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

24:50If it's a billion-dollar training run, then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you save much more than that with an ASIC, 20%, that's$200 million, you can tape out a chip for$200 million, right? So now you can literally like justify economically, not timeline-wise, that's a different issue, an ASIC per model. Because that's how much we leave on the table every single time we do like generic NVIDIA. Exactly, exactly. No, it's actually much more than that. You could probably get a factor of two, which would be$500 million.

25:22Typical MFE would be like 50. Yeah, yeah, yeah. And that's good. Exactly, yeah. So yeah, I mean, I just want to acknowledge like here we are in 2025 and opening eyes confirming like Broadcom and all the other custom Silicon deals, which is incredible. I think that, you know, speaking about AD, there's a really interesting tie-in that obviously you guys are hit on, which is like this sort of like America First movement or like sort of re-industrialize here and move TSMC here. if that's possible. How much overlap is there from AD to, I guess, growth and investing in particularly like, you know, US AI companies that are strongly bounded by their compute?

26:00Yeah, yeah. So I would view AD as more as a market segmentation than like a mission, right? So the market segmentation is it has kind of regulatory compliance issues or government, you know, sale or deals with like hardware. I mean, they're just set up to diligence those types of companies. So it's more of a market segmentation thing. I would say the entire firm, which has been since it's been incepted, has geographical biases, right? I mean, for the longest time, Bay Area is going to be where the majority of the dollars go. And listen, there's actually a lot of compounding effects for having a geographic bias, right?

26:36Everybody's in the same place. You've got an ecosystem. You're there. You've got presence. You've got a network. and I mean I would say the Bay Area is very much back you know like I remember during pre-COVID like it was like almost crypto had kind of pulled startups away from the Bay Area yeah New York because it's so close to finance came up like Los Angeles had a moment because it was so close to consumer but now it's kind of come back here and so I would say you know we tend to be very Bay Area focused historically even though of course we invest all over the world and then I would say like if you take the ring out you know one more it's going to be the US of course because we know it very well and then one remorse is going to be kind of using its allies.

27:11And yeah, and it goes from there. Yeah. Sorry. No, no, I agree. I think from a, but I think from the, that's sort of like where the companies are headquartered. Maybe your question's on supply chain and customer base. I would say our customers are, our companies are fairly international from that perspective. Like they're selling globally, right? They have global supply chains in some cases. I would say also the stickiness is very different. Yeah. Historically between venture and growth. Like there's so much company building in venture, so much. So like hiring the next PM, introducing the customer, like all of that stuff.

27:43Like, of course, we're just going to be stronger where we have our network. And we've been doing business for 20 years. I've been in the Bay Area for 25 years. So clearly I'm just more effective here than I would be somewhere else. Where I think for some of the later stage rounds, the companies don't need that much help. They're already kind of pretty mature historically. So like they can kind of be everywhere. So there's kind of less of that stickiness. This is definitely in the AI time. I mean, Sarah is now the chief of staff of like half the AI companies in the Bay Area right now. She's like, ops ninja, biz dev, biz ops.

28:16Are you finding much AI automation in your work? Like what is your stack? Oh, in my personal stack? I mean, because like, by the way, the reason for this is triggering, yeah, like I'm hiring ops people. A lot of partners I know are also hiring ops people and I'm just, you know, it's an opportunity since you're also like basically helping out with ops with a lot of companies. what are people doing these days? Because it's still very manual as far as I can tell. Yeah. I think the things that we help with are pretty network-based in that it's sort of like, hey, how do I shortcut this process? Well, let's connect you to the right person.

28:51So 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. That sounds so boring. But, you know, that's the kind of thing that a growth investor is like, you know, slaving away on late at night.

29:30Done in a few seconds. Yeah. You got to wonder what the whole Anthropic Labs, which is like their new sort of product studio, what would that be worth as an independent startup, you know? A lot. You got to hand it to them. They've been executing incredibly well. Yeah. I mean, to me, Anthropic, like building on CloudCo, I think it makes sense to me. The real pedal to the metal, whatever the phrase is, is when they start coming after consumer against OpenAI and that is like red alert at OpenAI. Oh, I think they've been pretty clear they're enterprise focused. They have been. But like here's... It's enterprise focused, it's coding, right?

30:11But here's Cloud Cowork. And here's like, well, apparently they're running Instagram ads for Cloud AI on, you know, for people just to know the chatbot. Right. And so like, it's kind of like this, the disruption thing of, you know, OpenAI has been doing, consumer, been doing, just pursuing general intelligence in every modality. And here's a topic that only focus on this thing. But now they're sort of undercutting and doing the whole innovators dilemma thing on like everything else. It's very interesting. 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 in the path and there's like a path this way, there's a path this way.

30:48Which 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 is 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.

31:30And 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 spent training the last model, they're gross margin positive.

31:58You're like, oh, that's really working. But if you look at like the current training that they're doing for the next model, they're 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. Does that make sense? I mean, it could be the case that the only reason this is working is because they can raise that next round and they can train that next model because these models have such a short life. And so at some point in time, And like, you know, they won't be able to raise that next round for the next model.

32:29And then things will kind of convert your fragment together. But right now it's not. Totally. I think the other, by the way, just a meta point. I think the other lesson from the last three years is, and we talk about this all the time because we're on this Twitter X bubble. But, you know, if you go back to, let's say, March 2024, that period, it felt like a, I think an open source model with like a, you know, benchmark leading capability was sort of launching on a daily basis at that point. And, and so that, you know, that's one period. Suddenly, it's sort of like open source takes over the world, there's going to be a plethora, it's not an oligopoly.

33:03You know, if you fast, you know, if you if you rewind time, even before that, GPT-4 was number one for nine months, 10 months, it's a long time, right. And of course, now we're in this era where it feels like an oligopoly, maybe some very steady state shifts. And, you know, it could look like this in the future, too. But it just it's so hard to call. And I think the thing that keeps, you know, us up at night in a good way and bad way is that the capability progress is actually not slowing down. And so until that happens, right, like you don't know what's going to look like. But I would say for sure it's not converged.

33:39Like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time. but at some point the market will rationalize it and just nobody knows what that will look like. Or like the drop in price of compute will save them, who knows. Yeah, I think the models need to asymptote to specific tasks. You know, it's like, okay, now Opus 4.5 might be AGI as some specific task and now you can like depreciate the model over a longer time. I think now, right now, there's like no old model.

34:15No, 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 aggregate 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.

34:45And unlike in the past where engineering stops me from doing that, is this very straightforward you just train so I also thought it was kind of like you must asymptote 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 I do wonder though to your point if there's a certain task that getting marginally better isn't actually that much better like we've asymptoted to you know we can call it AGI or whatever you know actually Ollie Goadzi talks about this like we're already at AGI for a lot of functions in the enterprise guys, that's probably, for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't coming from the model itself.

35:29There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples. So if you're looking at the legal profession or whatnot, and maybe that's not a great one because the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer. 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.

36:01It'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 for support, yeah. It's everything. I'm asking these models 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. It's having a full conversation with me while I engineer. And so it could be the case that the most AGI complete, I'm not an AGI guy, but the most AGI complete model will always win independent of the task.

36:37And we don't know the answer to that one either. But it seems to me that, listen, Codex, in my experience, is for sure better than Opus 4.5 for coding. It finds the hardest bugs that I work in with, like, 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 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 like capital investment and like frontier models and submodels?

37:15like what happened to all the special coding models like none of them worked right so did some of them they didn't even get released magic.dev there was a whole there was a whole host we saw a bunch of them and like there was this whole theory that like there could be and I think one of the conclusions is like there's no such thing as a coding model you know like that's not a thing like you're talking to another human being and it's good at coding but like it's got to be good at everything minor disagree only because I'm pretty like have pretty high confidence that basically Okunai will always release a GPT-5 and a GPT-5 codex.

37:48Like that's the coding one. The way I call it is one for Riz and one for Tiz. And then like someone internal and open it was like yeah. That's a good way to frame it. That's so funny. But maybe it maybe collapses down to Riz and Tiz and that's it. It's not like a hundred dimensions. It's two dimensions. Like in exactly Bidside manner versus coding. I think For anybody listening to this, when you're coding or using these models for something like that, actually just be aware of how much of the interaction has nothing to do with coding. And it just turns out to be a large portion of it. And so I think the best Soto-ish model is going to remain very important no matter what the task is.

38:34Yeah. 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 the good. giant team. What are you going to do? I help Fei-Fei at World Labs. It's one of the investments. And they're building a foundation model that creates 3D scenes. Yeah, we had our underpod. 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.

39:18It'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 splats. And it's just because, you know, you need that support. And right now there's kind of a 3JS moment. That's all meshes. And so it's become kind of the default in 3JS 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.

39:57And 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 background, but I actually have a background on this. And so a lot of it's fun. And, you know, but the whole goal is just for this rendering library to do it. Are you one of the most active contributors to their GitHub? Spark is? Yeah. There's only two of us. So, yes. No. So, by the way, the primary developer is a guy named Andreas Sundquist, who's an absolute genius. He and I did our PhDs together. And so, like, we studied for constant quality.

40:36It was almost like hanging out with an old friend, you know. And so, like, so he's the core, core guy. I do mostly kind of, you know, I run a venture fund. It's amazing, like five years ago, you would not have done any of this. And it brought you back. The activation energy was so high because you had to learn all the framework bullshit, man. I fucking used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmic so I can focus on the scaling. Yeah, yeah. And then I'll observe one irony and then I'll ask a serious investor question, which is like, the irony is Fei Fei actually doesn't believe that LLMs can lead us to spatial intelligence.

41:05And here you are using LLMs to like help achieve spatial intelligence. I see some, like, disconnected there. Yeah, so I think what she would say is LLMs are great to help with coding. Yes. But, like, that's very different than a model that actually, like, provides spatial. They'll never have the spatial intelligence. And listen, our brains clearly have both. Our brains clearly have a language reasoning section, and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems. Okay. I would say that the one data point I recently had against it is the DeepMind IMO gold.

41:44So typically, the typical answer is that this is where you start going down the neurosymbolic path, right? Like one sort of abstract reasoning thing and one formal thing. And that's what DeepMind had in 2024 with Alfred Proof, Alfred Geometry. And now they just use DeepThink and just extend the thinking tokens. And it's one model and it's an LLM. Yeah, yeah, yeah. And so that was my indication of like maybe you don't need a separate system. Yeah. So let me step back. I mean, at the end of the day, these things are like nodes in a graph with weights on them, right? You know, like. It can be modeled.

42:19You distill it down. But let me just talk about the two different substrates. Let me put you in a dark room, like totally black room. And then let me just describe how you exit it. Like to your left, there's a table, like duck below this thing, right? I mean, like the chances that you're going to like not run into something are very low. Now let me like turn on the light and you actually see and you can do distance and, you know, how far something away is and like where it is or whatever, then you can do it, right? Like language is not the right primitives to describe the universe because it's not exact enough.

42:55So that's all Fei Fei is talking about when it comes to like spatial reasoning is like you actually have to know that this is three feet far, like that far away. It is curved. You have to understand, you know, like the actual movement through space. Yeah. So I do think at the end of these models are definitely converging as far as models, but there's different representations of problems you're solving. One is language, which, you know, that would be like describing to somebody like what to do. And the other one is actually just showing them. And the spatial reasoning is just showing them. Yeah.

43:23Yeah, yeah, right. Got it, got it. The investor question was on Wall Labs is, well, like, how do I value something like this? What work do you do? I'm just like, Fefe's awesome, Justin's awesome, and, you know, the other two co-founders. But, like, the tech, everyone's building cool tech. But, like, what's the value of the tech? And this is the fundamental question. Let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture person. I mean, yeah, so, like, venture is always, like, kind of Wild West. You paid to dream, and she has to, like, actually— She has to make sure, like, I'm going to say— Be marked to reality.

43:57Exactly. So 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 100 bucks in an hour. the inference cost is a hundredth of a penny, right? So we've seen this with speech in very successful companies.

44:31We'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, awesome. 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 image, 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.

45:02So if you could do that for, you know, 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. So that would be like the venture kind of strategic dreaming map. Yeah, and for listeners, you can do this yourself on your own phone with like the Marble. Yeah, Marble. but also there's many Nerf apps where you just go on your iPhone and do this. Yeah, yeah, yeah. And in the case of Marble, though, what you do is you literally give it, so most Nerf apps, you kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.

45:37Things like Marble, just the whole generative 3D space will just take a 2D image and it'll reconstruct all the, like, Meaning it has to fill in Yeah, like the back of the table, under the table, like the images it doesn't see. So the generator stuff is very different than reconstruction and that it fills in the things that you can't see. Yeah. Okay. All right. So now the adult perspective. Well, no, I was going to say, these are very much a tag team. So we started this pod with that premise. And I think this is a perfect question to even build on that further because it truly is. I mean, we're tag teaming all of these together.

46:08But 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. They say that we're big, we're in everything.

46:41But, 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 one of 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 obviously we talked about talent wars. We do think at this particular moment in time, there are particular people that can move needles.

47:20Clearly, 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.

48:01Of course, the valuations, you know, they sound astronomical when you think about current revenue, the numbers. You know, there's sort of, one, I would say that's the market out there because they are raising larger dollars. They have compute needs, right? That's 80 % of a round that they typically raise, or typically of a round that they raise. But I think the thing that gets us excited about backing them is that the revenue growth has typically followed the capability breakthrough. So it sort of ties back to that question of the cyclical nature, like are you just funding it and you raise more funding?

48:37When there's a real capability breakthrough, the demand is there. And so the revenue growth is much faster than we've ever seen once it's turned on. There's a company, I can't share the name, but their product went GA in a few weeks, tens of millions of revenue, right? I've seen this myself, yes. Absolutely. We have SaaS companies that have been in business for seven years and they get to the same level seven years later. And the growth is eking to whatever it is. And by the way, great companies, not at all diminishing what they've accomplished. But the fact is to get to that revenue growth that quickly, it's not just the two companies that people talk about.

49:14It's really a lot of these sort of every domain has a specialist. And we think if you can win that, you become very large very quickly. and that's actually played out in the numbers. Yeah. Our viewers are going to, so first of all, thank you for that overall take. I think like it's important to hear you guys' perspective because the rest of us are just kind of looking at headlines and not knowing how to make sense of any of this. We can't mention, like our listeners will roast us if we mention Thinky and not discuss what happened. I mean, obviously, founder split happens. But like, I guess, is the thesis on change?

49:49Is like, you know, Like, what's going on in thinking? Yeah, we're more excited than ever about them. They have some things that we're not going to do breaking news in a pod. You know, obviously, they should share themselves. But they've, you know, I think when you bring a team of that caliber together, there's special things that happen. And I think 2026 is going to be a big year for them. Obviously, you know, some of the themes that we talked about before, even with just the media, news storm, like the whole something happens and then it's everywhere instantly. You know, I think that's a tough situation for any company to be in.

50:32But to come out of that stronger than ever, I think that, you know, we're more bullish about thinking than, you know, even before. And the story is Tinker, it's Custom Models RL. Is that what we're aiming for? Yeah, and a bunch of stuff we can't talk about here. Yeah, absolutely. But no, that team is cooking. And I think they'll be just fine. They'll recover from the events in January. Yeah, 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.

51:18Like, I guarantee you for any of these gossipy things, I guarantee you it's way off. Way, way off. Like the general sentiment. 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 he 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.

51:55So 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 on X, like the chances that it's, you know, it is actually representing what it's saying to is very, very low. Yeah. 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 of days stretch where I was like, oh, my God, Twitter is mind poison. And I love it. But we twice we tell that all the time because we actually know because we're there.

52:27Like 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 thing. 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. And so I think it's very tough for founders because, you know, it's tough enough fighting the real battle. Absolutely. Now they're fighting phantoms too. And so, you know, more and more we're just, like, and I got this from the cursor, you guys, which I really appreciate Michael Troll.

52:58He's like, listen, heads down, focus on the business. And they absolutely crushed it. Yeah. And I think that's right. I think all founders should do that right now because the noise is so hot. Now that team's been back to business for weeks, the Thinky team. So, yeah. Yeah. Well, thank you for acknowledging that. It's just the hot topic of the moment. We got to address the elephant in the room. Cursor, right? Obviously, you guys are big investors. 2025, I would say is Cursor's year. I mean, maybe a decade. but just like I think you know just going back to the discussion about how AGI would just kind of consume everything because just like the one like kind of the shiny example of like here's how you build application layer that's a wrapper but an extremely damn good one and I guess just like the general analysis I guess of cursor's development and what it means for everyone like is there a cursor in every industry to be built yeah so the interesting about cursors they actually for you know 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?

54:07Which 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?

54:41They 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. 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.

55:12Are these going to be the only models people use? Of course not. But they are in a great position to serve great models, and they've demonstrated that. Yeah, my sort of thesis, which we're not going to have to go into here is actually I think what I've been calling agent labs which are people who build on top of all the other models will probably have a better time with the margins because they price against the end user hours spent or like human labor whereas models get commodity price per token and so margin wise we know inference economics for model labs but agent labs the difference is the delta between token intelligence, which keeps going down, and human costs, which keep going up.

55:56Yeah, yeah, yeah. And so the margin should be higher. They should be. The caveat to that is if the models go first party, right? Yeah, yeah. And what they can do is they can... Which is the composer dream. Yeah, they can subsidize themselves. The models, they can subsidize themselves. Oh, Cloud Code. Cloud Code. They can subsidize themselves and then they can charge the third party more. and it's a very delicate dance because you're kind of competing with your own customers. And so, you know, we've seen this historically. We saw this with the cloud or the EC2. So this is not unusual. We saw this with the operating system.

56:28It's not unusual, but it's playing out very, very quickly. Yeah, thank you for joining us. That's all the time we have today. It's such a pleasure. You're welcome back anytime. And thank you for being so open and also like just leading the industry in so many areas. It's really inspiring to see. Thank you so much. Thank you for having us. Great, thank you. thanks for listening to this episode of the a16z podcast if you like this episode be sure to like comment subscribe leave us a rating or review and share it with your friends and family for more episodes go to youtube apple podcast and spotify follow us on x a16z and subscribe to our substack at a16z.substack.com thanks again for listening and i'll see you in the next episode This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product.

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