How I Invest with David Weisburd
00:38:18 · Joseph Coyne
[00:38:18] S1: The unknown unknown. But you have other folks that are devoting a lot of resources to the space that have billions of dollars in reasons to be highly defensive. The Googles, the Metas, the Amazons, etcetera. Right? And they they may take a different approach or a similar approach to building very very robust models, but that will only up the ante in terms of competitiveness over time. And then if you look at or make an assumption around whether they'll just be two players in the future, I think it's very unlikely that there'll be two dominant players in the LLM world. So if you start to model, and you can sort of stress test this, right, a few more entrants, and then you look at the market size, of course, it's gonna continue continue to grow. What do you... What's the implied capture rate of that future revenue in three, five years that Anthropic would have to have to justify their current valuation? I think right now, it is converging to a rational sort of pre IPO price, so you're probably in a in a good spot. But that's absent my assumption around increased competition and lower cost players. Now I think the other angle or lens is what is their... Who are the customers of all these LLMs? OpenAI tends to be slightly more consumer. Anthropic Anthropic tends, especially with Claude Code, tends to be slightly more enterprise. So for that reason, I would value every dollar revenue differently across those two. And so I give a little boost to Anthropic in that regard, and you can see that's probably why they've eclipsed OpenAI in terms of their growth rate.
00:39:38 · David Weisburd, Joseph Coyne
[00:39:38] S0: I'll tell you the bold case. Virtue of Lucas Swisher, he runs growth for Coyne, previous podcast guest, and he talked about the three s curves of adoption. So the first s curve, the early adopters in terms of LMs from a revenue standpoint have been developers. What's crazy about that is that there's only only 50,000,000 developers in the world out of 8,000,000,000 people. And on top of that, only a small percentage of developers have actually implemented, if you look at worldwide kind of engineers. So we're still at really the small end of a small market. Now, sure, enterprises have adopted it, but to your point, 95% of them have failed. There's not that mass adoption. Not... There... There's not many enterprises that are super users today. So we're still seem to be... And then, obviously, there's all of enterprise. First, have engineers, then you have all of enterprises, then you have consumers. And then you could argue a fourth s curve, which is agents. Mhmm. So some smart people would argue that your TAM is not actually 8,000,000,000. It's 8,000,000,000 people and their agents talk to each other. So, in terms of the adoption curve, you... We started this conversation talking about we're in the first inning of AI. [00:40:48] S1: Well, I think you just made my point a little bit there too. [00:40:50] S0: I I I there's an argument there.
00:40:50 · David Weisburd, Joseph Coyne
[00:40:50] S0: I I I there's an argument there. [00:40:52] S1: Side, there's room to run. Right? There's definitely room to run. So because you established that, you know, this market is growing and the TAM is expanding even from a very large base, it's extremely attractive. And I think that will also attract new entrants, new players, whether it's, you know, the FANG players or some player we haven't even started talking about. But that could erode, you know, pricing power. Right? So it could erode in two ways, which is over time, you just see switching costs and you see the markets divided among more players. You can see the entrance of sort of where we invest, which is bespoke enterprise software meets AI for specific industries that has foundational [00:41:27] S0: Verticalization. [00:41:28] S1: Yeah. That has more foundational, you know, subject matter expertise baked into it, which then leads to trust within specific verticals, like airlines, banking, insurance, etcetera. And then over time, I think you also have a pricing compression, which is like enterprises are gonna be willing to pay more, but how much more will they be willing to pay? In order for... To to really capture the the lion's share of the wallet, you need to have much higher r... Higher ROI than we're seeing right now in the in the first inning. So it's sort of multifactor. It'll be fascinating to watch, but I don't think you can lose coming in where you came in.
00:41:28 · Joseph Coyne, David Weisburd
[00:41:28] S1: Yeah. That has more foundational, you know, subject matter expertise baked into it, which then leads to trust within specific verticals, like airlines, banking, insurance, etcetera. And then over time, I think you also have a pricing compression, which is like enterprises are gonna be willing to pay more, but how much more will they be willing to pay? In order for... To to really capture the the lion's share of the wallet, you need to have much higher r... Higher ROI than we're seeing right now in the in the first inning. So it's sort of multifactor. It'll be fascinating to watch, but I don't think you can lose coming in where you came in. [00:42:01] S0: Also, investor in vertical AI solutions like Legora. How do you think about horizontal versus vertical? Are there gonna be different areas where vertical wins versus horizontal wins? And what's your framework for that?
00:42:15 · Joseph Coyne
[00:42:15] S1: When we think about horizontal, we think about more utilities that are foundational to AI. So it could be a company like Monitor, where it's helping any vertical or any one enterprise manage their own models. It could be the data loss prevention example I gave with with sort of like, where is my data flowing in and outside my enterprise? That, again, would be a horizontal solution to avert potentially a vertical problem. And then when I think about verticals that will win, you know, I think selling into any one of these verticals requires trust. And especially in the early days, building that trust within an air... Within an industry as esoteric as airlines or insurance underwriting or banking underwriting lending, that requires trust, understanding the customer process, the legacy process, understand the data, the KPIs, etcetera, can give you an early lead to start building that data flywheel within legacy industries. And so when we think about vertical AI, I think it's, you know, companies that have domain expertise plus the ability to build product very, very fast. So engineering led teams that have AI native workflows that also have deep understanding of what we call subject matter experts on the team that can earn the trust because they have some sort of asymmetric advantage to penetrate a market. And so we're oftentimes like underwriting teams in that way, which is technical prowess, what have they built today, how fast can they build their future roadmap, but then also, what does the go to market motion look like? Is it appropriate for their ACV, for example? So are they being rational about how they scale? But then the next thing is, you know, we don't wanna fight in a fair fight. Right? There's lots of players. People can build companies faster than ever. And so when we're looking at a company, we're looking for an unfair fight. Bringing a gun to a knife fight. Right? It's who knows this industry better? Who's better connected to this industry? Who has the license to win in this industry? That can often... In addition to having the best technology, you have to have base case very strong, if not the best technology. But having that other tipping point can really tip the scales in your favor in terms of how fast you can penetrate an esoteric market like airlines.
00:44:25 · David Weisburd
[00:44:25] S0: I think your answer will be very disappointing to engineers. I think since 2008 when I started in the startup world, engineers have been trying to replace salespeople. And I've like this high school jocks... Jocks versus nerds thing where for now almost twenty years, engineers have been trying to replace salespeople. The problem with that is they may be ultimately successful. It might take ten, twenty years. Why? Because there's still human beings running the organizations, and human beings need solution. They need trust. They need all these things. So this idea that you could come to the market with the best AI solution still needs those salespeople that help with implementation, still needs those people that help actually on the operations level, all these things that still require real live human beings, at least for now.