Understanding Hyperscalers — Masters in Business with Ankur Crawford

[00:00:00] Speaker 1: This week on the podcast, another extra special guest, Dr. Ankur Crawford, is co-head, a portfolio manager of large cap strategies at Alger. She's got a fascinating background. She was an engineer at Intel, won a number of patents and was the awardee of the Intel Ph.D. Fellowship. She's been recognized as one of the top women in asset management. If you're interested at all into the details of how artificial intelligence, semiconductors and software works, as I am, you're going to find this to be a fascinating conversation. With no further ado, my conversation with Alger's Ankur Crawford. Ankur Crawford, welcome to Bloomberg. [00:00:55] Speaker 2: Thank you for having me, Barry. [00:00:57] Speaker 1: So let's start with your background, which is really kind of fascinating. Bachelor's degree in mechanical engineering and material science and engineering. That's a double B.S. from UC Berkeley and then a master's and a Ph.D. in material science and engineering at Stanford. What was the original career plan? [00:01:18] Speaker 2: I didn't have one, to be honest. I think when I made the decision to become a mechanical engineer, I was kind of following my brother's footsteps, who was a mechanical engineer and became an orthopedic surgeon. And I realized if I didn't know what I wanted to do, I wanted to keep my options open. [00:01:38] Speaker 1: So he becomes an orthopedic surgeon with a mechanical engineering degree. Is he designing replacement joints and things like that? [00:01:47] Speaker 2: He does. He does actually bring that aspect of his engineering background into device, different device configurations. And he works a lot with the device companies as well. Um, but there's also, you know, as a kid, I loved, you know, figuring how, figuring out how things work, whether it was like a car or a calculator. And I would always be kind of fidgeting to understand how things work. So, um, mechanical engineering kind of felt like, you know, I'm, I'm just a curious person. So I like to, to satiate that need to know how things work. [00:02:26] Speaker 1: And I read somewhere that you originally wanted to be an astronaut. Is this correct? [00:02:31] Speaker 2: I did. So I grew up, um, until I was five. We, we lived in Florida, um, close to Cape Canaveral. And we would go watch the space shuttle take off. And I was so fascinated by space because it was almost, um, ethereal. You know, this thing goes up into the sky. And, um, for me, the astronauts were celebrities. So, um, you know, for a long time, I did want to be an astronaut. [00:03:00] Speaker 1: So I have Florida somewhere in between, you're born in Kansas. Is this correct? Yes, yes. Kansas, but you end up in the Middle East. [00:03:08] Speaker 2: Yes. [00:03:09] Speaker 1: And then you're sent to a convent boarding school in the Himalayas. Is this possibly right? That has to be an AI hallucination, right? [00:03:18] Speaker 2: No, that is all correct. [00:03:20] Speaker 1: And then you end up in Buffalo, New York. [00:03:22] Speaker 2: You got it. [00:03:24] Speaker 1: All right. So, so that's real human research, not, not chat. Um, I'm curious, that is a broad global life experience. How does that shape your views on, um, either international investing or just the concept of risk and reward? [00:03:42] Speaker 2: Yeah, um, I think it more so shapes the way I think about, you know, the cultural differences. Um, when I look at companies, when I look at management teams, um, I, I understand very well that there are certain cultural differences that are simply endemic, um, to businesses and to management teams. And, um, you know, just because a management team isn't necessarily always bullish or they're always telling you that the negative aspect of their, of their company doesn't necessarily mean that there's something wrong. And, um, um, so, you know, I think that, that has been like, like an example of this is this company called Nebius, where the CEO is a Russian CEO who is incredibly humble and he will never tell you what's right. He will always point out to you all the things that are wrong. And a lot of investors are like, you know, I don't, that doesn't sound good. And I'm kind of looking at the opportunity because that's just his culture, right? It's his culture, not to be boastful. Um, so just living in all these different countries and having exposure as a, as a kid to many different religions, it just gives a really unique perspective on, on any problem that you look at because, uh, it, it helps take kind of the blinders off. [00:05:05] Speaker 1: It, it, it's fascinating. I, I never really thought about how does, um, a societal cultural set of norms make its way to management. I mean, you, you think about the Japanese culture is the sort of, um, bravado and very aggressive, uh, forecast we tend to see in the United States. You would never see anything like that in Japan. That's right. I mean, how do you calibrate what is, uh, cultural diffidence and what is just, hey, there's a problem here and they're telling us this is an issue. [00:05:41] Speaker 2: Yeah. Um, you know, I think you have to know the business, right? That the first thing is, is know, know the business and then you can calibrate the tone of the management. And, you know, an example is Taiwan Semiconductor. I remember speaking to them over, over many of these years that we've owned the stock and I would always say, you guys are going to become the single supplier of leading edge. Why is it that you can't take up pricing and they would always talk me down and say like, oh no, you know, we are here to serve our customer. We are here to. And I was like, there's, there's absolutely no reason for you not to be raising pricing. And they would just push back and, and not really, um, cause that wasn't part of their philosophy. And it wasn't part of their philosophy that Morris had, Morris Chang had kind of put into place in the early years. Um, however, that is what they ended up doing. And so I had to take that with a grain of salt, understanding that's their philosophy. It was a little frustrating at the time, but you know, that a business is a business. And at, at, at some point, um, the realization of how good that business was came into the numbers. [00:06:53] Speaker 1: So you mentioned the advantage of really understanding the business. You're an Intel doctorate fellow. You worked as an engineer at Intel. You hold multiple patents. Um, how much of an advantage is that when you're looking at semiconductors or AI or any of the hyperscalers? What, what advantage does that give you? [00:07:16] Speaker 2: Um, look, I think understanding the technology is, it's kind of crucial right now, because in this world of AI, I think there's a lot of people who don't really understand what is happening under the covers and that's dangerous. And that's why you also see the volatility that you see today, because you know, they're kind of loose holders and not truly understanding the different dynamics of the technology. And it's just a hard, it's just a hard, it's a hard way to invest when you can get shaken out because you don't have conviction in the technologies. So it's, I, I feel like it's always helped. And in part because, you know, chips, I was a semiconductor analyst when I first started at Alger and I kind of immediately understood, well, I understand what a deposition tool is. You know, I used one, I understand what etching is, I used this tool, I understand what the issues are, um, in, in fabricating a chip and, you know, how hard it is to, to fabricate a chip. So, you know, it just gives you a little bit of a edge on the conceptual understanding and where the industry is going. So, you know, early, I remember in 2013, 11, 12 or 13, one of those years, I put together a presentation about how we're at the end of Moore's law. And what will happen if we're at the end of Moore's law, and I sent the presentation out to all of the companies that I covered, and I said, I would like your feedback and tell me why I'm wrong. But that was thinking kind of eight, nine years ahead because it had implications for the entire sector. And so those kinds of insights, I think, are easier, not that any, everyone can have them. They just come probably a little easier because I understand the technology. [00:09:11] Speaker 1: So, so I see the advantage of having the technical background as an analyst. I'm curious, what made you leave the technical field of being an engineer and working with semis to becoming an analyst in the space and working on the financing of semis? [00:09:31] Speaker 2: Yeah. Um, you know, I, I had gone through my graduate career and really had set some goals for myself. I want to write this many papers, I want to, you know, present, I want, you know, I want to be useful to society. And at the end of it, I felt like I had kind of achieved all those goals, but I wasn't happy. I just wasn't content and happy. And I thought to myself, my gosh, if I have achieved everything that I set out to do, and yet I'm still not happy, what happens if I become a professor and I'm, you know, and we just go through a tough spot on, you know, raising money or whatever it might be? Like in the research, uh, will I be able to, will, will I be even unhappier? And I think that self-awareness made me realize I, I needed to go look somewhere else. And when I came to Alger, I, it was really like, I was thinking I'd be here for two years and then go back and do a postdoc somewhere and be a professor. And, um, I never left. [00:10:38] Speaker 1: Huh. Really, really interesting. One of the complaints I've heard from people who are technologists or engineers or what have you is everything has become so increasingly specialized and narrow that you get put into a silo. You have no idea what's going on in any of the adjacent, um, sciences, more or less, even within your field, everybody gets too specific. Was that a concern? [00:11:03] Speaker 2: Oh, for sure. And that's a great insight. It is actually, um, you know, I was in a room, this, this is probably at, you know, 15 by 15 room. I spent, you know, three and a half years in the basement of a building at Stanford, taking care of a tool that was about this big. Wow. I was the plumber and the electrician and, um, carrying out cryo pumps and fixing them. And it was a, it was a very narrow, lonely experience. [00:11:30] Speaker 1: I can imagine. [00:11:31] Speaker 2: Um, and my, my advisor was fantastic. But, you know, just that process required, um, it was very narrow. And I'm very proud of the work that we did, but it was very, very niche. [00:11:45] Speaker 1: So you moved from a field governed by the laws of physics and nature to, uh, uh, another field kind of governed by the eccentricities of human behavior. What, what, what are the challenges in that transition? [00:11:59] Speaker 2: Um, you know, I, I don't really, I, I didn't know anything when I started in this business. I knew a lot about atoms and materials and magnets and, and, and how to, how to make a chip, but I really didn't know very much about, um, investing. So, uh, honestly, it was all new to me. So the, the challenge was really understanding, like, it was the, I was always asking why, like, well, why does this happen? Or why does the stock go up on this? Or why does stock not go up on this? Um, and understanding that human behavior aspect was more a fascination versus a challenge because this idea of expectations versus the truth, you know, I, I grew up in a world where there is a single answer, right? Where you, you, you, you write an equation and there is a way to do it versus, you know, people can skin cat in so many different ways. And in what we do, you can get to the same result in, you know, an infinite number of ways. Um, but, so that was, I, I suppose that was the challenge of understanding that there isn't just one way of doing it, but perhaps you have to understand the different ways than adopt your own way of approaching the problem. [00:13:33] Speaker 1: I, I love the Richard Feynman quote, "Imagine how much harder physics would be if, uh, electrons had feelings." All right, always, always cracks me up at, at the, um, intersection of, of science and investing. Um, so, you, you answer a recruiting ad from Alger, despite knowing nothing about investing, um, what made you think your skills might get you through the door, uh, at a shop like Alger? [00:14:04] Speaker 2: I didn't. [00:14:05] Speaker 1: Really? [00:14:06] Speaker 2: I, I really didn't. And I was reading a, a book, it was written by a bunch of McKinsey consultants at the time, and I, I forgot the name of the book, but it was all about, you know, profit and loss and, um, and just businesses, how businesses are run. And, um, I, I really didn't know, honestly, Barry, what I was applying for. I knew that I needed to do something else. I had worked at Merrill Lynch for a summer before I had started graduate school and I loved it. It was on, like, kind of the emerging markets debt desk. And, um, I was like, let me give this a go again. And when I applied to Alger, I knew that I was curious enough that I would, I would be able to cross the chasm and I would be able to, um, you know, learn and, and give back to our company. [00:14:59] Speaker 1: Huh, really, really? [00:15:00] Speaker 2: But I really didn't know. [00:15:02] Speaker 1: Well, well, that's really fascinating. We'll explore that more. Coming up, we continue our conversation with Ankur Crawford, co-PM of the Large Cap Strategy at Alger and PM of the Concentrated Portfolio ETF, talking about her career at Alger. I'm Barry Ritholtz. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Dr. Ankur Crawford. She is Portfolio Manager at Alger, where she co-PMs the Large Capital Appreciation Strategy, as well as running the Concentrated ETF. So, we were talking earlier about you answered an ad, um, that Alger had, had put up to, to hire people. I read a story that Alger's CEO, Dan Chung, hired you right on the spot. That, that's kind of unusual in this space. Tell us about what happened there. [00:16:08] Speaker 2: Um, it was funny. I, I actually walked into this meeting. I had just come back from Tahoe. I'm a big skier. And I was really frustrated because it was pouring outside. And I walked in, like, drenched and really, like, upset. And I was like, the only good thing about this is that it's snowing in Tahoe. And Dan happens to be a skier. I didn't know that. Um, and so we started this conversation talking about, you know, our mutual love of skiing. Um, after that, I think he realized I didn't know very much at all about investing. And he asked me my, uh, opinion of Intel versus AMD. And this was 2003, 2004. And... Peak Intel. It was peak Intel. And I remember saying, you know, I worked at Intel. And, you know, I think I prefer AMD versus Intel because this is, you know, kind of what I'm seeing inside of Intel. Not, not inside information, but more the culture that, that had developed. And, um, and we had this long discussion about it. That evening they hosted a, a kind of a get together for all of the applicants. And Dan and I got into an argument about NAND versus hard disk drives. [00:17:28] Speaker 1: Mm-hmm. [00:17:29] Speaker 2: And... [00:17:29] Speaker 1: You were on the NAND side, right? [00:17:31] Speaker 2: Well, he was telling me that all hard disk drives were, were going to go to zero. [00:17:36] Speaker 1: And he was eventually right. [00:17:37] Speaker 2: And he will be eventually right. And you know what's so funny? I just had this discussion with him yesterday. And I was like, Dan, we had this discussion 22 years ago. [00:17:47] Speaker 1: It only took you two decades to be right. You know, in, in trading, early is the same as wrong. [00:17:53] Speaker 2: Yeah. He, well, it was great because we had this really, it wasn't, it wasn't a heated conversation, but it was definitely kind of looking at this problem in two, two different ways. And as we were walking out, he was like, you're hired. [00:18:08] Speaker 1: Just like that. [00:18:08] Speaker 2: Just like that. [00:18:09] Speaker 1: I need someone who's not afraid of me, will stand up and make me think of this problem from multiple angles. [00:18:16] Speaker 2: Well, I think it's a little bit of, um, the culture that we have at Alger of it's always better to have different perspectives versus go along with the norm and be consensus. And to always encourage that debate. So, you know, and, and one thing I am for sure is, especially because, again, I come from a, from a place of we're always trying to find the truth. There is an answer. I do bring that to the table here too, and that there is an answer, right? Whether or not you look at it from, from one angle versus the other, there is an answer. Like the earnings are the answer. The trajectory of earnings are the answer. Um, and getting that right is, you know, it can be a topic of debate. And how you get there, we can debate it to make sure that we're getting to the truth. [00:19:07] Speaker 1: So you start, uh, in the analyst training program at Alger, you advanced to a research associate, then an analyst, then a tech sector head, and ultimately a portfolio manager. What transition was the most challenging? What changed the way you thought about the job? [00:19:27] Speaker 2: Um, I would say that the transition from kind of being an analyst to a portfolio manager. And even as a, as a tech sector head, it was, I kind of had my fingers in everything. Um, and I was, um, my, my little OCD tendencies were still able to play out a little bit. Um, that transition to portfolio manager, however, required a different skill set, which was allowing for other people to do the, the thinking and, or, or the, the detailed work, which I loved to do and kind of taking a much more macro perspective and a bigger picture perspective where, you know, it was a much more Socratic methodology of questioning and asking the right questions to guide the analysts in the right direction. So, um, you know, and that was like, I used to do that with the companies, right? I would ask all these questions of the companies, but doing it with your peer set and, and people that work with you is a little bit different. And so that was a, that was a, a kind of a tough transition for me. [00:20:44] Speaker 1: So you have a PhD, but not an MBA. Um, I'm curious, the apprenticeship you went through, going through all those steps at Alger, what do you think you learned through that process that, hey, a green MBA right at a school is going to take them a couple of years to figure out? [00:21:04] Speaker 2: Yeah. You know, when you are on the hook for real performance, for real clients and you make a wrong decision, it isn't like doing poorly on a test, right? It's simply not equivalent. Um, because you actually feel the, um, the pain of having made that decision that impacted someone else. So, you know, I think, I think learned, um, abilities that are experiential just have a different impact than, um, than when you're sitting in a classroom. Because I think in a classroom that the consequences are just lower than, than they are when it's, when you're really investing other people's money. [00:21:54] Speaker 1: Uh, that is the classic academia versus real life. All right. I didn't get a hundred. I got a 96. Isn't the same as this one position is ruining all of my performance for the quarter. That's right. It, it, it's very different. [00:22:09] Speaker 2: And, and I think what you learn from it is, I mean, there are certain, I have this book where I've, I, I used to write down and, and not so much anymore, but I have all my learnings from when I was a kid in the business where to remind myself not to make those same mistakes again. And, um, I haven't looked at it in a while. I probably should go back and see how, how I developed because there were so many learnings that I would carry with me and have shaped who I am today. [00:22:40] Speaker 1: I, I think that's how Ray Dalio wrote principles. Just kept writing all his mistakes and what he [00:22:45] Speaker 2: learned from them. Oh, really? I love principles. I actually have his book for kids. [00:22:50] Speaker 1: Oh, really? That one I haven't read. But he's described principles as just every mistake he's made, every adjustment he's made and ultimately how to turn errors into better performance. It's really, um, very insightful, especially from a time when wall street didn't love to admit they ever got anything wrong. It's, it's kind of fascinating. Um, so you ran Algiers tech sector and then took over with your colleague the capital appreciation strategies. Um, being hyper focused in one sector versus broad capital appreciation. What's that transition like? That sounds like a really big leap from something you're very comfortable with to, gee, there's a lot of risk and a lot of uncertainty around [00:23:39] Speaker 2: that sort of, of new job description. Yeah, absolutely. But, but look, there's, there's things that rhyme and I think that the, the, the sector I struggled with most was healthcare because it is [00:23:50] Speaker 1: so incredibly esoteric. And, um, why, why is healthcare so esoteric? You come out with a drug, you sell a few billion dollars worth, everybody's happy. Yeah, but it doesn't quite work that way all the [00:24:02] Speaker 2: time now, does it? So, and there's all this like legislative, you know, overhang, there's regulatory stuff that that's happening. There's subsidies that come and go. There's, um, there's a political backdrop that you have to always be aware of for healthcare. So, you know, healthcare actually was a part of the market where it didn't really rhyme with anything that I had done before. But if you think about industrials and financials, those were cyclicals, cyclicals of a different nature. You know, some were long, kind of long time cyclicals versus semis. Um, you know, financials were also cyclicals tied to the economy. Um, the, the emphasis more on the macro, um, um, was something I started to incorporate more, um, in, in my thinking. Um, but, you know, for someone who has a very curious, like, I'm, I'm always curious and I'm always asking questions. To me, it was, it was kind of a breath of fresh air to kind of expand my purview to understand and synthesize how the world works. Um, but it, it was, uh, I, I quite enjoy having that, that broader, broader perspective. And, and, you know, what, what is Elsa is super interesting. And, and I've only had this appreciation probably in the last decade is how history rhymes. So I have become a bit of a, you know, a history fan and in part because I got, I started when I was, I was working with my, my now 18 year old and doing history homework with her. And all of a sudden I started to realize, like, there is so much that is similar that is going on today as has been, as has happened before. So, um, you know, I, I think that is also really fascinating as you start to pull the big picture together because it just gives you a different perspective on sectors and how to invest. [00:26:10] Speaker 1: Really, really interesting. So you also are the sole manager of the Alger concentrated equity strategy, which is now an ETF form. Um, when I think of concentrated portfolios, we talking 15, 20, 25 names, how, how many names are, are concentrated and then how do you size them? Are they all equal weight or, you know, how, what does that look like? [00:26:35] Speaker 2: Yeah. So this, this portfolio is 20 to 30 stocks. It is an actively managed, fully transparent ETF. And, um, you know, when we, when we think about sizing for the portfolio and look, the concept of this portfolio is to just invest in the best businesses that are going to have the greatest change and have the most kind of have the best risk reward at any given point in time. So tell, tell us the full name of the, the ETF and the, it's the concentrated equity portfolio. Um, and the, the ticker is CNEQ. So, so the idea here is we want to invest in the best companies that are going to be benefited by, you know, the, the major, the, the best growing trends in the market and the compounding nature of earnings should drive the portfolio and drive the companies that are within that portfolio. So, you know, position sizing is just like any other portfolio, their risk reward dictates how big the companies are in the portfolio. Um, and there, there are some that, you know, Nvidia is currently at 13 and a half percent position in the portfolio. Whereas there's other companies that we're waiting at the bottom of the portfolio, kind of like figure technologies, um, that is smaller and waiting to see when the true traction in their market starts and the overhang of some of the selling and to take it up. But each of the businesses that are owned in this portfolio have large opportunity [00:28:17] Speaker 1: and big Tam. Total addressable market. Yes. CNEQ. All right. I'm going to make a note of that. Um, so I know Alger back when it was Alger Capital Growth or Alger Capital Management launched in 1964. What does growth investing mean in Alger? Because there are definitions that seem to be different from place to place. Um, what are you looking for that perhaps the market hasn't priced correctly? [00:28:46] Speaker 2: So, you know, I, I don't, well, I think what makes us interesting as growth investors is that the fundamental thing we look for is not necessarily growth, it is change. And the change begets the growth, right? So the growth is an output of the change. And I think that's an important differentiator because it's not just expressed as let's do a screen and find the companies that are growing the fastest. It is let us look for the change. Because where there is change, there is often unidentified opportunity. And because of that, we will get the growth. So we have a significant research team that is always looking for change. Now, the way that Fred had initially incepted this concept of change was to look at, you know, two different pillars. The first is what we call high unit volume growth. And that is typical kind of company that is growing their top line. Um, they become market dominant or, or have a, have a positioning that is, they're taking a lot of share, um, very forward thinking and, you know, expresses itself as high top line and growing bottom line. It could be small and mid cap companies. It could be larger companies. It really, it spans the gamut. Um, of, of, of change and growth. So that would be more of a typical, uh, growth company, traditional, kind of a traditional growth company. The other side, which I think makes us really unique is what we call life cycle change. And oftentimes we like to show this, this, this, it's almost like an S curve of, you know, we like to invest in the companies that are early on in the S curve and companies that have already gone through the S curve. They're kind of saturated out of their markets and they're starting to question who they are. [00:30:57] Speaker 1: Saturated as in fully priced or saturated as in, Hey, that's as big as their market share. [00:31:02] Speaker 2: Yeah, that's, that's as big as their market is going to get. So what you see is oftentimes companies that are, um, you know, their, their growth, their growth, they were great growth companies and all of a sudden their growth has stabilized or growth is starting to kind of approach GDP. I remember kind of more mature, right? So kind of a more mature company. And then the management has a decision to make. Am I still a growth company or am I going to just milk what we have? And oftentimes that, that begets change. So a new management comes in and decides we're going to kind of jettison all our low growth businesses and start buying higher growth businesses. And it changes the profile of the business. It could be a regulatory change that makes, um, the, the company a little bit more, um, growthy than it was historically. It could be, um, M and A that again, re-accelerates top line growth. It could be a technology technological change that they really embrace. And this was Microsoft in its early days back into, you know, when there was a, when Satya Nadella first came to the helm. So it, it's almost as if, the company had a decision to make and we're looking for changes that the decision from here is to get onto a growth trajectory and then study how they execute such that it drives both top line and bottom line growth. So that, you know, oftentimes when we buy companies that are on that side of the ledger, people will think they're value names and they're not really value, value names. They're actually unidentified and misunderstood growth. And that's how we think of them. And a great example of this is, you know, what's happening to the hard disk drive companies right now where, you know, they were trading at single digit multiples, but in an era of AI, all of a sudden you need a lot more data and you need to store all that data. So, you know, hard disk drives all of a sudden became, you know, in shortage and now they're taking pricing and their, their, their earnings power has, you know, gone up [00:33:13] Speaker 1: three, four, five fold over, over the last few years. Even though people thought it was a, um, at the tail end of their useful, uh, life cycle, they found a second life. That's right. And so, [00:33:26] Speaker 2: so that is a, that is also a change and it happens to be a change in the market broadly. Right. So, um, [00:33:33] Speaker 1: so that raises a really fascinating question. I have to ask you, there are companies that appear to be on the back end of their, of their life cycle. Um, their growth has plateaued. Maybe they're not gaining market share. Maybe the market itself isn't growing. Um, how can you identify when something is legitimately fading or potentially at the start? Like I know IBM just had a rough quarter, but how many times has that company reinvented itself and been left for dead only to surprise everybody? And, and there's a bunch of other companies. Microsoft you brought up is another example. Um, uh, what were they 30, 40 years old when Nadella came in that, that's a huge turnaround story. So, so how do you identify when, Hey, these guys are no, never going to be what they once were, or no, there's something [00:34:33] Speaker 2: real happening. Okay. So, so there is this publicly traded fintech company that, you know, was just struggling in part because they had saturated their markets and there was nowhere for them to grow. And it was becoming a lot more competitive, um, CEO and CFO leave new management comes in, you know, put together a brand new strategy. That is fantastic. Our team looks at it. It's like promising. However, the core issues of their business have not been resolved, right? Do you go from a four and five percent grower to a 10, 12, 15 percent grower with the strategy? We couldn't really, you know, resolve that they would be able to get there because the pressures in their markets were so significant, competitive mature, competitive maturity. The, you know, they were just fighting to kind of stay alive or stay, stay at that like three, four or five percent type growth. So that was, uh, one that we looked at. The catalyst was a new CEO, a new management team. They like the entire management team was different, but to us, it wasn't really logical that it would change or they could change the trajectory of the business. Um, Microsoft, a completely different story because Satya comes in. He says, we're going to turn the ship. We're going to develop cloud. And then we started to understand what it meant to go to a SaaS-based business. Gosh, it can be, you know, in the near term, it would be depressing their earnings, but longer term, it's really interesting, right? And they can get to a mid-teens type growth again, which they did get to. I mean, Microsoft, if you remember, everyone thought Google was going to take over, Google Sheets was going to take over Excel. And like, why, why do we all need Microsoft? [00:36:30] Speaker 1: I asked myself that question every time I launch and look at the annoying new ribbon that they changed a decade ago. Um, but I use both. But you use both. Yeah. And after all these years, [00:36:42] Speaker 2: and I, and I, and I assume in 10 years, we'll still be using Microsoft. So, you know, Satya then pivoted the ship and got into the cloud business with Azure. And, um, so we watched the actions as well. So we can dream the dream and then test the hypothesis and see whether or not they're executing [00:37:04] Speaker 1: against it. Really, really interesting. Let me reverse the question to you and say, what leads you when you're running a concentrated portfolio to say, I'm going to sell this. Is it the fundamentals deteriorating? The thesis not working out? Sometimes is based on valuation or is it simply we only have room for X number of companies and this opportunity is here and that opportunity is all the way up here. It's all of the above, [00:37:33] Speaker 2: right? There is examples of selling a company because there's a better opportunity and you don't want to take, you don't want to take double the risk. So to, to the same end market, yet the upside of one is greater than the upside in the other. Um, there are examples of, you know, you, you sell or at least trim because the price target has been achieved and maybe beyond the price target has been achieved. So the risk reward is simply different. There are examples of disappointments, you know, companies that disappoint relative to our expectations and they didn't deliver on what we expected them to do and the hypothesis didn't play out. So I think there's, there's all of the above, um, and every sale is, [00:38:21] Speaker 1: is, has a different reason. Right. Really, really interesting. Coming up, we continue our conversation with Dr. Ankur Crawford, uh, executive vice president and portfolio manager at Alger, diving in to her AI thesis. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest today is Dr. Ankur Crawford. She's portfolio manager at Alger, where she co-PMs the large capital appreciation strategy and runs the concentrated, uh, ETF for the firm. So we, we are legally obligated to discuss, uh, artificial intelligence, but you're the perfect person to have this conversation with. There, there's a quote of yours that I found fascinating. You said when software begins to write software, innovation becomes exponential. That's already happening. Walk us through what this means for earning powers for the semiconductors, for the hyperscalers, and then for the rest of the S&P 500. Okay. So that is a very big [00:39:36] Speaker 2: question. Um, look, I think we are at this, I mean, Elon will call it a singularity or, um, we're at this point in time where we have never seen this kind of innovation and imagine everything that, you know, let's take to, it's easiest to, to describe with software. We used to sit and code software, right? And we had to understand the, um, the coding. We had to debug it. It would take a long time. Well, when software begins to write software, that whole process is truncated and imagine what can be done in our largely digital world when software begins to code, decode and create. So let me, let me [00:40:30] Speaker 1: push you a little bit there. The large law language models that are out there give AI the ability to effectively cut and paste everything that's been done before. How good is AI at creatively innovating [00:40:48] Speaker 2: code that's never been written before? So look, I am not a coder, so I can't tell you whether the code is elegant or, um, you know, can be taken to production. I will tell you that I was able to build a pretty interesting app inside of a few months and I, and this is just doing it on the weekends, occasionally on the weekends, not even, you know, every weekend. So, um, you know, and, and that was all vibe coded. So it is adding this technology that is highly viable. You talk to coders, they are using it 90% of the time and are now just instructing and have to have this, the logical framework of how to use the code. And I think that the, the big picture here is that once the code begins to write the code, then it's not going to necessarily be creative. The creation still has to come from you. The insight still has to come from you. Um, but it can actually innovate, right? The innovation curve for you is significantly higher. So that's what we're seeing today, where, um, these digital assets are becoming more innovative or they're allowing us to be more innovative. And we've hit that point in time where we're getting exponential innovation and we've never really seen anything like this before. You know, humanity hasn't seen this before in such a short period of time. You know, if you look at previous industrial revolutions, they would be, you know, over generations. Um, it wouldn't be coming in the span of five years. And so this is what makes it really interesting because you asked like, how good is it for semiconductors and how good is it for, you know, the rest of the S and P and the hyperscalers, the impact on all of these differs. So, you know, software is that the, you know, we wrote a paper three years ago called AI and the declining cost to create. And it was all about how when software begins to write software, the cost to create software goes to zero. And what happens to the incumbents when the cost to create software is zero, right? One of the moats goes away. And that necessarily means that the operating profit of businesses must change, not that software is dead. It's just that the operating profile of all of the companies must change because it becomes more competitive, right? And, and where does that value go? We had $5.5 trillion of spending now $6 trillion of IT spending. 50% of that was IT services and software. And our contention was that the value would go from IT services and software into hardware networking, because that is really what is driving this innovation curve. So, you know, there's entire sectors that have been kind of grown a lot and others that are facing their own pressures. I would say the same thing for any sector in the market. You know, we spoke about healthcare earlier, like how can a United Healthcare actually use AI to bend the cost of care? And can there be incumbents that, that cross the chasm? Or, or there might be some that can't cross the chasm. And there are new companies that begin to use AI to bend the cost of care. [00:44:35] Speaker 1: So I'm glad you brought up healthcare. I'm, I've been fascinated not so much by bending the curve of the cost from somebody like United, but all of the small biotechs and new molecules and huge wealth of existing, um, chemistry and, and, uh, pharmaceuticals and studies we've done that nobody's really had the ability to go back to and say, Hey, maybe something's in here that we've missed. Um, you know, the, the most cliche example is, uh, I never pronounce it right. Sildenafel Viagra was supposed to be a heart treatment and had this unusual side effect. And now it's a multi-billion dollar med. I'm, uh, same thing with GLPs and originally for diabetes, but hey, everyone's losing a lot of weight on these. I'm curious, not so much on the cost side, but there's this giant body of, of, of unexcavated research that just seems like it's waiting for AI to attack it. Yeah. And so, you know, recently I met with the, [00:45:45] Speaker 2: I was actually on a panel where I was the moderator for a company and I've forgotten, I've forgotten the name of the CEO and the company, but they're, they're basically a new AI company that is taking this compendium of knowledge and taking it to companies and saying, marry it with, with the data that you have. And can we start finding not only the, the solutions, but you know, for your targets, but use this history to get there faster. So there's lots of efforts being made on this right now. I do think that we will accelerate drug discovery and the impact it will have to healthcare. I mean, look, the holy grail is to [00:46:29] Speaker 1: have personalized healthcare at some point. Um, well, wasn't, wasn't DNA testing supposed to give us [00:46:35] Speaker 2: that a couple of years ago? Well, DNA to yes, but DNA testing used to cost a million dollars a year, a million dollars per sample. And today it's a hundred. So we're getting to the point where we can actually look at our individual DNA and it just takes time. Um, and at some point, can we marry it with some AI insights? So look, I think healthcare is going to be greatly impacted. I think that I'm most excited actually to see how we can democratize healthcare because really our healthcare system here is kind of broken. Not kind of, um, to be polite and, and how can we take down that cost of care? And really like, I would love to have universal healthcare. It just can't be done in the construct of healthcare as it is today. So, you know, can we, can we use AI to provide universal healthcare? I think we can, it will take a few years, um, maybe a decade, but I think we can. And this is a global statement. It's not necessarily just the U S it's bringing the cost of care down enough such that, you know, anyone on this planet will have access to healthcare. [00:47:53] Speaker 1: So I'm going to assume that you think all the AI bubble talk is, is wildly overblown. [00:48:01] Speaker 2: Yes, I do think it's wildly overblown. I think there, look, I think the trade has gotten a bit harder, you know, and in part because the first two to three years of the trade was, oh, you just have to buy the GPUs and anything that the GPU touched was, was gold. Um, and then it became more nuanced. Well, agents use CPUs and we have a memory shortage and memory has now gone up for X in price. So CapEx budgets are going up. So that question of ROI is coming to the fore. And how much does CapEx have to go up to accommodate the supply chains being as tight as they are. And there's technological differences between a CPU versus a GPU and how they're used. And the Chinese might be coming and right. So, so there is like a lot of different aspects that have made it a little bit harder where, you know, open versus closed source debate, like the, the open model versus a closed model. That's another debate. Um, the debt and the CDS spreads widening. That's another. So all of a sudden we've gone from a relatively simple, we're going to need AI. We're going to need compute to there's a slew of different narratives that one can press on for the bear case. Now, I structurally believe that we just talked about healthcare and the innovation curve in healthcare and what that can give back to society. That is true value. Right. If we can bend the cost of care from X to X minus, that is value that's created for humanity. And we will pay for that value. Um, you know, the other day there, there's been this big debate about token maxing and there was, uh, define that for, for the lay listener. Yeah. Token maxing was this behavior that companies were encouraging their engineers to basically have leaderboards of who can use the most tokens, which sounds insane, right? It would almost be like, you know, telling your employees to see how much they can spend on lunch and whoever spends the most on lunch [00:50:20] Speaker 1: gets an award, right? Um, well, I imagine if you're a FedEx driver and the company holds a competition to who's going to go through the most amount of gas and, and tires, meaning making the most deliveries, [00:50:35] Speaker 2: not a bad thing for the company. Not necessarily a bad thing, but, um, you know, in, in this case, the tokens that were being used or the, the, the, what was being used is actually not necessarily, um, tied to deliveries. It was just use the most tokens as you can. It didn't kind of matter what you built with it, right? So, or it, there wasn't as much scrutiny as to how many quote unquote deliveries you made. You just burned through your tires. So, um, so it was kind of inefficient, but you know, they came out and they said, you know, we blew through our entire budget in, in a quarter for the year, the whole, for the year, the entire budget for the year in a quarter. And, uh, you know, and we haven't gotten an ROI. Well, no kidding. Well, they turned around last week and they laid off 10% of the people that worked for the company because of AI. Well, you know, somewhere along the way, the use of artificial intelligence allowed them to kind [00:51:41] Speaker 1: of refine their workforce. That, that sounds like they didn't lay off people because of AI. They sound like they laid off people because management was kind of misincentivizing the employees. Well, [00:51:55] Speaker 2: I mean, they said that they laid off, laid off people because of AI. There's been many companies like Jack Dorsey at XYZ. Um, you know, also, you know, he cut 40% of the staff blaming AI. Who knows really what the, the, the real reason is. It could be AI or it could be they just overhired. Which he has a [00:52:15] Speaker 1: history of. Which he has history of. If you track him over his various companies. Right. And many of [00:52:20] Speaker 2: these companies did. Right. Um, so I can't deconvolve that. However, you know, Uber in particular said it was because of AI. They, they had, they had kind of, they have been very front foot forward on the use of AI. And now they're able to increase productivity enough that they can titrate down their workforce. So, um, you know, I do think that there is value that is being created because of AI. I think that it is not necessarily a technology that's plug and play into an enterprise and there has to be some learnings and before you can get to that ROI. And we're seeing those stumbles in that learning curve. That's right. It doesn't mean that it's never going to work. And, and my viewpoint is that where there is value, we, we work in a system of rewarding value. So if you can create value, I believe that whoever uses, uses that system that creates the value, they will pay for it. [00:53:25] Speaker 1: So you've described the demand for computers insatiable. Um, what would have to happen for you to say, all right, we're getting to saturation or, or satiation? What, what does the top of the cycle look like? Or is it so far off in the future that we, we can't even think about it? [00:53:46] Speaker 2: What I would say is that this is not a question that can, I can say, oh, we'll never like in 2030, we won't need computer. I think it's a function of how much we put into the ground. Right. It's, it's a, it's a delicate balance of, you know, if we put X into the ground today, we put, you know, that the hyperscalers are spending $650 billion or whatever that that number is. [00:54:09] Speaker 1: It's circular. It's this, it's that. We've heard these complaints now for two years. [00:54:13] Speaker 2: But $650 billion, um, seemed like a really big number, yet we are still short compute. Right. You're hearing from the hyperscalers. We do not have enough. The neoclouds are telling you that there are four times as many, um, asks for compute as they have capacity. Wow. So if one says that we are short compute today, I don't really understand the logic. Now let's fast forward two and three years. If we put $3 trillion into the ground next year or the year after, which we cannot do today because we are short power. We are short people. We are short capacity. We are short, you know, we can't make those chips, but let's hypothetically say we put in $3 trillion of compute into the ground in 2028. I would say that that is over capacity, but we can't do it because there is almost a natural, a natural limiter to the growth of this market. And we don't have the chips. We don't have the people. We don't have the power. Right. Um, and so the market is being capped. If all normal forces and if we had an infinite supply of everything, I think we would be in over capacity today because it's such a big market, everyone would be building at a pace that, you know, they wanted to be that they would want to be first. But the fact is, it's actually a blessing that the market is being capped by all of these supply chain shortages. The fact that we don't have plumbers and electricians to actually work in our data centers, um, is capping the growth of data centers. And so it is allowing for duration versus kind of having a one time growth pop and which you were not going to pay a high multiple for. So, so I think that oversupply is a function of how much we put into the ground [00:56:09] Speaker 1: and how we use it. So, so let's, let's unpack some of that. Um, in the beginning of 25, when, when deep seek first kind of was released and everyone was startled and the initial reaction was, oh, we've overbuilt. We don't need, uh, this many GPUs. We don't need all these giant data centers. We just need slightly clever software that can do more with less, uh, didn't take long before that just was overrun with, no, we need horsepower. We really need the ability for big problems to not, not come up with clever little workarounds, but we, we need the, the firepower. And then again, more recently, we've seen a number of open source models out of China that seem to be doing a whole lot more with less. Um, at what point does it begin to become, hey, do we really need a $3 trillion worth of capacity? Don't we just need, uh, to take a little bit of that working out of the constraints we have the way [00:57:13] Speaker 2: the Chinese models have? Yeah. So one of the things that I think, um, is well understood is that the Chinese models didn't do this on their own. Mm-hmm. So, um, the way I like to think of it is, uh, you know, you have like these, uh, almost like, like an animal world, right? I just went on, on, on, um, safari to Kenya. Uh-huh. And, you know, giraffes almost always have like birds sitting on their necks and those birds are, you know, it's a mutually symbiotic relationship. I suppose it's not that symbiotic to the giraffe, but, um, you know, the bird gets to rest on the, the giraffe's neck and, and benefits from the fact that the giraffe is walking around. Um, so similarly, I think- What does the giraffe get out of that? Um, I, I suppose the bird might like- Keep the bugs away? Keep the bugs away, or, or like, eat at the ticks on the giraffe. I don't know. Um, but similarly, you know, the Kimi model is a little bit like the bird on the giraffe. Mm-hmm. Whereas, look, and I think that they are incredibly, um, like the Chinese are, are very innovative in their own right. Um, I think they're very good, fast followers. However, they need the giraffe, which is our LLMs in order to survive. And so, you know, I think there are many different ways to address what is happening. You know, my, my, the, the scenario that I think is actually most logical, which I'm not quite sure that, you know, the, the large language models will do, is basically to hold the N and N-1 model internal and allow for, you know, certain businesses, certain companies, the US government, um, other governments that, who are not going to distill this model and kind of feed a Kimi type model, um, and allow for them use of that model and only make public the N-2 model. And that way, it keeps any of the distillation at bay. Now, in order for that to happen, all of the frontier models will have to agree to do this. Because if there's any frontier model that, you know, is equivalently as good, then it, it kind of breaks the, breaks the ecosystem that I'm describing. But, um, so, so, so I think the point is that you need to spend the capex for, for the training in order to get that output so that Kimi can train on that output. So, um, these opening on the output, not on the, not creating their own LLM. Well, Kimi has created their own LLM by feeding off the, it's called distilling, feeding off the output from the large language models. So, you know, but a lot of the spending that is happening is actually coming from the use of the compute, like, so from the inference aspect. So, you train and then you have to infer. So then for the inference is what we experience as consumers. And so that in inference is driving a majority of the, the spend. And you look at the revenues of open AI anthropic. They're kind of like up, I've never seen growth like this. I don't think we is ever have seen growth. That is as significant as what we're seeing today. [01:00:47] Speaker 1: You know, people frequently make a comparison to the dot coms. And I always feel like that's a terrible comparison. Um, cause these are real companies with real revenue, real potential profits, like almost there, not clicks and eyeballs. But the one thing some of the skeptics have pointed out that almost resonate is, you know, during the internet era, we have this huge boom, um, where most of that value ended up landing in the consumers' laps, not the investors' laps, because so many of those companies crashed and burned. Um, how similar or different is this environment to that? [01:01:30] Speaker 2: So I think it's quite different. Um, look, there will, there may be parallels at some point, i.e. do we overbuild? Um, and, and how long does it take to, to actually eat through that overbuild? So you think about 2000s, one of the reasons we overbuilt is because we had dreamed this dream of what the internet would be. And we had, you know, pets.com was actually a brilliant idea. Just a little early. It was just early. Now, now it's chewy, but, but it's, um, but, but chewy became a significant business. Amazon has built, you know, a many trillion dollar business off the back of consumers buying on the internet. But we didn't have the internet, right? We had dial-up, right? Dial-up is not good enough to increase productivity back then. What I would argue today is that we actually have the tools, all we needed. We had the internet, we had the, the productivity, or we had the, um, kind of the, um, the, the, the infrastructure that was needed for ubiquitous intelligence. All we needed was the chips, right? We need the data centers and the chips. And that's what, what is happening today. And so if we actually need ubiquitous intelligence and infinite intelligence, to some extent, like, if we overbuild, we will eat through [01:03:05] Speaker 1: that overbuild as well. So, so what do you think the skeptics misunderstand about AI? Is it the scale, the economics, how, how durable the investment cycle is? What, what, what are the bears getting wrong here? [01:03:19] Speaker 2: I think it's the duration. I definitively think, I think it's the, I think maybe it's all of the above, really, but it's duration. It's the scale. It's the economics. Um, all three of those is where, where I think they're pushing on the wrong, on the wrong thread. [01:03:37] Speaker 1: So, so last question, before I get to all of my favorite questions, I ask all my guests, what do you think investors aren't talking about or thinking about, um, that perhaps they should be? What, what is getting overlooked here? And it could be any asset geography, policy, whatever. But what aren't people talking about, but should. Yeah. I think that people aren't really talking about, [01:04:04] Speaker 2: you know, the, the net positive benefits to humanity from AI. And, you know, we talked about healthcare, um, and, and how we can make healthcare kind of available to any human on this planet. The same goes for education. There's no reason why any child should be quote unquote left behind. I mean, I've been shocked at, you know, what I've been reading recently is kids going to college and they can't read. Right. That is a failure of our education system that can be solved using artificial intelligence. Um, you know, this is, and this is, again, it's a global issue. It is not a local issue. This is something that we can, there's, there's no one that should not be educated. And, um, and like the, the AI kind of, uh, the anti AI-ers are climate change. Right. I mean, I do think that using AI, will we be able to solve the problems that we have with climate change? Will we be able to engineer things that will help with, with the, the rapid rate of climate change? Um, and a lot of the AI, AI kind of doomers or AI naysayers who don't want the data center built in their backyard or data center built anywhere are ignoring the fact that there are many different aspects of AI that will be good for humanity. And does it require great change? And is change scary? It is. And it will require change. It will require change from all of us. But the endpoint is actually quite beautiful. [01:05:51] Speaker 1: I like that. You're such a techno optimist. All right. Let's jump to our favorite questions, starting with who your mentors who helped shape your career. Oh gosh, I think that's, [01:06:02] Speaker 2: that's a pretty easy one. Um, our CEO, Dan Chung is, has been pivotal in, in my career and my career growth. And, you know, I told you, like, he hired me from Stanford without knowing anything. Um, and, and without, I really knew nothing about this business. And he recognized that, you know, why not take a shot on someone who's non-traditional and he himself is a non-traditional thinker. He was a lawyer, um, and, you know, is, is, thinks very much outside of the box. So, you know, over the years, he's challenged me in ways that, um, have been sometimes frustrating, but I learned from. Um, he pushes me in ways that sometimes I don't understand. But, um, again, I learn from and grow from. So, um, yeah, I think Dan is, um, Dan's like my number one mentor. Let's talk about books. [01:07:04] Speaker 1: What are, what are some of your favorites? What are you reading currently? So, my favorite book is a, [01:07:10] Speaker 2: is a book called Think Again. It's by Adam Grant. Oh, of course. He was an organizational psychologist. And I know it's an odd, uh, he's, I think he was at Harvard, Wharton, I think. And, um, I know it's an odd book to have a, be a favorite book of mine, but in context of, of business, it definitively is. And in part, it's because it talks about how you can have a hypothesis, but you have to be humble enough to understand that you must, you can also change your hypothesis, but you have to have the confidence enough to hold a hypothesis. And really intelligence is about the ability opportunity to, to morph and be, be nimble without, um, without, and, and, and it's not about arrogance. Like it's not about, um, our business requires a constant questioning of what you think, right? And those that become very, uh, tied to a thesis and, uh, I think on the wrong side of, of a lot of trades. And so I just loved the book because the way he writes, um, about intelligence and the humility of questioning and of, of holding conversations with people. And I think this is true for society in general right now of having conversations where you may not, you may not agree, but to hear other [01:08:43] Speaker 1: people out, even if they don't agree with you. And any of the books, anything you're reading currently? [01:08:48] Speaker 2: Um, the last book I read was, uh, the, the, the recent one by Brad Jacobs, which was how to make a few more billions. Brad Jacobs is the CEO of QXO. And he wrote his first book, how to make a few billion. And then he wrote how to make a few more billions. And, and what I thought was so interesting about the book is the first two chapters is about how he centers himself. And he's an incredibly successful entrepreneur, um, has built many businesses really from scratch. He was, he's a self-made billionaire and, and he starts every morning meditating. Right. And, and how he finds that center. Um, and it just like, to me, it's, you know, we, we often don't talk about that aspect of, you know, investing in business. It, it feels sometimes really transactional. Um, but hearing that aspect of Brad, um, you know, it only puts him in even higher regard [01:09:49] Speaker 1: for me. Really interesting. Um, what are you streaming these days? What are you either listening [01:09:54] Speaker 2: to or, or watching? Oh gosh, I don't, I don't watch much. I don't have that much time. And usually when I do watch something with my kids, I, I fall asleep. So, but, um, but I am a runner. And so I have a lot of time, um, that I spend running and I'm constantly listening to podcasts. Um, my favorite ones happen to [01:10:14] Speaker 1: be macro voices. Um, I, I love the knowledge project. Oh, uh, Shane Parrish. Yeah. Yeah. He's, [01:10:21] Speaker 2: that's a regular on Sunday mornings for me. Yeah. And so he, I mean, the, the, the variety of conversations that he has with different people from, you know, from wellness and wellbeing. And, um, I, I've saw, I was listening to one about the alpha school and how, how education should be reshaped. Um, there's just like, uh, awesome amount of diversity of thought. Um, the circuit, which is all about semiconductors and, and chips. Um, I kind of think of other ones that I listen to [01:10:53] Speaker 1: regularly. That's all that comes to mind. That's a nice list to start with. Um, our final two questions. What sort of advice would you give to a recent college grad interested in a career in either engineering, material science or investing? Oh, wow. Um, well, look, I think for any college grad, [01:11:16] Speaker 2: make sure that you do something that you love, right? And it doesn't have to be, you love it every day, but you spend a lot of your time at work. Your, your life, a third, more than a third of your life is going to be spent from here on out at work. Make sure that you do something that you believe in that gives you great gratification, that you feel like you're contributing to society. Um, don't just do it because you're on a treadmill of, you know, I'm going to go do this because I set out to do this path and, you know, I just have to go trotting along. Um, allow yourself the grace to change and to change your mind. I did. Uh, and it was probably the best risk that I ever took. So, or the best gamble that I ever took on, um, was completely pivoting in my career. So, you know, allow yourself to explore because you, you change over time as well. You know, what you want today may be different what you want in five and 10 years, but, but definitively make sure that you love what you do because once you know that you love what you do, you will be the best [01:12:32] Speaker 1: at it. Huh. And our final question, what do you know about the world of investing today might have been useful, uh, 20 or so years ago when you were first starting out? Um, [01:12:45] Speaker 2: so you told me this question would stump me and it, it is stumped me. Well, it, because, you know, [01:12:52] Speaker 1: the answers that I'm not looking for are, you know, buy Amazon in 02 when it was $7. Yeah. It's, it's what insight might've been useful way back when, what have you be, uh, what have you learned? What expensive lessons came along that, uh, you know, I could have saved myself a lot of headache. Yeah. Had I figured this out sooner. [01:13:15] Speaker 2: You know what, Barry? I don't think I would, I would, in my way back machine, I wouldn't go tell [01:13:20] Speaker 1: myself anything. Uh huh. So it's the path and not necessarily. Yeah. It's the journey. Like my most painful [01:13:27] Speaker 2: moments as an investor have been the biggest learning moments for me that have had like, like they've branded me in some way with that, with that experience. And, um, so I wouldn't want to shortcut that because it has shaped me and every single time I've fallen on my face, it has shaped me and it has reminded me of, you know, the perils of, of not paying attention to X, Y, or Z, or I won't make that same mistake again. Because again, it is so like, it goes back to that first question you asked me about like academic versus learning on the job. Um, [01:14:11] Speaker 1: you need the real experience. You need the scars. You need the scars. And it's a little bit like [01:14:16] Speaker 2: your kids, right? You can tell your kids, don't do that. You're going to get hurt. Don't do that. You're going to get hurt. Well, sometimes they just have to fall down and get hurt to realize [01:14:25] Speaker 1: they're going to get hurt. So it makes a ton of sense. Ankur, thank you so much for being so generous with your time. We have been speaking with Ankur Crawford, uh, portfolio manager at Alger. If you enjoy this conversation, well, be sure and check out any of the 653 we've done over the past, uh, 12 years. We launched July, 2014. Um, you can find those at iTunes, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts. I would be remiss if I didn't thank the crack team that helps put these conversations together each week. Alexis Noriega is my video producer. Sean Russo is my researcher. Anna Luke is my producer. I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.