00:00 When you get 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. You know, the world doesn't think exponentially. Very few people believe you can accurately predict 2 3 4 years out.
00:20 But if you follow and understand the Scurve and you you know the moes and you know how to model, you really can uh predict these these great things. the enterprise AI or enterprise application AI market is less than 1% penetrated and we've never seen, you know, 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.
00:58 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, anthropic, the business, AI, everything. It's a great great way to zoom in. Why is it your highest conviction? And how did you get started? >> Yeah.
01:16 Well, when when the gun went off with OpenAI chat GPT in November 2022, we immediately took the firm and did a massive deep dive with our 10 person team. And we anytime you have a new compute paradigm, there's a new stack and on the and and that creates new winners and losers on the old stack. And in this stack, you know, it's 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.
01:52 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 uh 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 2 or 3 years, we started to get more clarity on how the foundational model, the layer would evolve.
02:25 And at the time, two or three years ago, there were 60 different companies going after it. OpenAI was kind of in the lead. And we did a webinar in April 2023. 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.
02:51 And what we saw over the following, you know, 3 years was that almost all the startups fell away and died. And then some of the largest companies in the world including Amazon and and Meta. Amazon really never really showed up. We'll see what happens with Meta, but they were they came in strong and then basically their effort faltered and they had to do a total reboot.
03:18 In the meantime, Anthropic kind of was this dark horse candidate, the startup and um they focused uh really purely on the enterprise and OpenAI had kind of won the consumer and then Gemini can never be counted out. We 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 uh cloud market evolved where three companies underpin the entire SAS cloud world and and have really excellent businesses and then we also were aware of the open- source risk
04:03 um from China and 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 and the um 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.
04:33 Meanwhile, the scaling laws and other means of improving the models, the feedback loops, etc. Uh 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. So we developed this thesis that it would be a three-horse race. And then the big kicker was code. And this is the true unlock of AI.
04:59 In the first few years, we knew AI would would be big, but we were skeptical. Also, we made large investments because we knew the training was 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 it there was a lot of uh some negative feedback from corporates and could they be truly agentic? We realized in 2025, the first cloud code and and the coding tools really began to explode and you saw the first gen was like Microsoft C-Pilot
05:38 which is like $20 a month and then and then it started and that could sort of improve your grammar of coding, maybe find a bug, maybe make a block of code like a paragraph and then Anthropic came out sometime in in the middle of the year and it could do so much more. Um, and it started to get to this point where it could run agentically and we kind of saw that happening and the coding market just exploded and then we started hearing that people who could use it unfettered.
06:11 We heard we heard that you know even within Anthropic at that time people were spending $100 a day on tokens which if you do the math comes out to 20 or $30,000 a year. And if you think about how many coders there are in the world, 20 million, you've got a half a trillion dollar market just from coding alone.
06:31 And mind you, that was on 7 8 9 month old technology. We could see just on the coding market alone that Anthropic had a tremendous opportunity ahead of it. So I think at the time, this is pretty funny, we wrote in our letter, you know, we made the investment um at the 180 valuation. And we said, and I think they were hoping to get to a nine billion >> one to nine. Yeah.
06:58 And and then 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 of 2025, we nobody had any idea what 2026 could be. the the the second big unlock lately which is that you know claude code has gone to almost completely agentic um where you had Andre Carpathy and Lionus Torvalds last year saying two of the smartest people in coding and they completely flip-fpped and Karpathy said you know last year's code tools could write 20% and 80% would be handwritten that flipped when the the latest model came
07:43 out and now he hasn't written a line of code not except in English and not to mention the pure unlocked that we're going to get for the people that never knew how to code. So just coding alone has completely taken off. Anthropic has been able to stay ahead in coding. And so one difference between the cloud, GCP, AWS, and the AI companies is the cloud's generally it's commodity.
08:15 They're they're selling you servers and storage. You know, they have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity. But there's tremendous differentiation with within. There's different training methods and different skills that they're good at.
08:34 And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity, but anthropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDF. And so there's a lot of like differentiation critical IP, which is a great competitive advantage.
08:55 and companies many companies have come after the coding franchise and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and anthropic is it's not just the API or the model. They're building a whole monopoly or whole ecosystem of products around the API.
09:20 So we've got the SDK claude for co-work uh orchestration layer and and all the tools and and they call it sort of a harness which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was oh people thought it was a commodity server up in a warehouse big deal and what they they saw this was a new way of do doing computing.
09:48 So they had 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 scurve and we have this infrastructure layer scurve which we think is somewhat like 10% penetrated. And by the way we think it's still uh one of the best ways to play AI and we'll talk about how that feeds back through.
10:16 Um but if you think about it, um even though you know 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 a search engine on steroids. But now with these new primitives where you have claw on your computer linking it in, then you build skills. Companies are going to build people and companies are going to start building skills and then they're going to build true AI bots and then big corporations are going to build much larger but where are we in terms of the amount of people doing that? I mean
10:47 Sunder said it's 10 bips of the uh knowledge workers 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 bips, it's classic Scurve 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.
11:12 But you're going to go from 10 bips to one to two or 3% to 5% to 15% 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. It's still >> like internet 1.0 I know when it's like you knew you needed a website in 1998 but it's like hard to build that website but this is coming together fast and so you know we think the I don't the enterprise AI or enterprise application AI market is is like less than 1% penetrated and we've never seen you know
11:54 we talk about S-curves we call this an L curve just straight up and then we'll take this to the infrastructure which is even we're at 10 basis points of people really using AI and we're already sold out of all the there's not enough compute in the world. So Anthropic has half of what they need right now and that's before this huge takeup.
12:20 So Mark Andre said in the next four years one thing he's sure of is there's not going to be enough compute. >> Most software companies try to maximize your time on their app to juice engagement. Ramp does the exact opposite. [sponsor — Ramp] OpenAI, Cursor, Enthropic, Perplexity, and Verscell all have something in common. They all use work OS. [sponsor — WorkOS] Every investor should know about Rogo [sponsor — Rogo]
14:16 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 Data Bricks or OpenAI or Anthropic. How do you get the positions at the size that you want coming from the legacy of been being able to just buy? How much of it is um creativity just directly with the company? If it is directly with the company, so they have it's a double opt-in. they have to
14:45 decide to let you in too. How do you do that? Like what what have you learned about getting the allocation you want or the amount of equity you want in a private company given that you know that wasn't your original background? >> In that case, you know, we we got to know the company. One of our analysts knew people in in the finance group there and we actually we had a look at the at the $60 billion round and we we didn't do it.
15:12 we didn't know the company as well and we um the gross margins were negative and and and frankly we hadn't seen coding explode the way it had and 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. I got a chance to spend some time with Daario.
15:35 I obviously listen to on podcasts and it 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. And uh it's one thing to grow from, you know, 100 to a billion, but it's another to do nine.
15:54 And then so we reached out to the company as much as we could. They took a meeting with us. We did a 90page PowerPoint deck where we used Claude Code to scour the internet for all the feedback we could about the coding market. and their 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.
16:19 They welcomed us into this round and then we stayed close with the CFO and uh it's it's been great to build a relationship with them and I think we p punched above our weight in terms of the allocation. So that one was a total home run. In the rest of the the world, we are in this period where the unicorn market is bigger than most stock markets in Europe, maybe even combined.
16:41 It's definitely bigger than Germany. It's definitely bigger than the UK. And we even before we invested in privates, the first one was 2020. We meet with these, 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 and have huge impact.
16:59 So we, you know, we do two to 3,000 face-to-face meetings with management teams a year and about 10 or 15% 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 and uh get involved in their rounds. And our first one was Stripe.
17:23 And we had a large investment at the time. This is 20 2018 2017 1819 we 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 you know the cloud the cloud modern payments was 5% of total you know $80 trillion market or what have you but you can't invest in aud unless you know Stripe like the back of your hand so we did tremendous amounts of due diligence talked to 200 customers in Audon but when we asked about audio and we asked about Stripe and we realized this is
17:58 Coke and Pepsi and um we said we got to find a way to invest and I finally got to meet the Coulson brothers in 2019 and so that was our first one. We weren't really known for privates. I've got a friend um who's who has a involved with a 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 co in April of 2020. We knew a lot about Stripe.
18:30 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 they disclosed we had over half a trillion of TPV. And we knew that Audience's take rate was 25 or 30 bips and we knew Stripes was 40 or 50. And we knew how many employees they had.
18:50 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 550. It was closer to the one 1 trillion. And you know, we underwrote the thing under our assumptions and it was much better.
19:07 And then we were able to upsize that from the seller to a $100 million block. Sometime they like it that you know 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 uh as well. all owned it for a long period of time in the public market as well.
19:26 >> 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. >> And I think everyone knows the basic idea of an S-curve and and the sort of uh the stages you mentioned, tinkerers and and early adopters and early majority.
19:53 But I'd love you to go into the 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 grading nuance detail of why S-curves can be so useful for investing. We have an investment framework. It's >> S-curve and we'll dive into each one.
20:13 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 modes, uh your earnings don't grow linearly, they grow exponentially.
20:31 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 $10.50 to 20. 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 pees. When we were buying Nvidia in 2023, we were paying four times earnings.
20:57 When we bought Tesla in 2019 for the car scurb, we were paying five times earnings. When we were owning Apple, we were paying four times earnings. When we bought Amazon for AWS, we we were getting it for free. And you know, the world doesn't think exponentially. and they're so focused on the next year, the next quarter.
21:19 Very few people believe you can accurately predict two, three, four years out. But if you follow and understand the S-curve and you you know the modes and you know how to model, you really can uh predict these these great things. So let's go to the scurve. So the S-curve is crucial because every technology follows this pattern where it comes out, you know, the I the smartphones were out 10 years before the iPhone.
21:48 The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until Chachi PT took it public uh and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical it because there were so many barriers to adoption.
22:12 The first smartphones, you know, they were clunky, they didn't have touchscreen, not Apple, there wasn't a wireless data system. And then uh and they were too expensive. They were $500 or $600. Steve Jobs got the price to 200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it.
22:33 So Annie 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.
22:53 The price was too high. Elon got the price to 40,000. Range anxiety was there. he got the the range to 300 miles. The supply chain was was finally in place so he could churn out millions of these things. So that triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now.
23:13 It's how tall, how big is this S-curve, how tall it is, so you know when to sell, how long to hold on, cuz we're underwriting out 2 or 3 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, not hardware chip, it was a new business model, what have you.
23:42 But we realized the TAM for this, it was the largest TAM in enterprise IT ever because previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. And so we figured out, 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.
24:06 And then we said it's probably going to be 50% deflationary. Therefore, we're 1 or 2% penetrated. But then over time, we realized it it actually wasn't deflationary. If you talk to anybody now, they say if you build it yourself, it's about the same price. So that means the TAM was so much bigger. So there's mega S-curves and there's subass curves.
24:28 You know, we've been lucky that we've had, you know, internet 1.0, uh, mobile, cloud, e-commerce, and now AI, which we can confidently say is the biggest and all these things build upon one another. So, you know, with with the electric vehicle S-curve, you you you have to pay attention too because, you know, at the time we we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at 10 or 15%.
24:58 Usually the S-curves go kind of all the way. Um, 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 you want to um when something gets to sort of 30 40% penetrated then you stop having exponential growth which means the sell side catches up and there's no longer big beats >> and is that when you sell typically >> generally we like we like the high growth and it was a mistake with Apple because in the first five or six years of Apple um it was awesome. I mean it
25:34 was our largest position. and it would go up 50 70% a year um except for '08 and then we sold in 20 2012 when it got to sort of 50% of the US had a smartphone and with Apple you know they maintained their leadership position it had a couple years of underperformance and then the multiple got low and they added several ancillary things and then they al also got to play in the uh the application because they get 30% % of the app.
26:07 So they were able to compound very nicely, say 20%, but the the big years were in the 50, you know, the the 0 to 50% part of the curve. >> I'm so fascinated by this uh you know, 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.
26:28 >> How do you measure that? Is it always different? What are the pitfalls that you've fallen into? How do you know when when to we talked about when to sell, but how do you know kind of when to start thinking about buying in one of these things? >> Yeah. And you know, Andy Grove says sort of when you have strategic inflection points, you can't trust the data.
26:47 And and strategic inflection points are about intuition, anecdotal evidence. I love this book called The Towel Jones Averages, a guide to whole investing, which is rightrain and leftrain. And the best investors have the right the creative side where they it's visual. It's connecting the dots. Um you know we invested in the mobile video game S-curve for so long.
27:11 Mobile video games were just the screens were small on the phones and the processing power wasn't good. So you had all these uh 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.
27:30 Um, enterprise is hard cuz you can't see it. We go to the Gartner IT Symposium. 30,000 American CIOS go there and like we saw this happen with Splunk where that used to be an amazing database company and like their their room where they were explaining was like standing room only or we saw that with VMware you know I'm talking like 30 years ago where they virtualized the server and like there was standing room only and you could just see the corporate demand just beginning and with AWS We went there and the grand ballroom was
28:08 completely packed and that was at nine o'clock and at 10 o'clock the grand ballroom was completely packed 11:00. So you could you could actually see the demand exploding before it happened. So um we look for for all kinds of clues and there's a whole pattern recognition that happens.
28:30 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, um 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 at Fidelity and he loved to mentor the young kids.
28:52 So, I got some time with him. He said, "Wite out the chart. It's all about the future." Um, and so it's okay to miss, but but what helps about the S-curve is is sort of how long it goes for. Then there's sort of the the slope of the Scurve, which is important. And a lot of people think cuz we're in a modern world, everything's so fast, but there's a lot of factors that determine the pace of the adoption.
29:18 And we we um commissioned this gentleman, Horus Du 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 Scurve is one of the fastest ever. It took 7 years to reach like 100% penetration. But the dishwasher Scurve is like that because it needs to be plugged into the back end.
29:46 >> What are some Yeah. What else did you learn? That's fascinating. What else did you learn? >> So like the B2B stuff can take a long time because it needs to be plugged into the existing systems. It's like it's got to be put >> the dishwasher >> inside the house and then um and consumers generally tend to go a lot faster.
30:05 Um >> I love that the radio and the dishwasher, the two models for adoption. >> Yeah. And and I I covered internet of fidelity. I c, you know, my first stock was Amazon. That's a whole other story which is a lot of fun. But I also did B2B internet and you know there was a whole huge bullcase on that. But the basically the underlying infrastructure wasn't in place for B2B to happen.
30:28 Ultimately happened 20 years later with SAS. And so that is a risk with AI in that you know these big companies are very security conscious. Uh they're can be slow to move. There's a lot of cultural issues with a with AI where you know you really need a few evangelists to push it through and the top management needs to push it through but then the IT's saying this is this is risky and that happened with cloud too that was one of the big things with cloud where it was too it was everybody was afraid it's unsecure to have your
31:05 data in the cloud and then we saw the CIA do it and we saw Capital One and we talked to the Capital One CIO we that it's more secure in the cloud and then it really started to take off. But but those takeoffs maybe because SAS is like the dishwasher and because cloud is like the dish it's got to be plugged in it it meant that yeah it was growing but it was sort of a 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
31:42 >> and so that's why we're getting this straight up >> and I think there's enough runway in the me in the near term going from 10 bits of people really using it to two to five or whatever which is going to cause it to keep on going straight up. So this we this we call it a backwards L curve. Um so it's really pretty exciting.
32:02 >> What have you learned about uh when the group that ends up being the leaders separates itself from one of these competitive packs? So you're talking there mostly about overall growth of the S-curve and demand. There's always multiple players fighting for it. You know, you've invested, it seems like you kind of invest after someone has separated themselves from the pack, not try to pick the winners from the pack.
32:24 Is that is that like roughly? >> Well, >> correct? >> Well, we're definitely So, 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 too fast.
32:47 Yeah. And so the Scurve 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 longived asset. And what we've found over the years is some of the competitive advantages within the digital world are more powerful, if not equally or more powerful than than in the offline world.
33:13 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, you know, they charge a lot and, you know, there's free versions, there's open- source Oracle, but they had all the database administrators.
33:36 They they had all the software that was tuned to work with them. So, they they basically had a chokeold on the relational database market forever. um you can get to scale very quickly because these scurves grow and all of a sudden Anthropic is doing 90 30 billion in sales or Amazon you know had so much scale and they got it quickly.
33:58 So they got a Walmart size scale advantage in 5 years versus 40 years for Walmart. So you can have network effects scale you can become 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, or ASML has critical intellectual property.
34:25 You can't make a chip without their lithography. And I think what's interesting is maybe these AI foundational companies, you know, they've got scale. Oh, you can also have brand. And brand's very important because Google, Amazon, they got to grow. They never had to advertise. Elon's never had to advertise for anything.
34:43 and cost to acquire versus lifetime. It's the whole business model. And so almost all the companies I mentioned have Apple, they have all of these rolled into one. Um, so we can sometimes we can notice these things before the rest of the world. And 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.
35:14 Amazon's won the war but before it even started and at that time we said there's Coke and there's no Pepsi. 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.
35:32 So they were 10 times the size of everybody else. Nobody could invest in the R&D to to catch them. So um but you're right that if you don't have a competitive advantage, you can be in the best S-curve of all time >> and still lose out. >> But if your name was Rim, Palm, Nokia, HC, LG, Motorola, I can go on forever. 0 negative negative negative negative.
35:53 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 and there's a lot of reasons to think they will continue to hold their position. >> So to take Google's a little trickier because they have this other huge massive complex business attached to the Gemini business.
36:18 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 in the fullness of time? >> Yeah. Of all the S-curves we've we've done, AI is by far the most complex and the fastest changing.
36:38 So it can be we have to keep in mind that there are risks but also the rewards are the highest cuz we're talking about a market in the trillions. You know we we just said cloud you know maybe cloud's 800 billion. This might be you know we now think 3 to five but there's higher risk higher reward. But let's just say with anthropic now they have it looks like they have critical intellectual property generally they've been able to maintain their their high market share and code.
37:10 Number two is uh they've built a a strong brand for enterprise to where go talk to any CIO and they'll just the first thing they'll say is claude. They're going to have escape velocity and scale. And what was scary for OpenAI and Anthropic fighting these big companies like Google was they had these huge cash cows.
37:32 And to both of the management teams credited Open 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, it looks like and their fundraising ability, it looks like they've reached escape velocity. So now they have scale.
37:52 And the other thing that Anthropic and OpenAI 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. >> Um, and so maybe they can have this liftoff stage.
38:16 you know, Open AI has, you know, they they were focused on so many different other sectors, but 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 it looks like enterprise right now is much better because you're, you know, you and I, we're willing to pay a lot because it's replacing human beings.
38:43 you know, consumer, maybe you can get advertising, but maybe they would pay for a a clawbot type assistant if you could make that perfectly well for them. Um, but they have gazillion eyeballs there. But you're right, things do shift, but it usually on the we have this charts that we almost do for all of our pitches.
39:07 On the internet, the leader goes bigger, faster, and wins. And it's it's it's happened you know most of the time the leader gets it. Shopify becomes the leader. It just keeps on going. Amazon the leader keeps on going. SAS company XYZ just you get the lead. It compounds on internet company compounds on itself.
39:24 And and another thing is you need to be big. Another is scale. You need the compute and you got to pay for the compute because so there's only so many people that can do that. So that those are some of the modes that we think are now showing up. Now there are some exceptions to that rule. usually with the paradigm shifts AOL and then dialup went to broadband and and they didn't make make the change.
39:46 You know, 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, you know, 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 that they'll be able to differentiate within those.
40:06 I'm so curious then what you think all of this means for software. Um when I look through your portfolio, I don't see a ton of uh big software companies, enterprise software companies. I I don't know if you once had them and sold them or 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 of, wow, you know, like if I spend enough time on this, even if I'm not technical, maybe I could build a, you know, an ERP equivalent replacement
40:35 or something for my company. There doesn't seem to be a fundamental reason why that's not possible and and then those companies could be in lots of trouble. Seems like everyone has a 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.
40:50 >> We were at certain points maybe 5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in in our April 2023 seminar, we said definitely invest in chips first and and we said but at the application layer initially we thought these companies are huge. They have huge sales forces.
41:14 They can take these AI APIs and and build products and they have the data. This is going to be amazing for software. And 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, almost all of our application software.
41:38 We still have one or two small ones, but entering this year, we were actually net net short. And uh it really helped us in the first quarter. There's 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.
42:06 It's so revolutionary changing that it feels like it has to be disruptive now. even if it's not disruptive now or right away. Uh so 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.
42:42 Um, second, um, 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. Um, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs cuz I don't, you know, there's smart people on both sides of that, but we are seeing some companies really gut their jobs or whatever, >> freeze hiring and so that hurts on seats.
43:17 Maybe in terms of them building their own apps, maybe it just um >> you know, if you want to be optimistic, it's it's taken them a while to do that. We talked about how early the primitives of AR are. So maybe they have just taken a while to get to something they can commercialize, but you know, they might not have the right people.
43:37 They might not know it's a different selling motion from selling a fixed system versus, you know, if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDE for deployed engineers and they might not have the right people internally to do that.
43:58 Then of course there's the 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 technology, old tech is very sticky. Like mobile video games didn't hurt console games and uh the tablet didn't hurt the PC and the smartphone didn't hurt the PC and uh there's a lot of integrations and work that goes into these software.
44:27 So that's all true and companies do like to buy from they don't like to build themselves that much. So that's all true but you can't imagine a world where in 1 2 3 4 5 years um you could have a brand new AI native company going after each one of these very strong incumbents and it might their data advantage could get obiated. it might be easy to take it out and put the new one in with AI and such.
44:57 So, what's good if you like so is the valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI um coding tools are just getting better and better. So, we'll we'll have to wait and see. And we're we're watching these software companies very closely to see if they're getting any revenue that can change that trajectory.
45:20 But it's hard because if you're a company like Salesforce, you've got 40 billion in sales and now you you might have 500 of ARR 700 of AR of AI. So you've got this huge base. Now maybe this starts to work but it takes a while. And in software there's the rule of 40 which is your growth rate plus your operating margin. And if you've got a 20% growth rate and 20% that's good.
45:50 For AI, we have a new kind of rule of 40. We call it well, it's really for chip investing. But if what percent 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 you've got a strong market position. Problem with software is their AI is 1 or 2% at this stage and it's a long way to go.
46:17 Um, one thing we are picking up though now lately and this is halfbaked, 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.
46:38 And so maybe these agents, 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, uh it seems like the commonality of the tools they might use that are the most sticky would be network-based tools.
46:56 Uh so Slack is a great obviously a great example of the software in Slack itself is I don't know leaves something to be desired. It's not the software is not the special part. It's that everyone is there, >> right? But I'm curious yeah what kinds of things you would want. Is it just network you know the presence of a network effect? Is that the only thing that really matters? >> It's still early in our thinking here but I don't know even even even maybe you know workday or the HR systems or um the big systems of record you know the agents may be running on
47:30 top of on top of them. CRM is going headless or they're making a headless version and that's sort of the bare case too that you get relegated to just being a database but you know there's a human interface to it then they need to make the AI interface which is no interface it's just them going right into the data and so you know you lose that customer interaction but if if the agents are going right to right to CRM and doing the work inside of CRM M that that will solidify CRM so you won't have to think it's going away.
48:07 >> Can we talk about chips? You've referenced them a few times. >> Inf infrastructure chips, you know, 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 the modified rule of 40 for percentage that's AI and percentage market share in the category.
48:22 That's an interesting stat. >> What companies shine on that today? What are what are lagards, you know, 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 '9s. And um and compute grew in the cloud era and it grew compute workloads grow you know 25 to 40% every year but Moore's law is improving at that rate.
48:57 So 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 you know there was no innovation you would go from one gig to 10 gig That would take 7 years.
49:30 And when you do switch in the first year, it does take some innovation to get to 10 gig and would create a little cycle, but then it would commoditize. And now you go to AI and 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.
49:57 And so, not only are you creating tremendous unit growth, but the industry, we call it the decommoditization of the hardware industry. And I I met with Shawn Maguire like 3 years ago, and he said, "I wish I could come back and be a 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.
50:23 And we're in this renaissance of chips. So not only do you have tremendous unit growth, but you it's requiring tremendous innovation and what that means, you know, at every aspect of the server. And so you know memory which used to be a pure commodity, this high bandwidth memory is stacked 10 chips on top. you know the input outputs are 10x what they were before like took Samsung for years to do it and it's a critical critical piece and then that is constantly upgrading so they're on the same you know they've got to be working with
51:05 Nvidia for three or four generations in advance we we had this with Celestica Celestica was a contract manufacturer and this has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on and they they kind of kept Celestica's heritage was IBM supercomputing and they kept all that talent and skill.
51:36 And then we noticed they were the sole supplier of the Google TPU server. We're like, "Oh my god, this was like three years ago. The stock was trading at eight times earnings." And they had this whole 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.
51:58 It it turns out that these are excellent businesses. Not only do they have tremendous growth, but to do an AI uh server computer, it's it's liquid cooled. It's running so much hotter and you know it's two or $300,000 piece of machinery whereas an old server was $5,000. If it breaks you just throw it away.
52:24 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. And then they they it turned out they were quite good at liquid cooling and you know a lot of other people tried to do it and failed and so they've retained that position. Then it also turned out that the Ethernet market was because you were in the old days you would go from 100 gig to 400 to 800.
52:56 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 the guys at at Celestica invented were some of the people that wrote that open- source software. They work very closely with Broadcom.
53:15 So 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 um AI because AI is incredibly network intensive. And then even something like the printed circuit board. I mean a regular server you need 10 layers.
53:36 These AI servers you need a 40 layer and there's very few PCB suppliers that can make this. And um there's all kinds of complexities in there. And we also own Elite Materials which makes the leading ingredient which is copper clad laminate which goes into these boards. And so the PCB uh units are growing, the layer counts are rising.
54:02 So you've got like a 50 to 60% keer 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 road map with us. So you've you've gone from a 5% grow or low margin to you know a 35% 40 50 topline kager for the next four years with rising margins and then on top of that there's shortages of everything.
54:38 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 Corning like they make the fiber. Um they've got some ridiculously high share of the fiber. I was reading this uh Microsoft data center they just built. There's enough fiber to circle the world four and a half times in that one thing.
55:04 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. And then they're doing, you know, in networking there's scale out which is kind of connecting all the server racks together.
55:26 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.
55:46 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 of the rack, >> everyone's overwhelmed. >> Everyone's overwhelmed.
56:10 But the story like in the power supplies, every Nvidia chip or rack uses n 50 to 125% more power. And like literally that drives the ASPs of Delta and Advanced Energy. I just I think it's it's 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.
56:41 The broader picture is like we're going to be the AI demand if we're right with this L curve. We're already short, you know, the DRAM market, the NAN market, the PCB. We're already like 30, we're 30% short all these things as we are now. >> [sponsor — Vanta] [sponsor — Ridgeline]
58:01 >> The measure of percent AI, percent market share. Do you care more about the absolute or the rate of change of those metrics? >> It's good because I took we did this presentation in two 2024 where we actually listed everybody's market share and everybody's and then I I asked Claude to plot plot it to a thing and it actually didn't get it right because what it didn't get is the rate of change.
58:29 So 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? Like if your whole framework is S-curve, competitive advantage, underappreciated earnings power.
58:48 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 it's why does the casino teach people how to play blackjack? It it's harder. It's really hard to do. It's it's you have to have a a deep you have to be comfortable investing.
59:07 You know, we've been doing I've been doing tech for 20 years at Whale Rock. We've got a team that's been doing this, covered many cycles. We know the different. So, very few people, 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. and Gavin's done a great job.
59:26 people weren't comfortable with it and it's it's harder to do than it seems and the chart you know a lot of these companies their charts are up so it's scary can I buy and then you also have to have the holistic view because if you don't have conviction so every you know every time with Nvidia over the last four years it's like 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 6 months of marking time it's got to be a bubble this is like getting out of hand this is pretty scary like and
59:57 the the bare cases are not like 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 so it helps to have have the big picture it helps to have you know decades and scores of scurves that you're looking at and and where it plays in different things.
1:00:28 >> What what in this whole picture, you know, I would describe your your stance so far in the first hour discussion as like very bullish on on the impact that AI is going to have and the returns available as a result. What makes you the most concerned or uncertain or is it just the rate at which all this stuff changes and like what keeps you worried amidst what seems like pretty extreme bullishness? I mean, 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
1:00:59 in some aspects of the government. You know, I think Maine just banned data centers and 80 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 sort of slows down in its improvements, I think there's a whole lot of AI adoption to happen even if the models didn't improve.
1:01:29 But Jensen said this, you know, years ago when he was talking about his GP crap, just the graphics chips. If good enough is good enough, I won't have a business. Now 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 and um and then it might be a race to the bottom and it might be you know it won't be good for the stocks probably.
1:02:03 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 if open source, you know, Jensen really wants open source to like take off. It's all he kept on mentioning at at his last GTC.
1:02:21 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, you know, Oracle cancelled a big deal and then Meta went right in.
1:02:43 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 we watch that very carefully and um in general, we see more, you know, more more companies truly going after this and even Microsoft going trying to build their own.
1:03:04 So I think those are 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 you know not 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. >> Yeah. >> Why focus so much on the bottom layers of Jensen's five layer cake versus things in the application layer that are actually getting used by consumers? Well, we do, you know, part of OpenAI is they have chatbt which which is an
1:03:31 application, but we think the application layer well a it always comes later. So, you know, the first three or four years of the iPhone and then the applications really took time. So, maybe it's just starting. Um but to date um we found that area to be pretty risky because where does the where does the foundational model end and where does the application begin and can can the applications build enough of a moat um where they can fend off and um and build and build businesses in that.
1:04:10 and um and we we thought we would see it in some of the incumbents like a a CRM and they're starting and maybe just a matter of time but we really haven't seen it in the enterprise world and there there are some you know very good startup application companies out there but the ecosystem is not clear you know like 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
1:04:44 because um but there will be great application companies built you know we really were watching Brett Taylor at Sierra Brett was CEO of CRM he wrote Google Maps he was CIO of Facebook and uh he 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.
1:05:09 We'll see. It's a matter of timing when these things really start to to to come in 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. >> [award wall / research-process discussion] The system we use is right out of common stocks and uncommon profits which was written by Philip Fischer in the 1950s. It's the scuttlebutt approach.
1:08:16 And like the job that the guys did on app 111 two years ago. I mean I think we got two of the best adte guys around and they you know they convinced me to buy I knew adte... but Michael and Sam really figured out the Apploven story like before anybody and they followed it when it was private. They know all the competitors... and they did the work on the model and developed a great relationship with Adam Ferogi. He's one of the best managers out there. And um I don't see AI doing that.
1:09:00 >> [closing: investor friendships, the "tripod" of conviction, product structure — long/short, long-only, hybrid privates, and the Whale Rock Mega Cap Tech Fund] >> we just think there's a huge structural underweight of the largest tech companies in the world... a lot of our performance over the years was from some of the largest companies whether it be Apple or Amazon or Tesla.
1:13:31 we created the Whale Rock Mega Cap Tech Fund which is the top 30 the universe is the top 30 market caps globally and then we pick you know the 12 or 13 that are the best and I think there's tremendous alpha in the largest cap... these companies by definition have wonderful modes... I mean, Nvidia sure is, and TSM is really levered to it, and Heinix is extremely levered to it, and ASML is levered to it.
1:14:58 >> [closing question — kindest thing anyone's done for you: his father, ex-Goldman Sachs, who joined Whale Rock as chairman/"the gray hair" until he passed away in 2011] >> Thanks so much.