Invest Like the Best with Patrick O'Shaughnessy
00:38:21 · Martin Casado
[00:38:21] S1: sublinear and it has to come with organizational communication which is one of the conclusions but the takeaway for those that are listening is that this comes from I think Fred Brooks, right, in the like the seventies which is basically like you had these software projects and you wanted to get them out faster and so they would throw more people at and then it would go slower. Engineering has always been this bottleneck and in my experiences and I had my own startup, I did it for ten years, right, it ended up being a very large business, this is totally true. You can't scale engineering. And to me, the biggest shifts... Like, we step back, like, industrial revolution level shift with AI is I can take 20 people and I can give them a billion dollars and they can use that. Now do they make 500,000,000 from that billion or 2,000,000,000? I have no idea. But the fact that you can productively use that money is so new and it's so interesting. And so at some level, the capital markets need to figure this out because they are taking this money and using it. And if you look at Anthropic and OpenAI, they've probably raised more money in aggregate than the entire downstream ecosystem combined by a factor of whatever. They are these mechanisms of turning money into growth. So that's one side. But there's another side which is the debt side, which is quite a bit different than the equity side, which is I do think that currently... I'm not a growth investor, not a public markets guy, I'm not a debt guy, but here's what I'm seeing on the ground. We're running out of available debt. And if you're the Norway pension fund, are you gonna give debt to random neocloud or buy T bills? And I think that we're just at a point where you don't have enough money to go around and so it's gonna prefer the investment grade companies and then those that are not investment grade will have a harder time.
00:39:59 · Patrick O'Shaughnessy, Martin Casado
[00:39:59] S0: So scale wins again? [00:40:00] S1: Scale will win and then it'll be a harder time raising debt. If your business model requires you to raise debt, there's just not enough debt to go out. So I I know you're talking about capital as in like general capital, you're gonna see a ton of liquidity. I think that that feels fine to me. The debt side, I don't know these markets but I definitely know it's harder to get debt now. And I think what this will mean, if we want to have smaller players around that are not IG, that get debt, they're gonna need to prepay which means their customers are gonna need to prepay. So I think over time this all flows back to the availability of cash and capital. Then you do have limited amounts of debt. [00:40:33] S0: This reminds me to ask about NeoLabs, NeoClouds, inference providers and companies. I don't know if you call these infrastructure or what part of the stack you [00:40:41] S1: have. Yeah, I've viewed those all as infrastructure. [00:40:43] S0: So if you take those categories in turn, there's lots of them. Lots of them have been funded. Very few of them are at scale. I'm curious how you think this plays out, like what roles they should and will play, what the market feels like. These things are growing so unbelievably fast in most cases. I'm very interested by these companies and I'm curious what you think of these categories. [00:41:04] S1: Well let's put NeoLabs aside because I
00:41:04 · Martin Casado, Patrick O'Shaughnessy
[00:41:04] S1: Well let's put NeoLabs aside because I [00:41:06] S0: think that's the least well defined of all of them. I included them to be clear because so many of them seem to be building their own [00:41:12] S1: They're becoming neoclass. Like every pitch, [00:41:16] S0: so many pitches turn into some version of like, we're building a token factory with some degree of vertical integration. And some neo labs are going all the way down to like buying land and power and all the way up through the token, you know what I mean? So there's this Yeah. [00:41:28] S1: I think so in my world that wouldn't be considered a neo lab, that would be it. Like all neo clouds wanna be token factories so I would view if your output is like a token of an existing model architecture. [00:41:38] S0: Or something.
00:41:39 · Martin Casado
[00:41:39] S1: Yeah. Neo cloud. Like all of them want to do that and they will all verticalize. The actual real limitation right now is harbor supply. That's it. It's not capital. It's not debt. That's the actual real limitation and power. So Neo Cloud to me is one that actually provides the physical infrastructure that ends at tokens. And so they can provide GPUs, they can provide fine tuning services or they can provide tokens. But it's like... Think of like a data center that actually has the GPUs in it. That's what Neo Cloud is to me. A Neo Lab to me is, give me a billion dollars, we're gonna come up with a new model architecture. Fair enough. I love a lot of these business plans are like, if we solve ASI, that's our business model. Like literally like that. And by the way, to be fair, that's what OpenAI's original thesis was and to some extent Anthropic, which is like, if you solve the intelligence problem, the business model part is easy. So that's to me a Neolab. It's new architecture, model training, and sometimes they'll buy their own infrastructure and buy their own GPUs, but, like, that's not what they're building towards or selling. The real constraint right now is power. It is chips. It is materials. With that sort of a limitation, 80% of these things are gonna go to the scale providers. 20% are just gonna be kinda hard to reach places, nichey type places. Like I have the relationship with a random government that has the power. You know, it's kind of funny. I met this company, a GPU marketplace, that's what it does. But it's like the most low level version of it and like many of their GPUs converted chicken sheds, you know, like like like chicken coops where like people in farms are like finding ways to provide it. So the market is so large, there will always be a significant portion that just doesn't make sense for the SpaceXs to do. Like SpaceX is not gonna go and find out how to create a marketplace of chicken coop GPUs. I think this is what end up happening is the 20% is large enough to have a very wide fragmented array of folks, but then the 80% of the market are gonna be these homogenous things like SpaceX.
00:43:28 · Patrick O'Shaughnessy, Martin Casado
[00:43:28] S0: I heard this story recently that someone asked Larry Page how much of compute will be orbital and he said five nines and the reason was it's just physics. Eventually it's all gonna be in space. [00:43:38] S1: The actual space thing actually pencils out but the reason it makes the most sense is just regulatory. [00:43:43] S0: Just because it skirts it? [00:43:44] S1: Yeah. There's 500 proposed legislations to stop data center building in The United States alone including Texas, right? Like if you can't build a data center in Texas, I don't know what we're doing anymore. If that regulatory pressure continues and you actually can't break ground and I've heard from many of the top data center people like it's very tough to break ground right now, then either you go international or you [00:44:03] S0: go into space. Your comment on these software companies growing 60% reminds me to ask about prevailing valuations in the market. And as a productive investor that's focused on disruptive assets being created, even if the disruptive asset is truly incredibly valuable and great, every one of those has a price that will ruin the return of investing in it if it's too high.
00:44:03 · Patrick O'Shaughnessy, Martin Casado
[00:44:03] S0: go into space. Your comment on these software companies growing 60% reminds me to ask about prevailing valuations in the market. And as a productive investor that's focused on disruptive assets being created, even if the disruptive asset is truly incredibly valuable and great, every one of those has a price that will ruin the return of investing in it if it's too high. [00:44:25] S1: So this is another one of my bugaboos. I don't know how you can be a VC and not believe tech creates value, which is the first conversation I had. Now I'm gonna have a second conversation which is I don't know how you can be a VC and not believe markets are efficient. What are we doing? Like, we're capitalists. You have to believe markets are efficient. I think private markets are very efficient and private markets figure out the price. And so I just don't try and bet against the market. I think a lot of people think that private markets aren't efficient and so you can find the deal and find a non consensus deal and all of those deals were incredibly competitive. And I didn't win by looking for a deal or whatever. I pay the market price and maybe I'll get a little discount for negotiating or whatever, but I never really worry about that. And so as a productive investor, I just trust that the markets are actually pretty smart. And I trust that the markets will value it given the risk adjusted and I believe that my diligence reduces that risk for me. I don't do it on market value. I'm not trying to find a better price. I'm just trying to find a less riskier asset.