Invest Like the Best with Patrick O'Shaughnessy
00:04:43 · Martin Casado
[00:04:43] S1: So I only do computer science investing and I only do infrastructure investing and I think infrastructure as an industry has a certain shape. Certain buying behaviors, the technology looks a certain way and the industry acts a certain way and so what you're trying to do is you're trying to take a technical approach and you're trying to map it into that techno strategic framework. And the way you do that, there's a number of approaches. One of them you're like, what are the leading companies doing internally? And this will give you a glimpse on maybe this is how platforms evolve or perhaps the team that is doing this did it in another context and now they're actually bringing it to this context. I think it's one of these things where like how much can you diligence without actually just using the numbers for a proxy? You can talk to customers, will they use this? You can see if there's adjacent budgets that are growing that could argue will fragment to become this Or maybe you have early signs of willingness to pay but they actually haven't figured out the business model. Like I always think every tech wave, you know, I've been in the industry for thirty years, every tech wave people like hand ring about margins. Everyone like I remember for the cloud, I remember for mobile, I remember for the internet, clearly now people worry about margins. So maybe you're like, there's a willingness to pay and there's a lot of interest but we haven't figured out the cogs yet in the actual viable business model. So like you're using partial indicators, not full indicators in order to do this. And so my team has a lot of technical people, product people, many have PhDs. We don't second guess the tech. Like the founder will know the tech better than us, but but we can ask the question, how does this tech fit into the broader platforms, fit into spending? How does this tech fit in with our understanding of stickiness and defensibility? And then we look at early market numbers and we'll say, hey, listen, a financial investor would puke at this but it actually shows that the customers are really interested to figure something out and will they be able to figure something out? So there's a lot of diligence you can do. Where with frontier investing, I don't think you do any of that. It's people, is this the best team to do this crazy thing and then you just yolo?
00:06:41 · Patrick O'Shaughnessy, Martin Casado
[00:06:41] S0: You were saying before we hit start that nobody understands network effects. Can you explain what you meant? [00:06:46] S1: I've realized that even the most common terms I use, there's no consensus on. And so for example, network effects is the favorite one that I pick up. I asked you before [00:06:55] S0: I messed it up. [00:06:56] S1: No, you did mess it up. You said you said what? Like a third of people say. So said, so what does a network affect? And what did you say? [00:07:01] S0: I said a product that gets better as it adds a marginal user.
00:07:01 · Patrick O'Shaughnessy, Martin Casado
[00:07:01] S0: I said a product that gets better as it adds a marginal user. [00:07:05] S1: Right. So to me this is almost a tautological definition because most products will get better as you add a user, right? They will find a bug, they will provide feedback, they will tell other people. That doesn't say anything about defensibility. But other people when they use the term network effects, they actually mean the original meaning of the term network effect from the seventies which is a telephone network where when users join, they interact with each other. That grows super linearly and that's why you get like natural monopolies. And other people use the term network effect more to mean like an actual flywheel, so like a self reinforcing flywheel. So not just like things get better but like it self reinforces in some sort of a way. This is where you hear the term like data network effects. But even in that sense, like you don't know if that self reinforcement is super linear or not. So you don't know if it makes it defensible or not. So you have this very common word that's on 70% of pitch decks where a founder's sitting in front doing a pitch and nobody has consensus on what it means and [00:08:03] S0: so how would you define it?
00:08:04 · Martin Casado
[00:08:04] S1: So I think the root of the confusion is not a definitional problem. I think the root of the confusion is conceptually we conflate the mechanism with the shape of the moat. There's many things that could create a moat, right? Like a brand, scale, a two sided marketplace, network effect. But that moat, to reason about that, is only really useful if it has some properties that makes the company defensible against somebody else. If that moat grows super linearly with scale, like a classic Metcalfe's law network effect would, that's really defensible. If it grows sub linearly with scale, it may not be useful at all. And so I think to you and I have a conversation of defensibility, I think the first thing we need to do is not conflate the shape of the moat with the mechanism that caused it. So first, I think we should say the mode that we have is super linear, it's linear, or it's sublinear with scale. And then the second thing, a lot of things actually contribute to a mode. For example, like we mentioned, network effects will or scale effect. What's a scale effect? A scale effect to me is as the company scale, it can reduce unit economics normally because it has pricing power for example or maybe CAC goes down because of brand. What's a two sided marketplace that's very different to me than a network effect? That's where basically you've got self reinforcing supply and demand. So I think that there are many mechanisms and then there's the shape and then to have a discussion I think you want to make sure that the shape is something that actually provides long term defensibility. Then the last thing I'll say is it's almost impossible to have these conversations in isolation because we're actually talking about competitive equilibria. Because if you have a network effect, then maybe the competitor has a network effect too. And maybe if they have more capital than you and grow faster, that probably impacts the shape of your moat. You may not grow as fast marginally because they're stealing your customers or something like that. And so I just think that we need a very very basic concepts. We need to not conflate the notion of the actual shape with the actual mechanism. And then I think we should enumerate the mechanisms because something like the definition that you had just doesn't really help anybody.
00:10:08 · Patrick O'Shaughnessy, Martin Casado
[00:10:08] S0: Can you give examples of super linear, linear, sub linear just to bring it to life? [00:10:12] S1: Yeah, for sure. A common view of where network effects came from was from the seventies. I forgot the name of the economist that did it but he worked at Bell Labs and he noticed for train networks and communications networks, the interactions of users grows with the square of the number of people that join. As a user joins they can communicate to many other people so the actual number of connections grows more than the number of users. Therefore, the value grows more than just legally within that. The same thing with destinations on a highway or destinations on a train. So that would mean that if you have two competing companies, one takes $5,000,000 of funding and the other takes $5,000,000 of funding but this one is marginally better, marginally larger. This one's a little bit larger. For the same amount of investment, it will grow faster than a challenger. The growth actually increases over time and that's why you get a monopoly. Whereas sublinear means if I'm a leader and I invest the same amount as a challenger, I grow marginally slower than the challenger. That would be sublinear. Clearly this is very important when it comes to modes because if you have two competing companies and there's a sublinear mode, the challenger will always catch up to the leader because the leader has to spend ever more money to grow, all things being equal. [00:11:28] S0: Favorite recent company examples of these different types?
00:11:28 · Patrick O'Shaughnessy, Martin Casado
[00:11:28] S0: Favorite recent company examples of these different types? [00:11:31] S1: How many actual network effects in the Metcalfe's law of super linear do you think there have actually been? [00:11:36] S0: Very few, I don't