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AI’s Capital Flywheel- Models, Money, and the Future of Power

The a16z Show

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  • Capital-Driven Capability Flywheel
    • Frontier model companies can raise enormous capital and rapidly translate it into capability and demand.
    • That creates a new capital flywheel unlike past internet cycles where supply sat idle. Transcript: Martin Casado There could be a systemic situation where the soda models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don’t think we’ve Ever seen before, just because we were so bottleneck in engineering. During the internet build-out, investors put money into fiber that nobody used. Alessio Fanelli Four years of supply overhang followed. This time, there are no dark GPUs. Every dollar going into compute has demand on the other side. But something else is different. A model company can raise capital, drop a model in a year with a team of 20, and produce something with immediate demand. If Frontier Labs can raise three times more than the aggregate of every company built on top of them, they may consume the entire application layer. Or the market fragments and value accrues to the companies closest to the end user. Nobody knows which path wins. (Time 0:00:22)
  • No Dark GPUs: Demand Matches Spend
    • There is no GPU supply overhang this cycle, so investment directly creates usage.
    • That fundamentally changes how risk from supply manifests versus the internet era. Transcript: Alessio Fanelli Years of supply overhang followed. This time, there are no dark GPUs. Every dollar going into compute has demand on the other side. But something else is different. A model company can raise capital, drop a model in a year with a team of 20, and produce something with immediate demand. If Frontier Labs can raise three times more than the aggregate of every company built on top of them, they may consume the entire application layer. Or the market fragments and value accrues to the companies closest to the end user. Nobody knows which path wins. In this conversation previously aired on the Latent Space podcast, Martin Casado and Sarah Wang, general partners at A16Z, speak with Alessio Finelli and Sean Wang about the capital Flywheel, talent wars, and why boring software is underinvested and whether every task is AGI complete. swyx Hey everyone, welcome to the Latent Space Podcast. (Time 0:00:41)
  • Apps And Infra Are Merging
    • App and infrastructure lines are blurring as model companies act like both.
    • This creates new financing strategies and faster platformization timelines. Transcript: Martin Casado There’s so many lines that are being crossed right now or blurred, right? So we already talked about venture and growth. Another one that’s being blurred is between infrastructure and apps, right? So, like, what is a model company? Like, it’s clearly infrastructure, right? Because it’s like, you know, it’s doing kind of core R&D, it’s a horizontal platform, but it’s also an app because it touches the users directly. And then of course, you know, the growth of these is just so high. And so I actually think you’re just starting to see a new financing strategy emerge. And, you know, we’ve had to adapt as a result of that. And so there’s been a lot of changes. You’re right that these companies become platform companies very quickly. You’ve got ecosystem built out. So none of this is necessarily new, but the timescales of which it’s happened is pretty phenomenal. And where we’d normally cut lines before is blurred a little bit. But that said, I mean, a lot of it also just does feel like things that we’ve seen in the past, like cloud build out and the internet build out as well. Sarah Wang Yeah. Yeah, I think it’s interesting. I don’t know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, you raise money for compute, you pour that or you Pour the money into compute, you get some sort of breakthrough, you funnel the breakthrough into your vertically integrated application. That could be ChatGPT, that could be Cloud Code, you know, whatever it is, you massively gain share and get users, maybe you’re even subsidizing at that point, depending on your strategy. You raise money at the peak momentum, and then you repeat, rinse and repeat. And so, and that wasn’t true even two years ago, I think. And so it’s sort of to your, just tying it to fundraising strategy, right? There’s a hiring strategy, all of these are tied. I think the lines are blurring even more today where everyone is, but of course, these companies all have API businesses. And so are these frenemy lines that are getting blurred in that. A lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there, but they’re competing on the app layer. Martin Casado Yeah, so this is a really, really important point. So I would say for sure, venture and growth, that line is blurry. App and infrastructure, that line is blurry. But I don’t think that changes our practice so much. But like where the very open questions are, like, does this layer in the same way compute traditionally has? Like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not be the case here. It may be the case that you actually can’t verticalize on the token string, like you can’t build an app, like it necessarily goes down just because there are no abstractions. So those are kind of the bigger existential questions we ask. Another thing that is very different this time than in the history of computer science is, is in the past, if you raised money, then you basically had to wait for engineering to catch Up, which famously doesn’t scale. Like the mythical man month, it’d take a very long time, but like that’s not the case here. Like a model company can raise money and drop a model in a year and it’s better, right? And it does it with a team of 20 people or 10 people. (Time 0:06:52)
  • Frontier Models Could Consume Apps
    • If a frontier model can raise more than the aggregate of its app ecosystem, it can outspend and displace those apps.
    • That creates a potential star-like expansion where verticalization eats the application layer. Transcript: Martin Casado That we don’t know the answer to, which involves the frontier models, which is, let’s take Anthropic. Let’s say Anthropic has a state-of model that has some large percentage of market share. And let’s say that a company is building smaller models that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more every subsequent round, they probably can raise more money than the entire app ecosystem that’s built on top of it. And if that’s the case, they can expand beyond everything built on top of it. Imagine like a star that’s just kind of expanding. So there could be a systemic situation where the SOTA models can raise so much money that they can outpay anybody that builds on top of them, which would be something I don’t think we’ve Ever seen before, just because we were so bottlenecked on engineering. And it’s a very open question. Alessio Fanelli Yeah, it’s almost like bitter lesson applied to the startup industry. Martin Casado Yeah, 100%. It literally becomes an issue of like, raise capital, turn that directly into growth, use that to raise three times more. And if you can keep doing that, you literally can outspend any company that’s built, not any company, you can outspend the aggregative companies on top of you, and therefore you’ll Necessarily take their share. (Time 0:11:10)
  • Investors: Don’t Ignore Boring Software
    • Don’t dismiss slower-growing enterprise software as uninvestable.
    • Boring, high-margin tools in large markets still offer attractive returns for patient investors. Transcript: Martin Casado Some of the areas that you think are under-discussed? I mean, I actually think that we’ve taken our eye off the ball in a lot of just traditional software companies. So, I mean, I think right now there’s almost a barbell. Like, you’re like the hot thing on the X, you’re a Tech. Right? But, you know, I feel like there’s just kind of a long, you know, list of, like, good companies that will be around for a long time in very large markets. Say you’re building a database, you know. Say you’re building, you know, kind of monitoring or logging or tooling or whatever. And there’s some good companies out there right now, but like they have a really hard time getting the attention of investors. And it’s almost become a meme, right? Which is like, if you’re not basically growing from zero to 100 in a year, you’re not interesting, which is the silliest thing to say. I mean, think of yourself as like an individual person, like your personal money, right? So your personal money, will you put it in the stock market at 7% or you put it in this company growing 5X in a very large market? Of course you can put it in the company 5X. So it’s just like, we say these stupid things, like if you’re not going from zero to 100, but like those, like who knows what the margins of those are. I mean, clearly these are good investments for anybody, right? Like our LPs want whatever, 3X net over, you know, the life cycle of a fund, right? So a company in a big market growing 5x is a great investment. Everybody would be happy with these returns. But we’ve got this kind of mania on these strong growths. And so I would say that that’s probably the most underinvested sector right now. Boring software. Boring enterprise software. Just traditional, like really good company. No AI here. Well, the AI, of course, is pulling them into use cases, but that’s not what they are. They’re not on the token path, right? Let’s just say that. They’re software, but they’re not on the token path. (Time 0:19:30)
  • Robotics Often Verticalizes
    • Hardware and robotics need different diligence because they tend to verticalize into end markets.
    • Horizontal robotics plays exist but are rarer and require deep domain knowledge. Transcript: Sarah Wang And it would probably be on the hardware side, actually, right? And the robotics sector, right? Which is, I don’t want to say that it’s not getting funding because it’s clearly, it’s sort of non-consensus to almost not invest in robotics at this point. But we spent a lot of time in that space. And I think for us, we just haven’t seen the ChatGPT moment happen on the hardware side. And the funding going into it feels like it’s already taking that for granted. Martin Casado Yeah, yeah. But we also went through the drone, you know, there’s a zipline right out there. Was that? Oh, yeah, there’s a zipline, yeah yeah. What’s the AV era? One of the takeaways is when it comes to hardware, most companies will end up verticalizing. Like if you’re investing in a robot company for agriculture, you’re investing in an ag company because that’s the competition and that’s the pricing and that’s the supply chain. And if you’re doing it for mining, that’s mining. And so the AD team does a lot of that type of stuff because they’re actually set up to diligence that type of work. But for like horizontal technology investing, there’s very little when it comes to robots just because it’s so fit for purpose. And so we kind of like to look at software solutions or horizontal solutions like applied intuition clearly from the AV wave, deep math clearly from the AV wave. I would say scale AI was actually a horizontal one for robotics early on. So that sort of thing, we’re very, very interested. (Time 0:21:33)
  • Use Copilots For Fast Data Analysis
    • Use modern co-pilot tools to get one-shot data analysis for investor or growth work.
    • Apply models like Claude to raw files to analyze cohorts and extract accurate insights quickly. Transcript: Sarah Wang Well, let’s connect you to the right person. So there’s not quite an AI workflow for that. I will say as a growth investor, Cloud Cowork is pretty interesting. Like for the first time, you can actually get one shot data analysis, right? Which, you know, if you’re going to do a customer database, analyze a cohort retention, right? That’s just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork. We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. (Time 0:28:50)
  • Two Divergent Industry Futures
    • Two macro futures exist: fragmenting applications with many specialized models, or consolidation where larger models consume everything.
    • Current capital flows and capability progress make the outcome uncertain. Transcript: Martin Casado Yeah, I mean, there’s a very open question. So for me, there’s like, do you know that meme where there’s like the guy on the path and there’s like a path this way, there’s a path this way. Which way, Western man? Yeah, yeah, yeah. And for me, like the entire industry kind of like hinges on like two potential futures. So in one potential future, the market is infinitely large. There’s perverse economies of scale because as soon as you put a model out there, it kind of sublimates and all the other models catch up. And it’s just like software’s being rewritten and fractured all over the place. And there’s tons of upside and it just grows. And then there’s another path, which is like, well, maybe these models actually generalize really well. And all you have to do is train them with three times more money. That’s all you have to do, and it’ll just consume everything beyond it. And if that’s the case, like, you end up with basically an oligopoly for everything. Like, you know, because they’re perfectly general. And, like, so this would be, like, the AGI path would be, like, these are perfectly general. They could do everything, and this one is, like, this is actually normal software. The universe is complicated. And nobody knows the answer. My belief is if you actually look at the numbers of these companies so generally if you look at the numbers of these companies if you look at like the amount they’re making and how much They they spent training the last model they’re gross margin positive you’re like oh that’s really working but if you look at like the current training that they’re doing for the next Model their gross margin negative. So part of me thinks that a lot of them are kind of borrowing against the future, and that’s going to have to slow down. That’s going to catch up to them at some point in time. But we don’t really know. (Time 0:30:40)
  • Capital Can Outcompete Generality
    • Raising more capital than the aggregate of your users lets model companies subsidize growth and attack apps.
    • This dynamic can matter more than pure model generality in determining market outcomes. Transcript: Martin Casado No, but let me just change that mental. That used to be my mental model. Let me just change it a little bit. If you can raise three times, if you can raise more than the ad grid of anybody that uses your models, that doesn’t even matter. It doesn’t even matter. See what I’m saying? So I have an API business. My API business is 60% margin or 70% margin or 80% margin. It’s a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that’s using it, I will consume them, whether I’m AGI or not. And I will know if they’re using it because they’re using it. And unlike in the past where engineering stops me from doing that, this is very straightforward to use as train. So I also thought it was kind of like you must ask some code AGI, general, general, general. But I think there’s also just a possibility that the capital markets will just give them the ammunition to just go after everybody on top of them. (Time 0:34:15)
  • Tasks Often Require Broad Models
    • Many tasks show ‘AGI-complete’ qualities meaning the best model will often need broad abilities, not narrow specialization.
    • Task value often depends on non-model services like implementation and integration. Transcript: Martin Casado One more thing I think is under-discussed in all of this is like to what extent every task is AGI complete. I code every day. It’s so fun. That’s a core question, yeah. And like when I’m talking to these models, it’s not just code. I mean, it’s everything, right? Like, you know, like it’s healthcare, it’s legal. But it’s exactly that. Yeah, that’s your support, yeah. It’s everything. Like, I’m asking these models to, yeah, to understand compliance. I’m asking these models to go search the web. I’m asking these models to talk about things I know in the history. Like, it’s having a full conversation with me while I engineer. And so it could be the case that, like, the most, you know, AGI complete, like, I’m not an AGI guy, like, I think that’s, you know, but like, the most AGI complete model will always win independent Of the task. And we don’t know the answer to that one either. Yeah. But it seems to me that, like, listen, Codex in my experience is for sure better than Opus 4.5 for coding. Like, it finds the hardest bugs that I work in with, like it’s, you know, the smartest developers I know work on it. It’s great. But I think Opus 4.5 is actually very, it’s got a great bedside manner. And it really, it really matters if you’re building something very complex because like it really, you know, like you’re, you’re, you’re a partner and a brainstorming partner for Somebody. And I think we don’t discuss enough how every task kind of has that quality. And what does that mean to capital investment and frontier models and submodels? What happened to all the special coding models? (Time 0:35:52)
  • Investor Who Codes 3D Rendering
    • Martin codes SparkJS to support Gaussian splat 3D rendering and builds demos to exercise the library.
    • He returns to coding to solve algorithmic scaling problems rooted in his earlier game-engine experience. Transcript: Alessio Fanelli Is going to remain very important no matter what the task is. Speaking of coding, I’m going to be cheeky and ask, what actually are you coding? Because obviously you could code anything and you’re obviously a busy investor and a manager of a giant team. What are you doing? I help Fei-Fei at World Labs. Martin Casado It’s one of the investments. And they’re building a foundation model that creates 3D scenes. Yeah, we had our underpod. Yeah, yeah. And so these 3D scenes are Gaussian splats just by the way that kind of AI works. And so you can reconstruct a scene better with Radiance fields than with meshes because they don’t really have topology. So they produce these beautiful 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn’t great. It’s always been meshes and things like Unreal use meshes. And so I work on a open source library called SparkJS, which is a JavaScript rendering library for Gaussian splits. And it’s just because, you know, you need that support. And right now there’s kind of a 3.js moment. That’s all meshes. And so it’s become kind of the default in 3.js ecosystem. As part of that, to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building. But all of that is just to exercise this library that I work on, because it’s actually a very tough algorithmics problem to actually scale a library that much. And just so you know, this is ancient history now, but 30 years ago, I paid for undergrad, you know, working on game engines in college in the late 90s. So I’ve got actually a back, it’s very old, but actually have a background in this. And so a lot of it’s fun, you know, but the whole goal is just for this rendering library to… Sarah Wang Are you one of the most active contributors to their GitHub? Martin Casado SparkJS? Yeah. There’s only two of us. So yes. (Time 0:38:31)
  • Generative 3D Could Be Transformational
    • Generative 3D scenes could collapse content creation costs by orders of magnitude.
    • That cost drop would open big markets in games, film, and AR/VR similar to prior leaps in speech and images. Transcript: Martin Casado I actually love to hear Sarah because I’m a venture person. Alessio Fanelli I’m also like venture is always like kind of wild west. You paid to dream and she has to like actually. She has to be like. Martin Casado I’m going to say the venture. And she can be like, okay, you little kid. So these diffusion models literally create something for almost nothing and something that the world has found to be very valuable in the past are real markets, right? Like a 2D image, I mean, that’s been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The influence costs a hundredth of a penny, right? So we’ve seen this with speech in very successful companies. We’ve seen this with 2D image. We’ve seen this with movies, right? Now, think about 3D scene. I mean, when’s Grand Theft Auto coming out? It’s been six, what, it’s been 10 years? I mean, how, like, honestly, how much would it cost to, like, to reproduce this room in 3D? If you hire somebody on Fiverr, like in any sort of quality, probably $4,000 to $10,000. And then if you had a professional, it would probably be $30,000. So if you could generate the exact same thing from a 2D device, and we know that these are used, and they’re used in Unreal, and they’re used in Blender, they’re used in movies, and they’re Used in video games, and they’re used in all. So if you could do that for less than a dollar, that’s four or five orders of magnitude cheaper. So you’re bringing the marginal cost of something that’s useful down by three orders of magnitude, which historically have created very large companies. (Time 0:43:47)
  • Back Proven, N-of-One Founders
    • Back founding teams with demonstrated track records and unique domain expertise.
    • Prioritize N-of-one founders who have repeatedly driven breakthroughs in their field. Transcript: Sarah Wang But I think every investment fundamentally starts with the same, maybe the same two premises. One is at this point in time, we actually believe that there are N of one founders for their particular craft. And they have to be demonstrated in their prior careers. Right. So we’re not investing in every, you know, now the term is Neolab, but every foundation model any company, any founders try to build a foundation model, we’re not, contrary to popular Opinion, we’re not invested in all of them, right? We have a very specific thesis. I don’t think people say that about you. No, they don’t. They say that we’re big, we’re in everything. But, you know, if you think about Ilya, right, he’s at SSI. He’s sort of been behind almost every foundational breakthrough for the last 15 years. If you think about, you know, the Thinking Machines team, right, Mira and John, right, John is the godfather of reinforcement learning. And so I go through this because, you know, if you think about for each of the bets that we’ve made, it goes back to a very specific thesis about that person, the team they’ve assembled, And what they’ve done in a prior life. And, you know, I think, you know, obviously we talked about talent wars. We do think at this particular moment in time, there are particular people that can move needles. Clearly, other companies believe that too. Otherwise, they wouldn’t be willing to pay such crazy prices for single individuals. So that’s one. And then two, we don’t think it’s a zero-sum game, right? Like if that were true, OpenAI or actually just DeepMind would be number one in everything, right? There’s clear value to specialization. It’s like 11 labs. There have been so many audio models that have hit the market. They’re still freaking number one, right? And so if you think about, and they’ve created a ton of value for their customers, for their investors, you know, for their team. And so if you think about those two put together, right, that’s sort of the foundation of our thesis when we back these foundation model companies. (Time 0:46:08)
  • Ignore The Twitter Phone Game
    • Public perception and social-media rumor often diverge dramatically from boardroom reality.
    • Founders must focus on business execution and ignore noisy, distorted narratives. Transcript: Martin Casado I will say this is the furthest. So we have a very privileged position on the boards of these companies. And like, I will say, I’ve never seen the perception of the truth be further from the truth industry-wide ever. Like, I guarantee you for any of these gossipy things, I guarantee you it’s way off. Okay. Way, way off. Like, the general sense of it. And like, and what happens is like, we’ve got this crazy game of telephone right now where there’s always like seeds of truth, but it gets so warped by the time like we hear all the time Rumors about stuff that we’re directly involved in, like we’re literally on the board, you know, like we’re the one that did the thing and by the time it gets to us, it’s gotten so warped And so twisted. I think this is like everybody’s excited, there’s a lot of focus. The shot and fried is so high that people just kind of will into being things that didn’t exist. So I’m not, you know, I don’t want to comment specifically on the thinking machines, but like, it’s an important message to the general audience. I will tell you, if you hear something at X, like the chances that it’s, you know, it is accurately representing what it’s saying to is very, very low. Yeah. Sarah Wang I have never lost so much faith in the non-counts on Twitter that just seem very confident in what they’re saying. Yeah, no, yeah. Could it be further from the truth? I had a couple day stretch where I was like, oh, my God, Twitter is mind poison. And I love it. Martin Casado But we talk to each other all the time because we actually know because we’re there. Like we’re there seeing these things and like, you know, Sarah will like text me, you know, like whatever. Like it’s like ridiculous. So for us, it’s like it’s like this ridiculous. But the problem is, is we realize that things start taking on a life of their own and then people assume that they’re real and everything. (Time 0:51:02)
  • Product-First Path To Model Strength
    • Cursor built a focused developer product and then moved down to build strong models for that niche.
    • Starting with product data can be a cheaper, effective path to model leadership. Transcript: Martin Casado Yeah, so the interesting thing about cursors, they actually, for a small fraction of the cost, a hundredth of the cost or less, developed an almost soda model, which for a period of time Was the most popular coding model in the world, right? Which is really crazy to think about. So I think they’re just kind of doing it in reverse, right? So there’s two approaches. You start with a foundation model and then you verticalize up, or you start with the app and all of the product data and you go down. And they’re the ones that are doing that. I think any company that’s doing an app has to ask the margin question, which is like, how do I extract margin on the tokens that are going through? Like everybody has to be on the token path and everybody has to ask that question. And I’ve just thought they’ve been incredibly thoughtful about it. And one reason is, is if you ask, you know, Michael, what type of company are you? They are a developer company for professional developers. That’s what they are. They’re a dev tool. So they’re just focused on coding. And that’s a huge, I mean, even if you didn’t do AI, that’s a, you know, they acquired Graphite. I mean, like, you know, listen, we were investors in GitHub. Like, we know how big this market is. So that’s a massive market even without becoming a model company. But they’ve also been quite successful in doing their own models. And so I think it just shows you that if you are focused, you have a large use case. There’s a huge opportunity not only to get the application, but to start building your own models. Are these going to be the only models people use? Of course not. (Time 0:53:52)