Invest Like the Best with Patrick O'Shaughnessy
00:38:12 · Alex Sacerdote
[00:38:12] S2: Five years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly, we realized their AI products were not very good, they weren't moving the needle, nobody could charge for them. We basically sold almost all of our software. Entering this year we were net short, It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper or it's like a horse and buggy. The new way of software is like a jet engine or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem which is their list on the to do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs because there's smart people on both sides of that but we are seeing some companies really gut their jobs.
00:40:10 · Patrick O'Shaughnessy
[00:40:10] S0: Free hiring or whatever Free hiring and
00:40:12 · Alex Sacerdote
[00:40:12] S2: so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives in AR. So maybe they have just taken a while to get to something they can commercialize, but they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you gotta be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into the software. That's all true, and companies do like to buy. They don't like to build themselves that much. That's all true, but you can imagine a world wherein one, two, three, four, five years, you could have a brand new AI native company going after each one of these very strong incumbents, and if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got 40,000,000,000 in sales, you might have 500 of ARR, 700 of ARR of AI. So you've got this huge base. Now maybe this starts to work but it takes a while. In software there's the rule of 40 which is your growth rate plus your operating margin and if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look cause you've got exposure and you've got a strong market position.
00:42:40 · Alex Sacerdote, Patrick O'Shaughnessy
Problem with software is their AI is one or 2% at this stage and it's a long way to go. One thing we are picking up though now lately, and this is half baked, but AI could make some of these software platforms more important because what's the first thing you do with Clog? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would. [00:43:17] S0: Just to pull on that thread, seems like the commonality of the tools they might use that are the most sticky would be network based tools. So Slack is a great example of the software in Slack itself. The software is not the special part, it's that everyone is there. I'm curious what kinds of things you would want. Is it just the presence of a network effect? Is that the only thing that really matters?
00:43:17 · Patrick O'Shaughnessy, Alex Sacerdote
[00:43:17] S0: Just to pull on that thread, seems like the commonality of the tools they might use that are the most sticky would be network based tools. So Slack is a great example of the software in Slack itself. The software is not the special part, it's that everyone is there. I'm curious what kinds of things you would want. Is it just the presence of a network effect? Is that the only thing that really matters? [00:43:38] S2: Still early in our thinking here but even maybe Workday or the HR systems or the big systems of record, the agents may be running on top of them. It's good and bad. I mean CRM is going headless or they're making a headless version. And that's the bear case too that you get relegated to just being a database. There's a human interface to it, then they need to make the AI interface, which is no interface. It's just them going right into the data. You lose that customer interaction. But if the agents are going right to CRM and doing the work inside of CRM, that will solidify CRM so you won't have to think it's going away. [00:44:21] S0: Can we talk about chips? You've referenced them a few times. Infrastructure chips, everything around the data center maybe, I don't know how you conceive of it. Why is this so interesting to you? I love the modified rule of 44 percentage that's AI and percentage market share in the category. It's an interesting stat. What companies shine on that? What are laggards you know that are surprising?
00:44:41 · Alex Sacerdote
[00:44:41] S2: For the past forty years, nothing has changed in the data center. Even with cloud, Intel x 86 became the data center chip sometime in the nineties. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's law is improving at that rate. It didn't require tremendous innovation. And then there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part, every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking, there was no innovation. You would go from one gig to 10 gig, that would take seven years. And when you do switch in the first year, it does take some innovation to get to 10 gig and would create a little cycle, but then it would commoditize. Now you go to AI, the workloads are growing 10 x every year. And they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like three years ago and he said, I wish I could come back and be a hardware hedge fund because all the companies are public and they all have powerful IP and Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server, memory, which used to be a pure commodity, this high bandwidth memory is stack 10 chips on top, input outputs are 10 x what they were before, took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for three or four generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server three years ago. The stock was trading at eight times earnings.