Invest Like the Best with Patrick O'Shaughnessy
00:54:35 · Alex Sacerdote, Patrick O'Shaughnessy
[00:54:35] S2: Yeah. And Gavin's done a great job. People weren't comfortable with it. It's harder to do than it seems. And a lot of these companies, their charts are up so it's scary. Can I buy? And then you also have to have the holistic view. Because if you don't have conviction every time with Nvidia over the last four years, it's, oh, they had a great year. Oh my god. It's gotta be a bubble. And then they had another great year and it's like six months of marking time. It's gotta be a bubble. This is like getting out of hand. This is pretty scary. The bare cases are not totally without merit. But if you can see the whole picture and understand how these things are unfolding and gain conviction in that, frankly, if you're just a semi analyst, so many semi analysts missed it because they didn't see what was really happening at the foundational model layer and how this broader picture. So it helps to have the big picture. It helps to have decades and scores of s curves that you're looking at and where it plays in different things. [00:55:37] S0: What makes you the most concerned or uncertain? Is it just the rate at which all this stuff changes? What keeps you worried amidst what seems like pretty extreme bullishness?
00:55:48 · Alex Sacerdote
[00:55:48] S2: One thing that bothers me is there's a lot of negativity in the general population about AI. And there's a lot of negativity in some aspects of the government. You know, I think Maine just banned data centers and only 20% of the people are optimistic about AI and potential for negative regulation. But I do think kind of the genie is out of the bottle. Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adaption to happen even if the models didn't improve. But Jensen said this years ago when he was talking about his graphics chips. If good enough is good enough, I won't have a business. Every year, he made the graphics a little bit better and people always wanted the best. In AI, if Anthropic sort of hits a wall and stops improving or OpenAI, then the open source models will catch up. It might be a race to the bottom and won't be good for the stocks probably. It could be good for the chip companies. Chip companies don't care who wins. So that's another positive. And they'll benefit. Jensen really wants open source to like take off. It's all he kept on mentioning at his last GTC. So that could be a risk. Another thing is if one or two of the players falters and loses its position and can't compete, that could be like a lot of compute that they don't need in the future. Now if AI is so big, somebody else will suck that up, and we saw that with Oracle canceled a big deal and then Meta went right in. But let's just say Meta decided not to be involved with AI, hey, we can't keep up, it's just gonna be a waste of our resources. We watch that very carefully. In general, we see more companies truly going after this and even Microsoft going trying to build their own. Those are some of the key risks.
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.