Alex Sacerdote - How to Invest Through Technology Cycles - [Invest Like the Best, EP.477]

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“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, and there was a whole huge bull case on that. The underlying infrastructure wasn't in place for B2B to happen. Ultimately happened 20 years later with SaaS. That is a risk with AI in that these big companies are very security conscious, can be slow to move. There's a lot of cultural issues with AI where you really need a few evangelists to push it through. The top management needs to push it through, but then the IT is saying this is risky. And that happened with cloud too. That was one of the big things with cloud where everybody was afraid it's unsecure to have your data in the cloud. And then we saw the CIA do it and we saw Capital One, and we talked to the Capital One CIO. He said it's more secure in the cloud, and then it really started to take off. Those takeoffs, maybe because SaaS is like the dishwasher and because cloud, it's gotta be plugged in, 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. And so that's why we're getting this straight up. And I think there's enough runway in the near term going from 10 bps of people really using it to 2 to 5 or whatever, which is gonna cause it to keep on going straight up. This, we call it a backwards L-curve.”

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly we realized their AI products were not very good. They weren't moving the needle. Nobody could charge for them. We basically sold almost all of our software entering this year. We were net short. It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper. Or it's like a horse and buggy. The new way of software is like a jet engine, or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs, 'cause there's smart people on both sides of that, but we are seeing some companies really gut their jobs. Freeze hiring or whatever. Freeze hiring. And so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are, so maybe they have just taken a while to get to something they can commercialize. But they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into these software. That's all true. And companies do like to buy they don't like to build themselves that much. That's all true, but you can imagine a world where in 1, 2, 3, 4, 5 years you could have a brand new AI-native company going after each one of these very strong incumbents. And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got $40 billion in sales. You might have $500 billion of ARR, $700 billion 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. And if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look. 'cause you've got exposure and you've got a strong market position. Problem with software is their AI's 1 or 2% at this stage, and it's a long way to go. One thing we are picking up though now lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so, Maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“For the past 40 years, nothing has changed in the data center. Even with cloud, Intel x86 became the data center chip sometime in the '90s. And compute grew in the cloud era and compute workloads grow 25 to 40% every year. But Moore's Law is improving at that rate. It didn't require tremendous innovation. And, and there really was almost no growth in hardware for years and years and years. And the whole industry basically commoditized every part every chip, every part of the server, the printed circuit board to the memory, to the enclosures, to the networking. There was no innovation. You would go from 1 gig to 10 gig. That would take 7 years. 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. Now you go to AI. The workloads are growing 10x every year, and they're pushing every single aspect of this hardware to the physical limits of what it can do. Not only are you creating tremendous unit growth, but we call it the decommoditization of the hardware industry. I met with Sean McGuire like 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. And Sequoia made some of their best investments back in the hardware day with Apple and Cisco and others. And we're in this renaissance of chips. So not only do you have tremendous unit growth, it's requiring tremendous innovation. That means every aspect of the server memory, which used to be a pure commodity, This high-bandwidth memory is stack 10 chips on top, input outputs are 10x what they were before. Took Samsung for years to do it, and it's a critical piece. And then that is constantly upgrading. So they've gotta be working with Nvidia for 3 or 4 generations in advance. We had this with Celestica. Celestica was a contract manufacturer. This has been a disaster industry since 1999. It went all offshore to China. It was commodity, but they hung on. Celestica's heritage was IBM supercomputing, and they kept all that talent and skill. And then we noticed they were the sole supplier of the Google TPU server. 3 years ago, the stock was trading at 8 times earnings. And then they also had this whole business of selling Ethernet white box, which is code word for commodity white box Ethernet switches into the clouds. It turns out these are excellent businesses. Not only do they have tremendous growth, but to do an AI server, it's liquid cooled. It's running so much hotter. It's $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 a commodity, like supplier to like selling a critical part on a plane. You'll never get swapped out. It turned out they were quite good at liquid cooling. A lot of other people tried to do it and failed, and so they've retained that position. Then it also turned out that the Ethernet market, in the old days, you would go from 100 gig to 400 to 800, it would be a 7-year cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source SONiC layer. The guys at Celestica invented— or some of the people that 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 like 50, 60% share of the cloud Ethernet switch market, which is a crucial market for AI because AI is incredibly network intensive. And then even something like the printed circuit board, a regular server, you need 10 layers. These AI servers, you need a 40-layer, and there's very few PCB suppliers that can make this. There's all kinds of complexities in there. Then we also own Elite Materials, which makes the leading ingredient, which is copper clad laminate, which goes into these boards. The PCB units are growing, the layer counts are rising. So you've got like a 50 to 60% CAGR 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 4 years to be like designing this roadmap with us. You've gone from a 5% grow or low margin to a 35%, 40%, 50% top line CAGR for the next 4 years with rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's going to be a great cycle. So we see that up and down the supply chain. You find these companies like I mean, Corning, they make the fiber. They've got some ridiculously high share of the fiber that I was reading. This Microsoft data center they just built, there's enough fiber to circle the world 4.5 times in that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs, and it's higher margin, and it's the fastest growing part of their business. In networking, there's scale out, which is connecting all the server racks together. Then there's scale across, which is connecting the data centers together. And when you want to build one of these huge clusters and you can't get all the power in one place for training, you want to wire them together. But the wires, you need like 10x, the wire has to be so much thicker. So that's creating huge growth., and where the real kicker comes in is when you do scale up, that's connecting every GPU in the rack to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens, that 2 to 3x's Corning's opportunity. So you just have at every layer of the rack, everyone's overwhelmed. Everyone's overwhelmed, but in the power supplies, every Nvidia chip or rack uses 50 to 125% more power. That drives the ASPs of Delta and Advanced Energy. I can't believe these stories when I hear, I'm like, wait, so your ASPs are gonna like go up 40% for the next 4 years in a row and it's higher margin. The broader picture is the AI demand, if we're right with this L-curve, we're already short the DRAM market, the NAND market, the PCB market. We're already 30% short all these things as we are now.”

“When the gun went off with OpenAI ChatGPT in November 2022, we immediately took the firm and did a massive deep dive with our 10-person team. Anytime you have a new compute paradigm, there's a new stack, and that creates new winners and losers on the old stack. Now Jensen talks a lot about it, but it's power at the bottom, chips at the bottom, the clouds, and then the foundational models, and then the applications on top. And at that time, this was 2023 early, we said we want to be in the chips and the infrastructure first., and not only do they get the demand first, but we know who the winners are. And no matter who wins above, which we weren't sure at the time, we know we're going to need tremendous amounts of compute. And we did a deep dive into that, which we can talk about later. But over the next 2 or 3 years, we started to get more clarity on how the foundational model layer would evolve. And at the time, 2 or 3 years ago, there were 60 different companies going after it. OpenAI was kind of in the lead, and we did a webinar in April 2023 and we said, look, this might be a winner-take-all. It might be a total commodity because there's open-source players. It might be a race to zero, or it might be an oligopoly where there's 3 or 4 leading players. And what we saw over the following 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 others, and Meta— Amazon never really showed up. We'll see what happens with Meta, but they came in strong. Basically, their effort faltered and they had to do a total reboot. In the meantime, Anthropic kind of was this dark horse candidate, the startup they focused really purely on the enterprise. OpenAI had kind of won the consumer, and then Gemini can never be counted out. We love Google as well. It's one of our largest positions. So it really started to look like a three-horse race and somewhat of an oligopoly, very similar to how the cloud market evolved, where three companies underpin the entire SaaS cloud world and have really excellent businesses. And then we also were aware of the open source risk from China. We started to get comfortable that the quality of the tokens from the leading edge were superior because if you're 80% close to the top of the benchmarks, going from 80 to 85 is a huge unlock. The open source guys, they don't have as much compute, so they can come close to the leading edge, but they can't leapfrog it, and then they kind of falter. Meanwhile, the scaling laws and other means of improving the models, the feedback loops, et cetera, we saw that there was a very strong runway, and everyone we talked to close to the industry saw that the scaling laws would continue. We developed this thesis that it would be a three-horse race. The big kicker was code, and this is the true unlock of AI. In the first few years, we knew AI would be big, but we were skeptical also. We made large investments 'cause we knew the training would be there, but we weren't sure how much revenue might come and if it could truly replace labor. 'Cause if you remember, the early versions of the models were good, but there was a lot of negative feedback from corporates. And could they be truly agentic in 2025, the first cloud code and the coding tools really began to explode. You saw the first gen was like Microsoft Copilot, which is like $20 a month, and that could sort of improve your grammar of coding, maybe find a bug, maybe make a block of code, like a paragraph. And then Anthropic came out sometime in, in the middle of the year, and it could do so much more. And it started to get to this point where it could run agentically, and the coding market just exploded. And then we started hearing people who could use it unfettered. We heard that, you know, even within Anthropic at that time, people were spending $100 a day on tokens, which if you do the math comes out to $20,000 or $30,000 a year., and if you think about how many coders there are in the world, 20 million, you've got a half a trillion-dollar market just from coding alone. And mind you, that was on 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 we made the investment at the $180 valuation. We said, and I think they were hoping to get to a 9.”

“When the gun went off with OpenAI ChatGPT in November 2022, we immediately took the firm and did a massive deep dive with our 10-person team. Anytime you have a new compute paradigm, there's a new stack, and that creates new winners and losers on the old stack. Now Jensen talks a lot about it, but it's power at the bottom, chips at the bottom, the clouds, and then the foundational models, and then the applications on top. And at that time, this was 2023 early, we said we want to be in the chips and the infrastructure first., and not only do they get the demand first, but we know who the winners are. And no matter who wins above, which we weren't sure at the time, we know we're going to need tremendous amounts of compute. And we did a deep dive into that, which we can talk about later. But over the next 2 or 3 years, we started to get more clarity on how the foundational model layer would evolve. And at the time, 2 or 3 years ago, there were 60 different companies going after it. OpenAI was kind of in the lead, and we did a webinar in April 2023 and we said, look, this might be a winner-take-all. It might be a total commodity because there's open-source players. It might be a race to zero, or it might be an oligopoly where there's 3 or 4 leading players. And what we saw over the following 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 others, and Meta— Amazon never really showed up. We'll see what happens with Meta, but they came in strong. Basically, their effort faltered and they had to do a total reboot. In the meantime, Anthropic kind of was this dark horse candidate, the startup they focused really purely on the enterprise. OpenAI had kind of won the consumer, and then Gemini can never be counted out. We love Google as well. It's one of our largest positions. So it really started to look like a three-horse race and somewhat of an oligopoly, very similar to how the cloud market evolved, where three companies underpin the entire SaaS cloud world and have really excellent businesses. And then we also were aware of the open source risk from China. We started to get comfortable that the quality of the tokens from the leading edge were superior because if you're 80% close to the top of the benchmarks, going from 80 to 85 is a huge unlock. The open source guys, they don't have as much compute, so they can come close to the leading edge, but they can't leapfrog it, and then they kind of falter. Meanwhile, the scaling laws and other means of improving the models, the feedback loops, et cetera, we saw that there was a very strong runway, and everyone we talked to close to the industry saw that the scaling laws would continue. We developed this thesis that it would be a three-horse race. The big kicker was code, and this is the true unlock of AI. In the first few years, we knew AI would be big, but we were skeptical also. We made large investments 'cause we knew the training would be there, but we weren't sure how much revenue might come and if it could truly replace labor. 'Cause if you remember, the early versions of the models were good, but there was a lot of negative feedback from corporates. And could they be truly agentic in 2025, the first cloud code and the coding tools really began to explode. You saw the first gen was like Microsoft Copilot, which is like $20 a month, and that could sort of improve your grammar of coding, maybe find a bug, maybe make a block of code, like a paragraph. And then Anthropic came out sometime in, in the middle of the year, and it could do so much more. And it started to get to this point where it could run agentically, and the coding market just exploded. And then we started hearing people who could use it unfettered. We heard that, you know, even within Anthropic at that time, people were spending $100 a day on tokens, which if you do the math comes out to $20,000 or $30,000 a year., and if you think about how many coders there are in the world, 20 million, you've got a half a trillion-dollar market just from coding alone. And mind you, that was on 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 we made the investment at the $180 valuation. We said, and I think they were hoping to get to a 9.”

“Yeah. 1 to 9. Yeah. 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, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Code has gone to almost completely agentic. You had Andrej Karpathy and Linus Torvalds, two of the smartest people in coding, and they completely flip-flopped. And Karpathy said last year code tools could write 20% and 80% would be handwritten. That flipped when the latest model came out, and now he hasn't written a line of code except in English. And not to mention the pure unlock that we're gonna get for the people that never knew how to code. So just coding alone has completely taken off. One difference between the cloud— GCP, AWS— and the AI companies is the clouds, generally it's commodity. They're selling you servers and storage. They have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity, but there's tremendous differentiation within. There's different training methods and different skills that they're good at. And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity. But Anthropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of like differentiation, critical IP, which is a great competitive advantage. Many companies have come after the coding franchise, and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and Anthropic is it's not just the API or the model. They're building a whole monopoly or whole ecosystem of products around the API. So they've got the SDK, Claude for Cowork, orchestration layer, and all the tools, they call it sort of a harness, which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse. Big deal. They saw this was a new way of doing computing, so they invented all these products that they could see before everybody else that slowly built lock-in. The other way we think about this is where are we on this S-curve? And we have this infrastructure layer S-curve, which we think is 10% penetrated. And by the way, we think it's still one of the best ways to play AI, and we'll talk about how that feeds back through. But if you think about it, 200 or I don't know how many, 800 million people are using AI. They're just using AI 1.0, which is like a search engine on steroids. But now with these new primitives where you have Claude on your computer linking it in, then you build skills and then they're going to build true AI bots. Big corporations are going to build much larger. But where are we in terms of the amount of people doing that? I mean, Sundar said it's 10 bps of the knowledge workers of the world. So Anthropic has something like 14 or 15 million DAUs. Probably a small portion of those are truly doing AI the way you can do it. So that 10 bps, it's classic S-curve where these are the tinkerers and then it's going to go to the early adopters, then it's going to go to the early mainstream. But you're going to go from 10 bps to 1 to 2 or 3% to 5% to 15% in the next 4 years. And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast. But it's still Internet 1.0 when it's like you knew you needed a website in 1998, but it's like hard to build that website. But this is coming together fast. And so the enterprise AI or enterprise application AI market is less than 1% penetrated. And, you know, we talk about S-curves, we call this an L-curve, just straight up. We'll take this to the infrastructure. We're at 10 basis points of people really using AI, and there's not enough compute in the world. So Anthropic has half of what they need right now, and that's before this huge takeup. Marc Andreessen said in the next 4 years, one thing he's sure of is there's not going to be enough compute.”

“Yeah. 1 to 9. Yeah. 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, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Code has gone to almost completely agentic. You had Andrej Karpathy and Linus Torvalds, two of the smartest people in coding, and they completely flip-flopped. And Karpathy said last year code tools could write 20% and 80% would be handwritten. That flipped when the latest model came out, and now he hasn't written a line of code except in English. And not to mention the pure unlock that we're gonna get for the people that never knew how to code. So just coding alone has completely taken off. One difference between the cloud— GCP, AWS— and the AI companies is the clouds, generally it's commodity. They're selling you servers and storage. They have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity, but there's tremendous differentiation within. There's different training methods and different skills that they're good at. And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity. But Anthropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of like differentiation, critical IP, which is a great competitive advantage. Many companies have come after the coding franchise, and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and Anthropic is it's not just the API or the model. They're building a whole monopoly or whole ecosystem of products around the API. So they've got the SDK, Claude for Cowork, orchestration layer, and all the tools, they call it sort of a harness, which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse. Big deal. They saw this was a new way of doing computing, so they invented all these products that they could see before everybody else that slowly built lock-in. The other way we think about this is where are we on this S-curve? And we have this infrastructure layer S-curve, which we think is 10% penetrated. And by the way, we think it's still one of the best ways to play AI, and we'll talk about how that feeds back through. But if you think about it, 200 or I don't know how many, 800 million people are using AI. They're just using AI 1.0, which is like a search engine on steroids. But now with these new primitives where you have Claude on your computer linking it in, then you build skills and then they're going to build true AI bots. Big corporations are going to build much larger. But where are we in terms of the amount of people doing that? I mean, Sundar said it's 10 bps of the knowledge workers of the world. So Anthropic has something like 14 or 15 million DAUs. Probably a small portion of those are truly doing AI the way you can do it. So that 10 bps, it's classic S-curve where these are the tinkerers and then it's going to go to the early adopters, then it's going to go to the early mainstream. But you're going to go from 10 bps to 1 to 2 or 3% to 5% to 15% in the next 4 years. And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast. But it's still Internet 1.0 when it's like you knew you needed a website in 1998, but it's like hard to build that website. But this is coming together fast. And so the enterprise AI or enterprise application AI market is less than 1% penetrated. And, you know, we talk about S-curves, we call this an L-curve, just straight up. We'll take this to the infrastructure. We're at 10 basis points of people really using AI, and there's not enough compute in the world. So Anthropic has half of what they need right now, and that's before this huge takeup. Marc Andreessen said in the next 4 years, one thing he's sure of is there's not going to be enough compute.”

“Of all the S-curves we've done, AI is by far the most complex and the fastest changing. We have to keep in mind that there are risks. The rewards are the highest because we're talking about a market in the trillions. Maybe cloud's $800 billion. This might be, we now think, $3 to $5, but there's higher risk, higher reward. But let's just say with Anthropic now, it looks like they have critical intellectual property. Generally, they've been able to maintain their high market share in code. Number 2 is they've built strong brand for enterprise to where go talk to any CIO and the first thing they'll say is Claude. 3, they're gonna 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. And to both of the management teams' credit at OpenAI and Anthropic, they were able to work in these super capital-intensive industries and find ways to raise capital. And certainly with Anthropic, with their 10x sales growth and their fundraising ability, it looks like they've reached escape velocity. So now they have scale. And the other thing that Anthropic and 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. Maybe they can have this liftoff stage. OpenAI, they were focused on so many different other sectors. They're starting to do better in enterprise, and their coding tool's good, and they're starting to see accelerating growth on that side. And then look, the consumer franchise, it looks like enterprise right now is much better because you and I, we're willing to pay a lot because It's replacing human beings. Now, consumer, maybe you can get advertising, but maybe they would pay for a Claude bot-type assistant if you could make that perfectly well for them. But they have gazillion eyeballs there. Things do shift. We have this charts that we almost do for all of our pitches. On the internet, the leader goes bigger, faster, and wins. Most of the time the leader gets it. Shopify becomes the leader, it just keeps on going. Amazon, the leader keeps on going. SaaS company XYZ, you get the lead, it compounds on itself. And another thing is you need to be big. Another is scale. You need the compute and you gotta pay for the compute. So there's only so many people that can do that. Those are some of the moats that we think are now showing up in this business. Now, there are some exceptions to that rule, usually with a paradigm shift. 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. And if you talk to anyone in the Valley or any startups, they'll tell you that they're building on top of these three. And the world's a huge place and the economy's a huge place that they'll be able to differentiate within those.”

“Yeah. 1 to 9. Yeah. 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, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Code has gone to almost completely agentic. You had Andrej Karpathy and Linus Torvalds, two of the smartest people in coding, and they completely flip-flopped. And Karpathy said last year code tools could write 20% and 80% would be handwritten. That flipped when the latest model came out, and now he hasn't written a line of code except in English. And not to mention the pure unlock that we're gonna get for the people that never knew how to code. So just coding alone has completely taken off. One difference between the cloud— GCP, AWS— and the AI companies is the clouds, generally it's commodity. They're selling you servers and storage. They have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity, but there's tremendous differentiation within. There's different training methods and different skills that they're good at. And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity. But Anthropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of like differentiation, critical IP, which is a great competitive advantage. Many companies have come after the coding franchise, and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and Anthropic is it's not just the API or the model. They're building a whole monopoly or whole ecosystem of products around the API. So they've got the SDK, Claude for Cowork, orchestration layer, and all the tools, they call it sort of a harness, which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse. Big deal. They saw this was a new way of doing computing, so they invented all these products that they could see before everybody else that slowly built lock-in. The other way we think about this is where are we on this S-curve? And we have this infrastructure layer S-curve, which we think is 10% penetrated. And by the way, we think it's still one of the best ways to play AI, and we'll talk about how that feeds back through. But if you think about it, 200 or I don't know how many, 800 million people are using AI. They're just using AI 1.0, which is like a search engine on steroids. But now with these new primitives where you have Claude on your computer linking it in, then you build skills and then they're going to build true AI bots. Big corporations are going to build much larger. But where are we in terms of the amount of people doing that? I mean, Sundar said it's 10 bps of the knowledge workers of the world. So Anthropic has something like 14 or 15 million DAUs. Probably a small portion of those are truly doing AI the way you can do it. So that 10 bps, it's classic S-curve where these are the tinkerers and then it's going to go to the early adopters, then it's going to go to the early mainstream. But you're going to go from 10 bps to 1 to 2 or 3% to 5% to 15% in the next 4 years. And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast. But it's still Internet 1.0 when it's like you knew you needed a website in 1998, but it's like hard to build that website. But this is coming together fast. And so the enterprise AI or enterprise application AI market is less than 1% penetrated. And, you know, we talk about S-curves, we call this an L-curve, just straight up. We'll take this to the infrastructure. We're at 10 basis points of people really using AI, and there's not enough compute in the world. So Anthropic has half of what they need right now, and that's before this huge takeup. Marc Andreessen said in the next 4 years, one thing he's sure of is there's not going to be enough compute.”

“5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly we realized their AI products were not very good. They weren't moving the needle. Nobody could charge for them. We basically sold almost all of our software entering this year. We were net short. It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper. Or it's like a horse and buggy. The new way of software is like a jet engine, or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs, 'cause there's smart people on both sides of that, but we are seeing some companies really gut their jobs. Freeze hiring or whatever. Freeze hiring. And so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are, so maybe they have just taken a while to get to something they can commercialize. But they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into these software. That's all true. And companies do like to buy they don't like to build themselves that much. That's all true, but you can imagine a world where in 1, 2, 3, 4, 5 years you could have a brand new AI-native company going after each one of these very strong incumbents. And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got $40 billion in sales. You might have $500 billion of ARR, $700 billion 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. And if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look. 'cause you've got exposure and you've got a strong market position. Problem with software is their AI's 1 or 2% at this stage, and it's a long way to go. One thing we are picking up though now lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so, Maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would.”

“5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly we realized their AI products were not very good. They weren't moving the needle. Nobody could charge for them. We basically sold almost all of our software entering this year. We were net short. It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper. Or it's like a horse and buggy. The new way of software is like a jet engine, or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs, 'cause there's smart people on both sides of that, but we are seeing some companies really gut their jobs. Freeze hiring or whatever. Freeze hiring. And so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are, so maybe they have just taken a while to get to something they can commercialize. But they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into these software. That's all true. And companies do like to buy they don't like to build themselves that much. That's all true, but you can imagine a world where in 1, 2, 3, 4, 5 years you could have a brand new AI-native company going after each one of these very strong incumbents. And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got $40 billion in sales. You might have $500 billion of ARR, $700 billion 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. And if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look. 'cause you've got exposure and you've got a strong market position. Problem with software is their AI's 1 or 2% at this stage, and it's a long way to go. One thing we are picking up though now lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so, Maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would.”

“5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly we realized their AI products were not very good. They weren't moving the needle. Nobody could charge for them. We basically sold almost all of our software entering this year. We were net short. It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper. Or it's like a horse and buggy. The new way of software is like a jet engine, or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs, 'cause there's smart people on both sides of that, but we are seeing some companies really gut their jobs. Freeze hiring or whatever. Freeze hiring. And so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are, so maybe they have just taken a while to get to something they can commercialize. But they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into these software. That's all true. And companies do like to buy they don't like to build themselves that much. That's all true, but you can imagine a world where in 1, 2, 3, 4, 5 years you could have a brand new AI-native company going after each one of these very strong incumbents. And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got $40 billion in sales. You might have $500 billion of ARR, $700 billion 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. And if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look. 'cause you've got exposure and you've got a strong market position. Problem with software is their AI's 1 or 2% at this stage, and it's a long way to go. One thing we are picking up though now lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so, Maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would.”

“Still early in our thinking here, but even maybe Workday or the HR systems or the big systems of record, the agents may be running on top of them. It's good and bad. I mean, CRM is going headless or they're making a headless version, and that's the bear case too, that you get relegated to just being a database there's a human interface to it, then they need to make the AI interface, which is no interface. It's just them going right into the data. You lose that customer interaction. But if the agents are going right to CRM and doing the work inside of CRM, that will solidify CRM. So you won't have to think it's going away.”

“5 years ago, we might have had 40 or 50% of our portfolio in software. And early on in our April 2023 webinar, we said definitely invest in chips first. But at the application layer, initially we thought these companies are huge. They have huge sales forces. They can take these AI APIs and build products and they have the data. This is gonna be amazing for software. Pretty quickly we realized their AI products were not very good. They weren't moving the needle. Nobody could charge for them. We basically sold almost all of our software entering this year. We were net short. It really helped us in the first quarter. There's so many layers to this. I mean, the old way of software is like using pen and paper. Or it's like a horse and buggy. The new way of software is like a jet engine, or frankly, the transporter from Star Trek. It's so revolutionary changing that it feels like it has to be disruptive. If it's not disruptive now or right away, the software companies have another problem, which is their list on the to-do list or priority list of any CIO has fallen a lot. So even if AI is not going to be disruptive, they're spending it on Anthropic tokens because there's faster ROI there. Second, if they're spending all that money over there, it pushes on the budget, so that hurts them. Third, a lot of software companies were able to raise price every year, and now they're probably nervous about doing that. Then fourth, we'll see what happens with jobs, 'cause there's smart people on both sides of that, but we are seeing some companies really gut their jobs. Freeze hiring or whatever. Freeze hiring. And so that hurts on seats. In terms of them building their own apps, if you wanna be optimistic, it's taken them a while to do that. We talked about how early the primitives of AI are, so maybe they have just taken a while to get to something they can commercialize. But they might not have the right people. It's a different selling motion from selling a fixed system versus if you're installing something that does human work, you got to be right at the side to make sure it's really getting done. So you need the FDEs, forward deployed engineers. They might not have the right people internally to do that. Then of course, there's the risk of you can build it yourself. The bulls will say, well, they're never gonna build their own ERP system. And that's probably right. And it is true that old tech is very sticky. Mobile video games didn't hurt console games, and the tablet didn't hurt the PC, and the smartphone didn't hurt the PC. There's a lot of integrations and work that goes into these software. That's all true. And companies do like to buy they don't like to build themselves that much. That's all true, but you can imagine a world where in 1, 2, 3, 4, 5 years you could have a brand new AI-native company going after each one of these very strong incumbents. And if their data advantage could get obviated, it might be easy to take it out and put the new one in with AI. The valuations are very high and everybody knows they're under pressure. Some people are tempted to buy these, but the AI coding tools are just getting better and better. We'll have to wait and see. We're watching these software companies very closely to see if they're getting any revenue that can change that trajectory. But it's hard because if you're a company like Salesforce, you've got $40 billion in sales. You might have $500 billion of ARR, $700 billion 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. And if you have 20% growth rate, 20%, that's good. For AI, we have a new rule of 40. What percent of your sales are AI? 30%. And what's your market share in that category? Say 30%. You'd be 60. That's a great place to look. 'cause you've got exposure and you've got a strong market position. Problem with software is their AI's 1 or 2% at this stage, and it's a long way to go. One thing we are picking up though now lately, and this is half-baked, but AI could make some of these software platforms more important because what's the first thing you do with Claude? You plug it into Slack. If that can become a key repository, that will make Slack a permanent fixture within the organization. And so, Maybe the next wave of AI will be these agents that use tools and they might operate inside of the existing incumbent software tools to use them like a human being would.”

“Well, A, it always comes later. So the first 3 or 4 years of the iPhone and then the applications really took time. So maybe it's just starting. To date, we found that area to be pretty risky because where does the foundational model end and where does the application begin? Can the applications build enough of a moat where they can fend off and build businesses in that? We thought we would see it in some of the incumbents of CRM, and they're starting, and maybe just a matter of time, but we really haven't seen it in the enterprise world. There are some very good startup application companies out there, but the ecosystem's not clear. When we started, the ecosystem 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 really were watching Brad Taylor at Sierra, Brett was CEO of CRM. He wrote Google Maps. He was CIO of Facebook. He's building this fantastic company called Sierra. We're not involved, but that's where the rubber hits the road. Will he be able to turn this into a huge company? And he's doing quite well, and we'll see. It's a matter of timing when these things really start to come into their own and prove they're sustainable. It usually doesn't start in the first 3 or 4 years. It comes a little bit later.”

“My guest today is Alex Sacerdote, founder of Whale Rock Capital Management. Whale Rock is a technology-focused investment firm that manages more than $17 billion across hedge fund, long-only, and hybrid strategies. Over the past 3 years, it's been one of the best-performing funds, compounding up roughly 44% per year. Alex invests through a single lens that he has refined over 20 years. He looks for technology S-curves, durable competitive advantages, and underappreciated earnings power. This conversation is a tour through how he applies that framework today. We start with his highest conviction position, which is Anthropic, and use it to work through the entire AI stack from chips to models to applications. Please enjoy my conversation with Alex Sacerdote. Alex, you were saying that your highest conviction position is Anthropic right now.”

“Yeah. And Gavin's done a great job. People weren't comfortable with it. It's harder to do than it seems. And a lot of these companies, their charts are up. So it's scary. Can I buy? And then 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, oh, they had a great year. Oh my God, it's gotta be a bubble. And then they had another great year and it's like 6 months of marking time. It's gotta be a bubble. This is like getting outta hand. This is pretty scary. The bear cases are not totally without merit, but if you can see the whole picture and understand how these things are unfolding and gain conviction in that, frankly, if you're just a semi-analyst, so many semi-analysts missed it because they didn't see what was really happening at the foundational model layer and how this broader picture— so it helps to have the big picture. It helps to have decades and scores of S-curves that you're looking at and where it plays in different things.”

“I would like to say that we're so advanced in our AI systems that It's a huge change, but so far it's helping us get up to speed and we have a handful of great apps, but it's not supplanting the job of the analyst. And so much of what we're doing is we're meeting with as many companies as humanly possible. We're developing relationships with the management teams that we cover. We're talking to the competitors. The system we use is right out of Common Stocks and Uncommon Profits. Which was written by Philip Fisher in the 1950s, and it's the scuttlebutt approach. It's growth investing. It's get out there and talk to suppliers, customers, competitors, looking for the key characteristics of these leading companies and really developing conviction in them. Now, if it's a new complicated area like ABF substrates or PCBs, we're able to get up to speed on those things quickly, but it can't pick stocks for you in any kind of a way. I will say that if you're an analyst who's good at the blocking and tackling, there's a role for that, but you need to have obviously the insight on top. So we're now like using AI to write notes or review the quarter, and those notes are much better, but there better be a really good paragraph on top Which is the wisdom. What does this mean? How does this deal with our thesis? What changed? Don't just be a reporter. So the AI can be a great reporter. It can't pick into the future. The job that the guys did on AppLovin 2 years ago, I think we got 2 of the best ad tech guys. I knew ad tech. I started actually nearby here in New York at an internet advertising startup, and after I did banking, I knew internet advertising and ad tech, which is historically a terrible industry, but Michael and Sam really figured out the AppLovin story before anybody, and they followed it when it was private. They know all the competitors, they know all the intricacies of terminology, and Sam went to the Las Vegas App Advertising Conference, and we went to Cannes, and we talked to scores and scores of people. So did the work on the model and developed a great relationship with Adam Ferrogi. He's one of the best managers out there. I don't see AI doing that.”

“We got to know the company. One of our analysts knew people in the finance group there. We had a look at the $60 billion round and we didn't know the company as well. The gross margins were negative, and frankly, we hadn't seen coding explode the way it had. And one thing about public markets is you get to know companies over a long period of time and you can kind of invest on your own schedule. Then I got a chance to spend some time with Dario. I started to realize these guys, their management team is excellent. The focus, the dedication, they had almost no turnover, the quality of code, and then the business plan was really starting to play out. It's one thing to grow from $100 to a billion, but it's another to do 9. And then, so we reached out to the company as much as we could. They took a meeting with us. We did a 90-page PowerPoint deck where we used Claude Code to scour the internet for all the feedback we could about the coding market and what their products were good at, where they might need to improve. And we also did our whole overview of what the coding market would be. They welcomed us into this round and then we stayed close with the CFO. It's been great to build a relationship with them, and I think we punched above our weight in terms of the allocation. So that one was a total home run. We are in this period where the unicorn market is bigger than most stock markets in Europe, maybe even combined. It's definitely bigger than Germany. It's definitely bigger than the UK. Even before we invested in privates, the first one was 2020. We have to know these companies and you really have to know them now because sometimes they're the biggest companies in the space and have huge impacts. So we, do 2,000 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, get involved in their rounds. Our first one was Stripe. We had a large investment at the time. This is 2017, '18, '19, and 2020. We owned Adyen, which is a fantastic payments company and they're a next-gen cloud payments company taking from Worldpay and the cloud modern payments was 5% of total $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, talked to 200 customers in Adyen. But when we asked them about Adyen, we asked about Stripe and we realized this is Coke and Pepsi. We said, we gotta find a way to invest. And I finally got to meet the Collison brothers in 2019. And so that was our first one. We weren't really known for privates. I've got a friend who's involved with a venture firm that has tremendous amounts, and I talked to him about it and I said, let me know if you ever want to sell some. And then I get a call from him during COVID in April of 2020. We knew a lot about Stripe. We didn't have the full financials, but we knew enough that at that valuation, I think it was $35 billion, They disclosed we had over half a trillion of TPV, and we knew that Adyen's take rate was 25 or 30 bps, and we knew Stripe's was 40 or 50, and we knew how many employees they had, so we could kind of get at the profitability. It turned out the take rate was higher. It turned out they were being modest about their TPV. It was much higher than the $550. It was closer to the $1 trillion. We underwrote the thing under our assumptions and it was much better. And then we were able to upsize that from the seller. To a $100 million block. The VCs are going to own and then most of them are going to sell. They like it that we'll own and own in the public market, which we did with Nubank as well, owned it for a long period of time in the public market as well.”

“One of the great things is just the friendships I've built with so many smart investors. And frankly, Philip Fisher said part of his process was get to know a good 10 or 15 like-minded people around the country. And share ideas. They're great friends to make. A lot of them have been on your podcast. You develop good friendships and then you share ideas, talk ideas. It's important that it's a two-way street. I call it the tripod. When I like something and then my analyst likes it, and then somebody who I really respect also likes it, that's three legs of the stool, can really help the conviction.”

“I would like to say that we're so advanced in our AI systems that It's a huge change, but so far it's helping us get up to speed and we have a handful of great apps, but it's not supplanting the job of the analyst. And so much of what we're doing is we're meeting with as many companies as humanly possible. We're developing relationships with the management teams that we cover. We're talking to the competitors. The system we use is right out of Common Stocks and Uncommon Profits. Which was written by Philip Fisher in the 1950s, and it's the scuttlebutt approach. It's growth investing. It's get out there and talk to suppliers, customers, competitors, looking for the key characteristics of these leading companies and really developing conviction in them. Now, if it's a new complicated area like ABF substrates or PCBs, we're able to get up to speed on those things quickly, but it can't pick stocks for you in any kind of a way. I will say that if you're an analyst who's good at the blocking and tackling, there's a role for that, but you need to have obviously the insight on top. So we're now like using AI to write notes or review the quarter, and those notes are much better, but there better be a really good paragraph on top Which is the wisdom. What does this mean? How does this deal with our thesis? What changed? Don't just be a reporter. So the AI can be a great reporter. It can't pick into the future. The job that the guys did on AppLovin 2 years ago, I think we got 2 of the best ad tech guys. I knew ad tech. I started actually nearby here in New York at an internet advertising startup, and after I did banking, I knew internet advertising and ad tech, which is historically a terrible industry, but Michael and Sam really figured out the AppLovin story before anybody, and they followed it when it was private. They know all the competitors, they know all the intricacies of terminology, and Sam went to the Las Vegas App Advertising Conference, and we went to Cannes, and we talked to scores and scores of people. So did the work on the model and developed a great relationship with Adam Ferrogi. He's one of the best managers out there. I don't see AI doing that.”

“It was a long-short fund and we want to be focused. And if you defocus, that can be hard. So we grew that and we got that to the scale that we wanted to. We're 20 years old, maybe 10 years in, people started to ask for a long-only product. Sometime in maybe 2015, we formalized that we might be doing privates. And so we gave investors the option to opt in or opt out., and you could do 15% or 25%. So we didn't break the seal on the privates until 2020. We just think there's a huge structural underweight of the largest tech companies in the world. We also realized that a lot of our performance over the years was from some of the largest companies, whether it be Apple or Amazon or Tesla. And so a lot of our largest pools of capital endowments or what have you, They realize they've been massively underweight the largest tech companies in the world because they have a lot of privates, they don't have a ton of public, and then maybe half the public is international. And then of their public bucket, there's a belief that there's no alpha in large cap, so they underweight large cap and they have a lot of small and mid managers that are stock pickers because it's intuitive that large cap can't have alpha., and then in their hedge fund portfolio, even if it's long bias, they're not going to have 15% in Nvidia and all these other things. We realize that people are worried that there's these big companies. This is just a product of the digital economy in that in tech, the leader usually grows bigger and wins and develops very high market share quickly, and there's great competitive advantages. They're also selling around the globe, so this is going to lead to massive profit pools and massive market caps. And it's just going to happen in, in the future. Most endowments are betting against this because they're completely underweight this. I think there's tremendous alpha in the largest cap because if you think about it, a small cap, it just takes one person to figure out it's good and move it up. But it takes 100 people, 100 diversified PMs to realize Google's not a loser, it's a winner., and can we figure that out before 95% of those generalist PMs?”

“It was a long-short fund and we want to be focused. And if you defocus, that can be hard. So we grew that and we got that to the scale that we wanted to. We're 20 years old, maybe 10 years in, people started to ask for a long-only product. Sometime in maybe 2015, we formalized that we might be doing privates. And so we gave investors the option to opt in or opt out., and you could do 15% or 25%. So we didn't break the seal on the privates until 2020. We just think there's a huge structural underweight of the largest tech companies in the world. We also realized that a lot of our performance over the years was from some of the largest companies, whether it be Apple or Amazon or Tesla. And so a lot of our largest pools of capital endowments or what have you, They realize they've been massively underweight the largest tech companies in the world because they have a lot of privates, they don't have a ton of public, and then maybe half the public is international. And then of their public bucket, there's a belief that there's no alpha in large cap, so they underweight large cap and they have a lot of small and mid managers that are stock pickers because it's intuitive that large cap can't have alpha., and then in their hedge fund portfolio, even if it's long bias, they're not going to have 15% in Nvidia and all these other things. We realize that people are worried that there's these big companies. This is just a product of the digital economy in that in tech, the leader usually grows bigger and wins and develops very high market share quickly, and there's great competitive advantages. They're also selling around the globe, so this is going to lead to massive profit pools and massive market caps. And it's just going to happen in, in the future. Most endowments are betting against this because they're completely underweight this. I think there's tremendous alpha in the largest cap because if you think about it, a small cap, it just takes one person to figure out it's good and move it up. But it takes 100 people, 100 diversified PMs to realize Google's not a loser, it's a winner., and can we figure that out before 95% of those generalist PMs?”

“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. It changed too fast. Yeah. And so the S-curve is our map for looking in the future. Now, a lot of people were worried about tech because they thought there was so much disruption, you could never trust a company to be a long-lived asset. What we found over the years is some of the competitive advantages within the digital world are more powerful, if not equally or more powerful than in the offline world. You've got the network effect that was so powerful for LinkedIn, Facebook, Alibaba, you name it. Then you can become an industry standard. Oracle and Bloomberg are the industry standard. Oracle charge a lot and there's free versions, there's open source Oracle, but they had all the database administrators, they had all the software that was tuned to work with them. They basically had a chokehold on the relational database market forever. You can get to scale very quickly because these S-curves grow and all of a sudden Anthropic is doing $30 billion in sales, or Amazon had so much scale and they got it quickly. So they got a Walmart-size scale advantage in 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. You can't make a chip without their lithography. 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. And cost to acquire versus lifetime. It's the whole business model. Almost all the companies I mentioned have all of these rolled into one. Sometimes we can notice these things before the rest of the world. One of our high points was we pitched Amazon for AWS at 2013 at the Robinhood Investors Conference, and we said the bulls have no idea what they're sitting on. Amazon's won the war before it even started. And at that time we said there's Coke and there's no Pepsi. Did turn out there was Pepsi, but it was big enough to last and we could see they had a 7-year lead. So first mover's important. Then they became a whole ecosystem and a platform. Then they got scale. So they were 10 times the size of everybody else. Nobody could invest in the R&D to catch them. But you're right that if you don't have a competitive advantage, you can be in the best S-curve of all time and still lose out.. But if your name was RIM, Palm, Nokia, HTC, LG, Motorola, I can go on forever, 0, 0, 0, negative, negative, negative, negative. And that's what we saw at the foundational model layer where there's like 50 companies trying to do that and they all have fallen away and 2 or 3 have emerged at the top. There's a lot of reasons to think they will continue to hold their position.”

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“It's S-curve, competitive advantage, and then underappreciated earnings power. And when you get the right part of the S-curve, you get exponential unit growth. If you have a very strong business model, which in tech there's so many of those for so many different types of moats, your earnings don't grow linearly, they grow exponentially. And that's the last piece. Invest when there's underappreciated long-term earnings power. And very often the earnings can grow from $1 to $10, and it happens way more than you think, and it allows you to buy some of the best companies in the world for extremely low 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 were owning 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, and they're so focused on the next year or the next quarter. Very few people believe you can accurately predict 2, 3, 4 years out. But if you follow and understand the S-curve, you know the moats, and you know how to model, you really can predict these great things. So let's go to the S-curve. So The S-curve is crucial because every technology follows this pattern where it comes out. The smartphones were out 10 years before the iPhone. The internet was out 20 years before Netscape. AI has been out hidden inside of these companies, but it wasn't until ChatGPT took it public and ignited what it was. So electric vehicles, Tesla went public 15 years before 2019 when it went vertical. Because there were so many barriers to adoption. The first smartphones, they were clunky. They didn't have touchscreen. There wasn't a wireless data system. And then they were too expensive. They were $500 or $600. Steve Jobs got the price to $200. There was AT&T had a 3G network. It was touchscreen. It was so easy your grandmother could do it. And he built an ecosystem and made it simple. So all the barriers to adoption were eliminated., and then you rocket when those barriers are removed. That's the tornado of demand that everybody in the world knows they need this right away. And so that's the flip that happens. It happened with electric vehicles. The price was too high. Elon got the price to $40,000. Range anxiety was there. He got the range to 300 miles. The supply chain was finally in place so he could churn out millions of these things. That triggers the inflection. Now the other nuance, it's not just, oh, it's taken off now. It's how big is this S-curve, how tall it is. So, you know, when to sell, how long to hold on, because we're underwriting out 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, we realized the TAM for this, it was the largest TAM in enterprise IT ever, 'cause previously the TAM was routers, memory, storage, Dell, EMC, but they were doing it all. You wanna know how tall the S-curve is? So we figured out they were addressing 600 billion of IT systems directly addressing that. And then we said, it's probably gonna be 50% deflationary., and then therefore were 1 or 2% penetrated. But then 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. So that means the TAM was so much bigger. There's mega S-curves and there's 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. And all these things build upon one another. With the electric vehicle S-curve, you have to pay attention too, because we thought probably maybe 40 to 50% of the cars would go electric, but it did hit a big wall at, at 10 or 15%. Usually the S-curves go kind of all the way, but in this case, for a variety of reasons, it didn't. So you have to adjust and you have to stay on top of it. And generally when something gets to sort of 30, 40% penetrated, then you stop having exponential growth, which means the sell side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth and it was a mistake with Apple 'cause in the first 5 or 6 years of Apple, it was awesome. I mean, it was our largest position and would go up 50, 70% a year. Except for '08. And then we sold in 2012 when it got to sort of 50% of the US had a smartphone. And with Apple, they maintain 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 also got to play in the application 'cause they get 30% of the apps. So they were able to compound very nicely, say 20%, but the big years We're in the 0 to 50% part of the curve.”

“Yeah. 1 to 9. Yeah. 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, nobody had any idea what 2026 could be. The second big unlock lately is that Claude Code has gone to almost completely agentic. You had Andrej Karpathy and Linus Torvalds, two of the smartest people in coding, and they completely flip-flopped. And Karpathy said last year code tools could write 20% and 80% would be handwritten. That flipped when the latest model came out, and now he hasn't written a line of code except in English. And not to mention the pure unlock that we're gonna get for the people that never knew how to code. So just coding alone has completely taken off. One difference between the cloud— GCP, AWS— and the AI companies is the clouds, generally it's commodity. They're selling you servers and storage. They have a lot of software on top and there is stickiness to it. But in the AI models, everyone thought it would be pure commodity, but there's tremendous differentiation within. There's different training methods and different skills that they're good at. And a lot of people have routers that switch in between, which sort of makes it sound like they're commodity. But Anthropic, they're very good for anything that has to do with private equity and finance. Google's very good for ingesting PDFs. There's a lot of like differentiation, critical IP, which is a great competitive advantage. Many companies have come after the coding franchise, and Anthropic has been able to keep ahead. The other thing that's good about the foundational models and Anthropic is it's not just the API or the model. They're building a whole monopoly or whole ecosystem of products around the API. So they've got the SDK, Claude for Cowork, orchestration layer, and all the tools, they call it sort of a harness, which is the software around the API that gets the most out of the model. This was one of the things we saw with AWS really early on in 2013 was, oh, people thought it was a commodity server up in a warehouse. Big deal. They saw this was a new way of doing computing, so they invented all these products that they could see before everybody else that slowly built lock-in. The other way we think about this is where are we on this S-curve? And we have this infrastructure layer S-curve, which we think is 10% penetrated. And by the way, we think it's still one of the best ways to play AI, and we'll talk about how that feeds back through. But if you think about it, 200 or I don't know how many, 800 million people are using AI. They're just using AI 1.0, which is like a search engine on steroids. But now with these new primitives where you have Claude on your computer linking it in, then you build skills and then they're going to build true AI bots. Big corporations are going to build much larger. But where are we in terms of the amount of people doing that? I mean, Sundar said it's 10 bps of the knowledge workers of the world. So Anthropic has something like 14 or 15 million DAUs. Probably a small portion of those are truly doing AI the way you can do it. So that 10 bps, it's classic S-curve where these are the tinkerers and then it's going to go to the early adopters, then it's going to go to the early mainstream. But you're going to go from 10 bps to 1 to 2 or 3% to 5% to 15% in the next 4 years. And kind of a light switch this year went off in the enterprise where everybody realizes they need to do this now and do it fast. But it's still Internet 1.0 when it's like you knew you needed a website in 1998, but it's like hard to build that website. But this is coming together fast. And so the enterprise AI or enterprise application AI market is less than 1% penetrated. And, you know, we talk about S-curves, we call this an L-curve, just straight up. We'll take this to the infrastructure. We're at 10 basis points of people really using AI, and there's not enough compute in the world. So Anthropic has half of what they need right now, and that's before this huge takeup. Marc Andreessen said in the next 4 years, one thing he's sure of is there's not going to be enough compute.”

“One thing that bothers me is there's a lot of negativity in the general population about AI, and there's a lot of negativity in some aspects of the government. You know, I think Maine just banned data centers, and only 20% of the people are optimistic about AI and potential for negative regulation. But I do think kind of the genie is out of the bottle. Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adoption to happen even if the models didn't improve. But Jensen said this years ago when he was talking about his graphics chips, if good enough is good enough, I won't have a business. Every year he made the graphics a little bit better and people always wanted the best. In AI, if Anthropic sort of hits a wall and stops improving, or OpenAI, then the open source models will catch up. It might be a race to the bottom and it won't be good for the stocks. Probably it could be good for the chip companies. The chip companies don't care. Who wins. So that's another positive, and they'll benefit. Jensen really wants open source to like take off. It's all he kept on mentioning at his last GTC. So that could be a risk. Another thing is if one or two of the players falters and loses its position and can't compete, that could be like a lot of compute that they don't need in the future. Now, if AI's so big, somebody else will suck that up, and we saw that with Oracle canceled a big deal and then Meta went right in. But let's just say Meta decided not to be involved with AI. Hey, we can't keep up. It's just gonna be a waste of our resources. We watch that very carefully. In general, we see more companies truly going after this and even Microsoft going, trying to build their own. Those are some of the key risks.”

“One thing that bothers me is there's a lot of negativity in the general population about AI, and there's a lot of negativity in some aspects of the government. You know, I think Maine just banned data centers, and only 20% of the people are optimistic about AI and potential for negative regulation. But I do think kind of the genie is out of the bottle. Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adoption to happen even if the models didn't improve. But Jensen said this years ago when he was talking about his graphics chips, if good enough is good enough, I won't have a business. Every year he made the graphics a little bit better and people always wanted the best. In AI, if Anthropic sort of hits a wall and stops improving, or OpenAI, then the open source models will catch up. It might be a race to the bottom and it won't be good for the stocks. Probably it could be good for the chip companies. The chip companies don't care. Who wins. So that's another positive, and they'll benefit. Jensen really wants open source to like take off. It's all he kept on mentioning at his last GTC. So that could be a risk. Another thing is if one or two of the players falters and loses its position and can't compete, that could be like a lot of compute that they don't need in the future. Now, if AI's so big, somebody else will suck that up, and we saw that with Oracle canceled a big deal and then Meta went right in. But let's just say Meta decided not to be involved with AI. Hey, we can't keep up. It's just gonna be a waste of our resources. We watch that very carefully. In general, we see more companies truly going after this and even Microsoft going, trying to build their own. Those are some of the key risks.”

“One thing that bothers me is there's a lot of negativity in the general population about AI, and there's a lot of negativity in some aspects of the government. You know, I think Maine just banned data centers, and only 20% of the people are optimistic about AI and potential for negative regulation. But I do think kind of the genie is out of the bottle. Another risk is that if AI slows down in its improvements, I think there's a whole lot of AI adoption to happen even if the models didn't improve. But Jensen said this years ago when he was talking about his graphics chips, if good enough is good enough, I won't have a business. Every year he made the graphics a little bit better and people always wanted the best. In AI, if Anthropic sort of hits a wall and stops improving, or OpenAI, then the open source models will catch up. It might be a race to the bottom and it won't be good for the stocks. Probably it could be good for the chip companies. The chip companies don't care. Who wins. So that's another positive, and they'll benefit. Jensen really wants open source to like take off. It's all he kept on mentioning at his last GTC. So that could be a risk. Another thing is if one or two of the players falters and loses its position and can't compete, that could be like a lot of compute that they don't need in the future. Now, if AI's so big, somebody else will suck that up, and we saw that with Oracle canceled a big deal and then Meta went right in. But let's just say Meta decided not to be involved with AI. Hey, we can't keep up. It's just gonna be a waste of our resources. We watch that very carefully. In general, we see more companies truly going after this and even Microsoft going, trying to build their own. Those are some of the key risks.”