How I Invest with David Weisburd
00:10:24 · David Weisburd, Matthew Harris
[00:10:24] S1: One of our companies as well. [00:10:26] S0: Oh, excellent. You know, I... That was not really consensus. I would say the consensus when we invested, Shirley, when you invested, was that, well, Harvey had won. [00:10:35] S1: Yep. [00:10:36] S0: And that this European upstart had managed to land a few European logos. But really, that's just because Harvey hadn't gotten there yet. And and now what we observe is that Liguria is catching up and in certain ways is the leader in certain segments of the market and I think has a case for a kind of a second mover advantage. And we're seeing that in some other markets, analysis, for instance, the companies that would have been anointed as obvious maybe are feeling less obvious now. So I think this idea of there are no diamonds in the rough, there are only diamonds takes a static view. And we observe this to be extremely dynamic to where leadership is actually not handed down from on high and protected forever, but is actually constantly under siege. So that's what I mean by there are I don't mean by diamonds in the rough that you should go to Des Moines, Iowa, and start looking for the next, you know, inference company. It's not really like a contrarian in the sense of geography or founder archetype, etcetera. It's more, I think, having an open mind about the dynamic nature of AI investing in particular. [00:11:55] S1: If you think about some new principles of investing that did not exist pre this AI market, what are some new principles that guide you as investor?
00:12:06 · Matthew Harris
[00:12:06] S0: The way we frame it, perhaps not unique to us, is in every one of our investments, stipulating that AGI is here or will be, That in fact, AI is no longer a project or a premise or even a tool or an ingredient, but rather that in in various ways, they'll become obvious over time. Superintelligence will be here. Abundant superintelligence will be here. And so the question is, what wins under those conditions? What are the types of companies you want to back under those conditions? Not under the conditions that AI exhibited two years ago, where it was still, I think, a question about not about the direction of travel, but about the speed of that travel and most importantly, whether it would plateau. This has been a really ongoing part of the narrative about AI is slightly less now. But if you go back and look through time, if you just take the New York Times as an example, you could find on different days of the same week articles that said AI is plateauing, is laughable, is a, you know, stochastic parrot, and clearly is not any good. And then the next day, AI is dooming our civilization and will ultimately take over everything. And it's sort of like, well, which one of those do you really have conviction in? Or are you just being sort of negative? And so this first category of critique, which has waned but persists, is the AI is a joke. It's a chat bot only. I mean, it's really harder and harder to argue that it's not an important technology, but there are still people seeking to relegate it to quite marginal status in terms of its importance. Our view and the view of most venture firms is is quite the opposite of that, that actually, if if every observer knew what was happening at the frontier, they would take the view that superintelligence is perhaps not comprehensively imminent, but is certainly inevitable and in the next one to three years. So that that is our view. And so as we develop this perspective, call it eighteen months ago, we asked ourselves the question you just asked me, which is, Okay, so then what? And I'd say there are two themes that emerge that explain 75% of our investing in the past eighteen months. One is the physical world. So we've always invested heavily in robotics.
00:14:36 · Matthew Harris
We, twenty years ago, invested in Kiva Systems, which was the first sort of warehouse robotics company that Amazon bought for over $1,000,000,000 and consistently throughout in manufacturing and distribution robotics. And here in the past eighteen months, just another kind of Cambrian explosion of amazing robotics companies, including household robots and all sorts of really kind of unprecedented advances. And so that's, you know, maybe a third of what we do. And then we've made 20 investments starting in late twenty twenty two in what people now call AI native services companies. This whole question of like, if you can actually compete in a major industry instead of just selling technology to that industry, depending on what the industry is, it's just a massive TAM expander and allows you to have a more defensible business, you know, relative to the labs themselves. We worry about the Foundation Model Labs and the pressure they will face to build application layer functionality, horizontal and vertical. And one element of defensibility is to move out of application layer applied AI and into actually doing the work. And so we our first version of this is a company called Reserve, which was actually a fun exercise that's still worth doing. Think we, you know, I'm a financial services investor, so we mapped all the financial services and all the business models. And we asked ourselves, if you had a machine that could magically read and write, what are the activities within financial services that would be most vulnerable to this machine? And the answer in our mind was insurance claims, which is really just a human swivel chair, multi screen. You get notice of loss on a policy. You have a policy. You have to read those things. You have to synthesize them into a loss adjustment. It is like what version one generative AI, if you remember, that was what it was born to do. And so we started incubating this with a with a customer, a design partner customer and AI native insurance claims business that ended up being called Reserve. And when we incubate companies, we don't take any common stock. We just put money in. We recruit a founder and then we give them all the common stock. And so they really are the founder and that company is they'll do well over 100,000,000 in this year in revenue and their second full year of life.
00:17:16 · Matthew Harris, David Weisburd
And it's been an extraordinary growth curve. So we were heartened by that. And we went on, as I said, we've done 20 more of these, including two law firms and automated earthmoving company and a home health agency and six or seven more insurance related native services companies. And so we think those companies and actually so far, it's been very clear that they've benefited from advances made by the Foundation Model Labs. And we want to be levered to that, not fighting against that. [00:17:49] S1: That's where I wanted to go, which is at some point, advanced intelligence gets so advanced that it's doing everything. And we're we're like that famous essay where we're just these... We're back in the jungle, and we have these overlords. But until that happens, why is going directly to services, why is that a moat? And maybe you could double click a little bit about how your strategy relates to the large LM models.
00:18:19 · Matthew Harris
[00:18:19] S0: Well, it certainly calls for speculation. I think these companies have the theoretical ability to do anything. So what we can do is look at their incentives. Right now, a vast amount and fast growing percentage of the revenue for OpenAI and Anthropic is coming from large corporates who either directly or via API through partners are accessing their models to automate core business functions. They will have to make a choice at some point along the way or at many different points along the way about which one of those industries they want to actually compete directly with. So right now, Anthropic is making a lot of money from the legal industry. And our view, probabilistically, is that that will inhibit them in starting their own law firm. And the same can be said of many other verticals. They make a lot of money from the insurance industry, and that will likely inhibit them from competing with Travelers, who is a massive customer of theirs, competing with Aon and Willis Towers Watson, who are massive customers of theirs. They feel like they have enough fish to fry selling, of course, just base intelligence. And then building vertical applications on top of that to sell to those corporate end users, not to mention consumer businesses, which they also have. So they may look at those three vectors, the intelligence vertical applications, consumer and horizontal applications like coding applications, for instance. They may look at that and say, that's not enough. We also have to start a law firm or a home health agency or an earthmoving business. But our probabilistic view is that will be pretty long down the path and may never happen. Like, for instance, in fintech land where I've been living for a long time, there's always this question of, you know, if you look at Google and Amazon and Facebook and Apple, which one of those will move aggressively into consumer financial services? And the ones that have always had the most capability actually were Google and Facebook. They had incredibly broad. You could argue Apple, but for a variety of reasons, Apple doesn't like to use data. And so it's they've always moved very, very carefully in financial services. But the most capabilities resided at Facebook and Google and they never have. And they've made little forays here and there, particularly Google.
00:20:58 · Matthew Harris, David Weisburd
They'll like announce something every two or three years and shut it down. And the reason is they're an advertising company. They make their money from the financial services industry in, you know, 20, 30% of their revenue. And so the incentive they have to go poke that industry in the eye by directly competing is a real problem. [00:21:21] S1: Is that because they've run this calculation, they've realized the best business model is advertising business? Or is that because culturally, they have this whole ad team that has 1,500 employees, and they can't cannibalize their own business and culturally just wouldn't work. Which one of those reasons is driving that?