain.

And one of the reasons is, is because, you know, this is really the first time, like, software development and software creation is being disrupted. And so on one hand, I'm like, I don't really know what to think. On the other hand, observationally, there's only been one sin. And that one sin is zero-sum thinking. We always worry about, like, oh, is this defensible? oh, 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's profitable companies. I mean, the business case is there, et cetera. And so I think the one sin is not playing the game. Do you agree with the playing the game on the field sentiment?

When we look back at 21, you know, I remember everyone was saying playing the game on the field. I wish I hadn't played the game on the field. To be transparent, Martine, do you agree that you have to play the game on the field in Buncher? I think behavior should follow business. It shouldn't follow Marks. And I think in 2021, behavior was following marks, right? It was like the public, Marcus just decided these companies were valued a whole bunch. You know, Tiger came in with a ton of money and deployed it a whole bunch.

And so, like, I think behavior following investment in Marx is, is, is a bad idea. But in this case, you have some of the fastest-growing companies we've ever seen by users, by revenue. I mean, the amount of value that's kind of shifted to this is so significant. And so I think investors' behavior should follow that. If not, I mean, what are we doing? When you think about a shifting value, again, I'm diving right in, but this is all first

going on. Let's go around about. A lot of people have fun, and you said about kind of 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? There are two futures to code.

In one future, you've got Anthropic as a monopoly. And another future you have, let's call it an oligopoly, or maybe even a bit more of a market of these coding models. And they're just very different futures. And I think when you answer this question, you have to consider both of these. I will say the timing of this conversation you and I are having right now is like pretty soon

after Claude 4 launched. And that's like a major model launch. And these models are so episodic. Every time one launches, everybody's like, it's the future. Everything's going to happen. Like remember the whole Jibli opening eye launch and we're like,

Oh, image is going to change forever. And then it comes, we're excited. And then it kind of, you know, passes. And maybe that'll happen here. Maybe that won't. I don't know. But like for sure, like our perception is colored by that launch. So let's consider both of these.

So I'm going to consider the first one. So historically, models don't really keep much of an advantage because they're so easy to distill. And so we've even in the last week have seen launches of models. you know, Quinn and I forgot Kimmy, that came out, and they're great, and people like them, and they adopt them. And in that world where you continue to have new models from different providers, you know, I would never count out Google. Their coding models are fantastic. The rumor is that GPD-5 coding is going to be great. So in this world where you've got lots of models coming out from lots of providers, you need to have a consumption layer that's independent,

right? And so then all of these companies are going to add that, that, that, you know, that consumption layer value, like, for example, to non-technical users or to Python users or to professional coders or whatever it is, and that's going to be a very healthy layer. The other features, let's assume that Anthropic is just a monopoly on coding models. And in that case, you have what you normally have in these situations is they will decide kind of where it's not profitable for them to enter or will change their business model. Like maybe they're like, listen, we want to have the consumption layer, but we'll decide. We're never going to be like an app dev tool company. It's just it's a different sales motion, a different sales team. And nobody knows where that stops, but they will put pressure on anybody they view in their core focus.

And they're going to, they will do whatever that they can to either capture that margin or just capture that market share. I just it's just the wrong time to have this conversation right after a major model launch. Because like I said, these models are so episodic. And we always think, like, we always assume every time a model launches is going to be a monopoly. and it just really hasn't been the case. 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?

Oligopoly. This is how the cloud, well, this is how the cloud played. I think probably the best analog we have is the cloud, right? You know, the other companies that are behind models can subsidize these things arbitrarily. I think about Gemini. And they don't have to do this in a way, where they have the same economics as an independent company.

And so if you look at how the cloud, remember the cloud, AWS was like 70 or 80% market share early on. Nobody thought they could ever catch up to them. You know, they were the massive market leaders that created the category. I mean, they had way more dominance than Anthropics has now.

And Microsoft and Google are like, you know, that's an important big market we have to be in it. And they just basically spun their way into it. And then you ended up with an oligopoly on the clouds. I see no reason. I mean, Gemini 2.5 is a great model. It's a great model.

And if you actually look at it, you know, on the price performance, I would say in many use cases, it's the one that I actually use as my standard model. It's better than anthropic. For some use cases, if you actually, you know, taking your price performance. And Google can arbitrarily subsidize that too. Never, you know, count out OpenAI. I mean, 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.

And so I just feel like it's, you know, the players, the money behind the players, the fact that these models distill, this will end up in an oligopoly. But I mean, that's just my guess. 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? I think that you end up with models with different flavors, and there's going to be a lot of new flavor models that will come out. You know, like, you know, we haven't even, you know, like, you know, Mira and Ilya are out there creating models.

I mean, you've got these very legit teams that were some of the pioneers. You know, we're just starting up models for the sciences. And as you get more into kind of R.R.L. territory, these models really get a certain flavor. They don't generalize nearly as much. And so, like, that's going to naturally from a technical perspective, fragment the models. And so I would say the core base model for like language, search, and code. I mean, I think even code actually, it's still so early. I mean, it's very, very early in the super cycle. In previous super cycles, remember, it took two or three generations for the winners

to emerge. I mean, Google was third generation search. Facebook was third generation social networking. Remember, there was Myspace, there was Friendster and then Myspace before that. And so I think there's a lot of change. There's a lot of change. There's a lot of change. come. But I do think that both Anthropic and Opener have done a remarkable job, remarkable, with brand independence and market share. And so I suspect there will continue to be stalwarts in the industry. Are you in either of them? We're investors in opening out, yeah. Got you. Okay. My question to you is fundamentally, there's many, but do you think models are fundamentally good investments for venture firms? When you look at employees' stock compend,

and the dilution that comes from it and then the dilutive nature of the businesses. Yeah. It's a hard sell. Okay, so if there's one thing I've learned, honestly, for anybody that's listening to this, this would be worth like your time. There is no one way to think of AI

and there is no like one way to think about models. And the models themselves are entirely different businesses depending on how you talk about the models. So to even answer that question, we have to tease apart what you mean by model. So for example, if you look at the, diffusion models, say, like 11 labs, Mid Journey, Black Forest Labs, Ideogram. These are wonderful businesses that have great economics because the models are smaller. The ecosystem isn't subsidized

in the same way, right? Like, Google subsidizes language and code and video, but not speech, right? And so from an investor, these are clearly great investments because, you know, if you just look on a metrics alone. On the other hand, the frontier language space, it's much more complicated because there's so much subsidization, right? You have meta and Google a bunch of Chinese players that are entering it. So for a subset of the players, and this is why it's a tricky question. For a subset of the players, you're like, yeah, clearly these are the fastest growing companies we've ever seen. There's tons of value. These are very valuable entities, right? You know, Anthropic. Open AI. But at the same time, even three years in, there've already been a number of companies

that, you know, have had to exit early. And so I would say it's kind of a high stakes game where the winners really win, but like it requires a lot of capital to enter the game. And if you're not in one of the leaders, like that, you know, capital is forfeit. We do a show every week with Rory O'Driscoll and Jason Lemkin. And Rory, very aptly, I think, just said, listen, with the transition to AI, every investor's just, accepted a willingness to go massively up the risk curve on investing. Do you agree with that? Well, I think it's the requirement of the game.

It's like these are very capital-intensive companies to build. You know, they have to get the capital from somewhere. They're also the fastest-growing companies. And so, you know, for the winners, it's justified. And so I think it's not that investors are willing to go up. I mean, we'd be very happy not to. I mean, I know you would, right? And we'd be great to have great returns with low risk.

But the nature of the system in the game which we're playing requires it. And that's just what this is, by way, this is the dissonance in all of this. It's just so important to call out, which is on one hand, you do have these great businesses that are very fast growing. and zero-sum thinking has been tremendously wrong. I mean, Nvidia is continuing to grow in value. The hosting providers, which everybody wrote off, is being kind of non-defensible business, continued to grow in value.

The model companies, which I can't tell you how many investors wrote off the models. I mean, this question has been around for three years. They continue to grow in value. So every layer of the stack continues to grow in value. So on one hand, you're like, it's all working. You should be in the leaders in every layer of the stack. On the other hand, we've seen tons of wipeouts already for the non-leaders.

And so it's almost this bipolar or paradoxical situation where you kind of have to play, but it's very, very high risk. And if you don't play, I mean, you're kind of missing one of the fastest gross in value that we've seen in, what, 20 years? Do you think you see the concentration of value to one or two players across markets in every market? Whether you look at voice, it's, you know, obviously you're 11 labs, whether you look at it's kind of a replet and lovable and open AI and Anthropic

Curse. This is such a great question. So here's one thesis. It's so early we don't know and maybe in a month all this gets proven wrong. But we actually talk about this a lot internally. And here's one thesis and this is the one that I'm attached to which is these markets are so large and they're growing so fast.

We're actually seeing brand effects take place. And we haven't seen that since the internet. And by brand effects, I mean, if you become the household name, you will get the adoption. Because it just does not require a lot of education. It does not require a lot of competitive discussion or competitive positioning in the field. You know, I would say for many of these models, I mean, you know, is one better than the other? Yeah, maybe, but they're pretty close. But like people know chat GPT.

It's like it's a household name. My mom knows chat GPT. You know, people. Honestly, when you're like, honestly, why did I do lovable? For the exact same reason, the CHAPT wins, I thought it was the consumer brand that would win. A hundred percent. And I just think these markets are so large. Brand effects work.

I mean, let's talk about Mid Journey. Mid Journey was the first that got above the quality bar. It's taken zero investment from institutions. It's still the market leader. And it continues to do great. And this is meanwhile what a bunch of other people have entered entered the market. And so I do think it's not unreasonable

to assume that these markets are very large. Leaders are going to have brand monopolies and brand modes. And they'll be able to maintain them until things slow down. And in general, I've found markets do this, which is when markets are expanding, so markets tend to expand and then contract, right? Think about cloud, right?

It was kind of like this funny thing and it became very massive. And then, of course, it slows down. When it slows down, then you have the consolidation and then, you know, competitive dynamics come in. I mean, we're clearly in a massive market expanse phase. It's just very clearly the case. And in which case, the leaders are going to continue to have, you know,

a distribution advantage just through brand recognition. When does that tail off or does it not tail off? When does the importance of brand and brand recognition dwindle and product prioritization or product quality trample? I mean, I think it's as soon as the market growth slows down. you know let's i mean again let's take cloud as an example where do these are actually tools of market

sorry i'm so sorry to interrupt you but market growth or actually just consumer intrigue which is there's a lot of people who want to try building a website on replete or lovable or bolter any of them there's a lot of people who want to try voice with 11 labs to what extent is it market intrigue versus expansion of market well i just think the expansion of market provides the dynamic so that you don't saturate the user with competing messages, right?

I mean, the idea of market expansion is the frontier continues to expand, and the first thing the frontier hears is the household names, and so the household names win. And so I just think that that's a natural artifact of expansion. As soon as like the expansion slows, then that frontier is going to hear both names, and then all of a sudden now you're in a discussion of which one to use and not to use. And again, I think for the longest time when the cloud market was expanding, everybody knew AWS, it was the leader,

it was 70-80% market share, and then as soon as that growth slowed down, then all of a sudden market share started to shift dramatically, and it just wasn't obvious. Do you do GCP? Do you do Azure, etc.? But I would say that's less an artifact of the fact that Google, Microsoft, decided to enter the game and much more

that the market growth itself started to slow down. So we see market growth slow down and then we see the dispersion of value across players more so. That's right. So the market slows down and once that happens, the frontier, it becomes more saturated, right?

Just because we're not adding people as much and so they will get more of the educated message and they'll start making more decisions and you can have more of a conversation. Like, of course, Anthropic would love to have the same brand as chat, GPT, a household name, but how do you reach that frontier, you know, if it's growing that fast. It's just, it's operationally tough to do.

Kind of the only way do it is just through brand recognition, which is kind of this word-a-mouthey type thing. It's like on every podcast and, you know, the friends and whatever. And so I do think, I do think we're seeing brand effects happen now. And we saw these in the early internet. The brand later tends to get 80% of the market. It just tends to break out Pareto for a while. And then over time, it'll slow down and these things even out based more on product differentiation.

How do you factor that into your thinking when investing today? Well, you just try to invest in the leader. And it's worth paying up for the leader, honestly. I mean, it's, you know, so I think for me, I ask two questions. Question number one is like for the area that it's focused on, is it the leader? If it is, it's definitely worth paying up. And then the second one is the story actually has been that, that in a competitive space,

almost everybody just found kind of a new nichey white space. So let's just take the example of OpenAI. I mean, open AI was the first to code, right? With GitHub co-pilot. I mean, they provided the weights, as far as I know, and they lost that. And they were first to image with Dali,

and they lost that. And they were the first to video with SORA, and as far as I can tell, they lost that. And yet there's still the massively dominant player in language and continue to be so and will be so. And arguably, that was the right thing for them because that's by far the largest market by far. And so Open AI acted totally rationally

and has the largest market. But that gave the ability for mid-journey to take image or BFL to take image. You know, Google seems to have grabbed video with V-O-3. Code, I mean, on the model side, Anthropic has, you know, turned that into, you know, this wonderful business.

And so when markets, spanned, not only do you have these brand effects that we were talking about, they also tend to fracture a bunch and what seems to have been a submarket will emerge as a leading market. And you even see this kind of on the image side, right? You've got a bunch of viable image players that focus on different things, right? Like, Ideogram is great for designers, a professional design community. BFL is the open source community that, you know, especially for developers that use these things and products. And then Mid Journey is for, you know, more of the fantasy, like, also professional designers, but it's a very stylized kind of opinionated view.

And all of these are independent, you know, viable companies. So I think we're going to see fragmentation for quite a while before we see consolidation. I need, the show is successful because I'm very open with my troubles. I need your advice. You know Abridge in the U.S. I'm not sure if you're in it, but I'm sure you know it. Very simple. There's a European player that does like medical transcription for nurses.

They went from 1 to 8 million in a year, and 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 U.S. is this going to be a big business. Is that a losing game where you are a European competitive? This is a great question. So another very interesting thing that we haven't seen in a very long time is we do have geographic biases showing up with AI. And the regulatory environments are quite balkanized.

You know, there's language and cultural biases that are also balkanized. And so we're actually seeing a lot of regional players show up. And so I think it's very legitimate. Now, 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. I promise that. And so I think a very legit thesis is, you know, this becomes a regional player in Europe and then maybe a portion of the U.S. market.

Can I ask you, a lot of people denigrate these businesses that we've discussed because of their margins. They're simply passed through funnels to the large language models. Do you think that is something that changes over time, and 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. And so there's always this question.

If you're a founder and you get access to relatively cheap private capital, And you can do a trade-off between margins and distributions and it's land grab time, what would you do? And the argument is the incremental user is someone you can monetize forever down the road. And then if you don't get that user during ladder ground, you could never monetize it. The rational business decision is to sacrifice margin for distribution. It's just the rational business decision. And we've seen this forever. I mean, hell, the web wasn't even monetized, right?

literally. I mean, like, this time we can actually monetize these things. Forget, forget, like, you know, break even or negative margins. It was literally like massively negative because we didn't even have a business model until the advertisements come up. So this is like the most rational thing that markets have been doing, at least tech markets forever. And it's no different this time with AI. I do think there's a question of, okay, so if you do want to then turn on margins, how do you do it? right? And then you can, of course, you'll either have to build a traditional moat, two-sided marketplace, a brand moat, the long-tail kind of integration and domain understanding. So for example, let's say your healthcare company, if they really crack the European market and they understand all the regulation, like Anthropics is not going to take the time to do that. Or, you know, so there's clearly pricing power you have on that side. Or you have to do actual technical differentiation. One thing, thing that we're learning is in this phase of model scaling, a lot of the approaches to scaling don't generalize. So if I want to be much better at like coding, I may not be so good at something else. This gives a ton of room for the application developers to build their own models that service certain areas that the large models just aren't focused on. And so I think there's even a ton of technical level to differentiate. So my sense is, and this is, I mean, you know, I don't want to talk too much about, you know, my portfolio and what I see just because there's sensitivity to rerun around the number.

But in my experience, most of these companies that are like, let's say break-even margins, it's like a board-level specific choice to prioritize distribution, not just because this is systemically something they have to do. We mentioned their sovereignty. I am intrigued how you think about safety and safety around. around AI and models. You've had Vinokosa be like, we have to lock this down. If this was not locked down, it would be like nuclear secrets being handed out. I remember then Mark came and was like,

fuck that, no way. How do you feel about the future of safety within this landscape? I mean, it's crazy to have VCs talking against open source, right? I mean, Fowder's Fund did too. And for me, it's just wild when pro-innovation, you know, in, you know, pro-initivation sectors of the economy academia, too,

have decided that, like, open, transparent innovation is somehow an antithesis of safety. I know that's not what you asked, but, like, I just want to make the point. It's just a, we were in very bizarre land for a while, and it seems like we're coming out of that now. So let me just draw a bit of a... Do you think we're coming out of that?

I think we're moving more and more into that. Great. You know, matter and Alex are going to turn long, fully closed. Great. So let's go back to that in just one second. I want to answer the question that, because you actually asked like a great question on how I view this,

and let's go to whether we're coming out or not. So how do I think about safety? So I, you know, I was actually very, very close to security during the rise of the Internet. You know, I worked for the intelligence community. I worked for Livermore, National Lab, And then, you know, when I did my PhD, like, you know, a good, you know, 50% of my work was in security. I taught, like, a cybersecurity policy course.

And the thing with the Internet is you had these very specific examples of new types of attacks that, like, impacted nation states. Like, critical infrastructure would go down. You know, you'd have things like the Morris Worm, like, you know, I mean, you had these really significant examples. And that kind of kicked off this large discussion on how you handle it. And it was so significant at the time that at the nation-state level, we started thinking that we have to actually change our doctrine. You know, you were kind of this Cold War era, mutually sure destruction. We had to change it to this notion of, like, defense asymmetry,

which meant the more we relied on these things, the more vulnerable we were, right, as opposed to like a country that didn't rely on them, as you can be attacked. And then, of course, kind of the whole terrorist information warfare stuff. And so the implications were so absolute, and you had so many proof points, and you could articulate them incredibly well.

And so if you look at the AI stuff, I mean, 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 and use from. And the thing that 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, and we have to kind of throw out everything that we've learned and kind of like revisit the discourse, even though we don't even have the same proof points. I mean, like, nobody can make a strong argument on asymmetry or need a shift to doctrine. And if they can, let's go ahead and have that discussion. You know, I still have yet to see the dramatic new attack.

It's going to come for sure, but we haven't seen it yet. And so I just feel like, like the discourse around this is not in line with the reality. It's not in line with historical precedents. And so we should absolutely take these things seriously, but we should draw on the information that we've learned from in the past and the approaches we've taken in the past. The last thing I'll say in it is the biggest difference this time

is in the past, the people created the technology were kind of pro-tech and the people that were like selling security, solutions were like the fear mongers, right? So you'd have somebody create like the internet and they're like, this is safe and it's great for everybody. But then you'd have somebody to create a firewall and like, oh, the internet's dangerous. Every sociopath of your next door neighbor. So you had both the same voices, but in two different bodies based on interests. The interesting thing this time is they're in the same body. So the person that's creating the thing is also like, oh, this thing is very dangerous. I don't recall the last time we had something like that, but it's created a dynamic

that's just been very confusing for everyone. Do you not think open source increases the opportunity set for hostile actors like China and Russia to harm us? I mean, I think it's tautologically true. Like, I think tautologically you can say, do you believe computers and the availability of computers increase their ability to harm us? And I would say absolutely computers and availability of computers do.

I would say, 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. and 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. So I think Chinese open source can be a national security issue, for sure. And any of this offer that 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. What do you think we can learn from China regulatory-wise that would enable us to have

the same or better open source ecosystem slash environments? I mean, to me, this is, you know, the United States is 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 apps involved. We should get academia involved. You know, we should make this a national priority, just like China does, and we should just, you know, a full-throated endorsement of all of this stuff. I think we should do closed stuff. I think we should do Dove and stuff.

And we've done this forever. You know, 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? I mean, 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 have then around compute. I mean, we actually stopped Saddam Hussein from like importing playstations because we were worried about, you know, using them for simulation. We'd put export controls on the hardware. And we'd say the same things. Like, oh, you know, computers out there, like computers, you know, they're going to enable, you know, the enemies and all sorts of stuff. And this is like nuclear weapons. This isn't like some abstract AI thing. This is like actual, actual on the ground weapons. The posture that we took at the time, and the conclusion is we're just going to be the least.

leaders and all of this stuff. And we funded academia and we funded the labs and we won. And we were able to control like 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 like they're going to learn from our, you know, our success and somehow, you know, we're not. Do Trump's cuts to university's research labs not impact your ability to do what you just said. Are you not actively going against what you should be doing? 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. Like I've, I still, I can't tell you, you know, 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, Obama tried to get rid of indirect costs. He was like, you know what? Universities, they have a tax-exempt status.

So why don't we just have them, you know, spend 5% of their endowments like any other tax-exempt organization. And, you know, that will cover a lot of indirect costs. And he couldn't get it through. So this is a bi-kind of partisan issue that is longstanding. And I mean, I would say that like a change is needed. Now, to the extent that, you know, I think these things are very hard to implement. but I would say concretely, yes, we should invest in these things.

Yes, we need a shift in how funding happens. I do think that like indirect costs have gotten way out of hand. And until it was like Trump doing it, everybody that I know in academia totally agreed. But yes, of course, change and shifts in funding will be disruptive. And so I think all things are true. I just want to do it. I don't want to redo this to a simple like 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 mean, I definitely think we should fund as much or more. I definitely think that shift in funding and change to the system is needed. And the right path through that is complex. I don't quite know it. You very kindly said that I asked a good question on the reversion back to closed source

when we mentioned Alex joining matter, what it meant for Lama. 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 as my statement now on the transition? 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, right? I mean, we just had the AI – what is the name of the bill that just came out. I mean, it's like 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, Open AI 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. Do you think they will? I just, I have no idea. I hope so. It would be a very rational. I mean, here's a great, maybe here's the, like, we say open source, but it's such a misnomer when it comes to AI.

I mean, the standard model of open sourcing AI is you open source the smaller model and you keep the more capable model closed source. And 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. Like to replicate it, you'd have to recreate the data pipeline

in the training pipeline. And so, you know, I think that there's just like a lot of concern of investing, you know, 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. And 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. What did you believe about the AI landscape that you now no longer believe?

We've touched on so many different elements. My mindsets have changed around so many. I mean, the one for me that I've just consistently got wrong is just how fast these coding models advance. And this is probably just sunk cost fallacy. My entire life, I've just been this nerdy program. I've been programming since the 90s. I mean, it's like it's my happy place. and I just never thought that they would advance to the level that they have.

I mean, I still develop most evenings, and it's just, you know, instead of watching a sitcom, I just goof off and mostly writing like old video games or whatever, just for fun. Like, it's silly stuff. And I'm already at the point that I just, I couldn't, I just couldn't work back to working without them. And I've spent, you know, 30 years without them.

And it's just their ability to all. offload, all of the shit I didn't want to learn is remarkable. The thing that kept me away from code for a while, which is I would kind of dabble with it. I would drop it is, yeah, just learn all of this, like, all these weird frameworks. And like, none of the knowledge is foundational. It's just like some fucking random dev came up with some weird way to do something and you've got to kind of learn, you know, some poor design decision to do it. And none of it made any fucking sense.

And it just felt like you're wasting your brain space on poor decisions made by random open source developers. And that was programming in the past. I probably in the programming. So let me just put it in context. In the late 90s, programming was you download your IED, you sit down to your computer, you'd program something, and then it would turn into a binary,

and then you'd run that binary. And so, like, you could, like, really get a lot done just by sitting down and writing code. You know, by, I would say, like, 2015 or so, you know, writing with something, it's like, you'd have to, like, fucking, like, download, like, 50 million packages and, like, to run it, you got to run some stupid dev server and to, like, actually have anybody else use it. You got to, like, learn how to host it. And, you know, like, it was a bunch of libraries that were, like, dealing with incompatibilities for all of us is a weird fucking platform. So, like, 90% of your time had nothing to do with code. Like, 90% of your time was just dealing with all the environment platform bullshit.

And so what's so nice now is you can just focus on your code. So like now I literally just, I mean, I use cursor and I just have like, I just have the AI tell me how to host the thing and tell me what package to use and whatever. And I just strictly focus on what I want in the logic. And so it's almost like it's brought coding back. And you can see this across the industry. Like all of like I've got, I mean, 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. they stop, they're running companies now or whatever,

and they're all back to programming at night. And I really think that, you know how, like, there's, like, the adage of, like, I don't know, like the old man that goes into the garage and, like, makes the train set for, like, nostalgic reasons. I think, like, the modern version of it is these old systems programmers, like, you know, vibe coding at night just because it's become pleasant again. And so I know you asked about the thing that's kind of 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. Do you think they make 1X engineers 10x or 10X engineers 100X? 10X engineers 100x would be what I said. But I don't actually think it's that. I think they make 10x engineers 2X. I would say every company I work with uses cursor, right? And then if I actually look at, has that increased the velocity of the products coming out? I don't think that much just because so much of...

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? I just think the things that are hard remain really hard. And so, you know, like, let's just talk about, like, creating a model. So let's say I'm creating a new model,

a new frontier model, right? And to create that new front term model, I've got to collect data, and I've got to run a pipeline, and I've got to, like, sit with my, you know, my Jupiter notebook, and I've got to, like, look at the lost curves, and I've got to rerun it. And, like, that's just a lot of kind of experimentation and so forth. You know, 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, you know, or visualization or write documentation, It's actually really good at that.

And so I would say that probably in the long run, having more robust, maintainable codebases with less bugs is just as likely to be the impact as feature velocity. Because in startups, again, I'm an infar. I'm an infar guy. This is probably different for the apps. Like, I've always thought apps had no technology to begin with. Like, every time I look at vertical SaaS,

I'm like, why do we even care about the technical team? It's fucking crud, man. And it's like, crud is like, create, you know, read, update,

delete. It's like they all do the same thing. They all just kind of look like a web app. They're all, like, who cares about the technology?

The technology is simple. These are all these kind of go-to-market things and whatever. But infrastructure is different. Infrastructure is like very real trade-offs in the design space that only some of the

understands computer science would know. So for infrastructure companies, I think it's quite unlikely that AI will really help like speed that up because it comes down to something that the developer has to decide on, has to articulate the tradeoffs. But I do think it could really help with the development process so you have less bugs and

things like that. And so I actually view it more as like a more robust developing methodology that necessarily speeds up the core product. Given the kind of dev productivity changes that occur because of these tools, how does that impact defensibility within companies today? If time to cost, which is Misha at Fiver said this on the show, he said time to copy has basically been reduced to nothing. To what extent does that change defensibility for companies? I mean, I still think we should just go back to the split between apps and infrastructure.

For apps, like, how long is it take to copy it anyways? I mean, you know that there are entire companies, that their stated purpose is just to copy another company in the app space. It's just so easy to do. I mean, there is no core technology for random app. I mean, there's no like different information technology for random app. Let's say that you're creating, I don't know, some health care vertical SaaS thing. Like you could contract and you have been forever the actual app.

I mean, the business is actually the long tail of understanding that domain. So I just don't think it changes that paradigm at all. And then when it comes to core infrastructure, which is what I focus on, things like, think like databases, foundations, models, there's no way that right now models can just copy. And the reason there's no way is it, it's not that the models aren't capable of doing the technology. It's just that there is a long tail of understanding of the tradeoffs for the particular use case and domain. And because it's a new market often, then you understand that through market exploration. And so I just don't feel

that I think these models really help with the software development process for, for you know, non-deeply technical areas like apps, sure they can help speed it up. But over time, all of these reduced to a long-tail understanding of the market. I mean, Aaron Levy said it so beautiful. I mean, do you know what the average, what do you think the average PR is,

pull request is for a production code base? Like how many lines of code is the average change that gets accepted, would you guess, for like some production enterprise app? I have no idea. It's two. It's two, yeah. It's very, very small.

It's actually two, but let's say it's 12, right? And what does that two or 12 lines signify? That two or 12 lines signify probably some learning in the field or some understanding of what is needed. And so 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. And so in many ways, I would say, you know, the AI is getting rid of the middle, right?

Like, so very new computer science like models, they don't know how to do just because nobody's done it before. And that's kind of pushing the state of the art. And then in the app space, all of the hard stuff is the business anyways, right? And this is why, like, the changes are very small. And, like, you learn everything to go to market, which the models don't know just because you're exploring a new market. And it's all the bullshit in the middle that they're helping us with. And so, you know, for me, it's just kind of neticreative. Do you think that CS holds the same weight as a study and education discipline

that it always did and you would always recommend it? Or does that change in a world that's funnly more democratized in terms of creation like we discussed? I mean, I feel very strongly that, like, if you care about building systems out of computers, you have to understand how they work. What do you think we do today, Martin, that we will look back. on in five or ten years time, and I can't believe we did that. It could be prompting. It could be choose the model that we're working on.

I find it ridiculous that we are supposed to choose which model. Like groc three, groc four, groc five, groc shopping, grot weather. What the fuck? Just figure it out. Well, I'm just taking it from a programmer's view. I mean, I just think hopefully we'll just stop worrying about frameworks altogether. And maybe even languages, maybe even a, like a proto-language evolves,

and we can just focus on logic and fundamental trade-offs. I mean, we've got 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. So I always worry, this is going to be

this weird philosophical rant, but I always worried, you know, while I was doing grad school and when I was doing research, that we kind of entered a space where there's so much research that's been done over the years that you never know

if you're doing something new. Like you just couldn't do the literature search. There's so much. And so like the entire industry just spent all of its time redoing research. You know, it's like, it's like, it's like you're like

cleaning a room and you're trying to like sweep out the dust. But rather than sweep it out the door, you're just kind of moving it. Like you'd move it to the bed or you move it to the wall. And then like, that's all you do is just kind of sweep the dust around, but you never actually get it out of the house.

That's what research felt to me. It was like we're in this mad delusion. And on top of that, it also felt like many of the most important problems were kind of between disciplines. And so, like, in order to even solve them, you just have to know too many things and we couldn't do that. And so I just felt like there's all, like the entire scientific industrial establishment was just kind of redoing the same stuff. And so in a way, I think AI has the ability to pull out of this mass craziness, this mass ineffectiveness, which, A, it's very good at telling you if you've done it before, right? You know, it's very good at that. It actually knows all the literature, knows all the history.

And it's also very good at tying different disciplines, right? It is an expert in all of these things. And so I think we've been stuck in this morass. And it's a bit of a liberator so we can actually focus on the new problems and know we're doing new things. And so I've got this very optimistic view of where it's pulling us. And so I know it's more of a philosophical answer to the question that 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.

In terms of like societal implications there, I mean, the worst question ever is like, oh, the job displacement question. But I am intrigued. Because like in the one hand, I see intense job displacement happening fast than ever. And then I'm also very aware of Bradfeld wrote a brilliant post where he basically said every single cycle, every time we've always said, oh, 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? Yeah. So listen, I'm very sympathetic to concerns around job displacement. And I think we should take him very seriously as a society. Like I'm in no way libertarian. I think that this is kind of where governments do step in and we do help out. But first we have to understand. And it's actually very unclear. So let me tell you just a quick anecdote. So I, you know, my cousins, are all pretty, like, I think high-end's the wrong term, but they're pretty established translators. And they have been for a long time, multiple languages,

and they visited recently. This is a husband and wife pair, and they're like, listen, like, you know, we have to change jobs because translation is all going to AII. And I asked, I said, you know, so the jobs are going away. And they said, well, no, they're shifting. And now instead we've got to like spot check these AIs, and the only way we can hold it up to our standards if we rewrite the entire thing, but they won't pay for that.

And I don't, you know, by the way, these are Italian, so they speak this way, but they're like, you know, I can't work on something without a soul, right? And I think that their dilemma is a good microcosm for the broader dilemma, which is one thing that's very unique about AI is that it actually requires today a human handler. I mean, they're just so unpredictable, you know. I mean, most of the use cases that we know, all the monetized use cases have a human on the other side of it, right? I mean, coding, you've got a professional coder, all the creative stuff. You've got, you know, somebody like doing all of the creation.

I mean, these are, it's kind of an enabler and that's a tool. But the nature of what you do does shift. And that's very different than, for example, electricity where, like, it doesn't require a human. Like it's like either you light the fire or like there's no fire to light. And so, you know, I think we as a society need to understand the level of displacement. We have to understand it. I think it's very important that we do.

I think these are things that governments should get involved in. I do just have to turn to your venture investing just 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 love it. I love it.

I love it. They said that they didn't think you enjoyed the administrative work that you now have to do with the size and scale of Andreessen. Oh, well, those are two different questions. I love the investing. I mean, 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 circle. I love it.

Actually, no, I love the – I actually really like the firm building side. It's, you know, I mean, frankly, I could do without, you know, endless meetings, but I've actually been pretty good at limiting those too. And so, no, no, I think this is actually the most exciting time to be in the industry and venture. I'm not trying to, I'm not trying to bullshit. No, no, no, dude, 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. Philosophically, for me, 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 like a single deal is going to like bend to my will. And so, I mean, philosophically how we think about investing in general is... You walk away because of price often? Price, no, ownership, yes.

What is the ownership we need? It all depends on the fund, the market, the size of the market, the understanding of everything comes down to ownership for us, not price. I mean, you just can't make the fund mechanics work, you know, if you don't get the ownership. Now, for very, very, 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 know, 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, say, a fifth of the fund or half of the fund. 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? So the way that I view it is a bit different, which is 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 special networks, special value, you understand a special size of the market. You understand a special size of the market. And that is kind of 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 like an AOM thing,

but it's like you have all of the products so that you can be adaptive in the market. Because, you know, 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. And so if you don't have, honestly, sometimes it's credit.

Sometimes we don't have a credit fund, but I can understand why people do it. And so the market is competitive. And everybody's scrambling for deals. and if you don't have the different funds or products to offer, then often that's kind of where people are going to squeeze you out or get alpha, et cetera. And so 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.

And so, again, I don't think it's a you, me thing. I think you play a very different game than we do, because I do think that on one side, like, you know, you have to go very specialized, very, very, focus very early, where for us, you know, we're trying to find out what is the right time to enter to, you know, to get the ownership that we need. What's the size of fund that you primarily invest out of day to day? I know you have flexibility. 1.2 billion. So I run the infrastructure fund, which is $1.2 billion fund. 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 that because I'm investing out of a $275 million series A fund and $125 million C fund. It's just like much more meaningful dollars for me than it is for you, which will affect my willingness. Yeah, well, my challenge is like we have to live with these investments forever and conflicts are very, very difficult for us to do. And so we don't enter very often at the stage that you do for this reason.

I mean this respectfully, everyone chastises Andreessen for their conflicts and for investing in many for conflicting companies. Do you think that's unfair? 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. Like, I don't recall, like, intentionally investing in a company. In fact, I mean, we routine, I would say the number one reasons we don't, that's not true. One of the top reasons we don't invest in companies because of conflicts. I mean, we do it.

I mean, just recently, I can't say the name of the company. We didn't invest because it was a hard conflict. And even though, by the way, the company, the portfolio company was not doing the thing, but it was on the roadmap. And the founder called me, he's like, Martine, you just can't invest this company. I said, okay. So I think we try our best to keep it. Okay. Sorry, just to push back on you there.

If it's not on the roadmap, I'm really sorry, founder. I have as much faith in conviction as you as possible. But if it's not on the roadmap, I'm not having you tell me how to do my job. So here's my talk track, and it's evolved over the years. And I stole this from Chris Dixon. which is I say, listen, you have one mortal enemy. You choose whoever that mortal enemy is and whoever it is I'm with you

if we're going to go kill that mortal enemy together, but you get one. You don't get an arbitrary number of mortal enemies. And so in this case, I'm like, listen, is this it? Is this your one mortal enemy? And the fighter said, yes, this is the one mortal enemy. I'm like, all right, fuck them, let's go kill them. And that's, and that's it.

That's kind of, 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. And we also do have the venture and the growth fund. And we try to minimize conflicts there, but sometimes they happen. You know, just 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.

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 Gill 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? So I don't want to speak for a lot, but that's not my experience working with a lot. And I've done many deals with him. A lot 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 a lot invests. Unpack that. Why and how does he do found a market fit that's the best? He will find a market that he really likes. And sometimes it's like even a fast follow market, right? Like, you know, and then he will find who he thinks is a great founder for that market. And so he's very good at like this kind of boy band construction based on

the market. The primary point I want to make is very much in his investment cycle. The founders have always mattered. Any of this, he's followed on deals. I've done. I've followed on deals. He's done. We've done a bunch of deals together. I've never, I've never gotten the impression. I mean, I actually always got the impression that the founder's, the primary decision once he's chosen the market. So I would say it's a primary concern for him. When you have misjudged a founder, what did you not see that you should have seen? So can I answer your previous question? Because you're like, okay, so how do we think about it?

So we think about it very simply, which is the only sin in investing, and I've sinned so much. The only sin in investing is missing the winner. Like there's no, it's fine to like invest in a category that doesn't work. It's fine to lose money. But like if you choose the wrong company, like that's not okay. And listen, it's just so hard to get. get it right all of the time. And so the way that we view it is we just look for viable, you know, what are viable spaces? And it's determined viable because someone said to me the other,

I'm so sorry to interrupt you that 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. Correct. That's exactly right. Yeah. Yeah. So the view is like there's basically no amount of work you can do to determine if a space is going to work or not. I mean, that's just, you know, 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. You think you can? The question is, can you beat the market with that strategy. Yes, I think you can beat the market. No, I do not think that you can equivocally 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. When did you most poignantly for you pick the market but pick the wrong horse? I just don't want to I don't want to call out any specific

company. Fair enough. When you think about like you mentioned sins there, what's a big sin that comes to your mind when you were... Well, yeah, I mean, I can answer the opposite. There's a bunch of markets that just haven't haven't really worked, right?

Like, you know, the entire streaming market has been very, very tough. Like the data streaming market. It's just turned out to be a subset of the analytics batch market. And so, you know, maybe, you know, Click House is, Aaron Katz is doing phenomenal and I'm not an investor, but he's doing phenomenal.

But that may be the one breakout since Confluent, but like that's just been a very, very tough space historically. Whether you're at the dashboard layer, you have the transformation layer, at the feature store layer. It's like there's been entire spaces where we played multiple bets where like it just didn't

it just didn't work out. And so many, many, many times we'll invest in the space where just none of them work. You know, I will tell you, there's definitely been companies reinvested where at the time the company was the very, very clear leader

and then something happens. Some macro shift, some, you know, something else happened. And, you know, I think that's just how the game goes. And you've probably heard this. I mean, the thing with actually having a strategy like that is if you're trying to scale a venture firm, you just need something

that you can articulate and teach other people. I just find it hard that 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 to be seen for that it's fucking hard to pick the horse, almost I'd fire the who picked the wrong market entirely. Where was your insight, at least? Yeah, and this is why you run your own venture firm, and you can have whatever strategy you want. Is that not, is that moronic? No, I learned. No, no, no, it's not. No, I just think it's philosophically different on the approach, right? And so I actually don't believe you can predict the

future of technology adoption. It's a very tough thing, right? I mean, you don't know what a big company is going to do can wipe out an entire market. You don't know what an innovation will wipe out entire markets. This happens all the time. I mean, 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, say, 10 companies that have some traction and you can talk to the founders, you know, the founders, you can diligence to the teams, you can diligence to the market,

you can do this to the project, you're doing to the technical approach. I think you can just say something a lot more concrete than, you know, is some future innovation going to wipe out this entire market. 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. I don't know. I mean, I would say humans are AGI and we still invest in enterprise SaaS. This is the problem is everybody somehow, they somehow think that AGI just means like unlimited powerful and anything I want to disappear in the future disappears.

Come on, you're AGI. I'm AGI. We invested a process. I think to be honest, Sam, Altman, so that's the definition of what AGI is. So whatever him and Microsoft decide is AGI is will be AGI. Dude, I want to do a quick fire round.

So I say a short statement, you give me your immediate thoughts. Yeah? Yeah. What's one of the most over-hyped AI categories today? ASI. What's one of the worst VC takes on AI you've heard recently? Open source is bad for national security. What one founder would you back in any category?

Whatever they did, I just want to wire them the money. Michael Trull. Why? Specifically. I've worked with them for a year. He's remarkable. What makes him remarkable? It's just 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. And that's a very, very potent combination. And then, of course, he's incredibly smart and he's got great product taste. What's your favorite trait in yourself that has been most impactful to your own success? Deep-seated anxiety from being poor? Seriously, I agree.

I mean, listen, I grew up like... You name it, food stamps, dirt road. Like, I mean, I come from Montana. It's so funny. People hear the name Martin and they're like, oh, he must be. And then, you know, I was actually born in Spain. I was Spanish and was Spanish citizens. So they're like, you know, he must be some like sophisticated European.

I'm like, motherfucker, dude, I grew up on a dirt road in Montana. Like, when there was hunting season, my school shut down. Like, I'm like a Western country boy. And so, you know, listen, I, you know, I mean, I had a great family. I didn't have any of those hardships. I had a wonderful family and educated family. And so, like, you know, we kind of muddled our way through, but, you know, you go through that and you see how hard your parents work and whatever.

You just don't take anything for granted. And, you know, listen, 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 or I haven't worked since basically forever. Now, listen, I'll take like a week off while I have a job, but I've never not had a job in, what, 20 years? It's just... Did that day feel fucking awesome?

Coming from a dirt track and bunny food stamps, as you said, you can retire today, and I know you didn't, but did it feel as good as you thought it would? You know, it's kind of an interesting thing. No, I mean, no, I mean, it was very bittersweet. I think you actually selling companies is very bittersweet for any founder, right? Like, you know, it's a death in a way.

I mean, you know, you spend so much time with something, and then it shifts. But here's the interesting thing. And maybe this is kind of advice to other founders, which is you always think about, you always think about that thing you'll do when you, like, you know, make the $100 million or whatever. You're like, you know, I'm going to go do that thing. But you only think about that thing in the most stressful times. So my thing was, so my cousin's a movie director, his name is Vincenzo Natale, a pretty legit guy. And I was like, you know what I'm going to do.

As soon as like I, you know, the money hits the bank, I'm going to drive down to Hollywood. And I'm going to help him make movies and be an actor and just kind of be one of those people. And I, and so, you know, it happened, the wire hit. And I was driving down the five. And I'm like, what the fuck am I doing? Like, I love technology. I love my job. I don't know.

I hate Hollywood. I have nothing in common with these people. you know, I probably got two hours out of town, and I just turned my car around and came right on back because I was like, you know, you only have those visions at the most stressful time, and when you're not stressed, you realize that there's something that brought you to this place

and his genuine interest and genuine love of it. And so my only advice to other people going through this is just don't use those dreams that you concocted when you were like really in the pressure cooker, like not sleeping, your relationships are falling apart, that whole thing. Like, that's not the thing that steady state you're going to want to do. Like, you're probably where you are because of for the love of. And letting that go tends to be pretty disastrous to some people. Was making money or having money what you thought it would be?

You know, I had to play all of these tricks. I actually borrowed one, which was very helpful. Which was, uh, so I just have a had a hard time spending money just because like, I mean, literally, I mean, like, for me, like, you know, when I got into like the Stanford PhD program, this is so embarrassing, but like, we always thought like $20 was like a lot of money growing up. Like, you know, and we'd call it like the yuppie food stamp because it was like 20 bucks. And, and I remember I was like, I was going to go to Bites Cafe and I was going to pay with $20. Like a $20 bill because like that's kind of like some like stamp of like having money. So I was just, you know, I was just so naive to all of these things.

And so like it was just very hard for me to like, you know, like, like, like, like, Once, you know, I made enough, you know, generational, you know, I made generational wealth to do it. And so I talked to a friend of mine who I went for similar things. Like, you know, I did. He said, I came up with, let's call him Brad. I came up with a Brad coin. And the Brad coin, let's say I'm worth, you know,

10 times more than like an average rich person. So my, the Brad coin is worth, you know, 10 times more. So I buy a thing in Bradcoins. And so if it's, you know, let's say it's a business class flight. Right? I mean, that's $10,000, but in Bradcoins, it's only $1,000. And $1,000 sounds a lot better than $10,000, so I feel good. So I actually had to adopt a lot of these mechanisms where, like, I'll make a Martine coin, and it's worth this much money. What got worse with money?

This is something I have to deal with all the time, but, like, I mean, my wife forces me to keep it real. I mean, she just won't abide by any of the shit. So, man, I got three fucking dogs that are crazy. Like, she doesn't like help in the house. Like, I drive a fucking Volkswagen. We have three chickens in the back. You know, I'm like fucking schleping the kid all the time. I mean, like, listen, man, if it were me, I would be living your life, man.

I'll be like 100%, you know. You know, being New York in the penthouse with a private jet. And instead, I'm in the fucking Volkswagen with three dogs in a messy house and no hell. So it's like, I, yeah. Dude, you were so whipped. You know, it's not even that, right? It's like, you know, like, I mean, this is what marriage is, man. Like, you know.

What's your biggest lessons on marriage? For me, I'm 29. I got a great relationship, but not quite that yet. What would you tell me about greatness in marriage that I should know? Well, listen, I got it wrong once. I'm not sure I can, I'm the right guy to ask here. Like, my startup was really tough. Like, you know, it was really tough.

And I think that burned through my first marriage. And she was great. Yeah, fuck, dude, I'm the wrong guy to ask. I'm really the wrong guy to ask. I mean, I will say something, I mean, which is a different question than he asked, but I think it's important, which is... I have found that men in particular

that have stable relationships just do a much better job in work. They're just much more stable. I think the best founders I have, tend to be, like, have families and et cetera. And I do think, again, like, you know, I don't want to make it a gender thing. Maybe it's not. Maybe just my observation.

I work with a lot of men that, like, families are really, really, really good for men, even though they can be a pain in the ass. And so I just think the only high level view is, like, it's just these things are super important. And so, like, whoever you have and you're working with it, like, it's an important thing that, like, kind of, like, it really is keeping you. grounded. I mean, in my case, listen, like, I mean, you got chickens, baby. I mean, you know, it's like what is Zorba the Greeks say? It's the full catastrophe. I know it's the only way I can

do what I do. There's there's no other way, right? I mean, like the level of pressure is the amount of work that I do. I mean, it probably work all in 80 to 100 hours a week. I've been doing it for 10 years. I mean, the amount of demands, I just, it's very, very hard to do. I mean, it's very hard to do with like without like you know support and grounding and so you know in a way again like I'm not the right person to ask like how do you treat your well like I just whatever like I'm a fucking autistic nerd like I have no idea but I do know that these things are incredibly important for for us and and you should value them and treat them as such if you think about andrewson in 10 years time where do you think andreason will be then like what does the 10 years ago when you remember

it was a fucking different firm. Amazing and innovative in its own time, but it was from where it was now night and day. Where is the 10-year Andrewson in 2035? The most remarkable thing about the firm, in my opinion, is that it's able to evolve and adapt very aggressively because the way it's structured. I mean, Mark and Ben really are the top of the firm.

They really are. And I think it's a feature, not a bug. And I think it's very, I mean, it's kind of a historical quirk that VC was created around a partnership model. Like that's the same thing you use for a dentist office or a law firm. And I think it's, there's positives in that there's a bunch of different agendas that kind of sit at the same level. But for like decision, velocity and disruptive change, it's death.

And so I think that 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. So 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. 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. Yeah, likewise. Harry, always a pleasure. You're the best. Thanks for listening to the A16Z podcast. If you enjoyed the episode, let us know by leaving a review at rate thispodcast.com slash A16Z.

We've got more great conversations coming your way. See you next time. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include pay promotional advertisements, other company references, and individuals unaffiliated with A16Z. Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16Z, or any of its affiliates. Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.