Invest Like the Best with Patrick O'Shaughnessy
· Dan Sundheim, Patrick O'Shaughnessy
Anthropic took a different approach and just said, “We are going to focus on enterprise.” They tried consumer early on, but it came clear they didn't have traction, so then they just went all in on enterprise. They've had a lot of success with coding and enterprise. Because they've now taken a market leading position, generally the sentiment is that Anthropic is winning and they are like now the Uber, if you want to use that analogy. I think this is going to go back and forth over time and people probably get carried away in both directions, but I think those are the biggest differences. I would probably err on the side of focus, but I do understand the economic rationale for trying to do as many things at once. The only thing I – early on, we invested in OpenAI, this is probably a year and a half ago. I said to them “You have to do ads.” I understand, I've seen it so many times. People in Silicon Valley, the idea of ads is like… [00:00:00] S0: Gross to them or something. [00:00:00] S1: They’re allergic, because it's like – I have this amazing pure technology product and you want me to like [00:00:00] S0: Taint it?
· Patrick O'Shaughnessy, Dan Sundheim
[00:00:00] S0: Taint it? [00:00:00] S1: Taint it with ads. And like, you see Anthropic’s Super Bowl commercial. That being said, even the companies that were the most adamant about never getting into ads, like Netflix. If you go back and just listen to what Netflix was saying even 15 years ago, it was like, “Getting into ads?” Even Reed would've been like, “You are out of your mind. We would never do that.” Ultimately, they did it. And to me it's like, if you're going to do it, ultimately – one, you can't really compete against companies that are using ads if you're not. Very hard. If you're ultimately going to do it, you might as well start earlier because you have to build a culture around it. It just takes time. I don't think it's a big deal that OpenAI waited, but I was probably – rightly or wrongly, I was pushing for ads sooner than they've chosen to do it. I think now they're probably going to get it right. [00:00:00] S0: I'm so curious what you think is going to happen to the hyperscalers now. I saw this news report the other day that Anthropic is considering securing 10 gigawatts now of their own power. Which just makes me think, “Okay, they're gonna have the power, the scale's gonna be so big, why don't they just create their own clouds effectively?” The hardware might be different, more focused on inference, et cetera. Does that jeopardize what these business models might be – which I think people have thought of as pretty damn good at the hyperscalers – do you think the future is different as a result of AI?
· Dan Sundheim
[00:00:00] S1: I do. I've kind of thought this for probably about a year now. I am more confident in the thesis that the hyperscalers are a worse business model going forward. Now it's interesting because usually when you say something's a worse business model, you're implying that growth is going to slow, margins are gonna contract. I actually think you're gonna see the opposite. I think that AWS and Azure – maybe Azure doesn't accelerate, but certainly GCP – I think these businesses are going to accelerate for a while just because they are – their customer bases, Anthropic, OpenAI are growing at enormous pace, and as they get to be a bigger part of the business, the growth accelerates. The problem is that you went from a dynamic where AWS, Azure, to some extent GCP – their customer base was like every corporation in the world. So therefore they had fragmentation and they had the benefits of massive economies of scale that no single company could get, and it was a good, very good business. The problem going forward is that I think that economically it's highly unlikely that LLMs are not very concentrated in the hands of four or five companies. Those companies right now, they are obviously, as we discussed, they're investing a ton and they're cashflow negative, and therefore they're looking for compute anywhere they can get it. But if we're correct, and if anyone who owns these companies is correct, at some point in the next five to ten years, they'll be generating enormous amounts of free cash flow. When that happens, I think that they're likely to insource the compute, and every year AI is going to be a bigger percentage of the workloads at any hyperscaler. And so if you roll out 10 years from now, I think that the majority of the workloads will probably be AI. The LLMs will probably be providing a lot of those workloads, and I think that it will make economic sense to take it in house. Right now, I think that they look at the hyperscalers as more of a financing mechanism. These are well capitalized companies with big balance sheets. But I don't think these companies are better than them at building data centers. Like, building CPU clusters is different than building GPU clusters. Running inference on GPUs is very different than workloads on CPUs. And I think these – the LLMs are actually better at inference than the hyperscalers.
· Dan Sundheim, Patrick O'Shaughnessy
Then you have this whole dynamic of neoclouds. I think that the initial view for most public investors was that this was pure overflow capacity. There weren't enough GPUs and these things would be dead as soon as Microsoft got their GPUs. [00:00:00] S0: Caught up. [00:00:00] S1: Yeah. I absolutely would not make the case that they're fantastic businesses, but I don't think they're going away like people thought, because I think they're better at running GPU clusters than the traditional hyperscalers are, and I think there's a lot of interest from Nvidia and other chip companies to make sure that their customer base is diversified. Nvidia has a very big balance sheet and they want to keep these players in business. So over the next 10 years I think that these hyperscalers – AWS, Azure – will grow fast. I think the margins, my guess is, will be challenged both because the businesses are getting a lot more capital intensive, because AI is capital-intensive, more capital-intensive than traditional workloads. And also the customer base is getting more concentrated. Meta is not a hyperscaler, but they insourced all their compute. Why would they pay? I mean, they're just too big to use somebody on the outside.
· Patrick O'Shaughnessy
[00:00:00] S0: If you think about the last couple of years, probably the best thing you could have done is just be long the AI build-out in all its various forms, and maybe that will remain true going forward. But it seems like the market a little bit is starting to think now ahead to the other implications of AI. Software – we're talking the week after software got absolutely decimated in the market, and everyone thinks that because of Claude Code and the amazing experiences that they're having with Claude Code, software businesses are just screwed. I'm curious how you're starting to think now beyond just the AI build. Okay, it seems like, here's the thing, like it's gonna be here. Now the rest of the world has to start to absorb this technology. How are you thinking through that? I'm just super curious what you think about the software sell-off. But even more broadly, the real economy now has to start to swallow this new technology. I'm so curious how you think that's going to happen.
· Dan Sundheim
[00:00:00] S1: It is incredibly difficult to know, and I don't think that's because I don't have perfect information. I think it's just these models are improving at a rate which is exponential, and understanding how that makes its way into the real economy and the implications, is difficult. I think that you probably want to use a few frameworks. It really comes down to like, which companies do you think will have a moat in most circumstances. It's fairly straightforward to identify moats that are protected from digital LLMs, like just the proliferation of digital intelligence. Once you get into robotics and other areas, you start to have to question the moats around some other traditional industrial companies. And also, globally, how do countries that were arbitrating labor do relative to a developed economy? So there's gonna be phases of this, where the first phase is software. And that's really because Claude Code entered the zeitgeist and it's like, all of a sudden, people have seen Claude Code, and all of a sudden they just see on Twitter that people are saying like, “Oh, you know, I created a CRM system in like a day.” And I was like, “Oh my god, this isn't good.” That's kind of where people are now. We wrote in our letter at the end of year, I said, “Look, the build-out is still gonna be a thing in terms of like, places to invest in the public markets. But it's increasingly going to become which companies are affected, and it's gonna become – there haven't been any shorts in AI. There were like, basically no shorts prior to 2026, really. If you wanted to just say, “I'm gonna short something because of AI,” you didn't make a lot of money. In our letter, I said, “There are going to be a lot of shorts – some longs – because of AI.” Software's the first one. I think the market tends to swing to extremes. My guess is that software will have to evolve, will probably be a worse business model going forward. But I think the same way that Walmart evolved with e-commerce – and yes, would they have, all else equal, preferred that e-commerce never happened? Probably, at least at the beginning. It required enormous amount of investment, their margins took a hit, they had new competitors. I think that'll be the case with software too, where companies that have really great distribution and great business models and are systems of record for companies.