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

0:02 Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories, and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at Colossus.com. Mm-hmm. Chick O'Shaughnessy is the CEO of Passitive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Some.

0:36 This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. My guest today is Alex Sacerdot, founder of Whale Rock Capital Management. WhaleRock is a technology focused investment firm that manages more than 17 billion dollars across hedge fund, longly, and hybrid strategies. Over the past three years it's been one of the best performing funds, compounding up roughly forty four percent 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.

1:17 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 Saccherdo. Alex, you were saying that your highest conviction position is anthropic right now. Can you tell the story of

1:38 Discovering it, making the investment, using this anecdote as an excuse to talk about All the things that I think you and I are mutually interested right now. Investors like you investing in private markets, entropic to business, AI, everything, it's a great way to zoom in. Why is it your highest conviction and how did you get started? When the gun went off with open AI chat GPT in November twenty twenty two. We immediately took the firm and did a massive deep dive with our ten person team.

2:05 Any time 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. Ships at the bottom.

2:17 The clouds. And then the foundational models. And then the applications on top. And at that time this was twenty twenty three early, we said.

2:28 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.

2:41 We know we're going to need tremendous amounts of compute, and we did a deep dive into that, which we can talk about later. But over the next Two or three years. We started to get more clarity on how the foundational model Layer.

2:55 would evolve and at the time Two or three years ago. There were sixty different companies going after it. OpenAI was kind of in the lead and We did a webinar in April.

3:07 twenty twenty three and we said Look, this might be a winner take all. It might be a total commodity because there's open source players. It might be a race to zero. Or it might be an oligopoly where there's three or four leading players.

3:22 And what we saw over the following Three years Was that Almost all the startups. Fell away.

3:31 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

3:44 Their effort faltered and they had to do a total reboot. In the meantime, anthropic kind of was this dark horse candidate, the startup Stay focused. Really purely on the enterprise.

3:57 Open AI had kinda 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.

4:12 Very similar. to how the cloud market evolved where three companies Underpin. The entire

4:21 SAS 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 eighty percent

4:40 Close to the top of the benchmarks. Going from eighty to eighty-five 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 kinda falter. Meanwhile The scaling laws And other

4:57 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.

5:12 The big kicker. Was Code. And this is The true

5:18 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, because if you remember the early

5:34 versions of the models were good, but There was a lot of negative feedback from corporates. And could they be truly agentic? In Twenty twenty five.

5:46 The first claude code And the coding tools really began to explode. You saw The first gen was like Microsoft Co pilot, which is like twenty dollars a month. And that could sort of improve your grammar of coding, maybe find a bug, maybe make a block.

6:02 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

6:18 people who could use it unfettered. We heard that, you know, even within Anthropic at that time. People were spending a hundred dollars a day on tokens. Which if you do the math comes out to twenty or thirty thousand dollars a year. And if you think about How many coders there are in the world, twenty million, you've got a half a trillion dollar market.

6:38 Just from coding alone. And mind you, that was on Seven, eight, nine month old technology. We could see just on the coding market alone that Anthropic had a tremendous opportunity ahead of it.

6:52 So We made the investment At the one eighty valuation, we said And I think they were Hoping to get to a nine. Yeah, one to nine. Yeah. One to nine. Yeah. And then and then the numbers were

7:05 Like nothing we'd ever seen before. One hundred On the way to nine. But when we did it in August of twenty twenty five, nobody had any idea what twenty twenty six could be.

7:17 The second big unlock lately. Is that Claude Code has gone to almost completely agentic. You had Andre Carpathy and Linus Torvald, two of the smartest.

7:29 People encoding And they completely flip flopped and Carpathi said. Last year it's code tools could write twenty percent

7:38 And eighty percent would be handwritten. That flipped. when the 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

7:54 Just coding alone. has completely taken off. One difference Between the cloud G C P A W S

8:03 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

8:14 A I models, everyone thought it would be Pure commodity. But there's tremendous differentiation within there's Different training methods. And different skills.

8:24 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 the 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.

8:42 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

8:56 It's not just the API or the model. They're building a whole Panopoly or whole ecosystem of products around the API. So we've got the SDK Cloud for Cowork. Orchestration layer.

9:11 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 twenty thirteen was oh people thought it was a commodity server up in a warehouse big deal.

9:30 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

9:44 We have this Infrastructure layer S curve. Which we think is ten percent penetrated and by the way we think it's still one of the best ways to play A AI, and we'll talk about how that feeds back through.

9:57 But if you think about it two hundred or I don't know how many eight hundred million people are using AI. They're just using AI one point oh, 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 gonna build true

10:15 AI bots. big corporations are gonna build much larger but Where are we in terms of the amount of people doing that? I mean, Sunder said it's ten bips of the knowledge workers, the world. So Anthropic has uh something like fourteen or fifteen million DAUs.

10:32 probably a small portion of those are truly doing AI the way you can do it. So that ten bips, it's classic. S curve where these are the tinkerers. And then it's gonna go to the early adopters, then it's gonna go to the early mainstream, but you're gonna go from ten bips to one to two or three percent to five percent

10:51 T. fifteen percent in the next four years and kind of a light switch this year went off in the enterprise Where everybody realizes they need to Do this now.

11:01 And do it fast. And it's still Internet one point oh when it's like you knew you needed a website in nineteen ninety eight, 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

11:19 Less than one percent 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 ten basis points of people really using AI. There's not enough compute.

11:35 in the world. So Anthropic has half of what they need right now, and that's before This huge Take up Mark Andreessen said in the next four years, one thing he's sure of is there's not gonna be enough compute. I'm so curious when an investor like you, who historically was a public markets investor, you could hit buy and buy whatever you want. is now operating in lots of the most important private market companies. We can talk about Stripe or Databricks or OpenAI or Anthropic.

12:00 How Do you get the positions at the size that you want? coming from the legacy of being able to just buy How much of it is creativity just directly with the company? If it is directly with the company, So they have to it's a double opt in, they have to decide to let you in too.

12:16 How do you do that? What have you learned about getting the allocation you want? or the amount of equity you want in a private company. Given that That wasn't your original background. We got to know the company one of our analysts knew

12:31 People and You finance group there. We had a look at the sixty billion dollar round and we didn't know the company as well. The gross margins were negative and Frankly, we hadn't seen coding explode the way it had.

12:45 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.

13:04 The quality of the code. And then the business plan was really starting to play out. It's one thing to grow from One hundred to a billion. But it's another to do nine.

13:14 And then so we reached out to the company as much as we could. They took a meeting with us. We did a ninety page PowerPoint deck. Where we use cloud code to scour the internet. for all the feedback we could about the coding market.

13:29 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

13:50 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

14:12 And have huge impact. So we do two to three thousand face to face meetings with management teams a year. And about ten or fifteen percent of those are with privates. And then we kind of focus in on the Companies

14:26 That we really want to learn about and find ways to meet with them. Get involved in the rounds. Our first one was stripe. We had a large investment at the time. This is

14:38 twenty seventeen, eighteen, nineteen and twenty twenty, we own Audion, which is a fantastic Payments company. And they're a next gen cloud payments company taking from world pay and the cloud modern payments was Five percent of total

14:51 eighty trillion dollar market or what have you. But you can invest in Audion. Unless you know stripe like the back of your hand. So We did tremendous amounts of due diligence, talked to two hundred customers in Adian, but When we ask about Adian, we ask about Stripe and we realized this is Coke and Pepsi.

15:07 We said we gotta find a way to invest, and I finally got to meet the Calson brothers in twenty nineteen. And so that was our first one. We weren't really known for privates. I've got a friend It was Involved with the

15:19 venture firm that has tremendous amounts and I talked to him about it and I said Let me know if you ever wanna Sell some And then I get a call from him during Covid in April

15:30 of twenty twenty we knew a lot about stripe. We didn't have the full financials, but we knew enough. Then at that valuation, I think it was thirty five billion. They disclose we had over half a trillion of TPV. And we knew that Audion's take rate was

15:46 twenty five or thirty bips and we knew. Stripes was forty or fifty. And we knew how many employees they had, so we could kinda get at the profitability. It turned out the take rate was higher. It turned out they were being modest about their T P V. It was much higher than the five fifty. It was Closer to the one trillion.

16:04 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 one hundred million dollar Block. The V Cs are gonna own and then p most of them are gonna sell. They like it that we'll own. and own in the public market, which we did with New Bank.

16:21 as well, owned it for a long period of time in the public market as well. Maybe now's the right time to lay out everything you've ever learned about S curves. Obviously your firm is sort of predicated on this idea. of technology adoption Cycles.

16:35 And investing in companies at the right time. Amidst a certain platform change or S curve change. I'd love you to go into the super deep detail of what you've learned. Since this is the lens through which you've viewed markets and stocks for a long time.

16:49 Bring us into like the nitty gritty fine green nuanced detail of why S curves can be so useful for investing. We have an investment framework. It's S curve. Competitive advantage. And then underappreciated earnings power. And when you get the right part of the S curve.

17:05 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 motes. 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 one dollar to ten dollars, 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 PEs.

17:35 when we're buying NVIDIA in twenty twenty three. We were paying four times earnings. When we bought Tesla in twenty nineteen. For the car S curve. We were paying five times earnings when we were owning Apple. we were paying four times earnings. When we bought Amazon for AWS, we were getting it for free.

17:53 The world Doesn't think exponentially. And they're so focused on the next year, the next quarter, very few people. Believe You can accurately predict two, three, four years out.

18:05 But if you follow and understand the S curve, you know the motes 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

18:18 Technology follows this pattern. Where it comes out. The smartphones were out ten years before the iPhone. Іннет вот. twenty years before Netscape. AI has been out hidden inside of these companies, but it wasn't until Chat GPT

18:36 took in public. And ignited. What it was. So Electric vehicles Tesla went public fifteen years before twenty nineteen when it went vertical. Біздер со мнірсьом the first smartphones.

18:49 They were clunky, they didn't have touch screen, there wasn't a wireless data system, and then They were too expensive. They were five or six hundred dollars. Steve Jobs got the price to two hundred. There was ATT and a three G network. It was touch screen. It was so easy. Your grandmother can do it.

19:06 And he build it. 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.

19:18 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 forty thousand. Range anxiety was there. He got the range to three hundred miles.

19:34 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

19:49 How long to hold on? 'Cause we're underwriting out two or three years. We have to know what the growth looks like thereafter. And these S curves can be dynamic. So when Amazon had AWS. And it was

20:03 A hidden line item. Inside of Amazon covered by retail internet analysts. We realized the TAM for this, it was the largest TAM in enterprise IT ever, because previously the TAM was routers. Memory. Storage Dell.

20:19 EMC But they were doing it. Oh. You want to know how tall the S curve is. So we figured out they were addressing six hundred billion of IT

20:28 systems directly addressing that. And then we said It's probably gonna be fifty percent deflationary. And then therefore we're one or two percent penetrated. But then over time we realized it actually wasn't deflationary if you talk to

20:43 Anybody now, so 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 one point oh mobile Clown.

20:58 E commerce. And now AI. Which We can confidently say is the biggest and all these things build upon one another. With the

21:07 Electric vehicle as curve. You have to pay attention to because We thought probably maybe forty to fifty percent of the cars will go electric, but it did hit a big wall at at ten or fifteen percent usually

21:21 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 thirty, forty percent penetrated. Then you stop having exponential growth.

21:38 Which means the south side catches up and there's no longer big beats. And is that when you sell? Generally, we like the high growth. And it it was a mistake with Apple,'cause in the first five or six years of Apple, it was awesome. I mean, it was our largest position and would go up fifty, seventy percent a year. Except for O eight. And then we sold in twenty twelve when it got to sort of fifty percent of the US had a smartphone.

22:01 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

22:15 application'cause they get thirty percent of the app. So They were able to compound very nicely, say twenty percent, but the big years Or in the zero to fifty percent part of the curve. I'm so fascinated by this sometimes decade plus long flat line at the beginning of one of these curves. Which makes me wonder. What you've learned about

22:33 the right moment to buy or even start paying attention before you buy. How do you measure that? Is it always different? What are the pitfalls that you've fallen into. How do you know when start thinking about buying one of these things?

22:46 Andy Grove says When you have strategic inflection points You can't trust the data. And strategic. Inflection points are about intuition

22:56 Anecdotal evidence. I love this book called The Tow Jones Averages, A Guide to Whole Brain Investing, which is right brain and left brain, and the best investors have the right the creative side where it's visual, it's connecting the dots. We invested in the mobile video game S curve for so long. The screens were small on the phones and the processing power wasn't good. So you had all these casual games. But then I was in China and I saw this little twelve year old boy with a huge phone and he was like

23:25 Playing a Awesome video game. I'm like, Oh my God, it's now coming to the phone. So It's visual. Enterprise is hard'cause you can't see it. Smartphone you can see, oh my God.

23:36 I got it. It's amazing. With AI, there's some intuition there. But enterprise, you don't really get to do that. We go to the Gartner IT symposium. thirty thousand

23:48 American CIOs go there. We saw this happen with Splunk. Where That used to be an amazing data base company and their room where they were explaining was like

23:58 Standing room only. Or we saw that with VMware. thirty years ago where they virtualized the server There was standing room only, and you could just see the corporate demand just beginning. And with AWS

24:12 We went there and the grand ballroom was Completely packed. And that was a nine o'clock and at ten o'clock the grand ballroom was completely packed. Eleven o'clock. So you could see the demand exploding before it. happened. We look for all kinds of clues and there's a whole pattern recognition.

24:32 That happens, and by the way, it's okay to be late. It's okay to miss the first one, two, three years in a lot of cases, because If the top of the S curve is Half a trillion the growth can go on for a long time. So you don't always have to be right there.

24:48 It's okay to m miss the first hundred percent. Peter Lynch. I started at Fidelity and He loved to mentor the kids, so I got some time with him. He said White out the chart.

25:00 It's all about the future. So it's okay to miss, but what helps about the S curves is is sort of how long it goes for. Then there's the slope of the S curve, which is important. And a lot of people think 'Cause we're in a modern world, everything's so fast.

25:15 But There's a lot of factors that Determine the pace of the adoption. And we commission This gentleman, Horace Dadu, used to work with

25:24 Clayton Christiansen to go look in history and we have the big S curves on our wall over the last hundred years. And the radio S curve. was one of the fastest ever. It took seven years to reach like a hundred percent penetration. But the dishwasher as curb

25:41 is like that because it needs to be plugged into the back end. B to B stuff can take a long time because it needs to be plugged into the existing systems. Consumers generally tend to go a lot faster. I love that. The radio and the dishwasher are the two models for adoption.

25:59 I covered Internet of Fidelity. My first stock was Amazon. That's a whole other story, which was a lot of fun. But I also did B to B internet and there was a whole Huge bull case on that. The underlying infrastructure wasn't in place.

26:14 For B to B to happen. ultimately happened twenty years later with SAS. That is a risk with AI in that These big companies are very security conscious.

26:27 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 ITs

26:39 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.

26:52 And we talked to the Capital One CIO we 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.

27:05 It meant that Yeah, it was growing, but it was sort of a thirty to forty, maybe a fifty percent 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.

27:22 And I think there's enough runway In the near term going from ten bips of people really using it to two to five or whatever, which is gonna cause it to keep on going straight up. This we call it a backwards L curve.

27:35 So it's really pretty exciting. What have you learned about when the group that ends up being the leader separates itself from one of these competitive Pack so You're talking there mostly about

27:47 Overall. growth of the S curve in demand. There's always multiple players fighting for it. It seems like you kind of invest after someone has separated themselves from the pack, not try to pick the winners from the pack. You look for the S curve, then we do an exhaustive

28:03 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. Yeah. And so the S curve is our map for looking in the future.

28:22 Now a lot of people were worried about tech c 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

28:39 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

28:51 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.

29:03 Hey, 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 thirty billion in sales. Or Amazon.

29:21 had so much scale and they got it quickly. So they got a Walmart size scale advantage. In five years versus forty years for Walmart. So you can have Network effects, scale, you can become industry. standard

29:35 You can be a platform that people build on top of. You can have critical інтелектуal 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.

29:49 without their lithography. You can also have brand. And brands very important because Google, Amazon, they got to grow they never had to advertise. Elon's never had to advertise for anything.

30:02 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

30:16 Amazon for AWS a twenty thirteen at the Robin Hood Investors Conference. And we said the bulls have no idea what they're sitting on. Amazon's won the war before it even started. And at that time we said there's Coke and there's no Pepsi.

30:31 Did turn out there was Pepsi, but It was Big enough to last. And we could see they had a seven year lead. So first mover is important. Then they became a a whole ecosystem and a platform

30:42 Then they got scale. So they were ten times the size of everybody else. Nobody could invest in the R and 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 lives out. But if your name was Rim, Pa, Nokia, H C C L G, Motorola, I can go on forever. Zero zero zero zero negative negative negative negative. And that's what we saw at the foundational model layer where there's like fifty companies trying to do that and they all

31:11 Have fallen away. And Two or three have emerged at the top. There's a lot of reasons to think they will continue to hold their position. Google's a little trickier because they have

31:23 This other huge, massive complex business attached to the Gemini business, but If you take anthropic and open AI as pure plays. And you dig through those and you reason through their competitive advantages. Why aren't they susceptible to erosion of those things?

31:37 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

31:50 Or The highest because we're talking about a market in the trillions. Maybe cloud's eight hundred billion. This might be We now think three to five, but

31:59 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 two is they've built a

32:14 Strong brand. For enterprise to where Go talk to any CIO and the first thing they'll say is Claude. Three, they're gonna have escape velocity and scale. And what was scary for Open AI and Anthropic

32:28 fighting these big companies like Google. was they had these huge cash cows. And to both of the management teams credited open and anthropic, they were able to Work in these super capital intensive industries and find ways to raise capital. And certainly with anthropic

32:45 With their ten X sales growth and their fundraising ability, it looks like they've Reached escape velocity. So now they have scale. And the other thing That

32:55 Anthropic and open AI 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

33:10 It's accelerating. Maybe they can have this lift off stage. Open AI. They were focused on so many different Other sectors.

33:20 They're starting to do better in enterprise and their coding tools good and they're starting to see accelerating growth on that side. And then look the consumer franchise. It looks like enterprise right now is much better because you and I we're willing to pay a lot because It's replacing human beings. Now consumer

33:39 Maybe you can get advertising, but maybe they would pay for a claw bot type assistant if you could make that perfectly well for them. But they have to do it. Gazillion eyeballs there.

33:51 Things do shift. We have this. charts that we almost do for all of our pitches on the internet. The leader goes big, 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.

34:08 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 motes that we think are now showing up. In this business.

34:21 Now there are some exceptions to that rule, usually with the paradigm shift AOL. And then dial up went to broadband and and they didn't make the change. Netscape came out early. And it wasn't as strong of a business model. If you talk to

34:37 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. I'm so curious then what you think all of this means for software.

34:52 When I look through your portfolio, I don't see a ton of big software companies, enterprise software companies. I don't know if you once had them and sold them. But It's hard to have the experience of building

35:03 really useful cool little tools, even if they're still toys. And not have the thought of wow. If I spend enough time on this, even if I'm not technical. Maybe I couldn't. build a ERP e equivalent replacement or something for my company.

35:17 there doesn't seem to be a fundamental reason why that's not possible. And then those companies could be in lots of trouble. Seems like everyone has a strong view on this one way or the other. I'm curious how you've approached those sorts of companies, given that you don't seem to own a ton of them. Five years ago we might have had forty or fifty percent of our portfolio in software. And early on in our April twenty twenty three.

35:39 webinar. We said definitely invest in ships 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.

35:52 And they have the data. This is gonna be amazing for software. Pretty quickly. We realized They're AI products.

36:01 We're 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.

36:12 It really helped us in the first quarter. There's so many layers. To this. I mean

36:19 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

36:29 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.

36:41 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

36:56 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.

37:07 Third. Алатов софтве компанії. We're able to raise price every year. And now they're Probably

37:15 Nervous about doing that. Then Fourth. We'll see what happens with jobs because There's smart people on both

37:23 sides of that, but we are seeing Some companies really got their job. In terms of them building their own apps. If you want to be optimistic, it's taken them a while to do that. We talked about how early the primitives of AR. So maybe they

37:41 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

37:52 If you're installing something that does human work. You gotta be right at the side to make sure it's really getting done, so you need the F D E's For deployed engineers. They might not have the right people internally to do that.

38:06 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.

38:21 console games and The tablet didn't hurt the P C 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

38:36 They don't like to build themselves that much. That's all true, but You can imagine a world where in One, two, three, four, five years. You could have a brand new AI native company going after

38:49 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

39:06 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

39:19 If you're a company like Salesforce, you've got forty billion in sales. You might have five hundred of ARR, seven hundred of AR of AI. You've got this. huge base now maybe this starts to work. But it takes a while.

39:33 In software there's the rule of forty. Which is Your growth rate. plus your operating margin and if you have twenty percent growth rate, twenty percent that's good. For AI we have a new

39:44 Rule of forty. What percent of your sales are AI? Thirty percent. And what's your market share in that category, say thirty percent? You'd be sixty. That's a great place to look. 'Cause you've got exposure and you've got a strong market position.

39:59 Problem with software is their AIs. one or two percent at this stage, and it's a long way to go. One thing we are picking up though now, lately This is half big, but AI could make some of these software platforms more important because what's the first thing you do with Cloud? You plug it into Slack.

40:17 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

40:29 Inside of the existing incumbent. software tools to use them like a human being would. Just to pull on that thread, seems like the commonality of the tools they might use that are the most sticky would be network based tools. So Slack is a great example of The software insight itself.

40:44 The software is not the special part. It's that everyone is there. I'm curious what kinds of things You would want is it just the presence of a network effect is that the only thing

40:55 That really matters. Still early in our thinking here, but Even maybe Work day or The HR systems or

41:04 The big system is a 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.

41:14 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

41:31 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 it's going away. Can we talk about chips? You've referenced them a few times, infrastructure chips. Everything around the data center, maybe I don't know how you conceive of it. Why is this so interesting to you? I love the modified rule of forty for percentage that's AI and percentage market share in the category. That's an interesting stat.

41:54 Or companies shine on that. What are laggards, you know, that are surprising? For the past forty years. Nothing has changed in the data center. Even with cloud.

42:06 Intel X eighty six. became the data center chip sometime in the nineties. And compute grew in the cloud. And compute workloads grow. twenty five to forty percent every year.

42:20 But Moore's Law is improving at that rate. It didn't require tremendous innovation. And in there really was almost no growth in hardware. For years and years and years. And the whole industry

42:34 Basically Commoditized every part Every chip, every part of the server. The printed circuit board to the memory. to the enclosures, to the networking.

42:46 There was no innovation. You would go from One gig to ten gig That would take Seven years and

42:53 When you do switch in the first year. It does take some innovation to get to ten gig and would create a little cycle, but then it would commoditize. Now you go to AI. The workloads are growing ten X Every year.

43:07 And they're pushing Every Single aspect of this hardware. To the physical limits of what it can do. Not only are you creating

43:18 Tremendous Unit growth. But we call it the decommoditization of the hardware industry. I met with Sean McGuire like three years ago and he said I wish I could come back and be a Hardware hedge fund.

43:30 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 ships. So not only do you have Tremendous

43:46 Unit growth. It's requiring tremendous innovation. That means Every aspect Of the server.

43:54 Memory, which used to be a pure commodity. This high bandwidth memory is Stacked. Ten ships on top. input outputs are ten x what they were before

44:06 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 three or four generations in advance. We had this with Celestica.

44:20 Celestica. Was a contract manufacturer. This has been a disaster industry since Nineteen ninety nine.

44:29 It went all off shore to China. It was commodity. But they hung on. Celestica's heritage was IBM supercomputing. And they kept all that talent and skill.

44:41 And then we noticed They were the sole supplier of the Google TPU server. Three years ago. The stock was trading at eight times earnings. And then they also had this whole business of selling Ethernet white box.

44:54 Which is code word for commodity. White box Ethernet switches. into The clouds. It turns out

45:03 These are excellent. businesses not only do they have tremendous growth But To do an AI Server.

45:11 It's liquid cool. It's running so much hotter. two or three hundred thousand dollar piece of machinery, whereas an old server was five thousand dollars. If it breaks, you just throw it away. If this thing breaks, the whole thing goes down.

45:25 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.

45:36 A lot of other people tried to do it and fail, and so they've retained that position. Then it also turned out That the Ethernet market In the old days you would go from One hundred gig to Four hundred to eight hundred.

45:49 It would be a seven year Cycle to upgrade. Now they're upgrading every year. That's really hard to do. Then there's a whole software layer, the open source Sonic layer.

46:00 The guys at Celestica invented there were 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 fifty, sixty percent share. of the cloud ethernet switch market, which is a crucial

46:18 market for AI because AI is incredibly network intensive. And then even something like the printed circuit board. A regular server, you need ten layers. These AI server you need a forty layer.

46:31 And there's very few P C B 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.

46:46 The P C V Units are growing. The layer counts are rising, so you've got like a fifty to sixty percent Kager just in the Units.

46:55 And then the ASPs are rising. And then the gross profits are rising. And your visibility, which used to be, hey, we'll call you next week if we need you to like, hey, we need you for the next four years to be like designing this roadmap with us. You've gone from a five percent crow or low margin to

47:15 a thirty five percent Forty, fifty top line Kager for the next four years. With rising margins. On top of that, there's shortages of everything. So even if it is a commodity, it's gonna be a great Cycle.

47:28 So we see that. Up and down. The supply chain, you find these companies like I mean corning. They make the fiber.

47:36 They've got some ridiculously high share. Of the fiber. Microsoft Data center they just spill. There's enough fiber to

47:45 Circle the world four and a half times. In that one thing. And their fiber is thinner and more bendable and can be specially manufactured to the exact specs. And it's higher margin and it's the fastest growing part of their business.

48:01 In Networking, there's Scale it out. Which is connecting all the server racks together. And their scale across, which is

48:10 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.

48:21 But the wires you need like ten X, 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.

48:34 to the other ones. That's done over copper. Eventually that'll be done over fiber. When that happens That Two to three X's.

48:43 Corning's opportunity. So you just have And every layer of the rack Everyone's overwhelmed. Евріонсь ом, банус.

48:53 Every NVIDIA chip. Or rack. Uses fifty to a hundred and twenty five percent more power. That drives the ASP's

49:03 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 forty percent for the next four years in a row? And it's higher margin. The broader picture is

49:19 The AI demand if we're right with this L curve. We're already short. The D RAM market, the NAN market. B P C B we're already

49:28 Thirty percent short. All these things as we are now. A measure of percent AI, percent market share. Do you care more about the absolute or the rate of change of those metrics? We did this presentation and

49:41 twenty twenty four where we listed everybody's market share and then I asked Claude to plot it to a thing and it actually Didn't get it right. 'Cause what it didn't get is the rate of change. So the rate of change is important, and that's incredible too, because You go from

49:56 ten percent to thirty percent, and your growth rate accelerates and your margins accelerate. So rate of change is very important. Why don't more people get this right in public markets? If your whole framework is S curve competitive advantage, underappreciated earnings power. It feels like the movie's been played out. a lot over the last twenty five, thirty years. My mom said, Why do you tell everyone your secret? It's like

50:20 Why does a casino Teach people how to play blackjack. It's really hard to do. You have to be comfortable investing. I've been doing tech for twenty years at Whale Rock, we've got a team

50:31 That's been doing this, covered many cycles. No one's paid attention. to hardware. And ships. At all. So you've got all these newbies coming into it. You and Gavin, that's it.

50:41 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 And then you also have to have the holistic view because if you don't have conviction

50:56 Every time with NVIDIA over the last four years it's Oh, they had a great ear. Oh my god. It's gotta be a bubble. And then they had another great year and it's like six months of marking time.

51:08 It's gotta be a bubble. This is like getting out of hand. This is pretty scary. The Bare cases. Or not. Totally without merit, but

51:17 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

51:37 decades and scores of S curves that you're looking at and Where it plays in different things. What makes you the most concerned or uncertain? Is it just the rate at which all this stuff changes and What keeps you worried amidst what seems like pretty extreme bullishness?

51:54 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 twenty percent of the people are optimistic about AI

52:12 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.

52:24 I think there's a whole lot. of AI adaptation 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

52:36 Is good enough. I won't have a business. Every year he made the grafics. A little bit better, and people always wanted the best. In AI if

52:46 Anthropic sort of hits a wall and stops improving or open AI. then the open source models will catch up. It might be a race to the bottom and won't be good for the stocks, probably. It could be good for the chip companies. The chip companies don't care.

53:03 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.

53:15 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. Is that

53:26 they don't need in the future. Now if AI is so big, somebody else will suck that up. And we saw that with Oracle canceled a big deal and then Matt I went right in. But let's just say meta. decided not to be involved.

53:39 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.

53:54 Seems like you really have done very little in the application layer of AI. Historically the apps ended up being most of the market cap. Not the infrastructure and there wasn't really a model layer in the past. I guess you could say it was the clouds or something. Why focus so much on the bottom layers of Jensen's five layer cake versus things in the application layer that are actually getting used by consumers. Well, A it always comes later, so

54:18 the first three or four years of the iPhone and then The applications really took time. So maybe it's just starting. To date, we found that area to be pretty risky. Because where does the foundational model end and where does the application begin?

54:34 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. a CRM and they're starting and maybe just a matter of time. But we really haven't seen it in the enterprise world.

54:52 There are some. Very good. startup application companies out there. But The ecosystem's not clear.

55:00 When we started the ecosystem and ships 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

55:16 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

55:32 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 their sustainable

55:43 It usually doesn't start in the first three or four years. It comes a little bit later. At your office you have this Trying. award wall for the best research job or project of the year given to an analyst. And I think you won it and you gave it self awarded and I was alone when you were by yourself. But you've got this now long twenty year history of every year one or more people put their name on this wall for having done the best job on a research project that year.

56:08 I'm so curious about the nature of that research and how it's changing as a result of all of this. say, you know, the person that's gonna win the award this year and the sort of work That that requires a human to do. When so much of the work that probably would have won you the award in I don't know, two thousand nine or something. could probably be fully automated or done in an hour with cloud code or something today.

56:29 How is the nature of research and what gets you on that? Will rock award wall changing. In real time. I would like to say that we're so advanced in our AI systems that It's a huge change.

56:41 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 of the competitors.

57:02 The system we use is right out of Common stocks and uncommon profits. Which was written by Philip Fisher in the nineteen fifties. And it's the scuttle butt approach, it's growth investing, it's Get out there and talk to suppliers, customers, competitors.

57:18 looking for the key characteristics of these leading companies and really developing conviction in them. Now if it's a new complicated area like A B F substrates or P C Bs were able to get up to speed on those things. quickly. But it can't

57:34 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.

57:42 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

57:56 A really good paragraph on top. Which is the wisdom. What does this mean? How is this Deal with our thesis. What changed. Don't just be a reporter.

58:06 So the AI can be a great reporter. It can't pick into the future. The job that the guys did on Apple and two years ago I think we got two of the best ad tech guys. I knew Ad Tech f I started actually

58:18 Nearby here in New York, at Internet advertising startup and After I did banking. I knew internet advertising and ad tech, which is historically a terrible industry.

58:30 But Michael and Sam really figured out the Applevin story. before anybody and they followed it when it was private. They know all the competitors, they know all the intricacies of

58:41 terminology and Sam went to the Las Vegas app advertising conference and we went to Conn and We talk to scores and scores of people, so did the work on the model and developed a great relationship with Adam Farougi, he's one of the best Managers out there.

58:58 I don't see AI doing that. What role does talking to other investors outside of your firm play in your life? One of the great things is just the friendships I've built With so many smart investors.

59:12 And frankly, Philip Fisher said Get to know a good ten or fifteen like minded people around the country. And share ideas.

59:22 They're great friends to make a lot of been have been on your podcasts. You develop good friendships and then You share ideas, talk ideas, it's it's important that it's a two way street. I call it the tripod when I like something

59:37 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.

59:45 The conviction. What have you learned about shaping the products that you offer your investors? across the history of the firm. It's not just one monolithic structure anymore. There's several things that if I'm an investor and I want to give you money, there's a couple of ways I can do that.

1:00:01 How did you arrive at those things and how can you turn that experience into advice for other investors that are trying to provide their LPs with the right set of options. So the first fifteen years it was a long short fund and we wanna be focused and if you defocus That can be hard. So we grew that.

1:00:20 And we got that to the scale that we wanted to. were twenty years old, maybe ten years in, people started to ask for a a long only product. Sometime in maybe twenty fifteen we formalized that we might be doing privates and so we gave Investors.

1:00:36 option to opt in or opt out. And you could do fifteen percent or twenty five percent. So We didn't break the seal on the privates until twenty twenty. We just think there's a huge structural underweight of the largest tech companies in the world. We also realize that a lot of our performance.

1:00:54 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.

1:01:05 They realize they have been massively underweight. The largest tech companies in the world. Because They have a lot of privates. They don't have

1:01:14 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.

1:01:29 'Cause 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 gonna have fifteen percent and 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

1:01:45 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 gonna happen and in the future.

1:02:02 Most endowments are betting against this. 'Cause 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.

1:02:17 But it takes a hundred people. A hundred diversified PMs. To realize Google's not a loser, it's a winner. And can we figure that out? Before

1:02:29 Ninety five percent of those generalists. PMs And We've been able to do it. We like your odds in that. Yeah, we like our odds in that. And so

1:02:37 There is alpha to be had there. And then as an asset category, it's great because These companies by definition have wonderful modes. And maybe they're not the Super ask her, but sometimes they are. I mean NVIDIA sure is.

1:02:51 And T S M is Really levered to it and Heinz is extremely levered to it and ASML is levered to it. We're four months into that one. It sort of sounds like really what you've built is a research

1:03:04 Machine. to understand the world through the lens of companies. The thing you're constantly trying to improve is that research machine. And then the way that you then express that through products is multiplied. But if I was to try to understand Whale Rock, it would be to investigate the research machine first and foremost. We call it the Whale Rock Learning Machine, and it's a group of ten highly experienced individuals.

1:03:26 Warren Buffett reads books and we read books and we read blogs. In tech, you gotta go out and talk to people, so we do. twenty five hundred, three thousand face to face meetings with management teams. Munger and Buffett talk about compounding knowledge. We've been compounding that knowledge for twenty years.

1:03:43 There's changes to the team, but Broadly, there's a lot of Consistency to it. Andrew and Michael have been with me for eighteen years. And the average experience level on the team is ten or so years and that includes Some of the newer people.

1:03:58 That research engine can support all these products and it's the same people that do Public and the private. So we're not gonna Scour the world and turn over every A B, but when we see something that fits into our s system, we're able to act on it.

1:04:14 It's so much fun to do this with you. When I do this, I ask the same traditional closing question of everybody. What is the kindest thing that anyone's ever done for you? It's Definitely my father. I was super lucky, my father. graduated Cornell uh double E

1:04:28 Electrical engineering. pivoted to Wall Street and Had a great career at Goldman Sachs. He ran corporate finance in the eighties and then ran Private equity.

1:04:40 As chairman in the nineties. He was just Whip smart, but he He had such humility and was such a great gentleman. When I started whale rock.

1:04:51 Friends and family. He was the first call, but he said Yeah, I've been at Goldman. For forty one years. How about I come and join you? I'll be the gray hair, I'll be the oversight, I'll be the chairman, you do what you do, you build the firm. in Boston, build the team, run the money.

1:05:07 I'll help raise some money and we got to work together for six years until he passed away in twenty eleven. But I just feel so lucky to have Worked with them. But it's not easy running a fund. We never raised our voice and

1:05:21 He was just An amazing mentor to so many people, and when he passed away. I got so many letters. From people who said Your father was just

1:05:32 such an influence on me. He was such a gentleman. He was such a great mentor to me. I just feel so lucky. to have worked with them and If I could be half the person that he is, I'd be completely winning. How did he do that? What was his method?

1:05:46 Why do so many people say that? He was modest, he was whip smart. He was wise, he was also Known as a Great.

1:05:54 investor, which isn't the most common thing at a lot of investment banks. He also was on their commitments committee and kept him out of a lot of Tougher situations and He was very warm and People could go into his office. With problems, and he handled it with grace.

1:06:10 whether it's a personal problem or a work issue or what have you, and he just had this Soft way, and he also had a great sense of humor. And I'm so lucky. Alex, thanks so much for your time. Thanks so much. If you enjoyed this episode, visit Colossus.com. You'll find every episode of this podcast complete with hand edited transcripts. You can also subscribe to Colossus, our quarterly print, digital, and private audio publication, featuring in-depth profiles of the founders, investors, and companies that we admire most. Learn more at Colossus.com slash subscribe.

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