Invest Like the Best with Patrick O'Shaughnessy
00:02:00 · S1
[00:02:00] S1: Patrick O'Shaughnessy is the CEO of Positive Some. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of PositiveSum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of PositiveSum may maintain positions in the securities discussed in this podcast. To learn more, visit pspsum.vc.
00:02:28 · Patrick O'Shaughnessy
[00:02:28] S0: My guest today is Alex Sakerdot, founder of Whale Rock Capital Management. Whale Rock is a technology focused investment firm that manages more than $17,000,000,000 across hedge fund, loan only, and hybrid strategies. Over the past few years, it's been one of the best performing funds, compounding at roughly 44 per year. Alex invests through a single lens that he has refined over twenty years. He looks for technology s curves, durable competitive advantages, and underappreciated earnings power. This conversation is a tour through how he applies that framework today. We start with his highest conviction position, which is anthropic, and use it to work through the entire AI stack from chips to models to applications. Please enjoy my conversation with Alex Acordeaux. Alex, you were saying that your highest conviction position is anthropic right now. Can you tell the story of discovering it, making the investment, using this anecdote as an excuse to talk about all the things that I think you and I are mutually interested right now? Investors like you investing in private entropic the business, AI, everything. It's a great way to zoom in. Why is it your highest conviction and how did you get started?
00:03:28 · Alex Sacerdote
[00:03:28] S2: When the gun went off with OpenAI ChatGPT in November 2022, we immediately took the firm and did a massive deep dive with our 10 person team. Anytime you have a new compute paradigm, there's a new stack. And that creates new winners and losers on the old stack. Now Jensen talks a lot about it, but it's power at the bottom, chips at the bottom, the clouds, and then the foundational models, and then the applications on top. And at that time, this was 2023 early, we said, we wanna be in the chips and the infrastructure first. And not only do they get the demand first, but we know who the winners are and no matter who wins above, which we weren't sure at the time, we know we're going to need tremendous amounts of compute. And we did a deep dive into that, which we can talk about later. But over the next two or three years, we started to get more clarity on how the foundational model layer would evolve. And at the time, two or three years ago, there were 60 different companies going after it. OpenAI was kind of in the lead. And we did a webinar in April 2023, and we said, look, this might be a winner take all. It might be a total commodity because there's open source players. It might be a race to zero, or it might be an oligopoly where there's three or four leading players. And what we saw over the following three years was that almost all the startups fell away and died. And then some of the largest companies in the world including Amazon and others and Meta, Amazon never really showed up. We'll see what happens with Meta but they came in strong. Basically, their effort faltered and they had to do a total reboot. In the meantime, Anthropic kind of was this dark horse candidate, the startup. They focused really purely on the enterprise. OpenAI had kind of won the consumer and then Gemini can never be counted out. We love Google as well, it's one of our largest positions. So it really started to look like a three horse race and somewhat of an oligopoly. Very similar to how the cloud market evolved where three companies underpin the entire SaaS cloud world and have really excellent businesses. And then we also were aware of the open source risk. From China, we started to get comfortable that the quality of the tokens from the leading edge were superior because if you're 80% close to the top of the benchmarks, going from 80 to 85 is a huge unlock.
00:06:15 · Alex Sacerdote
The open source guys, they don't have as much compute so they can come close to the leading edge, but they can't leapfrog it and then they kind of falter. Meanwhile, the scaling laws and other means of improving the models, the feedback loops, we saw that there was a very strong runway. And everyone we talked to close to the industry saw that the scaling laws would continue. We developed this thesis that it would be a three horse race. The big kicker was code. And this is the true unlock of AI. In the first few years, we knew AI would be big but we were skeptical also. We made large investments because we knew the training would be there but we weren't sure how much revenue might come and if it could truly replace labor. Because if you remember the early versions of the models were good but there was a lot of negative feedback from corporates. And could they be truly agentic? In 2025, the first clogged code and the coding tools really began to explode. You saw the first gen was like Microsoft Copilot, which is like twenty dollars a month. And that could sort of improve your grammar of coding, maybe find a bug, maybe make a block of code like a paragraph. And then Anthropic came out sometime in in the middle of the year and it could do so much more. And it started to get to this point where it could run agentically and the coding market just exploded. And then we started hearing people who could use it unfettered. We heard that, you know, even within Anthropic at that time, people were spending $100 a day on tokens, which if you do the math comes out to 20 or $30,000 a year. And if you think about how many coders there are in the world, 20,000,000, you've got a half $1,000,000,000,000 market just from coding alone. And mind you, that was on seven, eight, nine month old technology. We could see just on the coding market alone that Anthropic had a tremendous opportunity ahead of it. So we made the investment at the 180 valuation we said, and I think they were hoping to get to a nine
00:08:36 · Patrick O'Shaughnessy
[00:08:36] S0: Yeah, one to nine, yeah.
00:08:37 · Alex Sacerdote
[00:08:37] S2: One to nine, yeah. And then and then the numbers were like nothing we'd ever seen before. 100 to a billion on the way to nine. But when we did it in August 2025, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Code has gone to almost completely agentic. You had Andrei Karpathy and Linus Torvalds, two of the smartest people in coding, and they completely flip flopped. And Karpathy said, last year's code tools could write 2080% would be handwritten. That flipped when the latest model came out, and now he hasn't written a line of code except in English. And not to mention the pure unlock that we're gonna get for the people that never knew how to code. So just coding alone has completely taken off. One difference between the cloud GCP AWS and the AI companies is the clouds, generally it's commodity. They're selling you servers and storage. They have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity, but there's tremendous differentiation within. There's different training methods and different skills that they're good at. And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity, but then Thropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of like differentiation critical IP, which is a great competitive advantage. Many companies have come after the coding franchise and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and Anthropic is it's not just the API or the model, they're building a whole monopoly or a whole ecosystem of products around the API. So they've got the SDK, Claude for co work, orchestration layer, and all the tools, they call it sort of a harness, which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse, big deal. They saw this was a new way of doing computing so they invented all these products that they could see before everybody else that slowly built lock in. The other way we think about this is where are we on this s curve?