Invest Like the Best with Patrick O'Shaughnessy - Alex Sacerdote - How to Invest Through Technology Cycles - [Invest Like the Best, EP.477]

Episode Date: June 9, 2026

My guest today is Alex Sacerdote, founder of Whale Rock Capital Management.  Whale Rock is a technology focused investment firm that manages more than $17 billion across hedge fund, long only, and hybrid strategies. Over the past three years it has been one of the best performing hedge funds, compounding at roughly 44 percent a 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 right now. 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 Sacerdote. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp’s⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like The Best (00:00:00) Welcome to Invest Like The Best (00:02:29) Alex Sacerdote (00:03:08) Anthropic: Highest Conviction Position (00:13:23) Investing in Private Markets at Scale (00:19:08) S-Curves: The Full Framework (00:25:08) When to Buy Tech Companies (00:30:20) Identifying the Leader from the Pack (00:34:04) Anthropic & OpenAI's Competitive Moats (00:37:31) AI's Threat to Enterprise Software (00:43:18) Network Effects in the Agent Era (00:44:22) The Hardware Renaissance: Chips & Infrastructure (00:53:56) Why So Few Investors Get This Right (00:55:36) Key Risks to the AI Bull Case (00:57:47) The Application Layer (00:59:40) How AI Is Changing Research at WhaleRock (01:02:53) The Role of Investor Networks & Idea Sharing (01:03:40) Building a Multi-Product Firm (01:07:58) WhaleRock as a Learning Machine (01:09:15) The Kindest Thing

I know firsthand how complex the tech stack is for asset managers. And seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridgeline offers a better way forward, one unified platform that automates away all that complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more. All at scale. Ridgeline is revolutionizing investment management, helping ambitious firm scale faster, operate smarter, and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at Ridgeline. Apps.com. Open AI, cursor, anthropic, perplexity, and Versel all have something in common. They all

use WorkOS. And here's why. To achieve enterprise adoption at scale, you have to deliver on core capabilities like SSO, Skim, Rback, and Audit logs. That's where WorkOS comes in. Instead of spending months building these mission critical capabilities yourself, you can just use WorkOS APIs to gain all of them on Day Zero. That's why so many of the top AI teams you hear about already run on WorkOS. WorkOS is the fastest way to become enterprise ready and stay focused on what matters most, your product. Visit workOS.com to get started. Felix by Rogo is a personal finance agent that turns a single prompt into finished client ready work using your firm's own templates, context, and standards. Send Felix an email like, take these comments and turn them for me, or update my

tracker with the context of these emails, or run the ability to pay math on this buyer, and Felix sends back finished PowerPoint decks, Excel models, and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.a.ai slash Felix. Hello and welcome, everyone. I'm Patrick O'Shaughnessy, and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people-shaping business and investing. You can find Colossus, along with all of our podcasts at colossus.com.

Patrick O'Shaughnessy is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Some may maintain positions in the securities discussed in this podcast. To learn more, visit PSUM. My guest today is Alex Sackerdote, founder of Whale Rock Capital Management.

Whale Rock is a technology-focused investment firm that manages more than $17 billion across hedge fund, long-only, and hybrid strategies. Over the past three years, it's been one of the best-performing funds, compounding up roughly 44% per year. Alex invests through a single lens that he has refined over 20 years. He looks for technology S-curves, durable, competitive advantages, and underappreciated earnings power. This conversation is a tour through how he applies that.

that framework today. We start with this 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 Sackerdot. 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 markets, entropic the business, AI, everything, it's a great way. way to zoom in? Why is it your highest conviction and how did you get started? 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 want to 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, opening. I 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, this startup. They focused really purely on the enterprise. Open AI 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 overwork 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. 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 money. models, the feedback loops, et cetera, 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 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 Claude Code and the coding tools really began to explode. You saw the first gen was like Microsoft co-pilot, which is like $20 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 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 $20,000 or $30,000 a year. And if you think about how many coders there are in the world, $20 million, you've got a half a trillion dollar 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 billion. Yeah, one to nine. Yeah. 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 of 2025, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Cod Cote has gone to almost. almost completely agentic. You had Andre Carpathie and Linus Torvalds, two of the smartest people in coding, and they completely flip-flopped. And Carpathie said, last year's code tools could write 20% and 80% 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 going to 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 a 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 their commodity, but Anthropic, they're very good for anything that has to do with private equity in 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 code, franchise, Ananthropic has been able to keep ahead.

The other thing that's good about the foundational models Ananthropic is it's not just the API or the model. They're building a whole conopoly or 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 software. 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? And we have this infrastructure layer S curve, which we think is 10% penetrated. And by the way, we think it's still one of the best ways to play AI. And we'll talk about how that feeds back through. But if you think about it, 200 or I don't know how many 800 million people are using AI,

they're just using AI 1.0, which is like a search engine on steroids. But now with these new primitives where you have clawed on your computer linking it in, then you build skills, and then they're going to build true AI bots. Big corporations are going to build much larger, but where are we in terms of the amount of people doing that? I mean, Sunder said it's 10 bips of the knowledge workers of the world. So Anthropic has something like 14 or 15 million DAUs, probably a small portion of those are truly doing AI the way you can do it. So that 10 bibs, it's classic S-curve where these are the tinkerers, and then it's going to go to the early adopters, then it's going to go to the early mainstream.

But you're going to go from 10 bips to 1 to 2 or 3 percent to 5 percent to 15 percent in the next four years. And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast. But it's still Internet 1.0 when it's like, you knew you. you needed a website in 1998, but it's like hard to build that website. But this is coming together fast. And so the enterprise AI or enterprise application AI market is less than 1% penetrated. And, you know, we talk about S curves. We call this an L curve, just straight up.

We'll take this to the infrastructure. We're at 10 basis points of people really using AI. And there's not enough compute in the world. So Anthropic has half of what they need right now, and that's before this huge take-up. Mark Andreessen said in the next four years, one thing he's sure of is there's not going to be enough compute. I'm so curious when an investor like you, who historically was a public markets investor, you could hit buy and buy whatever you want, is now operating in lots of the most important private market companies. We can talk about Stripe or Databricks or Open AI or Anthropic.

How do you get the positions at the size that you want coming from? from the legacy of being able to just buy. How much of it is creativity just directly with the company? If it is directly with the company, so it's a double opt-in, they have to decide to let you in too. How do you do that? What have you learned about getting the allocation you want or the amount of equity you want in a private company

given that that wasn't your original background? We got to know the company, one of our analysts, new people in the finance group there. We had a look at the 60, billion dollar round and we didn't know the company as well. The gross margins were negative and frankly we hadn't seen coding explode the way it had. And one thing about public markets is you get to know companies over a long period of time and you can kind of invest on your own schedule. Then I got a chance to spend some time with Dario. I started to realize these guys, their management

team is excellent. The focus, the dedication, they had almost no turnover, the quality of code. And then the business plan was really starting to play out. It's one thing to grow from 100 to a billion, but it's another to do nine. And then so we reached out to the company as much as we could. They took a meeting with us. We did a 90-page PowerPoint deck where we used Claude Code to scour the internet for all the feedback we could about the coding market and what their products were good at, where they might need to improve. and we also did our whole overview of what the coding market would be.

They welcomed us into this round, and then we stayed close with the CFO. It's been great to build a relationship with them, and I think we punched above our weight in terms of the allocation. So that one was a total home run. We are in this period where the unicorn market is bigger than most stock markets in Europe, maybe even combined. It's definitely bigger than Germany. It's definitely bigger than the U.K.

even before we invested in privates, the first one was 2020, we have to know these companies and you really have to know them now because sometimes they're the biggest companies in the space and have huge impacts. So we do two to three thousand face-to-face meetings with management teams a year and about 10 or 15 percent of those are with privates. And then we kind of focus in on the companies that we really want to learn about and find ways to meet with them, get involved in their round. Our first one was Stripe. We had a large investment at the time. This is 2017, 18, 19, and 2020. We own Audion, which is a fantastic payments company. And they're a next-gen cloud payments company taking from World Pay. And the cloud modern payments was 5% of total $80 trillion market or what have you. But you can invest in Audion unless you know Stripe like the back of your hand. So we did tremendous amounts of due diligence, talked to 200 customers in Audion. But you can invest in Audion, but you can invest in Audion, unless you know Stripe like the back of your hand. So we did tremendous amounts of due diligence, talked to 200 customers in Audion. But you can. But you can. But you can. But you can But when we asked him about Audien, we asked about Stripe, and we realized this is Coke and Pepsi. We said, we got to find a way to invest. And I finally got to meet the Carlson brothers in 2019. And so that was our first one. We weren't really known for privates. I've got a friend who's involved with a venture firm that has tremendous amounts.

And I talked to him about it, and I said, let me know if you ever want to sell some. And then I get a call from him during COVID in April of 2020. We knew a lot about Stripe. We didn't have the full financials, but we knew enough that at that valuation, I think it was $35 billion, they disclosed we had over half a trillion of TPV. And we knew that Audien's take rate was 25 or 30 bibs, and we knew Stripes was 40 or 50. And we knew how many employees they had so we could kind of get at the profitability. It turned out the take rate was higher. It turned out they were being modest about their TPV. It was much higher than the 50, it was closer to the $1 trillion. We underwrote the thing under our assumptions and it was much better. And then we were able to upsize that from the seller to a $100 million block. The VCs are going to own and then most of them are going to sell. They like it that we'll own and own in the public market, which we did with New Bank as well,

owned it for a long period of time in the public market as well. As your business scales up, everything gets more complex, especially your compliance and security needs, With so many tools offering band-aids and patches, it's unfortunately far too easy for something to slip through the cracks. Fortunately, Vanta is a powerful tool designed to simplify and automate your security work and deliver a single source of truth for compliance and risk. There's a reason that Ramp, Cursor, and Snowflake all use Vanta. It frees them to focus on building amazing differentiated products, knowing that compliance and security are under control. Invest like the best listeners get a special offer of $1,000 off Vanta when you go to vanta.com slash invest. I know firsthand how complex the tech stack is for asset management firms.

And seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount, and more risk. Ridgeline offers a better way forward, one unified platform that automates away that complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more, all at scale. Ridgeline is revolutionizing investment management, helping ambitious firms scale faster, operate smarter, and stay ahead of the curve. See what Ridgeline can unlock for your firm. schedule a demo at ridgeline.ai. Maybe now is the right time to lay out everything you've ever learned about S-curves. Obviously, your firm is sort of predicated on this idea of technology adoption life cycles and investing in companies at the right time amidst a certain platform change or S-curve change.

I'd love you to go into the super deep detail of what you've learned since this is the lens through which you've viewed markets and stocks for a long time. Bring us into like the nitty-gritty, fine-gring, nuance detail of the, of why S-Curves can be so useful for investing? We have an investment framework. It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech, there's so many of those for so many

different types of moats, your earnings don't grow linearly. They grow exponentially. And that's the last piece. invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10. And it happens way more than you think. And it allows you to buy some of the best companies in the world for extremely low-key ease. When we were buying Nvidia in 2023, we were paying four times earnings. When we bought Tesla in 2019 for the car S-curb, we were paying five times earnings. When we were owning Apple, we were paying four times earnings. When we bought Amazon for AWS, we were getting it

for free. The world doesn't think exponentially, and they're so focused on the next year, the next quarter, very few people believe you can accurately predict two, three, four years out. But if you follow and understand the S curve, you know the MOTS and you know how to model, you really can predict these great things. So let's go to the S curve. So the S curve is crucial, because because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies,

but it wasn't until Chatchip T took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical because there were so many barriers to adoption. The first smartphones, they were clunky, they didn't have touchscreen. There wasn't a wireless data system, and then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T at a 3G network.

It was touchscreen. It was so easy your grandmother can do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated, and then you rocket when those barriers are removed, that's the tornado of demand, that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the

inflection. Now, the other nuance, it's not just, oh, it's taken off now. It's how big is this S curve, how tall it is, so you know when to sell, how long to hold on, because we're underwriting out two or three years. We have to know what the growth looks like thereafter. And these S curves can be dynamic. So when Amazon had AWS and it was a hidden line item inside of Amazon covered by retail internet analysts, we realized the TAM for this, it was the largest TAM in enterprise IT ever, because previously the 10 was routers, memory, storage, Dell, EMC, but they were doing it all.

You want to know how tall the S-curve is. So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said it's probably going to be 50% deflationary. And then therefore we're one or two percent penetrated. But then over time, we realized it actually wasn't deflationary. If you talk to anybody now, I say if you build it yourself, it's about the same price. So that means the Tam was so much bigger. There's mega S curves and there's sub-ass curves.

We've been lucky that we've had Internet 1.0, mobile, cloud, e-commerce, and now AI, which we can confidently say is the biggest. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention, too, because we thought, probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at 10 or 15%. Usually the S curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally, when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the cell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth. And it was a mistake with

Apple because in the first five or six years of Apple, it was awesome. I mean, it was our largest position and would go of 50, 70 percent a year except for 08. And then we sold in 2012 when it got to sort of 50 percent of the U.S. had a smartphone. And with Apple, they maintain their leadership position. It had a couple of years of underperformance. And then the multiple got low. And they added. added several ancillary things, and then they also got to play in the application because they got 30% of the app. So they were able to compound very nicely, say 20%, but the big years were in zero to 50% part of the curve. I'm so fascinated by this sometimes decade plus long flatline at the beginning of one of these curves, which makes me wonder what you've learned about the right moment

to buy or even start paying attention before you buy. How do you measure that? Is it always different? What are the pitfalls that you've fallen into? How do you know when start thinking about buying one of these things? Andy Grove says when you have strategic inflection points, you can't trust the data. And strategic inflection points are about intuition, anecdotal evidence. I love this book called the Tao Jones averages a guide to whole brain investing, which is right brain and left brain. And the best investors have the right, the creative side where it's visual.

It's connecting the dots. We invested in the mobile video game S-curve for so long. The screens were small on the phones and the processing power wasn't good. So you had all these casual games. But then I was in China and I saw this little 12-year-old boy with a huge phone and he was like playing a awesome video game. I'm like, oh, my God, it's now coming to the phone. So it's visual enterprise is hard because you can't see it. Smartphone, you can see, oh, my God, I got it.

It's amazing. With AI, there's some intuition there. But Enterprise, you don't really get to do that. We go to the Gartner IT Symposium. 30,000 American CIOs go there. We saw this happen with Splunk, where that used to be an amazing database company and their room where they were explaining

it was like standing room only. We saw that with VMware, 30 years ago, where they virtualized a server. There was standing room only, and you could just see the corporate demand. just beginning. And with AWS, we went there and their grand ballroom was completely packed. And that was a 9 o'clock. And a 10 o'clock, the grand ballroom was completely packed. 11 o'clock. So you could see the demand exploding before it happened.

We look for all kinds of clues and there's a whole pattern recognition that happens. And by the way, it's okay to be late. It's okay to miss the first one, two, three years in a lot of cases, because if the top of the S curve is half a trillion, the growth can go on for a long time. So you don't always have to be right there. It's okay to miss the first 100%. Peter Lynch, I started a fidelity, and he loved to mentor the kids. So I got some time with him. He said, white out the chart. It's all about the future. So it's okay to miss. But what helps about the S curve is sort of how long it goes for. Then there's the slope of the slope of the the S curve, which is important. And a lot of people think, because we're in a modern world,

everything's so fast. But there's a lot of factors that determine the pace of the adoption. And we commissioned this gentleman Horace Daydu used to work with Clayton Christensen to go look in history. And we have the big S curves on our wall over the last 100 years. And the radio S curve was one of the fastest ever. It took seven years to reach like 100 percent penetration. But the dishwasher S-curb is like that because it needs to be plugged into the back end. B-to-B stuff can take a long time because it needs to be plugged into the existing systems. Consumers generally tend to go a lot faster. I love that. The radio and the dishwasher, the two models for adoption. I covered Internet of Fidelity. My first stock was Amazon. That's a whole

other story, which is a lot of fun. But I also did B-to-B Internet. And There was a whole huge bullcase on that. The underlying infrastructure wasn't in place for B2B to happen. Ultimately happened 20 years later with SaaS. That is a risk with AI in that these big companies are very security conscious, can be slow to move. There's a lot of cultural issues with AI where you really need a few evangelists to push it through. The top management needs to push it through. within the ITs saying this is risky.

And that happened with Cloud too. That was one of the big things with Cloud where everybody was afraid it's unsecured to have your data in the cloud. And then we saw the CIA do it. And we saw Capital One. And we talked to the Capital One CIA. I always said it's more secure in the cloud. And then it really started to take off.

Those takeoffs maybe because SaaS is like the dishwasher and because Cloud it's got to be plugged in, it meant that, yeah, it was growing, but it was sort of a thing. 30 to 40, maybe a 50% growth rate. But what's amazing about AI is you just, at least with consumers or even business, you just open up the browser and it's there. And so that's why we're getting this straight up. And I think there's enough runway in the near term going from 10 bibs of people really using it to 2 to 5 or whatever, which is going to cause it to keep on going straight up. This we call it a backwards L curve. So it's really pretty exciting. What have you learned about when the group that ends up being the leader separates itself from one of these competitive packs?

So you're talking there mostly about overall growth of the S-curve in demand. There's always multiple players fighting for it. It seems like you kind of invest after someone has separated themselves from the pack, not try to pick the winners from the pack. You look for the S-curve. Then we do an exhaustive study of everybody with exposure in that. area and try and find the one with a very powerful competitive advantage. And a lot of people didn't like tech. Warren Buffett didn't like tech because he couldn't predict the future. Change too fast. Yeah. And so the S curve is our map for looking in the future. Now, a lot of people were worried about

tech because they thought there was so much disruption you could never trust a company to be a long-lived asset. What we found over the years is some of the competitive advantages within the digital world are, more powerful, if not equally or more powerful than in the offline world. You've got the network effect that was so powerful for LinkedIn, Facebook, Alibaba, you name it. Then you can become an industry standard. Oracle and Bloomberg are the industry standard. Oracle charge a lot and there's free versions. There's open source Oracle, but they had all the database administrators. They had all the software that was tuned to work with them. They basically had a chokehold on the relational database market forever. You can get to scale very quickly because these S curves grow and all of a sudden Anthropics

is doing 30 billion in sales or Amazon had so much scale and they got it quickly. So they got a Walmart size scale advantage in five years versus 40 years for Walmart. So you can have network effects, scale. You can become industry. You can be a platform that people build on top of. You can have critical intellectual property, which was what Qualcomm had. You couldn't make a phone without paying them. Or ASML has critical intellectual property. You can't make a chip without their lithography.

You can also have brand. And brands very important because Google, Amazon, they got to grow. They never had to advertise. Elon's never had to advertise for anything. And cost to acquire versus lifetime. It's the whole business. model. Almost all the companies I mentioned have all of these rolled into one. Sometimes we can notice these things before the rest of the world. One of our high points was we pitched Amazon for

AWS at 2013 at the Robin Hood Investors Conference, and we said the Bulls have no idea what they're sitting on. Amazon's won the war before it even started. And at that time, we said there's Coke and there's no Pepsi. It did turn out there was Pepsi, but it was big enough to last. And we could see they had a seven-year lead, so first mover is important. Then they became a whole ecosystem and a platform. Then they got scale. So they were 10 times the size of everybody else. Nobody could invest in the R&D to catch them. But you're right that if you don't have a competitive advantage, you can be in the best S-curb of all time. And still lose out. But if your name was RIM, Pong, Nokia, HECC, LG, Motorola, I can go on forever. Zero, zero, zero, negative, negative, negative.

And that's what we saw at the foundational model layer where there's like 50 companies trying to do that, and they all have fallen away. And two or three have emerged at the top. There's a lot of reasons to think they will continue to hold their position. Google's a little trickier because they have this other huge, massive, complex business attached to the Gemini business. But if you take Anthropic and Open AI as pure plays and you dig through those and you reason through their competitive advantages, Why aren't they susceptible to erosion of those things? Of all the S curves we've done, AI is by far the most complex and the fastest changing. We have to keep in mind that there are risks.

The rewards are the highest because we're talking about a market in the trillions, maybe clouds 800 billion. This might be, we now think three to five, but there's higher risk, high reward. But let's just say with Anthropic now, it looks like they have critical intellectual property. Generally, they've been able to maintain their high market sharing code. Number two is they've built a strong brand for Enterprise to where go talk to any CIO. And the first thing they'll say is clawed. Three, they're going to have escape, velocity, and scale. And what was scary for Open AI and Anthropic fighting these big companies like Google was they had

these huge cash cows. And to both of the management teams credited OpenA and Anthropic, they were able to work in these super capital intensive industries and find ways to raise capital. And certainly with Anthropic, with their 10x sales growth and their fundraising ability, it looks like they've reached escape velocity. So now they have scale. And the other thing that Anthropic and Open AIA could have is Anthropic. Now that they're leading in code, they set that code back onto their model, and it's this concept of the recursive improvement. And if you look at the pace of their innovation, it's accelerating. Maybe they can have this liftoff stage. Open AI, they were focused on so many different other sectors. They're starting to do better in enterprise and their

coding tools good, and they're starting to see accelerating growth on that side. And then, look, the consumer franchise, it looks like Enterprise right now is much better because you and I were willing to pay a lot because it's replacing human beings. Now, consumer, maybe you can get advertising, but maybe they would pay for a claw bot type assistant if you could make that perfectly well for them, but they have gazillion eyeballs there. Things do shift. We have this, charts that we almost do for all of our pitches. On the internet, the leader goes bigger, faster, and wins. Most of the time the leader gets at Shopify becomes the leader, just keeps ongoing. Amazon, the leader keeps on going. SaaS company, XYZ, you get the lead. It compounds

on itself. And another thing is you need to be big. Another is scale. You need the compute, and you've got to pay for the compute. So there's only so many people that can do that. Those are some of the modes that we think are now showing up in this business. Now, there are some exceptions to that rule, usually with the paradigm shift, AOL, and then Dialup went to broadband, and they didn't make the change. NetScape came out early, and it wasn't as strong of a business model. And if you talk to anyone in the Valley or any startups, they'll tell you that they're building on top of these three, and the world's a huge place, and the economy is a huge place, that they'll be able to differentiate within those. I'm so curious then what you think all of this

means for software. When I look through your portfolio, I don't see a ton of big software companies, enterprise software companies. I don't know if you once had them and sold them, but it's hard to have the experience of building really useful, cool little tools, even if they're still toys, and not have the thought of, wow, if I spend enough time on this, even if I'm not technical, if I could build a ERP equivalent replacement or something for my company, there doesn't seem to be a fundamental reason why that's not possible, and then those companies could be in lots of trouble. It seems like everyone has a strong view on this one way or the other. I'm curious how you've approached those sorts of companies, given that you don't seem to own a ton of them.

Five years ago, we might have had 40 or 50 percent of our portfolio and 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. huge sales forces, they can take these AI APIs and build products and they have the data, this is going to 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 they're smart people on both sides of that, but we are seeing some companies really got their jobs. Freeze hiring or whatever, despite growth. And so that hurts on seats. In terms of them building their own apps, if you want to be optimistic, it's taken them a while to do that.

We talked about how early the primitives of they are. So maybe they have just taken a while to get 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've got to be right at the side to make sure it's really getting done, so you need the FTEs for 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 going to 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 these 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't imagine a world where in 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 billion in sales. You might have $500 of ARR, 700 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 because you've got exposure and you've got a strong market position. The problem with software is their AI's 1 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 Claude? 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. Just to pull in that thread, it 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? 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 bare 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. 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. 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. the category that's interesting stat. What companies shine on that? What are laggards, you know, that are surprising? For the past 40 years, nothing has changed in the data center. Even with cloud, Intel X86 became the data center chip sometime in the 90s. 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 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 we'd create a little cycle, but then it would commoditize. Now you go to AI. The workloads are growing 10X 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, or 10x 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 got to 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 it 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. And then they also had this whole business of selling Ethernet, White Box, which is code word for commodity, white box ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. it's two or three hundred thousand dollar piece of machinery, whereas an old server was $5,000. If it breaks, you just throw it away. If this thing breaks, the whole thing goes down.

So you become a commodity like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and fail. And so they've retained that position. Then it also turned out that the Ethernet market, in the old, days you would go from 100 gig to 400 to 800. It would be a seven-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open-source

sonic layer, the guys that Celestica invented, or some of the people that wrote that open-source software, they worked very closely with Broadcom. What we thought was just a great growth driver turned out to be great competitive advantages, and they have like 50, 60% share of the cloud Ethernet Switch market, which is a crucial market for AI, because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI server, you need a 40 layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own elite materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards.

The PCB units are growing. The layer counts are rising. So you've got like a 50 to 60% Kager just in the units. And then the ASPs are rising. And then the gross profits are rising. And your visibility, which used to be, hey, we'll call you next week if we need you to like, hey, we need you for the next. four years to be like designing this roadmap with us. You've gone from a 5% grower with low margin to a 35% 40, 50 top line Kager for the next four years with rising margins. On top of that,

there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like, I mean, Corning, they make the fiber, they've got some ridiculously high share of the fiber. I was reading this Microsoft Data Center they just built, there's enough fiber to circle the world four and a half times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs. And it's higher margin. And it's the fastest growing part of their business. In networking, there's scale out. which is connecting all the server racks together.

Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters, and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth. And where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack

to the other ones, that's done over copper. Eventually, that'll be done over fiber. When that happens, that two to three X's Corning's opportunity. So you just have, at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every invidia chip or rack uses 50 to 125% more power.

That drives the ASPs of Delta, and advanced energy, I can't believe these stories when I hear, I'm like, wait, so your ASPs are going to like go up 40% for the next four years in a row and it's higher margin? The broader pictures, the AI demand, if we're right with this L curve, we're already short, the DRAM market, the NAN market, the PCB, we're already 30% short all these things as we are now. A measure of percent AI, percent market share. Do you care more about the absolute or the rate of change of those metrics? We did this presentation in 2024 where we listed everybody's market share and then I asked Claude to plot it to a thing.

And it actually didn't get it right because what it didn't get is the rate of change. So the rate of change is important. And that's incredible too because you go from 10 percent to 30 percent and your growth rate accelerates and your margins accelerate. So rate of change is very important. Your finance team isn't losing money on big mistakes. It's leaking through a thousand tiny decisions nobody's watching. Ramp puts guardrails on spending before it happens. Real-time limits, automatic rules, zero firefighting. Try it at ramp.com slash invest.

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Why don't more people get this right in public markets? If your whole framework is S-curve, competitive advantage, underappreciated earnings power, it feels like the movie's been played out a lot over the last 25, 30 years. My mom said, why do you tell everyone your secret? It's like,

why does the casino teach people out of play blackjack. It's really hard to do. You have to be comfortable investing. I've been doing tech for 20 years at Whale Rock. We've got a team that's been doing this covered many cycles. No one's paid attention to hardware and chips at all. So you've got all these newbies coming into it. You and Gavin. That's it. 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 bind. 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 got to be a bubble. And then they had another great year, and it's like six months of marking time. It's got to 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. What makes you the most concerned or uncertain? Is it just the rate of which all this

stuff changes and what keeps you worried amidst what seems like pretty extreme bullishness? 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 adoption 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 Anthropics sort of hits a wall and stops improving or open AI, then the open source models will catch up. It might be a race to the bottom and it 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 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 going to be a waste of our research. sources. We watch that very carefully. In general, we see more companies truly going after this, and even Microsoft trying to build their own. Those are some of the key risks. 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. I 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 that the application, later that are actually getting used by consumers? 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. To date, 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 and 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. 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 when you were by yourself. But you've got this now long 20-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 going to 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 Clod Code or something today. How is the nature of research and what gets you on that Whale Rock Award wall changing in real time? 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 analysts. 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 1950s. 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 Apple 11 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, which is historically a terrible industry.

But Michael and Sam really figured out the Apploven 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 Kahn. And we talked to scores and scores of people. also did the work on the model and developed a great relationship with Adam Farooggi.

He's one of the best managers out there. I don't see AI doing that. What role does talking to other investors outside of your firm play in your life? 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 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.

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 want to give you money, there's a couple of 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? So the first 15 years, it was a long, short fund. And we want to 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 10 years in, people started to ask for a long only product. 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 in the privates until 2020. We just think there's a huge structural underweight of the largest tech companies in the world. We also realize 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 realize they have 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 cap. 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 going to have 15% in Nvidia and all these other things. we realize that people are worried that there's these big companies. This is just a product of the digital economy in 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 going to lead to massive profit pools and massive market caps. And it's just going to happen in the future. Most endowments are betting against this because they're

completely underweight this. I think there's tremendous alpha in the largest cap, because if you think about it, a small cap, it just takes one person to figure out it's good and move it up. But it takes 100 people, 100 diversified PMs to realize Google's not a loser. It's a winner. And can we figure that out before 95% of those generalist PMs? And we've been able to do it. You like your odds in that. Yeah, we like your odds in that. And so there is alpha to be had there. And then as an asset category, it's great because these companies, by definition, have wonderful moats. And maybe they're not the super S curve, but sometimes they are. I mean, NVIDIA sure is.

And TSM is really levered to it. And Hynix is extremely levered to it. And ASML is levered to it. We're four months into that one. It sort of sounds like really what you've built as a research machine to understand the world through the lens of companies. The thing you're constantly trying to improve is that research machine. And then the way that you then express that through products is multiplied. But if I was to try to understand Whale Rock, it would be to investigate the research machine first and foremost.

We call it the Whale Rock Learning Machine, and it's a group of 10 highly experienced individuals. Warren Buffett reads books and we read books and we read blogs. In tech, you've got to go out and talk to people. So we do 2,500, 3,000 face-to-face meetings with management teams. Munger and Buffett talk about compounding knowledge. We've been compounding that knowledge for 20 years. There's changes to the team, but broadly there's a lot of consistency to it. Andrew and Michael have been with me for 18 years, and the average experience level on the team is 10 or so years, and that includes some of the newer people.

That research engine can support all these products, and it's the same people that do publics and the private. So we're not going to scour the world and turn over every A, B. But when we see something that fits in our system, we're able to act on it. It's so much fun to do this with you. When I do this, I ask the same traditional closing question of everybody. What is the kindest thing that anyone's ever done for you? It's definitely my father. I was super lucky.

My father graduated Cornell, a double-e electrical engineering, pivoted to Wall Street, and had a great career at Goldman Sachs. He ran corporate finance in the 80s and then ran private equity as chairman in the 90s. He was just whip smart, but he had such humility and was such a great gentleman. When I started Whale Rock, friends and family, he was the first call, but he said, you know, I've been at Goldman for 41 years. How about I come and join you? I'll be the gray here.

I'll be the oversight. I'll be the chairman. You do what you do. You build the firm in Boston. the team, run the money, I'll help raise some money. And we got to work together for six years until he passed away in 2011. But I just feel so lucky to have worked with him. It's not easy running a fun. We never raised our voice. And he was just an amazing mentor to so many people. And when he passed away, I got so many letters from people who said, your father was just

such an influence on me. He was such a gentleman. He was such a great mentor to me. I just feel so lucky to have worked with him. And if I could be half the person that he is, I'd be completely winning. How did he do that? What was his method? Why does so many people say that? He was modest. He was whip smart. He was wise. He was also known as a great investor, which isn't the most common thing at a lot of investment banks. He also was on their commitments committee and kept him out of a lot of tougher situations. And he was very warm and people could go into his office with problems. And he handled it with grace, whether it's a personal problem or a work issue or what have you. And he just had this software. And he also had a great sense of humor. I'm so lucky. Alex, thanks so much for your

time. Thanks so much. If you enjoyed this episode, visit colossus.com. You'll find every episode of podcast complete with hand-edited transcripts. You can also subscribe to Colossus, our quarterly print, digital, and private audio publication featuring in-depth profiles of the founders, investors, and companies that we admire most. Learn more at colossus.com slash subscribe. You know how small advantages compound over time. That's true in investing and just as true in how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default, better data, better decisions, better economics over time. See how at Ramp.com slash invest. As your business grows, Vanta scales with you, automating compliance and giving you a single source of truth for security and risk.

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