Top 5 of 2025: #4: Alex Sacerdote

0:00With the year in review, we also kick off our countdown of the most popular episodes of 2025. We'll drop two this week and the top three next week. And at number four, it's Alex Sasserdote from Whale Rock Capital. Alex is a passionate TMT investor who describes how he finds companies ascending their S-curve of adoption. Next week, we'll drop the top three. I'm wishing you a relaxing, enjoyable, and very happy holiday.

0:34Hello, I'm Ted Seides, and this is Capital Allocators. This show is an open exploration of the people and process behind capital allocation. Through conversations with leaders in the money game, we learn how these holders of the keys to the kingdom allocate their time and their capital. You can join our mailing list and access premium content at CapitalAllocators.com. All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of capital allocators or their firms. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

1:13Clients of capital allocators or podcast guests may maintain positions in securities discussed on this podcast. My guest on today's show is Alex Sasserdote, the founder of WhaleRock Capital Management, a technology-focused investment firm that manages$8 billion across hedge fund, long-only, and hybrid strategies. Our conversation covers Alex's path to running WhaleRock, shaped by early exposure to the markets through his father, a longtime partner at Goldman Sachs, and his formative years at Fidelity. We dive into the key lessons he learned at Fidelity, the development of his investment framework centered around S-curves, competitive advantages, and underappreciated earnings power, and the application of the framework to AI, the Mag7, cloud computing, electronic vehicles, and blockchain technologies.

2:05Before we get going, after eight years of being told by my friends that I have a face for podcasting, we decided to rip off the Band-Aid and produce video recordings. It turns out that YouTube is the fastest growing channel for listening to podcasts. Not Apple, still the biggest. And not Spotify, still the biggest among younger listeners. It's YouTube. Now, I, for one, don't understand this at all. It's hard for me to imagine watching two people talk to each other, especially when so much of my listening comes when I'm on the move. But it seems that's not the trend. And the trend is your friend.

2:43at least according to Cliff Asness and others with a momentum factor strategy. So we're giving it a shot. That's despite my hesitancy, and despite quite enjoying the many times I speak to both friends and new friends who tell me it seems strange to hear me talk while seeing me at the same time. Now, I'm not sure if that's a sensory thing, or if my friends were right about my face for podcasting, but either way, it seems I'll be a little more visible going forward. The irony of this is most of you hearing this already are listening, so you may be the exact wrong audience to share that we've added video.

3:18But regardless, if you do have a friend who's addicted to YouTube, please let them know we're now stimulating an additional one of the five senses. Thanks so much for spreading the word about Capital Allocators videos now available on YouTube. Please enjoy my conversation with Alex Sasserdote. Alex, thanks so much for joining me. Ted, I'm very excited to be on the show. We've been friends for a long time, since 1997 at Harvard Business School, classmates, class of 99. That is true. Why don't you take me back before that? When did you first get interested in stocks and businesses? Well, I think actually the first stock that I owned was Apple back when the PCs were coming out.

4:04And I owe it to my father. He was a longtime Goldman Sachs partner. He ran corporate finance in the 80s and then chairman of the private equity group in the 90s and into the 2000s. I lived in New York and I always was exposed to a lot. We had a few shares of Apple and we were so excited when it split. And then in college, I remember buying Boeing on the theory that they were somewhat of a monopoly and air travel had a huge future. I've always been interested in investing from an early stage, and I was lucky to just be exposed to my father and a lot of others along the way. How did your father transmit that knowledge or interest to you?

4:47He always had so many stories of the characters he met, the IPOs, the M &A, the deals he would do. Sometimes we'd meet those guys along the way. He was involved in the Alaskan pipeline and meeting some of the entrepreneurs that were trying to build that out. and just hearing his stories of the deals he would do all over the world. He was chairman of the Commitments Committee at Goldman Sachs, and he talked about some of the deals he was able to keep them out of and then told us about some of the stocks he would own. He would always read Barron's. And I grew up reading the newspaper and in college would read Barron's.

5:23Actually, one of our classmates basically decided to invest in my firm early on because we were on a weekend at HBS. and he saw me reading with a flashlight Barron's at 2 a.m. Sean's wire. I always had a small account and was investing along the way. I started in the business in investment banking, and I worked at Smith Barney, which became Citigroup. I was lucky to join the tech, media, and telecom group there. That's a big thing for me is I was involved in technology early on. Then I realized I wanted to be an investor during that period where we would spend three months with a company. And then we'd do the IPO and I'd show up at Fidelity.

6:08And there was a guy my age or a little bit older who just knew everything about the industry. And he was talking directly to the CEO. His job was to evaluate, is this a good company or not? I immediately was like, that's what I want to do. In the year before business school, I worked at an internet advertising startup called Interactive Imaginations. In their finance group, 90 people, they did internet games as well as they started one of the first internet advertising networks. It was a good, not great company. It ultimately went public. It was one of the competitors of DoubleClick. And then I knew I wanted to get to the buy side.

6:48And I was enrolled at Harvard Business School, I got a summer internship at Fidelity. And that was a great moment for me where the seven or eight interns were each assigned a different industry. And I said, oh, you know about the internet. There's this new sector called e-commerce and our current internet thinks Amazon's a fraud. Why don't you cover these eight companies? And it was Amazon, egghead.com, preview travel, on sale, N2K, CD Now, which sold CDs online. I was like a kid in a candy store. I spent the entire summer visiting every one of these companies. I was at Amazon's first investor day, had lunch with Jeff Bezos.

7:30He still had that laugh. I wrote this 40-page report, and we all had to present to the entire equities group. It basically said the leader grows bigger, faster, and wins. buy Amazon and short everything else in the space. Half the people at Fidelity thought I was crazy because here was this book company that was going to beat Barnes and Nobles and made no sense. But we looked very carefully into the unit economics, the growth. At that point, even if the internet did not grow, they only had 2 million customers and there were 90 million internet users at that point. Hindsight makes it seem easy, but at that point, it was a harder call.

8:12And one guy who bought it was Will Danoff, who has proved to be one of the best PMs in history, frankly. And it was great to have him as a mentor at Fidelity. But I was totally hooked. I had found my calling. I spent the next six years at Fidelity. And Fidelity is an amazing training ground because there's so many different types of PMs. They give you free reign. They give you a computer, a credit card, and a sector to cover. And you get tremendous access and tremendous travel to go do primary research. At Fidelity, they rotate you on the different sectors. I started with internet. Then I did software.

8:56Then I moved out of tech and I did gaming, lodging, leisure, and media. I ran the Select Leisure Fund, and then I also ran the Aerospace and Defense Fund. In the last two years, I was a sector PM running a large cap tech and a mid tech. Ultimately, I saw such a huge opportunity in tech, and I really wanted to specialize in tech. And at that point, really the career track at Fidelity was to get a big fund diversified that runs against the S &P 500. So as you're learning at Fidelity, how did you think about the mentorship that comes from having all these different PMs around? Fidelity is a very interesting culture because it's a huge team, but it's very individualistic.

9:46I don't want to say it's sink or swim. There's some training. You have to do industry reviews and write notes. But it's really up to you to learn how to do creative research. But you learn a lot by watching all the different PMs and their various styles. And of course, you've got to pitch your stocks to all these PMs. So there's so many great characters of Fidelity. There's value managers, growth managers, GARP managers, high yield debt managers. And then you're just in an environment where you're constantly talking about stocks, other people's sectors, your own group, and your own peers, your peer analysts as well.

10:25I've had a few great mentors who really taught me to push and do creative research in different ways. What's the gross margin? What are all the factors that drive into that? And then how can you research those to get even deeper on those? But there's so many different styles of investing. You learn what your style is very organically. What did you find and learn along the way that made for an effective stock pitch to a portfolio manager? Peter Lynch, he wasn't active when I was there, but he would meet with the analysts individually three or four times a year. And he always had his thing, the egg timer, where you go into his office and he turns the egg timer.

11:08In fact, at one point, I had one here. You've got to figure out how to do it quickly. Obviously, it's the key points. It's the thesis. It's where you're differentiated. You've got to get your earnings estimates, your PE, get to the crux of it very quickly. And I find that's a hard skill for a lot of people where everybody's pressed for time, getting to the key points very succinctly and quickly, and just cutting down to what really matters and why is this so much better. What was it that you saw in the tech sector that led you to be excited about the future and wanting to push in that direction?

11:46The amount of innovation and growth, you could just see it. I mean, we had the internet come and that spawned whole new companies. Each tech innovation from the mainframe to the PC to the client server computing to internet builds upon the previous one and unleashes more growth and more innovation. And you could see that tech was such a small part of the GDP, but through the internet was rapidly expanding. This was right at the cusp of the iPhone. Because I started WhaleRock in 06, the iPhone was launched in 2007. And at that point, the big product for Apple was the iPod. You could just see digital music and the explosion of the iPod and what that did to the earnings power.

12:41Moving from the iPod to the phone was such an obvious move because all you really had to do was put a modem into that iPod and it was one device that could do more. Obviously, at that time, I didn't even realize how powerful that would be. what's been so fantastic that I didn't see is each of these cycles gets bigger and bigger and takes more and more of the economy and it doesn't stop. And frankly, I was a little worried when I first started doing internet. I was like, okay, I'm going to be great for two years and then the rest of the world is going to figure out all about the internet and there won't be that much change.

13:25And then I'm going to have to find other ways to make alpha. But what's been so dramatic and shocking is the amount of major innovations and S-curves and how big and broad they become. I mean, mobile was just massive, but now it's just table stakes. After that, we had the cloud. Now we've got another one with AI. Each one gets bigger and bigger, and each one enables the previous one. So I knew inherently tech was going to be a great place for growth and also stock picking. But frankly, I didn't think it would be as huge as it has become at the time. So when you spent your six years at Fidelity and you were exposed to all these different styles, you knew that you were interested in the growth of tech sector.

14:16What did you find was your style of investing and how you wanted to pursue the trade? One of the things I was good at understanding was that you really wanted to be behind product cycles and true growth. And in tech, there are areas that don't grow that much. And frankly, the period after the Internet crashed and before the mobile revolution happened, there weren't any major megatrends happening, but there were minor trends. And I would make sure that I was invested behind those. And then, of course, I started in the internet itself in 97, 96, and got the early days of Amazon. And even before that, I invested in AOL for my own PA at the time, which was just growing like crazy.

15:03I devised this three-part framework, which we're known for at Whale Rock, which is S-curve, competitive advantage, underappreciated earnings power. And the first one is all technologies start slowly. They have a lot of barriers to adoption. It might be too expensive or complicated. There might not be the right ecosystem. There might be a lot of inertia. There were smartphones before the iPhone, but they were big, clunky, hard to use. There was no wireless network and they were expensive. Steve Jobs fixed all that with a$200 phone, a touchscreen monitor that your grandmother or child could use, and then he connected it to the 3G network.

15:50And so all those barriers were immediately removed. And then you hit that mainstream takeoff phase, that inflection, where you go from 1 % of people having a smartphone to 50 % in a four or five year period. That creates just incredibly rapid unit growth, some of the fastest growth in the global economy. Not only is it fast, but it actually can be predictable. So one of the reasons Warren Buffett has not liked tech is he found it to be so unpredictable. But this S-curve framework gives you insight into how big the market can be, where it might be in four or five years. It's like a map into the future.

16:32And that's why you want to invest in tech. What are the characteristics of these big multi-year, now trillion dollar companies? How do we find the next Microsoft or Dell or IBM in the mainframe world? So find a powerful S-curve. And then second, look at all the companies in that ecosystem, trying to find the one or two that have an incredibly powerful competitive advantage. And the thing about the digital world is some of these competitive advantages can be much stronger than in the offline world and accrue faster. In software, you can be the operating system. On the internet, you can have a really powerful network effect.

17:17If you're LinkedIn, the more people on LinkedIn, the more valuable it is, the faster it grows. It makes it impossible for the competition. And in e-commerce, you can have brand, you can have scale, you can have scale that helps you in logistics. You can be a platform company where everyone needs to be on your system. Or you might have critical intellectual property like Qualcomm, for example. Everybody had to pay them a 3%, 4 % royalty if they wanted to do cell phones. Or ASML has critical intellectual property in their lithography. So you've got these really great competitive advantages, learning how to spot those.

17:58And then when you get the S curve, plus a competitive advantage, that's the third thing, which creates exponential earnings growth, which we call it underappreciated, because very few people look out two, three, four years into the future. When you get S curve and a rising margin, your earnings don't grow linearly, they grow exponentially. And it's very hard for the world to think exponentially because very few things in our day-to-day lives do this. So if you can get those two things right, very often you can buy amazing companies at extremely low multiples. And it might look expensive on the first or second year PE, but three, four, five years out, it might look crazily cheap.

18:51Examples of that are when we first bought Tesla at the end of 2019 and 2020, when the EVS curve really took off, because Elon got the price of the Model 3 down to$40 ,000. And then we saw they had very good competitive advantages. We said, wow, they can do 2 million cars and a 50 ,000 ASP with an X gross margin. And we knew what the OpEx would be. And so we calculated the earnings. And looking back at it, we were buying Tesla at literally four times the earnings they did three years later. And similarly, we loaded up on NVIDIA right after ChatGPT in January 2023. We were buying NVIDIA at four times the earnings they're going to do this year.

19:41When you're thinking about figuring out that S-curve, let's assume you find a trend that you think is going to climb an S-curve. How do you decide where you want to buy in on a stock on the S-curve? It's great if you can get it right at the inflection point. But the thing about the S-curve that's so important is it tells you how long you can still own it. And sometimes it's okay to be late because maybe it's unclear why or how it's totally inflecting or you're not sure how strong the competitive advantage of a company in that space is. But generally, once you get to 30 % to 40 % penetrated on the S-curve, that's usually when that exponential growth becomes linear.

20:30Because in the first few years, you go from 1 % to 3%, 3 % to 6%, penetration along the S-curve. And then a value of the S-curve is how big is this market and how long can this trend keep on going? A lot of times the S-curves can be dynamic because these are new things that we don't always know for certain how big they're going to be. So we do a tremendous amount of research to figure out how big the S-curve is. And some can be very quantifiable and some are more you're directionally right. A good dynamic S-curve would have been the cloud computing. When cloud first came out, there was no real estimate for it.

21:11And no one really knew what it was. Is it just a server and a warehouse? And we realized it's not just the server. It's the networking. It's the storage. It's the database. It's all the software layers. And we looked at in the traditional world, that was$600 billion of spend. And AWS was going after all of it. This was the biggest market in business IT ever because previous markets were just the server market or just Cisco was good in networking, EMC was good in storage. And the analysts were set up in different compartments and they couldn't see this coming together. That was$600 billion. And then we said it's going to be 50 % deflationary.

21:53So we sized it at$300 billion. And then we looked at Amazon's revenue and that was$1 billion. Then Amazon grows like a weed. And what happened? It actually wasn't deflationary. It was equal. So now we're saying we thought it was 300 billion. It's actually 600 billion. We have another five, maybe even 10 years to go on this. And it's still growing. A lot of these technology trends, the competitive landscape can change quickly. How do you conduct your research so that at any point in time, if there's a wobble in a quarter, you can have conviction in a longer term trend? We've got a team of 10 of us, and we're doing 2 ,500 face-to-face meetings with management teams a year using our S-curve competitive advantage underappreciated earnings power framework to guide where we look.

22:52So we're focusing on these different S-curves or sub-S-curves or trends. And then we're going to industry trade shows, many different countries. We're talking to all the competitors, suppliers, customers. And we've been doing this for 18 years. We call it the Whale Rock Learning Machine. We look for clues as to what's changing or what looks like another history rhymes in a lot of ways. You have to have deep research to really understand. And then when you have that deep research, when something like DeepSeek comes along, it scares the rest of the investment community. AI had been around for 20 years, but serious last five years, but it was hidden inside the clouds.

23:39And it really wasn't exposed to anybody or consumers or businesses. And then bam, OpenAI put out ChatGPT. it's just like the Netscape moment. And then four of us really spent 110 % of our time just sprinting to learn everything we could possibly learn, meeting with private companies, public companies, mega caps, chip companies in Taiwan, in the US, software companies, venture capitalists, you name it, to put together the pieces and anticipate that we might not have all the answers, but we knew the questions to ask and where the fulcrum issues would be. As you pull that thread now, a couple of years later, how are you thinking about AI as it relates to your framework?

24:28I think AI is definitely one of the major mega S-curve trends, and it's great that we have another one to invest in. It's going to be a multi-decade story like the cloud, like mobile. This is, in a lot of ways, more complicated than the previous ones. With smartphone, you had units and ASP. You could really quantify easily how big the market was with precision. When you have a new computing cycle, you've got the whole stack. This was pioneered by Lou Gerstner. He talked about the new stack in a client-server world versus the mainframe world. In the AI world, there's a new stack. And at the bottom, always first comes the infrastructure layer because you've got to build the compute out.

25:15And when you have a new stack, that's when the inflections happen, creating winners and losers. Then above that is the cloud. Most of the AI is going to take place in the cloud. There's the cloud delivery layer with AWS, Azure. There's some of the new Neo clouds. And then some will be delivered on-prem. And then above that is the foundational model layer. These are the big LLM companies like OpenAI, XAI, Google Gemini, Meta. And then above that are the applications that can be software applications or internet applications. And they can come from startups or incumbents. Our thesis was invest in the infrastructure layer first, because that's always the first to inflect on the S-curve.

26:06And also, no matter who wins above, we know we're going to need tremendous amounts of AI infrastructure. And then third, we also know what the ecosystem's like. We know it's going to happen on GPUs. We know it needs a lot of networking. And most of the die has been cast, still shifting a little bit. But this is the competitive advantage. We were able to understand and identify winners. At the foundational level and at the application level, especially with startups, it's very challenging to figure out the ecosystem's not baked. It's a lot like Internet 1.0 when you had seven or eight search engines, and then later Google came out and crushed them all.

26:51So we found that to be a dangerous place to invest at the infrastructure layer, the safest place to invest, because not only was it coming first, but it was analyzable who the winners could be. Then we did all kinds of work to quantify how big this S-curve would be. We were quite bullish, but we really underestimated how big the CapEx could be. That's another dynamic S-curve situation. There's a lot of ways to skin the cat, but we looked at how much capacity was getting put in place. How many chips were they able to make because it was under such short supply? And then, of course, that wasn't enough.

27:29We got to figure out, are we really going to need all this compute? How many major foundational models will there be? What's it going to cost to train all these? How many small models will there be? and then there's the training in AI, there's the inference when people use it. So then we tried to forecast how many times would people go to the internet and ping chat GPT? What if Copilot gets adopted? How many people are going to be using open AI? And then anticipating the future of what kind of applications we're going to hit and how compute intensive those would be. Our thesis has been AI CapEx has been the place to be, but it's not over.

28:11We're only about 14 % penetrated on the AIS curve for infrastructure. Within the cloud, it's only 3 % or 4%. But everybody got so scared about, oh my God, the first year NVIDIA's revenues did this, it has to go down. We feel good about that because these changing models, particularly with inference reasoning, have these models where you ping it and then the model thinks and reasons against itself, checks its work, does a multi-part question, and it might ping the model 100 or 1 ,000 times. When we were doing our calculations, we didn't bank on the 1 ,000 to 1 inference ratio. And now DeepSeek is basically solidifying this inference time reasoning is going to be adopted by all the models.

29:03That's another reason we feel positive about the AI infrastructure story from here. How do you think about layering on shorts? The S-curve framework is good for shorting as well. And you can find great shorts all across the S-curve. The classic would be mature and getting disrupted. So traditional media by Netflix or newspapers or CPUs losing to GPUs. Then you can have great shorts in the best part of the S-curve because you might be in the sweet spot of the S-curve and selling into it. But if you don't have an airtight competitive advantage, you're a zero. You're going to destroy value. In smartphones, if you were RIM, Palm, Nokia, HTC, Motorola, Lenovo, the list goes on, you are a complete zero.