Invest Like the Best with Patrick O'Shaughnessy
· Dan Sundheim
[00:00:00] S1: They were very different, in that when we first invested in OpenAI, I wouldn't say it was contrarian at all, to some extent. We invested originally in the $125 billion round. So I don't think people were entirely sold on LLMs as a business model, but if you wanted to invest in LLMs as a business model, OpenAI was the one. Whether you invest in LLMs or didn't invest in LLMs was debated quite a bit. I mean, I think there was a lot of uncertainty about the ultimate business model of these companies, so that was what we had to figure out. Anthropic was a different situation, in that when we first invested in Anthropic, a number of people that I spoke to who I think are very smart, drew the analogy of Uber versus Lyft. Or, “why are you going to invest in the second player?” In most industries, investing in the second player is not the path to glory. But the way I viewed it was – it was incredibly difficult at that stage to say who was going to be first and who was going to be second. The pattern recognition, to answer your question, for me on Anthropic was just reading Dario’s essays and listening to him on podcasts. When I look back at my career and look back at the companies we missed – Amazon in the early days – and I think, “What could I have seen?” If you look at their income statement, you would just see a sea of red. The only telltale sign was reading Jeff Bezos' 1997 shareholder letter, which was like – the clarity of thought, his understanding of what he wanted to achieve and how to create value for shareholders was greater than almost any public CEO I dealt with. If I had read that and almost ignored everything else, it would've been a really important sign, and very profitable. Dario struck me like that. It wasn't that the models at that point were so differentiated. I think they were considered to be one of – probably at that point – five, six, seven players that could ultimately be important. There's still a lot of debate around LLMs as a business model, but I felt like he was incredibly skilled and extremely focused. And I place a lot of weight, rightly or wrongly, on clarity of thought and the ability to communicate as a CEO. What you want to achieve and how you're going to achieve it. Especially in written form.
· Dan Sundheim, Patrick O'Shaughnessy
Because taking the time to write something down – you actually really have to go through everything you plan to do and express it in a way that makes sense to everybody else. And Dario just did that better than almost any CEO I've seen since Bezos. [00:00:00] S0: How would you frame the debate today about LLMs as a business model now that we know a bit more?
· Dan Sundheim
[00:00:00] S1: Back then it was like, are these businesses going to ever generate an economic return? I think one analogy was, “AI will be huge. So was air travel.” Airlines were not a good business. There's nothing differentiating about one airline versus the other, and so therefore the returns go down to the cost of capital. Obviously we took a different view, but that was like a 65-35, 70-30 degree of confidence in that. At that point, it was more about the skew. If things played out like we thought and the business models were actually moated, it would be huge. I think at this point we're in a different place in terms of the debate that’s important. The debate that's important now – if you want to look through a positive lens, which we do – you'd say that the businesses have taken slightly different lanes and have excelled at different things within AI. So, OpenAI has been great at consumer and has had good traction in enterprise. Anthropic has been incredibly successful at coding. There was a thesis when we first invested that APIs, or the business of having other software companies plug into you, or other developers plug into your model, would be commoditized. It would just be a race to the bottom. I think that debate is more or less irrelevant, because as you've just seen with Claude Code and even OpenAI's API business, these are durable businesses. And yes, can you switch? You can, the same way you could switch from AWS or Azure, but it's not worth it for a lot of businesses to do it. And there's sufficient differentiation among the models. If you look at the underlying margins of these companies, they are not the margins that you see in a commoditized industry. The gross margins are quite high. The competitive landscape, I think, is not heavily debated. At this point, you probably have four or five LLMs that will be relevant in the long term. I don't see that changing. Not that there's not sufficient talent out there, it's just that the capital required to get into this business is too great and these companies are too big at this point. And then you kind of get the snowball of – the more capital you have, the more compute, you get better researchers. I think it would be very difficult. So the competitive landscape is not really in question. I don't think anyone would say that these business models are commoditized.
· Dan Sundheim
I think the real debate is, these are extremely capital-intensive businesses, capital-intensive to a degree that we've never seen before in the history of business. And the question is, you're spending a ton of capital and the ultimate return on that capital is unknown. So it's not like a normal business who builds a factory and knows what they're going to sell. You are spending tons of capital to train a model. The question is, do the scaling laws work such that the returns on that capital continue to be attractive, which means that you'll be able to attract more capital and build better models? Or, are you going to get to a point where everyone looks back and says, “We raised too much money, we spent too much training models, we didn't get the economic return.” Or – I think equally likely, not more likely – people would say, ultimately you will get the economic return, but it just happened slower than you would've thought. Enterprise adoption just didn't take off as quickly as you thought. And therefore, the problem is when you are this capital-intensive as a business, it introduces financial leverage and operating leverage to a degree you don't see in normal businesses. So you don't have the luxury of two or three years of things going slower than you otherwise would expect. I think the scaling laws, the returns on capital, and the speed at which these tools and AI is adopted throughout the economy are the questions.
· Patrick O'Shaughnessy
[00:00:00] S0: Is there anything that Netflix or something like that could teach us? That's another business that comes to mind where there's a crazy amount of capital that was spent to build an asset, and then it gets amortized over a bigger and bigger user base. And that's turned out to be a great stock and one that I know you've owned a lot. Is there any analogy between those two that's interesting to you?
· Dan Sundheim
[00:00:00] S1: When I was speaking to the executives at the LLMs, the way I framed it is I said, “Look, I think your business is some kind of combination between Netflix and Spotify. Netflix in that, unlike other tech companies, you are spending a ton of money upfront to train these models. Once these models are trained, you go sell them at extremely high incremental margins. You don't know what the revenues are going to be from that fixed asset that you've built. But to the extent that you've built that asset, you want to sell it as much as possible, so that you can get the cash flows to build the next model, and so on and so forth. That's very similar to Netflix in that they invested in content. And when you're an early mover, this kind of fixed asset business – you invest heavily, you get the capital to invest heavily, you get the revenues, you spread it out over an increasing number of people. You invest more in that fixed asset, and that kind of has a flywheel effect of generating more revenue, more content, more revenue, more content. And eventually, you get to the point where it's almost impossible to compete because the first mover advantage is too great. The difference – if you were to say like, what is an important difference of Netflix versus these models – is Netflix's content was differentiated. The models are more similar than they are different. At any given time, OpenAI may have a better model, Anthropic may have a better model, but a lot of the expertise and the innovation gets disseminated pretty quickly. So, these models are not terribly different. And that's where the Spotify analogy comes in, in that I think if you are Google or you are OpenAI, the differentiating factor will not necessarily be that Google gives you a better answer. Like, if we were just like to query Gemini or ChatGPT on something, I don't think it's the case that we would say definitively one will give you a better answer over time. However, the personalization matters, and the first mover advantage is like – the more that these models know about you, how you live your life, your health, all the things that are important to you – you build up this data and history, and it becomes very sticky. The music on Spotify is no different than Apple Music or Amazon Music. Theoretically, it's a pure commodity. What makes Spotify have pricing power, what makes it differentiated?