a16z's Martin Casado on Anthropic vs OpenAI: Where Value Accrues

So excited to be here.

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 twenty years. It’s just just the future is very uncertain. And and 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, was 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, etc. And so I think the one sin is not playing the game.

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. Tiger came in with a ton of money and deployed it a whole bunch. And so, like, I think 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. 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?

There are two futures to code. In one future, you’ve got Anthropic as a monopoly. 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 Cloud four 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 gonna happen. Like, remember, like, the whole Ghibli OpenAI launch and we’re like, oh, image is gonna 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, 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. You know, the rumor is is that g p d five coding is gonna be great. So in this 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 consumption layer value, like, for example, to nontechnical users or to Python users or to professional coders or whatever it is, and that’s gonna 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 they’re like, listen, we want to have the consumption layer, but we’re never gonna 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 that they view in their core focus, and they will do whatever that they can to either capture that margin or to capture that market share. I just think 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, it’s gonna be a monopoly.

It just really hasn’t been the case.

Oligopoly. This is not the cloud played. I 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, you know, 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.

They were the massive market leaders that created the category. I mean, they had way more dominance than Anthropic 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 spunt 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 on the price performance, I would say in many use cases, the one that I actually use as my standard model, it’s better than Anthropic.

If you actually, you know, take into account price performance. And Google can arbitrarily subsidize that too. 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 gonna show up. And so I just feel like the players, the money behind the players, the fact that these models distill this one up in an oligopoly.

I think that you end up with models with different flavors, and there’s gonna be a lot of new flavor models that will come out. You know, Mara and Ilia are out there creating models. I mean, you got these very legit teams that were some of the pioneers. We’re just starting up models for the sciences. And as you get more into RL territory, these models really get a certain flavor. They don’t generalize nearly as much. And so, like, that’s gonna naturally, from a technical perspective, fragment the models.

And so I would say the core base model for, like, language search and code, 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 there’s a lot of change to come, but I do think that both Anthropic and OpenAI have done a remarkable job with brand independence and market share, and so I suspect they’ll continue to be stalwarts in the industry.

Are you in either of them? Or investors in OpenAI. Yeah.

Okay. So if there’s one thing I’ve learned, honestly, for anybody that’s listening to this, this this would be worth, like, your time. There is no one way to think of AI, and there is no, like, one way way to think about models. And the models themselves are entirely different businesses depending on how you talk about the model. So to even answer that question, have to tease apart what you mean by model. So for example, if you look at the diffusion models, say like eleven 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. Google subsidizes language and code and video, but not speech. And so from an investor, these are clearly great investments because of you know, if you just look on the metrics alone. On the other hand, the frontier language space, it’s much more complicated because there’s so much subsidization. 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 best growing companies we’ve ever seen. There’s tons of value. These are very valuable entities. Right? You know, Anthropic, OpenAI. 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, you know, capital is forfeit.

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, you know, for the winners, it’s justified. And so I think it’s not that that investors are willing to go up. I mean, we’d be very happy not to. I mean, I know you would, right? And it be great to have great returns with low risk. But the nature of the system and the game which we’re playing requires it.

By the way, this is the dissonance in all of this. This is 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 as being kind of non defensible business, continue to grow in value. The model companies, which I can’t tell you how many investors wrote off the models. I mean, this question’s 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 on every, you know, 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 growths in value that we’ve seen in, what, twenty years?

a great question. So so here’s one thesis. I mean, it’s so early. We don’t know. Maybe And in a month, all this gets proven wrong. But we actually talk about this a lot internally. And here 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. For many of these models, I mean, is one better than the other? Yeah. Maybe, but they’re pretty close, but, like, people know ChatGPT. It’s like it’s a household name. My mom knows ChatGPT.

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, 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 gonna 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 so markets tend to expand and then contract. Right? Think about cloud. Right? It’s it’s kind of like this funny thing, and it became very massive. Then, of course, it slows down. And 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 expand space. It’s just very clearly the case. And in which case, the leaders are gonna continue to have, you know, a distribution advantage just through brand recognition.

I mean, I think it’s as soon as the market growth slows down. Take Cloud as an example.

Well, I just think 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, and the first thing the frontier hear hears is the household names. And so the household names win. You know, that’s a natural artifact of expansion. As soon as like the expansion slows, 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.

For the longest time, when the the cloud market was expanding, everybody knew AWS. It was the leader. It was 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 was just wasn’t obvious. Do you do GCP? Do you do Azure, etcetera? 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.

That’s right. So so so the market slows down. And once that happens, the frontier, it becomes more saturated just because we’re not adding people as much. And so they will get more of the educated message. 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 as a household name. 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 that you do it is is just through brand recognition, which is kind of this word-of-mouth y type thing.

It’s like on every podcast and, you know, the friends and and 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 leader 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 outpace more on product differentiation.

Well, you just try to invest in the leader. And it’s worth and it’s worth paying up for the leader, honestly. I mean, it’s for me, I ask two questions. Question number one is like for the area that it’s focused on, is it the leader of it is, it’s definitely worth paying up. And then the second one is, the story actually has been 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, the OpenAI was the first to code with GitHub Copilot.

I mean, they provided the weights, 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, they’re still the massively dominant player in language and and continue to be so and will be so. And and arguably, that was the right thing for them because that’s by far the largest market by far. And so OpenAI acted totally rationally and has the largest market, but that gave the ability for mid journey to take image or BFL to take image.

Google seems to have grabbed video with v o three code. I mean, on the model side, Anthropic has turned that into, you know, this wonderful business. And so when markets expand, not only do you have these brand effects that we were talking about, they also tend to fracture a bunch of 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, especially for developers that use these things in products. And then mid journey is for, you know, more of the fantasy, you know, also professional designers, but it’s a very stylized kind of opinionated view. And all of these are independent viable companies. So I think we’re gonna see fragmentation for quite a while before we see consolidation.

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 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 of The US market.

I just don’t buy that these are endemic to the business model. Like, 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, you know, 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 land grab, you can never monetize it.

The rational business decision is to sacrifice margin for distribution. It’s just a rational business decision, and we’ve seen this forever. I mean, hell, the web wasn’t even monetized. Literally. I mean, like, this time we can actually monetize these things. Forget forget, like, breakeven or negative margins. I 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 how do you do it? 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, Anthropic is not gonna take the time to do that, you know, so there’s clearly pricing power you have on that side.

Or you have to do actual technical differentiation. One 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 most of these companies that are like, let’s say, breakeven margins, it’s like a board level specific choice to prioritize distribution, not just because this is systemically something

I mean, it’s crazy to have VCs talking against the open source. Right? I mean, founders fund pro innovation sectors of the economy, academia too, have decided that, like, open transparent innovation is somehow an antithesis of safety. And I know that’s not what you but, like, I just wanna make the point. It’s just that we’re we we were in very bizarro land for a a while, and it seems like we’re coming out of that now. So let me let me just draw a bit of a tear.

Great. So let’s let’s let’s go back to that in just one second. I wanna answer the question that you because you actually asked, like, a great question on on how I view this, and let’s go to whether we’re we’re coming out or not. So how do I think about safety? 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 I worked for Livermore National Labs. And then, you know, when I did my PhD, like, 50% of my work was in security.

I taught cybersecurity policy course. And the theme 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 have these really significant examples and 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, you know, we started thinking that we have to actually change our our our doctrine.

You know, you go to kind of this cold war era, mutually assured destruction. We had to change it to this notion of, like, defense asymmetry, which meant the more we relied on these things, more vulnerable we were, right, as opposed to, like, a country that didn’t rely on them because you could 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, for every computer system, you have security considerations.

But we’ve got this thirty, forty 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 gonna come for sure, but we haven’t seen it yet. And so I just feel 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 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 is 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.

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 harness? And I would say, absolutely, computers and the availability of computers do.

So I think that right now, 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. Chinese open source can be a national security issue for sure. And and any of the software that produced by a nation state that we view, you know, quasi adversarially, the way that we combat that is we also are incredibly open, and we also do a

of technology.

You know, The United States has a long history of being pro innovation, 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. You know, we should make this a 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 close stuff. I think we should do dope and stuff, and we’ve done this forever.

My first job out of college, this is, you know, 1999, was working at Lawrence Livermore National Labs in the ASCII program. And what were we doing then? We’re I mean, the the broad program was stipulating nuclear weapons. I mean, this is what it was. And a lot of the the concerns we have today were concerns we had then around compute. I mean, we actually stopped Saddam Hussein from importing PlayStations because we were worried about, you know, using them for simulation. We 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 the enemies and and 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 gonna be the leaders in all of this stuff. And we funded academia. 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 wanna put our head in the sand and let somebody else do it. So, like, they’re gonna learn from our our success, and somehow we’re

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. You know, I, 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 a tax exempt status, so why don’t we just have them, you know, spend 5% of their endowments, any other tax exempt organization?

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 long standing. I would say that, like, a change is needed. 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 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 I just don’t wanna do it. I don’t wanna 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 the shift in funding and the change to the the system is needed, and the right path through that is complex.

I don’t know. I don’t quite know it.

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 policy and recommendations as 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, you’re right. I I do think it’s quite likely that we’re gonna see less open source. Now listen. OpenAI has said that they’re gonna 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 have no idea. I hope so. We say open source, but it’s such a misnomer when it comes to when it comes to AI. I mean, the the the standard model of 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 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, like, recreate the data pipeline and 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, source has only been about 20% of the total market value. Would say it’s much higher than that for AI. So in a way, we’re doing better than software has historically.

I mean, the one for me that I’ve just I’ve just consistently got wrong is just how fast these coding models advance. This is probably just sunk cost fallacy. My entire life, I’ve just been this nerdy program. I’ve been programming since the nineties. I mean, it’s like it’s my happy place. 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 it’s silly stuff. And I’m already at the point that I just I just couldn’t work back to working without them, and I’ve spent, you know, thirty years without them. And it’s just their ability to offload all of the shit I didn’t wanna learn is remarkable. The thing that kept me away from code for a while, which is I I would I would kinda dabble with it. I would drop it. It’s have to just learn all of this, like, all these weird frameworks. 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 gotta kind of learn 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. Let me just put it in context. In in the late nineties, programming was you download your IDE, you’d sit down to your computer, you program something.

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. I would say like twenty fifteen or so, you know, writing with something is like, you’d have to like fucking like download like 50,000,000 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 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, 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 and the logic. And so it’s almost like it’s brought coding back and and you can see this across the industry.

Like all of like, I’ve got yeah. 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 stopped. They’re, like, running companies now or whatever, and they’re all back to programming at night. And I 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 the the modern version of it is these old systems programmers, like, vibe coding at night just because it’s become pleasant again.

And so I know you asked about the thing that’s kinda surprised me the most, but I I I really think it’s such a marvel what these coding models are able to do, and they add very real value.

100x would be what I said. But I don’t I don’t actually think it’s that. I think they make 10x engineers two x. I would say every company I work with uses Cursor. And then if I actually look at, has that increased the velocity of the products coming out? I don’t think that much.

the things that are hard remain really hard. So let’s say I’m creating a new model, a new frontier model. To create that new frontier model, I’ve gotta collect data, and I’ve got to run a pipeline, and I’ve gotta, like, sit with my my Jupyter notebook, and I’ve gotta, like, look at the loss curves, I’ve gotta rerun it. That’s just a lot of kind of experimentation, And, you know, there’s no coding model that’s going to do that for you. But but if I wanted to run create tests or a test suite 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 code bases with less bugs is is is just as likely to be the impact as feature velocity. Because, you know, in startups so, again, I’m an info 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. It’s like crud is like create, you know, read, update, delete.

It’s like they all do the same thing. They all just kinda look like a web app. 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 somebody that understands computer science would know. So for infrastructure companies, I think it’s quite unlikely that AI will really help in 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 and things like that. And so I I actually view it more as, like, a more robust development methodology that necessarily speeds up the core product.

I 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? I mean, you know that there are entire companies that their stated purpose is just a copy. Another company in the app space is just so easy to do. I mean, there is no core technology for random app. I mean, that there’s there’s no, like, differentiated 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 databases, foundation models. There’s no way that right now models can just copy. And the reason that 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 trade offs 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 think these models really help with the software development process. For non deeply technical areas like apps, sure, 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 beautiful. 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?

What would you guess for, like, some production enterprise app? 12. 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 the thing that’s the hard thing is to understand the specific deployment environment and market you went 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 the AI is getting rid of the middle.

Right? Like, 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. 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 net accretive.

I mean, I feel I feel very strongly that, like, it’s very hard to work with computer systems and be effective if you don’t understand how the computers

Well, I got I’m just taking it from a programmer’s view. I mean, I I just 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. I mean, it’s it’s we’ve gotten this very backwards world where these days programmers think about all the nonfundamental stuff, and they don’t think about the fundamental stuff. Let me give you an example.

So I I always worry this is gonna 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 has 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. 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 door, the 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 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. 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 could 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.

Yeah. So I’m listen. I’m very sympathetic to concerns around job displacement, and I think we should take them very seriously as a society. 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. Let me tell you just a quick anecdote. You know, cousins are all pretty I think high end is the wrong term, but they’re they’re they’re they’re pretty established translators. And they have been for a long time, multiple languages, and, you know, they visited recently.

This is a husband and wife pair, and they’re like, listen. Like, we have to change jobs because translation is all going to AI. And I asked, I said, no, they’re 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. By the way, these are Italian, so they speak this way, but they’re like, you know, I I can’t work on something without a soul.

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 a human handler. I mean, 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, you know, somebody, like, doing all of creation. I mean, these are are it’s kind of an enabler, and it it’s 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. Either you light the fire or there’s no fire to light. And so 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

Oh, well, those are two different questions. I love the investing. I mean, investing is great. It’s just the most exciting time in the industry since the late nineties. It’s great to be part of a super cycle. I love it.

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 bend to my will. Do you walk away because of price often? Price? No. Ownership. Yes. What is the ownership you 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 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.

So the way that I view it is a bit different, which is I think there’s 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 network, special value. You understand the special size of the market. Like, you’re very, very much a specialist 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 AUM 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 ten 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. Honestly, 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. And if you don’t have the different funds or products to offer, then often that’s kind of where people are going to, you know, try and squeeze you out or get alpha, etcetera. And so I think that for, 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 focused, very early, where for us, you know, we’re trying to find out what is the right time to enter to get the ownership that we need.

one one point two billion. I I so I run the infrastructure fund, is $1,200,000,000 fund.

My challenge is, like, we have to live with these investments forever, and and conflicts are very, 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

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, one of the top reasons we don’t invest in companies because of conflicts. I mean, we do it. Mean, I just did it. I mean, just recent I I can’t say the name of the company. We we didn’t invest because it was a hard conflict. Even though, like, by the way, the portfolio company was not doing the thing, it was on the roadmap.

And the founder called me and was like, Martin, you just can’t invest in this company. I said, okay.

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 gonna 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 provider said, yes. This is the one mortal enemy.

I’m like, alright. Fuck them. Let’s go kill them. Listen. We have a number of companies where they pivot mid stream 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 I would say that we try very, very hard to steer away from conflicts.

So I don’t listen. I don’t wanna speak for a but that’s not my experience working with a lot. And I’ve done many deals with him a lot. He’s 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.

He will find a a market that he really likes. And sometimes it’s like even a fast follow market. 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 primary point I wanna make is 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 I’ve never gotten the impression I mean, I’ve actually always got the impression that they actually the founders the primary decision once he’s chosen the market. So I would 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? The only sin in investing, and I’ve sinned so much, the only sin in investing is missing the winner. 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 that’s not okay. And listen. We I it’s just so hard to to 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 it’s determined viable because

right. There’s basically no amount of work you can do to determine if a space is gonna 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 the best. 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. You mentioned sins there. What was the big sin that comes to your mind? I mean, I can answer the opposite. There’s a bunch of markets that just haven’t really worked. You know, the entire streaming market has been very, very tough. It’s just turned out to be a subset of the analytics batch market. And so click houses Aaron Casado is phenomenal with 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, you have the feature store layer. It’s like, there’s been entire spaces where we played multiple bets where, like, it just didn’t work out. And so many, many, many times we’ll invest in 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, then something happened, some macro shift, some, you know, something else happened. And, you know, I think that’s just how the the game goes.

Yeah. And this is why you run your own venture firm, and you can have whatever strategy you want. I just Is that not

not. No. I just think it’s 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 gonna 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 is really invalidating tons of markets, 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, you know, the founders, you can diligence the teams, you can diligence the market, can diligence the project, you can diligence the technical approach, I think you could just say something a lot more concrete than, you know, is some future innovation gonna wipe out this entire market.

I don’t know. I mean, I would say humans are AGI, and we still invest 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. Like, it’s like, come on. You’re AGI?

ASI.

Michael True.

worked with him for a year.

What makes him remarkable? He has three things. He 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.

Deep seated anxiety from being poor. I mean, listen. I grew up, like, you name it. Food stamps, dirt road. I I mean, come from Montana. It’s so funny. People hear the name Martin and they’re like, oh, he must be so and then, you know, I was actually born in Spain, so I’m a Spanish citizen. 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 a I’m like a western country boy. And so, like, you know, we kinda 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. And, you know, listen, I sold a very successful outcome from a company and I could have retired on that day. And I I still have not taken a day off where I haven’t worked since basically forever. Now, no, listen, I’ll I’ll like I’ll I’ll take like a week off while I have a job, but I’ve never not had

job in What?

You know, it’s a kind of an interesting thing. No. I mean, no. I you know, I mean, it was it was very bittersweet. I think actually selling companies is very bittersweet for any founder. Right? It’s like, you know, it’s it’s a death in a way. I mean, you know, you spend so much time with something, and then it it shifts. But here’s the interesting thing, and maybe this is kind of advice to other founders, which is, you always think about that thing you’ll do when you, like, you know, make the $100,000,000 or whatever.

You know, like, you know, I’m gonna go do that thing, but you only think about that thing in the most stressful times. So my thing was so my my my cousin’s a movie director. His name is Vincenzo Natali. Pretty legit guy. And I was like, you know what I’m gonna do? As soon as, like, I you know, 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 kinda be one of those people.

It happened, The Wire hit, and I was driving down to 5, and I’m like, what the fuck am I doing? I love technology. I love my job. I hate Hollywood. I don’t 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 this genuine interest and genuine love of it.

And so my only my only advice to other people going through this is just don’t use those dreams that you concocted when you’re, 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 gonna wanna do. 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

Yeah. I had to play all of these tricks. I actually borrowed one, which is very helpful. So I just have a had a hard time spending money just because I I mean, look. 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. And I remember I was, like, I was gonna go to bites cafe, and I was gonna 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, 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. You know, I did. He said, came up with let’s, you know, 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 10 times more. So I buy thing in Brad coins. Let’s say it’s a business class flight. I mean, 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. So I actually had to adopt a lot of these mechanisms where, like, I’ll make a Martin coin and it’s worth this much money.

What got worse with money? Man, my I mean, it was I I I this is something I I I have to deal with all the time, but, like, man, my wife forces me to keep it real. I mean, she just won’t abide by any of this shit. So, man, I got three fucking dogs that are crazy. Like, she doesn’t like helping the house. Like, I drive a fucking Volkswagen. We have three chickens in the back. You know? I’m, like, fucking schlepping the kid all the time.

I mean, like, listen, man. If it were me, I would be your life, man. I’ll be, like, 100%. Be in New York in in the penthouse with a private jet, and instead, I’m in a fucking Volkswagen with three dogs in a messy house and no hell. So I was just like, I Dude, you’re so whipped. You know, it’s not it’s 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

Well, listen, I got it wrong once. I’m not sure I can I’m the right guy to ask here. Like, my my start my start up was really tough. And I think that burned through my first marriage. And she’s she was great. Yeah. Fuck it. I’m the wrong guy to ask. Really the wrong guy to ask. I mean, I I will say 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 etc. You know, I don’t want to make it a gender thing. Maybe it’s not as many just my observation. I’ve worked 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 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, it really is keeping you grounded.

I mean, in my case, listen, like, I mean

I mean, you know, it’s like the it’s like the what what what is Zorba the Greeks say? It’s the full catastrophe, but I know it’s the only way I can do what I do. There’s there’s no other there’s no other way. Right? I mean, like, the level of pressures, the amount of work that I do. I mean, I probably work all in eighty to a hundred hours a week. I’ve been doing it for ten years. I mean, the amount of demands, I just it’s very, hard to do with without, like, support and grounding.

In a way, again, like, I’m not the right person to answer, like, how do you treat your like, I just whatever. Like, I’m I’m a fucking autistic nerd. Like, I have no idea, but I do know that these things are incredibly important for us, and and you should value them and treat them as such.

This is a very dangerous question, Harry.

You’re exceptional. You’re an exceptional interviewer.

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, Marc 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’d use for a dentist office or a law firm. There’s positives in that. There’s a bunch of different agendas that kinda kinda sit at the same level, but for, like, decision velocity and disruptive change, it’s death.

And so I think that that’s a a massive benefit to the firm. I’m 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 gonna look like in ten years. I guarantee it’s gonna look different as it evolves with with the landscape.

Yeah. Likewise. Harry, always a pleasure. You’re the best. Seriously.