Invest Like the Best with Patrick O'Shaughnessy
00:00:00 · Patrick O'Shaughnessy
[00:00:00] S0: Ramp is the only platform built to make your finance team leaner, faster, and better, saving businesses 5% annually on average so you can stay focused on growth. Ramp just opened for business in The United Kingdom. So if you're running a business in The UK, you can now use Ramp's AI powered finance platform to manage cards, expenses, bills, approvals, and accounting all in one place. I run my business on Ramp and so should you. Learn more at ramp.com/invest. Rogo is the AI platform purpose built for financial institutions serving tens of thousands of bankers and investors at hundreds of leading firms worldwide. Rogo's AI agents autonomously execute large chunk of your firm's workflows. They can screen deals, draft sims, run buyer outreach, and due diligence on data rooms. All with full security and compliance handled. Every action feeds rogo intelligence, the firm's context layer, turning years of deal experience into a secure, governed institutional memory that makes each new transaction stronger than the last. To learn more, visit rogo.com/invest. OpenAI, Cursor, Anthropic, Perplexity, and Vercel all have something in common. They all use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, SCIM, RBAC, and audit logs. That's where WorkOS comes in. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on day zero. That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise ready and stay on what matters most, your product. Visit workos.com to get started. You recently wrote about four buckets of styles of investing that might be applied, call it venture, private market tech, let's call it. Yeah. Can you enumerate those and just like say a sentence on effect of an h?
00:01:58 · Martin Casado
[00:01:58] S1: I'm just in this point in my career. I've been doing this for a decade. I've done hundreds of deals that I sit in these discussions and I just realized people aren't communicating very well. So I did this exercise where I'm like, what are the mindsets people bring to investing? And I think there's basically four types of mindsets. So the one that we were talking about before is like this traditional finance mindset which is the correct proxy for the health of a business is the market it's going into and its finances. What are the SaaS metrics? What is the size of the market? And that is a proxy for the entire business. So that's one. Clearly growth investors do this. But like more and more you see early stage series a people do this, which to me is a little bit discordant. Then there's what I call productive investors, which you believe you're investing in a productive asset and the productive asset is what's creating value. And if you believe in that, as it creates value, it will create market. And as it creates value, you're gonna have to find ways for the business to catch up to that value. And so the type of diligence you do if you're a productive investor is more like what is the technical approach, what are the adjacent markets, how fast is this likely to evolve, how close is it to capturing value, like that sort of stuff. Is this the right team to build it? Then there's what I call frontier investing. I think this is the romantic idealized version of VC from like the sixties and seventies which is kinda YOLO, man. It's like founders know better than VCs and they've seen something that I don't see and they're gonna go out there and do it and like who am I to second guess them? They almost look like talent networks. Right? Like I would say Chris Dixon. I mean, remember when he was investing in Nootropics, he invested in Soylent, he invested in Oculus, he invested in Coinbase and in crypto. I mean, it's like every science fiction trope from the seventies and eighties you kind of invest in and you just don't overthink any of these things. So it's almost like the strong form of the productive. And then the last one, and it is always so surprising how prevalent it is, is speculative investors. They don't even care about the asset. They just want to time at market. You'll see this like in crypto and in secondary markets.
00:03:51 · Martin Casado, Patrick O'Shaughnessy
They don't know about the underlying asset. They probably don't even know about the actual business metrics, but they're like, Well, this looks cheap. It's clearly gonna go up in value. Oh, this is the top of the market, you know, like I'm gonna get out. They're trying to like time markets. They're actually just doing speculative. They're not really interested in like will the business grow? And again, the problem is if you have all of these archetypes, you can justify any deal or not justify any deal. So if you don't know what your beliefs are, you'll always justify whatever personal bias you have. [00:04:20] S0: If you think about yourself as a productive investor focused on, I love this idea that disruptive technologies, you should not look at some of these traditional things that so many investors talk about. Talk about what you do look at. Like you look at so many companies, what is your method by which to assess whether or not someone might be creating a truly disruptive piece of technology? A valuable asset.
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?