Invest Like the Best with Patrick O'Shaughnessy
00:57:48 · Patrick O'Shaughnessy
[00:57:48] S0: Seems like you really have done very little in the application layer of AI. Historically, the apps ended up being most of the market cap, not the infrastructure and there wasn't really a model layer in the past. Guess you could say it was the clouds or something. Why focus so much on the bottom layers of Jensen's five layer cake versus things in the application layer that are actually getting used by consumers?
00:58:10 · Alex Sacerdote
[00:58:10] S2: Well, a, it always comes later. So the first three or four years of the iPhone and then the applications really took time. So maybe it's just starting. Today, we found that area to be pretty risky because where does the foundational model end and where does the application begin? Can the applications build enough of a moat where they can fend off and build businesses in that? We thought we would see it in some of the incumbents of CRM and they're starting and maybe just a matter of time. But we really haven't seen it in the enterprise world. There are some very good startup application companies out there, but the ecosystem's not clear. When we started, the ecosystem in chips was clear. When we started the foundational model ecosystem wasn't clear, now it's clearer to us. And at the application layer, it's still kind of unclear and a little bit dangerous, But there will be great application companies built. We really were watching Brett Taylor at Sierra. Brett was CEO of CRM. He wrote Google Maps. He was CIO of Facebook. He's building this fantastic company called Sierra. We're not involved. But that's where the rubber hits the road. Will he be able to turn this into a huge company? And he's doing quite well and we'll see. It's a matter of timing when these things really start to come into their own and prove they're sustainable. It usually doesn't start in the first three or four years, it comes a little bit later.
00:59:41 · Patrick O'Shaughnessy
[00:59:41] S0: At your office, you have this giant award wall for the best research job or project of the year given to an analyst and I think you won it. You gave self award to the Neuronization when you're by yourself. But you've got this now long twenty year history of every year one or more people put their name on this wall for having done the best job on a research project that year. I'm so curious about the nature of that research and how it's changing as a result of all of this. Say, you know, the person that's gonna win the award this year and the sort of work that that requires a human to do when so much of the work that probably would have won you the award in 2009 or something could probably be fully automated or done in an hour with ClodCode or something today. How is the nature of research and what gets you on that Whale Rock award wall changing in real time?
01:00:29 · Alex Sacerdote
[01:00:29] S2: I would like to say that we're so advanced in our AI systems that it's a huge change. But so far, it's helping us get up to speed and we have a handful of great apps. But it's not supplanting the job of the analyst. And so much of what we're doing is we're meeting with as many companies as humanly possible. We're developing relationships with the management teams that we cover. We're talking to the competitors. The system we use is right out of common stocks and uncommon profits, which was written by Philip Fisher in the nineteen fifties. And it's the scuttlebutt approach. It's growth investing. It's get out there and talk to suppliers, customers, competitors, looking for the key characteristics of these leading companies and really developing conviction in them. Now if it's a new complicated area like ABF substrates or PCBs, we're able to get up to speed on those things quickly, but it can't pick stocks for you in any kind of a way. I will say that if you're an analyst who's good at the blocking and tackling, there's a role for that, but you need to have obviously the insight on top. So we're now like using AI to write notes or review the quarter, and those notes are much better, but there better be a really good paragraph on top, which is the wisdom. What does this mean? How does this deal with our thesis? What changed? Don't just be a reporter. So the AI can be a great reporter. It can't pick into the future. The job that the guys did on app eleven two years ago, I think we got two of the best ad tech guys. I knew ad tech. I started actually nearby here in New York at an internet advertising startup. And after I did banking, I knew internet advertising and ad tech, is historically a terrible industry. But Michael and Sam really figured out the AppLovin story before anybody and they followed it when it was private. They know all the competitors. They know all the intricacies of terminology. And Sam went to the Las Vegas App Advertising Conference, and we went to con, and we talked to scores and scores of people. So did the work on the model and developed a great relationship with Adam Feroge, he's one of the best managers out there. I don't see AI doing that.
01:02:54 · Patrick O'Shaughnessy, Alex Sacerdote
[01:02:54] S0: What role does talking to other investors outside of your firm play in your life? [01:02:59] S2: One of the great things is just the friendships I've built with so many smart investors. And frankly, Philip Fisher said, part of his process was get to know a good 10 or 15 like minded people around the country and share ideas. They're great friends to make a lot of them have been on your podcasts. You develop good friendships and then you share ideas, talk ideas. It's important that it's a two way street. I call it the tripod when I like something and then my analyst likes it and then somebody who I really respect also likes it, that's three legs of the stool can really help the conviction. [01:03:40] S0: What have you learned about shaping the products that you offer your investors across the history of the firm? It's not just one monolithic structure anymore. There's several things that if I'm an investor and I wanna give you money, there's a couple ways I can do that. How did you arrive at those things and how can you turn that experience into advice for other investors that are trying to provide their LPs with the right set of options?
01:04:05 · Alex Sacerdote
[01:04:05] S2: So the first fifteen years, it was a long short fund and we wanna be focused. And if you defocus, that can be hard. So we grew that and we got that to the scale that we wanted to. We're 20 years old. Maybe ten years in, people started to ask for a long only product. In 2020, we launched the long only fund. So we're six years on that. That's now larger than the long short. The bulk of the assets are in these two. Sometime in maybe 2015, we formalized that we might be doing privates, and so we gave investors the option to opt in or opt out. And you could do 15% or 25%. So we didn't break the seal on the privates until 2020. In 2021, we offered a hybrid fund that could be 80% into privates, sort of similar approach, but if you wanted more exposure to privates. Very recently, we launched the Whale Rock Mega Cap Tech Fund. We just think there's a huge structural underweight of the largest tech companies in the world. We also realized that a lot of our performance over the years was from some of the largest companies, whether it be Apple or Amazon or Tesla. And so a lot of our largest pools of capital endowments or what have you, they realized they've been massively underweight the largest tech companies in the world because they have a lot of privates, they don't have a ton of public, and then maybe half the public is international. And then of their public bucket, there's a belief that there's no alpha in large caps, so they underweight large cap and they have a lot of small and mid managers that are stock pickers because it's intuitive that large cap can't have alpha. And then in their hedge fund portfolio, even if it's long biased, they're not gonna have 15% in Nvidia and all these other things. We realized that people are worried that there's these big companies. This is just a product of the digital economy and that in tech, the leader usually grows bigger and wins and develops very high market share quickly and there's great competitive advantages. They're also selling around the globe. So this is gonna lead to massive profit pools and massive market caps. And it's just gonna happen in the future. Most endowments are betting against this because they're completely underweight this. Finally, somebody came to us and said, what should we do? I'm on the board of Hamilton college and on their investment committee, were trying to figure out this.