Invest Like the Best with Patrick O'Shaughnessy
00:34:40 · Martin Casado, Patrick O'Shaughnessy
[00:34:40] S1: I sit on the boards of a lot of great companies that have the problem that you have. Profitable, growing 40, 50, 60% at scale, $304,105 100,000,000 in ARR. What do those companies do? You can't really IPO because they're subscale but they're great companies and I think about my own personal portfolio, I'm like, I'm in muni bonds, man. I'd rather be in these companies, right? And so I think that everybody would be happy with an asset that grows 40%. Like you'd be happy with your personal money being in there And if you could guarantee so I still think these are very good companies. I feel it's more a question. Again, this is my naivety of not understanding this of like, are we gonna see a shift in the market so that people can get exposure to this asset class? Because I think the risk profile is very, very different than AI. Nobody knows what's gonna happen to AI when the market slows down. Nobody knows. It could be easy come, easy go. It could keep going forever. Like nobody really knows. And so the only free lunch I've ever seen in investing is diversification. And I would love to personally have Ask it. A basket that includes these slower growing but these assets that don't disappear. [00:35:46] S0: Are most of the ones though that are in your mind extremely subjected to this disruption risk? There's lots of companies with that rough profile which seem terrifying in the face of how easy it's gonna be to create software.
00:35:46 · Patrick O'Shaughnessy, Martin Casado
[00:35:46] S0: Are most of the ones though that are in your mind extremely subjected to this disruption risk? There's lots of companies with that rough profile which seem terrifying in the face of how easy it's gonna be to create software. [00:35:59] S1: I'm an infra guy, so for me, more software means you need more infra. We don't vibe code databases yet. You do have your data [00:36:06] S0: Snowflake be a well known example of kind of what you're talking about?
00:36:06 · Patrick O'Shaughnessy, Martin Casado
[00:36:06] S0: Snowflake be a well known example of kind of what you're talking about? [00:36:09] S1: Yeah. It's a great example. It's not just building it. It's really hard to have the long tail of features, the long tail of performance tweaks to actually operate these things is really really tough. And so let's say that Snowflake is growing 30 to 40% and the economics are good. Like I don't think that they're at any risk of being disrupted by AI. Maybe by Databricks but that is like pre AI type of thing. And so, again, I'm very naive when it comes to these things. I'm a very simplistic thinker. My view is like I don't think AI is gonna disrupt these. I think they are good businesses. I think more and more, the industry is gonna want to wrap these up and get exposure. And I don't know what that looks like. It's kind of interesting. Like, we're even seeing SPACs come back. I get a lot of calls now for companies that sit on the board, which are good companies, where these SPACs are like, hey, listen. Like, we can make this stuff go public. So I think that the financial markets have to figure that out. There's a lot of companies that will be disrupted by AI but there's a whole bunch that will not and I just think the market doesn't know what to do with them right now.
00:37:02 · Patrick O'Shaughnessy, Martin Casado
[00:37:02] S0: Most meetings that you have around here, at least for me in the last let's say three months, half the meeting is about compute. And that the product across categories just needs more compute that is available to accomplish its goals. This is another way of asking about the capital thing. You guys manage a ton of money, everyone's deploying their funds at very fast paces. Each company that's working requires lots of capital. I'm trying to get a sense of the feel of how much capital itself is the constraint that like if we just air dropped a trillion dollars more in all the funds, that would be a good thing. [00:37:37] S1: I said this before but I'm gonna reiterate because it's so important. I really think the big disruption of AI is the fact that you can throw capital at problems. In the history of tech, you haven't been able to do that because it was always an engineering problem which just doesn't scale. The mythical man month is a mythical man month for a reason. Ten years ago, if I had five founders show up and they asked for a billion dollars and I gave it to them, what would happen? [00:38:00] S0: Sits there. [00:38:01] S1: Or they would detonate, right? They'd like hire a bunch of engineers and then you're really fucked. You can't throw capital in it. Can you [00:38:08] S0: explain the mythical man month? It's a very interesting idea. [00:38:11] S1: Yeah. Mythical man month basically says that you can't increase the speed of engineering. It's like a natural law [00:38:19] S0: of It's very
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