Why the AI Boom Is Just Getting Started

When you get to the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, your earnings don't grow linearly; they grow exponentially. The world doesn't think exponentially, and very few people believe you can accurately predict 2, 3, or 4 years out. But if you follow and understand the S-curve, know the moats, and know how to model, you really can predict these great things.

The enterprise AI, or enterprise application AI, market is less than 1% penetrated. We've never seen—when we talk about S-curves, we call this an L-curve—just straight up. Alex, you were saying that your highest-conviction position is Anthropic right now. Can you tell the story of discovering it, making the investment, and use this anecdote as an excuse to talk about all the things that I think you and I are mutually interested in right now: investors like you investing in private markets, Anthropic, the business, AI, everything? It's a great way to zoom in. Why is it your highest conviction, and how did you get started?

Yeah. When the gun went off with OpenAI's 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. In this stack, Jensen talks about it a lot now: there's power at the bottom, chips at the bottom, the clouds, then the foundational models, and then the applications on top.

At that time, in early 2023, we said, “We want to be in the chips and infrastructure first.” Not only do they get the demand first, but we know who the winners are. No matter who wins above—which we weren't sure about at the time—we knew we were going to need tremendous amounts of compute. We did a deep dive into that, which we can talk about later, but over the next 2 or 3 years, we started to get more clarity on how the foundational-model layer would evolve.

At the time, 2 or 3 years ago, there were 60 different companies going after it. OpenAI was kind of in the lead. We did a webinar in April 2023 and said, “Look, this might be winner-take-all. It might be a total commodity because there are open-source players. It might be a race to zero, or it might be an oligopoly where there are 3 or 4 leading players.”

What we saw over the following 3 years was that almost all the startups fell away and died. Then some of the largest companies in the world, including Amazon and Meta, entered the market. Amazon really never showed up. We'll see what happens with Meta, but they came in strong, their effort faltered, and they had to do a total reboot.

In the meantime, Anthropic was this dark-horse candidate, the startup that focused really purely on the enterprise. OpenAI had kind of won the consumer, and 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 3-horse race and somewhat of an oligopoly, very similar to how the cloud market evolved, where 3 companies underpin the entire SaaS cloud world and have really excellent businesses.

We were also aware of the open-source risk from China, and we started to get comfortable that the quality of the tokens from the leading edge was superior. If you're 80% close to the top of the benchmarks, going from 80 to 85 is a huge unlock. The open-source players don't have as much compute, so they can come close to the leading edge, but they can't leapfrog it, and then they falter.

Meanwhile, with the scaling laws and other means of improving the models, the feedback loops, and so on, we saw that there was a very strong runway. Everyone we talked to who was close to the industry saw that the scaling laws would continue. So we developed this thesis that it would be a 3-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. We made large investments because we knew the training would be there, but we weren't sure how much revenue might come from it or whether it could truly replace labor. If you remember, the early versions of the models were good, but there was a lot of negative feedback from corporates about whether they could be truly agentic.

We realized in 2025 that the first Claude Code and the coding tools really began to explode. The first generation was Microsoft Copilot, which was $20 a month. It could sort of improve your grammar of coding, maybe find a bug, or maybe make a block of code like a paragraph. Then Anthropic came out sometime in the middle of the year, and it could do so much more. It started to get to the point where it could run agentically, and we saw that happening. The coding market just exploded.

Then we started hearing that people who could use it unfettered were spending $100 a day on tokens. 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. If you think about how many coders there are in the world—20 million—you've got a half-trillion-dollar market just from coding alone. Bear in mind, that was on 7-, 8-, or 9-month-old technology. We could see, just in the coding market alone, that Anthropic had a tremendous opportunity ahead of it.

At the time, this is pretty funny, we wrote in our letter that we made the investment at the $180 valuation. We said—and I think they were hoping to get to a 9—

One to 9.

Yeah. The numbers were like nothing we'd ever seen before: 100 to a billion on the way to 9. But when we did it in August 2025, nobody had any idea what 2026 could be.

The second big unlock lately is that Claude Code has gone to being almost completely agentic. You had Andrej Karpathy and Linus Torvalds, 2 of the smartest people in coding, saying that last year's coding tools could write 20%, while 80% would be handwritten. That flipped when the latest model came out, and now Karpathy hasn't written a line of code—not except in English. Not to mention the pure unlock we're going to get for the people who never knew how to code. Just coding alone has completely taken off.

Anthropic has been able to stay ahead in coding. One difference between the cloud companies—GCP and AWS—and the AI companies is that the cloud is generally a 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, and there's tremendous differentiation within them.

There are different training methods and different skills that they're good at. A lot of people have routers that switch in between, which sort of makes it sound like they're commodities, but Anthropic is very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of differentiation and critical IP, which is a great competitive advantage. Many companies have come after the coding franchise, and Anthropic has been able to keep ahead.

The other thing that's good about the foundational models and Anthropic is that it's not just the API or the model. They're building a whole monopoly, or a whole ecosystem, of products around the API. We've got the SDK, Claude Cowork, the orchestration layer, and all the tools. They call it a harness, which is the software around the API that gets the most out of the model.

This was one of the things we saw with AWS really early on, in 2013. People thought it was a commodity server up in a warehouse—big deal—but what they saw was a new way of doing computing. So they invented all these products that they could see before everybody else, which slowly built lock-in.

1. AI's L-Curve

The other way we think about this is: where are we on this S-curve? We have this infrastructure-layer S-curve, which we think is somewhat 10% penetrated. 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.

Even though 800 million people are using AI, they're just using AI 1.0, which is like a search engine on steroids. Now, with these new primitives, where you have Claude on your computer linking it in, you build skills. People and companies are going to start building skills, then they're going to build true AI bots, and then big corporations are going to build much larger ones.

But where are we in terms of the number of people doing that? Sunder said it's 10 basis points of the knowledge workers in the world. Anthropic has something like 14 or 15 million daily active users. Probably a small portion of those are truly doing AI the way you can do it.

That 10 basis points is a classic S-curve. These are the tinkerers, and then it's going to go to the early adopters, then to the early mainstream. You're going to go from 10 basis points to 1% to 2% or 3%, to 5%, to 15% in the next 4 years. A light switch went off this year in the enterprise, where everybody realized they need to do this now and do it fast.

It's like Internet 1.0. You knew you needed a website in 1998, but it was hard to build that website. This is coming together fast.

And so we think the enterprise AI, or enterprise application AI, market is less than 1% penetrated. We've never seen anything like it. We talk about S-curves; we call this an L-curve—just straight up.

Then we'll take this to the infrastructure, where we're even earlier. We're at 10 basis points of people really using AI, and we're already sold out of all the compute. 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 Andre said that, in the next 4 years, one thing he's sure of is that there isn't 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 a lot of the most important private-market companies, how do you get positions at the size that you want, coming from the legacy of being able to just buy?

2. Finding AI Winners

We can talk about Stripe, Databricks, OpenAI, or Anthropic. How much of it is creativity directly with the company? If it is directly with the company, 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 or amount of equity you want in a private company, given that that wasn't your original background?

In that case, we got to know the company. One of our analysts knew people in the finance group there, and we actually had a look at the $60 billion round, but we didn't do it. 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.

One thing about public markets is that you get to know companies over a long period of time, and you can kind of invest on your own schedule. I got a chance to spend some time with Dario. I had obviously listened to him on podcasts, and I started to realize that these guys—their management team is excellent. The focus, the dedication, the fact that they had almost no turnover, the quality of the code, and then the business plan were really starting to play out.

It's one thing to grow from, you know, $100 million to $1 billion, but it's another to do $9 billion. We reached out to the company as much as we could. They took a meeting with us, and 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. We also did our whole overview of what the coding market would be.

They welcomed us into the 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.

In the rest of the world, 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 United Kingdom.

Even before we invested in private companies—the first one was in 2020—we met with these companies. 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 a huge impact. We do 2,000 to 3,000 face-to-face meetings with management teams a year, and about 10% to 15% of those are with private companies. Then we focus on the companies that we really want to learn about and find ways to meet with them and get involved in their rounds.

Our first one was Stripe. We had a large investment at the time. This is 2017, 2018, 2019, and 2020. We owned Adyen, which is a fantastic payments company. They're a next-generation cloud-payments company taking share from Worldpay.

Modern cloud payments were 5% of the total, you know, $80 trillion market, or what have you. But you can't invest in Adyen unless you know Stripe like the back of your hand. So we did tremendous amounts of due diligence and talked to 200 customers about Adyen. When we asked about Adyen and Stripe, we realized this is Coke and Pepsi, and we said, “We've got to find a way to invest.”

I finally got to meet the Collison brothers in 2019, and so that was our first one. We weren't really known for private investments. I have a friend who's involved with a venture firm that had a tremendous amount of it, and I talked to him about it. I said, “Let me know if you ever want to sell some.” Then I got 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—we knew they had disclosed that they had over half a trillion dollars of TPV. We knew that Adyen's take rate was 25 or 30 basis points, and we knew Stripe's was 40 or 50. 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 $550 billion. It was closer to $1 trillion. We underwrote the thing under our assumptions, and it was much better. Then we were able to upsize that from the seller to a $100 million block.

Sometimes they like that the VCs are going to own it and then most of them are going to sell. They like that we'll own it and own it in the public market, which we did with Nubank as well. We also owned it for a long period of time in the public market.

3. Whale Rock's S-Curve Playbook

Maybe now's 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 think everyone knows the basic idea of an S-curve and the stages you mentioned—tinkerers, early adopters, and the early majority. But 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 the nitty-gritty, fine-grained nuance and detail of why S-curves can be so useful for investing.

We have an investment framework. It's S-curve, competitive advantage, and underappreciated earnings power. 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 technology, there are so many of those with so many different types of moats—your earnings don't grow linearly; they grow exponentially.

That's the last piece: invest when there's underappreciated, long-term earnings power. Very often, the earnings can grow from $1 to $10, $50 to $20, and it happens way more than you think. It allows you to buy some of the best companies in the world for extremely low P/Es.

When we were buying Nvidia in 2023, we were paying 4 times earnings. When we bought Tesla in 2019 for the car S-curve, we were paying 5 times earnings. When we owned Apple, we were paying 4 times earnings. When we bought Amazon for AWS, we were getting it for free.

The world doesn't think exponentially. People are so focused on the next year and the next quarter. Very few people believe you can accurately predict 2, 3, or 4 years out. But if you follow and understand the S-curve, know the moats, and know how to model, you really can predict these great things.

So let's go to the S-curve. The S-curve is crucial because every technology follows this pattern where it comes out. Smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI had been hidden inside these companies, but it wasn't until ChatGPT took it public and ignited what it was.

For electric vehicles, Tesla went public 15 years before 2019, when it went vertical, because there were so many barriers to adoption. The first smartphones were clunky. They didn't have touchscreens—not like Apple—there wasn't a wireless data system, and they were too expensive. They were $500 or $600. Steve Jobs got the price to $200.

AT&T had a 3G network. It was touchscreen, and it was so easy your grandmother could do it. Apple built an ecosystem and made it simple, so all the barriers to adoption were eliminated. Then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need right away.

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 is that it's not just, "Oh, it's taken off now." It's how tall, how big, is this S-curve? How tall is it, so you know when to sell and how long to hold on, because we're underwriting out 2 or 3 years. We have to know what the growth looks like thereafter.

These S-curves can be dynamic. When Amazon had AWS, it was a hidden line item inside of Amazon, covered by retail internet analysts, not hardware or chip analysts. It was a new business model, or whatever have you. But we realized the TAM for this was the largest TAM in enterprise IT ever, because previously the TAM was routers, memory, storage, Dell, and EMC, but they were doing it all.

We figured out that they were directly addressing $600 billion of IT systems. Then we said it was probably going to be 50% deflationary. Therefore, we were at 1% or 2% penetration. But over time, we realized it actually wasn't deflationary. If you talk to anybody now, they say if you build it yourself, it's about the same price. That means the TAM was so much bigger.

There are mega S-curves and there are sub-S-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. All these things build upon one another.

With the electric vehicle S-curve, you have to pay attention, too. At the time, we thought probably 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 all the way, but in this case, for a variety of reasons, it didn't. You have to adjust and stay on top of it.

Generally, when something gets to 30% or 40% penetration, you stop having exponential growth. That means the sell side catches up, and there are no longer big beats.

Is that when you sell, typically?

Generally, we like the high growth. It was a mistake with Apple, because in the first 5 or 6 years of Apple, it was awesome. It was our largest position, and it would go up 50% or 70% a year, except for 2008. Then we sold in 2012, when it got to roughly 50% of the U.S. having a smartphone.

4. Spotting Inflection Points

With Apple, they maintained their leadership position. It had a couple of years of underperformance, and then the multiple got low. They added several ancillary things, and they also got to play in the application because they get 30% of the app. So they were able to compound very nicely, say 20%, but the big years were in the 0% to 50% part of the curve.

I'm 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 to start thinking about buying in one of these things?

Andy Grove says that when you have strategic inflection points, you can't trust the data. Strategic inflection points are about intuition and anecdotal evidence. I love this book called The Tao Jones Averages: A Guide to Whole-Brain Investing, which is right brain and left brain. The best investors have the creative side, where it's visual and they're connecting the dots.

We invested in the mobile video game S-curve for so long. Mobile video games had screens that 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 saw this little 12-year-old boy with a huge phone, and he was playing an awesome video game. I thought, "Oh my God, it's now coming to the phone." So it's visual.

Enterprise is hard because you can't see it. We go to the Gartner IT Symposium, where 30,000 American CIOs go. We saw this happen with Splunk, which used to be an amazing database company. The room where they were explaining it was standing-room only. We saw that with VMware, too—I'm talking like 30 years ago—when they virtualized the server. There was standing room only, and you could just see the corporate demand beginning.

With AWS, we went there and the grand ballroom was completely packed at 9:00. At 10:00, the grand ballroom was completely packed. At 11:00, it was completely packed. You could actually see the demand exploding before it happened. We look for all kinds of clues, and there's a whole pattern recognition that happens.

By the way, it's okay to be late. It's okay to miss the first 1, 2, or 3 years in a lot of cases, because if the top of the S-curve is half a trillion dollars, the growth can go on for a long time. You don't always have to be right there. It's okay to miss the first 100%.

Peter Lynch—I started at Fidelity, and he loved to mentor the young kids, so I got some time with him—said, "Wipe out the chart. It's all about the future." It's okay to miss. What helps about the S-curve is how long it goes on for. Then there's the slope of the S-curve, which is important.

A lot of people think that because we're in a modern world, everything is so fast, but there are a lot of factors that determine the pace of adoption. We commissioned a gentleman who used to work with Clayton Christensen to look at history. We have the big S-curves on our wall over the last 100 years.

The radio S-curve is one of the fastest ever. It took 7 years to reach roughly 100% penetration. But the dishwasher S-curve is like that because it needs to be plugged into the back end.

What else did you learn? That's fascinating.

B2B stuff can take a long time because it needs to be plugged into the existing systems. It's like it has to be put inside the house.

The dishwasher.

Consumers generally tend to go a lot faster.

I love that: the radio and the dishwasher, the 2 models for adoption.

I covered internet at Fidelity. My first stock was Amazon—that's a whole other story, which is a lot of fun—but I also did B2B internet. There was a huge bull case on that, but the underlying infrastructure basically wasn't in place for B2B to happen. It ultimately happened 20 years later with SaaS.

That is a risk with AI, in that these big companies are very security-conscious. They can be slow to move, and there are a lot of cultural issues with AI. You really need a few evangelists to push it through, and top management needs to push it through, but then IT is saying, "This is risky."

That happened with cloud, too. One of the big things with cloud was that everybody was afraid it was insecure to have your data in the cloud. Then we saw the CIA do it, and we saw Capital One. We talked to the Capital One CIO, who said that it's more secure in the cloud, and then it really started to take off.

Those takeoffs may be because SaaS is like the dishwasher, and cloud is like the dishwasher: it's got to be plugged in. It meant that it was growing, but it was sort of a 30% to 40%, maybe a 50%, growth rate.

What's amazing about AI is that, at least with consumers or even businesses, you just open up the browser and it's there.

The browser.

That's why we're getting this straight up. I think there's enough runway in the near term, going from 10 basis points of people really using it to 2% to 5%, or whatever, which is going to cause it to keep on going straight up. We call this a backwards-L curve. It's really pretty exciting.

What have you learned about when the group that ends up being the leaders separates itself from one of these competitive packs? You're talking mostly about the overall growth of the S-curve and demand. There's always multiple players fighting for it. You've invested, it seems like, after someone has separated themselves from the pack, rather than trying to pick the winners from the pack. Is that roughly correct?

We're definitely looking for the S-curve. Then we do an exhaustive study of everybody with exposure in that area and try to find the one with a very powerful competitive advantage.

A lot of people didn't like tech. Warren Buffett didn't like tech because he couldn't predict the future—it moved too fast. The S-curve is our map for looking into the future. A lot of people were worried about tech because they thought there was so much disruption that you could never trust a company to be a long-lived asset.

What we've found over the years is that some of the competitive advantages within the digital world are more powerful, if not equally or more powerful, than those in the offline world. You've got the network effect, which 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, you know, charges a lot, and there are free versions and open-source Oracle, but they had all the database administrators. They had all the software tuned to work with them, so 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 Anthropic is doing $9 or $30 billion in sales. Amazon had so much scale, and they got it quickly, so they got a Walmart-size scale advantage in 5 years versus 40 years for Walmart.

You can have network effects and scale. You can become an industry standard. 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. ASML has critical intellectual property; you can't make a chip without its lithography. I think what's interesting is that maybe these AI foundation-model companies have scale.

You can also have brand, and brand is very important because Google and Amazon got to grow without ever having to advertise. Elon has never had to advertise for anything. The cost to acquire versus lifetime value—it's the whole business model. Almost all the companies I mentioned, like Apple, 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 when we pitched Amazon for AWS in 2013 at the Robin Hood Investors Conference. We said, “The bulls have no idea what they're sitting on.” Amazon had 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 a Pepsi, but it was big enough to last. We could see they had a 7-year lead.

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: if you don't have a competitive advantage, you can be in the best S-curve of all time and still lose out.

And still lose out. But if your name was RIM, Palm, Nokia, HTC, LG, or Motorola, I can go on forever. All negative, negative, negative, negative.

And that's what we saw at the foundation-model layer, where there were like 50 companies trying to do that. They all fell away, and 2 or 3 emerged at the top. There are a lot of reasons to think they will continue to hold their position.

So, to take Google, it's a little trickier because they have this other huge, massive, complex business attached to the Gemini business. But if you take Anthropic and OpenAI as pure plays and dig through those and reason through their competitive advantages, why aren't they susceptible to erosion of those things in the fullness of time?

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, but the rewards are also the highest because we're talking about a market in the trillions. We just said cloud—maybe cloud is $800 billion. This might be, we now think, $3 to $5 trillion, but there's higher risk and higher reward.

Let's just say with Anthropic, now they have what looks like critical intellectual property. Generally, they've been able to maintain their high market share in code. Number 2 is that they've built a strong brand for enterprise, to where you go talk to any CIO and the first thing they'll say is Claude. They're going to have escape velocity and scale.

What was scary for OpenAI and Anthropic, fighting these big companies like Google, was that those companies had these huge cash cows. To both management teams' credit, OpenAI and Anthropic were able to work in these super-capital-intensive industries and find ways to raise capital. 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.

The other thing that Anthropic and OpenAI could have is this: Anthropic, now that they're leading in code, can feed that code back into their model. It's this concept of recursive improvement. If you look at the pace of their innovation, it's accelerating.

And so maybe they can have this liftoff stage.

OpenAI has been focused on so many different sectors, but they're starting to do better in enterprise, their coding tool is good, and they're starting to see accelerating growth on that side. Then look at the consumer franchise. It looks like enterprise is much better right now because you and I are willing to pay a lot because it's replacing human beings.

For consumers, maybe you can get advertising, but maybe they would pay for a Claude-bot-type assistant if you could make that perfectly well for them. They have a gazillion eyeballs there. But you're right, things do shift, though it usually goes this way: we have these charts that we make for almost all of our pitches. On the internet, the leader goes bigger, faster, and wins.

It's happened most of the time. The leader gets it. Shopify becomes the leader; it just keeps on going. Amazon, the leader, keeps on going. A SaaS company—XYZ—you get the lead, and it compounds. An internet company compounds on itself.

Another thing is that you need to be big. Another is scale. You need the compute, and you have to pay for the compute, because there are only so many people who can do that. Those are some of the moats that we think are now showing up.

5. AI vs Software

There are some exceptions to that rule, usually with paradigm shifts. AOL and then dial-up went to broadband, and they didn't make the change. Netscape came out early, and it wasn't as strong of a business model. But I think if you talk to anyone in the Valley or any startups, they'll tell you that they're building on top of these 3. The world is a huge place, and the economy is a huge place, so 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, or how you thought about it, 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, “Wow, if I spend enough time on this, even if I'm not technical, maybe I could build an 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.

At certain points, maybe 5 years ago, we might have had 40% or 50% of our portfolio in software. Early on, in our April 2023 seminar, we said, “Definitely invest in chips first,” and at the application layer, initially we thought these companies were huge. They had huge sales forces; they could take these AI APIs and build products, and they had the data. This was going to be amazing for software.

Pretty quickly, we realized their AI products were not very good. They weren't moving the needle, and nobody could charge for them. We basically sold almost all of our software, almost all of our application software. We still have 1 or 2 small ones, but entering this year, we were actually net short. It really helped us in the first quarter.

There are so many layers. The old way of software is like using a pen and paper, or it's like a horse and buggy. The new way of software is like a jet engine or, frankly, like the transporter from Star Trek. It's so revolutionary and changing that it feels like it has to be disruptive now, even if it's not disruptive now or right away.

The software companies have another problem, which is that their place on the to-do list, or priority list, of any CIO has fallen a lot. Even if AI is not going to be disruptive, companies are spending 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 prices every year, and now they're probably nervous about doing that.

Then, fourth, we'll see what happens with jobs, because I don't know—there are smart people on both sides of that. But we are seeing some companies really gut their jobs or freeze hiring, and so that hurts on seats.

If you want to be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are. Maybe they've just taken a while to get to something they can commercialize, but they might not have the right people. They might not know that it's a different selling motion from selling a fixed system. If you're installing something that does human work, you've got to be right at their side to make sure it's really getting done.

So you need the FDEs, or forward-deployed engineers, and they might not have the right people internally to do that. Then, of course, there's the risk that you can build it yourself. The bulls will say, “Well, they're never going to build their own ERP system.” That's probably right.

It is true that old technology is very sticky. Mobile video games didn't hurt console games, the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integration and work that goes into this software.

So that's all true, and companies do like to buy from others; they don't like to build themselves that much. But you can't imagine a world where, in 1, 2, 3, 4, or 5 years, you could have a brand-new AI-native company going after each one of these very strong incumbents, and their data advantage could get obviated. It might be easy to take the old one out and put the new one in with AI and such.

What's good, if you like them, is that 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, and now you might have $500 million of ARR, $700 million of ARR of AI. So you've got this huge base. Now maybe this starts to work, but it takes a while.

In software, there's the Rule of 40, which is your growth rate plus your operating margin. If you've got a 20% growth rate and a 20% operating margin, that's good. For AI, we have a new kind of Rule of 40. We call it—well, it's really for chip investing—but if what percentage of your sales are AI, say 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 a strong market position.

The problem with software is that their AI is 1% or 2% at this stage, and it's a long way to go. One thing we're picking up lately—and this is half-baked—is that AI could make some of these software platforms more important. 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.

Maybe these agents—the next wave of AI will be these agents that use tools—might operate inside the existing incumbent software tools and 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. Slack is a great example. The software in Slack itself leaves something to be desired. It's not that the software is the special part; it's that everyone is there, right?

But I'm curious: What kinds of things would you want? Is it just network—the presence of a network effect? Is that the only thing that really matters?

It's still early in our thinking here, but maybe even Workday or the HR systems, or the big systems of record—the agents may be running on top of them. CRM is going headless, or they're making a headless version, and that's sort of the bear case too: that you get relegated to just being a database.

There's a human interface to it, and then they need to make the AI interface, which is no interface. It's just them going right into the data. So 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.

6. The Hardware Renaissance

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 40 for the percentage that's AI and the percentage of market share in the category. That's an interesting stat.

What companies shine on that today? What are laggards that are surprising?

For the past 40 years, nothing has changed in the data center. Even with cloud, we're basically Intel x86. It became the data center chip sometime in the '90s. Compute grew in the cloud era, and compute workloads grew 25% to 40% every year, but Moore's Law was improving at that rate. So it didn't require tremendous innovation.

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, the memory, the enclosures, the networking. There was no innovation. You would go from 1 gig to 10 gig; that would take 7 years. When you do switch, in the first year it does take some innovation to get to 10 gig, and it would create a little cycle, but then it would commoditize.

Now you go to AI, and the workloads are growing 10x every year. They're pushing every single aspect of this hardware to the physical limits of what it can do. So not only are you creating tremendous unit growth, but you're also getting what we call the decommoditization of the hardware industry.

I met with Shawn Maguire 3 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.” Sequoia made some of its best investments back in the hardware days with Apple, Cisco, and others. We're in this renaissance of chips.

Not only do you have tremendous unit growth, but it's requiring tremendous innovation at every aspect of the server. Memory, which used to be a pure commodity, now has high-bandwidth memory stacked with 10 chips on top. The inputs and outputs are 10x what they were before. It took Samsung years to do it, and it's a critical, critical piece. It's constantly upgrading, so they've got to be working with NVIDIA for 3 or 4 generations in advance.

We had this with Celestica. Celestica was a contract manufacturer, and this had been a disaster industry since 1999. It all went offshore to China. It was a commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill.

Then we noticed they were the sole supplier of the Google TPU server. We were like, “Oh my God, this was 3 years ago.” The stock was trading at 8 times earnings. They also had this whole business of selling Ethernet white-box—which is code for commodity white-box Ethernet switches—into the clouds.

It turns out that these are excellent businesses. Not only do they have tremendous growth, but to do an AI server computer, it's liquid-cooled. It's running so much hotter, and it's a $200,000 or $300,000 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 like critical infrastructure, 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 failed, so they've retained that position. Then it also turned out that the Ethernet market was changing. In the old days, you would go from 100 gig to 400 to 800. It would be a 7-year cycle to upgrade. Now they're upgrading every year, and that's really hard to do.

Then there's a whole software layer, the open-source SONiC layer. The guys at Celestica were some of the people who wrote that open-source software. They work very closely with Broadcom. What we thought was just a great growth driver turned out to be great competitive advantages, and they have 50% to 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network-intensive.

Even something like the printed circuit board: a regular server needs 10 layers; these AI servers need 40 layers, and there are very few PCB suppliers that can make this. There are all kinds of complexities in there. We also own Elite Material, which makes the leading ingredient, copper-clad laminate, that goes into these boards.

The PCB units are growing, and the layer counts are rising. So you've got a 50% to 60% CAGR just in the units, and then the ASPs are rising, the gross profits are rising, and your visibility—which used to be, “Hey, we'll call you next week if we need you”—has become, “Hey, we need you for the next 4 years to be designing this roadmap with us.”

So you've gone from a 5% grower with low margins to a 35%, 40%, or 50% top-line CAGR for the next 4 years, with rising margins. On top of that, there are shortages of everything. So even if it is a commodity, it's going to be a great cycle. We see that up and down the supply chain.

You find these companies like Corning. They make the fiber, and they've got some ridiculously high share of the fiber. I was reading about this Microsoft data center they just built. There's enough fiber to circle the world 4.5 times in that one thing. Their fiber is thinner and more bendable, and it can be specially manufactured to the exact specifications. It's higher-margin and 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. 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 you need 10 times the wire, and it has to be so much thicker. So that's creating huge growth.

The real kicker comes in 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 2 to 3x's Corning's opportunity. So you have, at every layer of the rack—

Everyone's overwhelmed.

Everyone's overwhelmed. But the story, in the power supplies, is that every NVIDIA chip or rack uses 50% to 125% more power.

And literally, that drives the ASPs of Delta Electronics and Advanced Energy. I just can’t believe these stories when I hear them. I’m like, wait, so your ASPs are going to go up 40% for the next 4 years in a row, and at higher margins?

The broader picture is, what is going to be the AI demand if we’re right with this S-curve? We’re already short the DRAM market, the NAND market, and the PCB market. We’re already 30% short on all these things as we are now.

7. Why Investors Miss AI

When you measure percentage of AI and percentage of market share, do you care more about the absolute or the rate of change of those metrics?

It’s good, because we did this presentation in 2024 where we actually listed everybody’s market share. Then I asked Claude to plot it, and it didn’t get it right because what it didn’t get was the rate of change.

The rate of change is important, and that’s incredible too, because you go from 10% to 30%, and your growth rate accelerates and your margins accelerate. So, rate of change is very important.

Why don’t more people get this right in public markets? If your whole framework is S-curve, competitive advantage, and underappreciated earnings power, it feels like the movie has played out a lot over the last 25 or 30 years.

My mom said, “Why do you tell everyone your secret?” [laughter] It’s like, why does the casino teach people how to play blackjack? It’s harder. 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 and has covered many cycles. We know the differences. Very few people have paid attention to hardware and chips at all, so you’ve got all these newbies coming into it.

You and Gavin, that’s it.

Gavin’s done a great job. People weren’t comfortable with it, and it’s harder to do than it seems. A lot of these companies’ charts are up, so it’s scary: Can I buy?

You also have to have the holistic view, because if you don’t have conviction, every time with NVIDIA over the last 4 years, it’s like, “Oh, they had a great year. Oh my God, it’s got to be a bubble.” Then they had another great year, and it’s like, “6 months of marking time. It’s got to be a bubble. This is getting out of hand. This is pretty scary.”

The bear cases are not totally without merit, but if you can see the whole picture, understand how these things are unfolding, and gain conviction in that, it helps. Frankly, so many semiconductor analysts missed it because they didn’t see what was really happening at the foundational-model layer.

It helps to have the big picture, decades and scores of S-curves that you’re looking at, and an understanding of where they play in different things.

In this whole picture, I would describe your stance so far in the first hour of our discussion as very bullish on the impact that AI is going to have and the returns available as a result. What makes you the most concerned or uncertain? Is it just the rate at which all this stuff changes? What keeps you worried amidst what seems like pretty extreme bullishness?

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. I think Maine just banned data centers, and only 20% of people are optimistic about AI. There’s also the potential for negative regulation. But I do think 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 GPU business—just the graphics chips. “If good enough is good enough, I won’t have a business.” Every year, he made the graphics a little bit better, and people always wanted the best in AI.

If Anthropic hits a wall and stops improving, or OpenAI does, then the open-source models will catch up, and it might be a race to the bottom. It probably won’t be good for the stocks. It could be good for the chip companies. Chip companies don’t care who’s winning tokens, right?

Who wins?

So, that’s another positive, and they’ll benefit if open source takes off. Jensen really wants open source to take off. It’s all he kept mentioning at his last GTC. So, that could be a risk.

Another thing is if 1 or 2 of the players falters, loses its position, and can’t compete. That could be a lot of compute that they don’t need in the future. Now, if AI is so big, somebody else will suck that up. We saw that with Oracle canceling a big deal and then Meta going 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 resources.” So, we watch that very carefully. In general, we see more companies truly going after this, and even Microsoft is trying to build its own. I think those are some of the key risks.

It seems like you’ve really done very little in the application layer of AI. Historically, the applications 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 5-layer cake versus things in the application layer that are actually getting used by consumers?

Yeah. We do. OpenAI has ChatGPT, which is an application, but we think the application layer always comes later. The first 3 or 4 years of the iPhone were like that, and the applications really took time. Maybe it’s just starting.

To date, we’ve 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 competition and build businesses in that?

We thought we would see it in some of the incumbents, like a CRM, and they’re starting. Maybe it’s 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 is not clear.

When we started, the ecosystem in chips was clear. When we started, the foundational-model ecosystem wasn’t clear. Now it’s clearer to us, and at the application layer it’s still kind of unclear and a little bit dangerous. But there will be great application companies built.

We were really watching Bret Taylor at Sierra. Bret was co-CEO of Salesforce, he wrote Google Maps, and he was CTO 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? He’s doing quite well. We’ll see.

8. Whale Rock's Research Machine

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 3 or 4 years. It comes a little bit later.

At your office, you have this giant award wall for research—I can’t remember exactly what it is. It’s for the best research job or project of the year, given to an analyst, and I think you won it. You self-awarded it when you were by yourself, but you’ve got this now-long 20-year history of 1 or more people putting 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 the person who’s going to win the award this year, and the sort of work that requires a human to do, when so much of the work that probably would have won you the award in, I don’t know, 2009 or something could probably be fully automated or done in an hour with Claude 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 so far. I mean, it’s helping us get up to speed, and we have a handful of great apps, but it’s not yet supplanting the job of the analysts.

And so much of what we're doing is meeting with as many companies as humanly possible. We're developing relationships with the management teams that we cover, and 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. It's the scuttlebutt approach.

It's growth investing. It's getting out there and talking to suppliers, customers, and 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 AI 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—and there's a role for that—you need to have, obviously, the insight on top. So, we're now 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.

The AI can be a great reporter. It can't quite peek into the future. And look at the job that the guys did on AppLovin 2 years ago. I think we have 2 of the best ad tech guys around, and they convinced me to buy. I knew ad tech—I started, actually, nearby here in New York, at that internet advertising startup, 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 AppLovin story before anybody, and they followed it when it was private. They know all the competitors, and they know all the intricacies. There's all this terminology, and Sam went to the Las Vegas app advertising conference, and we went to Cannes, and we talked to scores and scores of people.

They did the work on the model and developed a great relationship with Adam Foroughi. 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. 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 them have been on your podcast, and you develop good friendships and share ideas, talk ideas.

It's important that it's a 2-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 3 legs of the stool that 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 1 monolithic structure anymore. There are several things that, if I'm an investor and I want to give you money, I can do. There are a couple of ways I can do that. How did you arrive at those things, and how could you turn that experience into advice for other investors that are trying to provide their LPs with the right set of options?

For the first 15 years, it was a long-short fund, and you want to be focused. If you defocus, that can be hard. So, we grew that and got it 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. So, in 2020, we launched the long-only fund. We're 6 years into that, and that's now larger than the long-short fund.

The bulk of the assets are in those 2 products. In maybe 2015, we formalized that we might be doing privates, and we gave investors the option to opt in or opt out. You could do 15% or 25%, but we didn't break the seal on the privates until 2020. In 2021, we offered a hybrid fund that could be 80% into privates—sort of a similar approach, but if you wanted more exposure to privates.

Very recently, we launched the Whale Rock Mega Cap Tech Fund. We just think there's a huge structural underweight of the largest tech companies in the world because we also realize that a lot of our performance over the years was from some of the largest companies, whether it be Apple, Amazon, or Tesla. People just find it hard to overweight these companies to the amount that they should.

A lot of our largest pools of capital—endowments or what have you—realize they have been massively underweight the largest tech companies in the world. They have a lot of privates. They don't have a ton of public equities, and maybe half the public equities are international. In their public bucket, there's a belief that there's no alpha in large cap.

So, they underweight large cap and have a lot of small- and mid-cap managers that are stock pickers, because it's intuitive that large cap can't have alpha. 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 there's a huge opportunity because people are worried about 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. There are great competitive advantages, and they're also selling around the globe. This is going to lead to massive profit pools and massive market caps, and it's just going to happen into the future.

Most endowments are betting against this because they're completely underweight it. Finally, somebody came to us and said, “You know, what should we do? Which index should we go to?” I'm on the board of Hamilton College, and they were trying, on their investment committee, to figure this out. We kept hearing it, and finally one of our clients came to us, and we said, “We'll do this for you,” because there's a lot of alpha to be had.

The Mag 7, or the FAANG companies, or whatever, are going to be different. In 2022, they all rallied, but last year they were very divergent, and this year they're down. So, we created the Whale Rock Mega Cap Tech Fund. The universe is the top 30 market caps globally, and then we pick the 12 or 13 that are the best.

I think there's tremendous alpha in the largest cap because, if you think about it, with a small cap, it just takes 1 person to figure out it's good and move it up. But it takes 100 people—100 diversified PMs—to realize Google isn't a loser; it's a winner. Can we figure that out before 95% of those generalist PMs do? We've been able to do it.

We like your odds in that.

Yeah, we like our odds in that. So, there is alpha to be had there. As an asset category, it's great because these companies, by definition, have wonderful moats. Maybe they're not on the super S-curve, but sometimes they are. Nvidia sure is, and TSM is really levered to it, and SK Hynix is extremely levered to it, and ASML is levered to it. So, it's a great asset category. That's a new one; we're 4 months into that.

The right way maybe to think about it—it sounds like, really, what you've built is 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 the way that you would 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 blogs, but we're also in tech, so you've got to go out and talk to people.

We do 2,500 to 3,000 face-to-face meetings with management teams, and Munger and Buffett talk about compounding knowledge. We've been compounding that knowledge for 20 years. There are changes to the team, but broadly, there's a lot of consistency to it.

Andrew and Michael have been with me for 19 and 18 years, and the average experience level on the team is 10 or so years. That includes some of the newer people. That research engine can support all these products, and it's the same people that do the public and the private.

We're not going to scour the world and turn over every rock. But when we see something that fits into 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?

I have to say it's definitely my father. I was super lucky. My father graduated from Cornell in 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 whip-smart, but he had such humility and was such a great gentleman. When I started Whale Rock, he was the first call among friends and family. But he said, “I've been at Goldman for 41 years. How about I come and join you? I'll be the gray hair. I'll be the oversight. I'll be the chairman. You do what you do. You build the firm in Boston, build the team, run the money, and I'll help raise some money.”

We got to work together for 6 years until he passed away in 2011. I just feel so lucky to have worked with him. It's not easy running a fund.

We never raised our voices. He was just an amazing mentor to so many people. When he passed away, I got so many letters from people who said, “Your father was 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. 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 did so many people say that?

I don’t know. He was modest, whip-smart, and wise. He was also known as a great investor, which isn’t the most common thing at a lot of investment banks. He was on their commitments committee, which kept him out of a lot of tougher situations.

He was very warm, and people could go into his office with problems. He handled them with grace, whether it was a personal problem or a work issue or what have you. He just had this soft way, and he also had a great sense of humor.

Lucky.

Yeah. I’m so lucky.

Alex, thanks so much for your time.

Thanks so much.