Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: This week on the podcast another extra special guest, Doctor
Speaker 2: Encore Crawford is cohed, a portfolio manager of large cap
Speaker 2: strategies at Alger. She's got a fascinating background. She was
Speaker 2: an engineer at Intel, one a number of patents, and
Speaker 2: was the awardy of the Intel PhD Fellowship. She's been
Speaker 2: recognized as one of the top women in asset management.
Speaker 2: If you're interested all into the details of how artificial intelligence,
Speaker 2: semiconductors and software works, as i am, you're gonna find
Speaker 2: this to be a fascinating conversation with no further ado.
Speaker 2: My conversation with Algers Encore Crawford. I'm Core Crawford. Welcome
Speaker 2: to Bloomberg.
Speaker 1: Thank you for having me. Very so, let's.
Speaker 2: Start with your background, which is really kind of fascinating.
Speaker 2: Bachelor's degree in mechanical engineering and material science and engineering.
Speaker 2: That's a double BS from UC Berkeley, and then a
Speaker 2: master's and a PhD in material science and engineering at Stanford.
Speaker 2: What was the original career plan.
Speaker 1: I didn't have one, to be honest.
Speaker 3: I think when I made the decision to become a
Speaker 3: mechanical engineer, I was kind of following my brother's footsteps,
Speaker 3: who was a mechanical engineer and became an orthopedic surgeon.
Speaker 3: And I realized, if I didn't know what I wanted
Speaker 3: to do, I wanted to keep my options open.
Speaker 2: So he becomes an orthopedic surgeon with a mechanical engineering degree.
Speaker 2: Is he designing replacement joints and things like that.
Speaker 1: He does.
Speaker 3: He does actually bring that aspect of his engineering background
Speaker 3: in to device different device configurations, and he works a
Speaker 3: lot with the device companies as well. But there's also
Speaker 3: you know, as a kid, I loved, you know, figuring
Speaker 3: how figuring out how things work, whether it was like
Speaker 3: a car or a calculator, and I would always be
Speaker 3: kind of fidgeting to understand how things work. I loved building,
Speaker 3: so mechanical engineering kind of felt like, you know, I'm
Speaker 3: just a curious person. So I like to satiate that
Speaker 3: need to know how things work.
Speaker 2: And I read somewhere that you originally wanted to be
Speaker 2: an astronauts.
Speaker 1: This correct did. So I grew up till I was five.
Speaker 3: We lived in Florida close to Cape Canaveral, and we
Speaker 3: would go watch the Space Shuttle take off and I
Speaker 3: was so fascinated by space because it was almost ethereal,
Speaker 3: you know, this thing goes up into the sky. And
Speaker 3: for me, the astronauts were celebrities. So you know, for
Speaker 3: a long time I did want to be an astronaut.
Speaker 2: So I have Florida somewhere in between. You're born in Kansas,
Speaker 2: is this correct?
Speaker 1: Yes?
Speaker 2: Yeah, canvas, but you end up in the Middle East, yes,
Speaker 2: and then you're sent to a convent boarding school in
Speaker 2: the Himalayas. This is possibly right, right, No, that is
Speaker 2: all correct, and then you end up in Buffalo, New York.
Speaker 2: You got it all right. So so that's real human research,
Speaker 2: not not chat. I'm curious that is a broad global
Speaker 2: life experience. How does that shape your views on either
Speaker 2: international investing or just the concept of risk and reward?
Speaker 3: Yeah, I think it more so shapes the way I
Speaker 3: think about, you know, the cultural differences. When I look
Speaker 3: at companies, when I look at manage teams, I understand
Speaker 3: very well that there are certain cultural differences that are
Speaker 3: simply endemic to businesses and to management teams. And you know,
Speaker 3: just because a management team isn't necessarily always bullish or
Speaker 3: they're always telling you the negative aspect of their company
Speaker 3: doesn't necessarily mean that there's something wrong, and.
Speaker 1: So, you know, I think that that has.
Speaker 3: Been like an example of this is this company called Nebeus,
Speaker 3: where the CEO is a Russian CEO who is incredibly
Speaker 3: humble and he will never tell you what's right. He
Speaker 3: will always point out to you all the things that
Speaker 3: are wrong. And a lot of investors are like, you know,
Speaker 3: I don't that doesn't sound good, And I'm kind of
Speaker 3: looking at the opportunity because that's just his culture, right,
Speaker 3: It's his culture.
Speaker 1: Not to be boastful.
Speaker 3: So just living in all these different countries and having
Speaker 3: exposure as a kid to many different religions, it just
Speaker 3: gives a really unique perspective on any problem that you
Speaker 3: look at because it helps take kind of the blinders off.
Speaker 2: It's fascinating. I never really thought about how does a
Speaker 2: societal cultural set of norms make its way to management.
Speaker 2: You think about the Japanese culture is the sort of
Speaker 2: bravado and very aggressive forecast we tend to see in
Speaker 2: the United States, You would never see anything like that
Speaker 2: in Japan. How do you calibrate what is cultural diffidence
Speaker 2: and what is just hey, there's a problem here and
Speaker 2: they're telling us this is an issue.
Speaker 3: Yeah, you know, I think you have to know the
Speaker 3: business right that the first thing is is know the business,
Speaker 3: and then you can calibrate the tone of the management.
Speaker 3: And you know, example, as Taiwan Semiconductor, I remember speaking
Speaker 3: to them over many of these years that we've owned
Speaker 3: the stock, and I would always say, you guys are
Speaker 3: going to become the single supplier of leading edge. Why
Speaker 3: is it that you can't take up pricing? And you
Speaker 3: would always talk me down and like, oh, no, you know,
Speaker 3: we are here to serve our customer. We are here
Speaker 3: to and I was like, there's absolutely no reason for
Speaker 3: you not to be raising pricing, and they would just
Speaker 3: push back and not really because that wasn't part of
Speaker 3: their philosophy, and it wasn't part of their philosophy that
Speaker 3: Morris had Morris Chang had kind of put into place
Speaker 3: in the early years. However, that is what they ended
Speaker 3: up doing, and so I had to take that with
Speaker 3: a grain of salt, understanding that's their philosophy. It was
Speaker 3: a little frustrating at the time, but you know that
Speaker 3: a business is a business, and at some point the
Speaker 3: realization of how good that business was came into the numbers.
Speaker 2: So you mentioned the advantage of really understanding the business.
Speaker 2: You're in an Intel doctorate fellow, you worked as an
Speaker 2: engineer at Intel, you hold multiple patents. How much of
Speaker 2: an advantage is that when you're looking at semiconductors or
Speaker 2: AI or any of the hyperscalers. What advantage does that
Speaker 2: give you?
Speaker 3: Look, I think understanding the technology is it's kind of
Speaker 3: crucial right now because in this world of AI, I
Speaker 3: think there's a lot of people who don't really understand
Speaker 3: what is happening under the covers, and that's dangerous. And
Speaker 3: that's why you also see the volatility that you see today,
Speaker 3: because you know they're kind of loose holders and not
Speaker 3: truly understanding the different dynamics of the technology.
Speaker 1: And it's just a hard it's a hard.
Speaker 3: Way to invest when you can get shaken out because
Speaker 3: you don't have conviction in the technologies. So it's I
Speaker 3: feel like it's always helped, and in part because you
Speaker 3: know chips. I was a semiconductor analyst when I first
Speaker 3: started at alger and I kind of immediately understood, well,
Speaker 3: I understand what a deposition tool is.
Speaker 1: You know, I used one.
Speaker 3: I understand what etching is I used this tool, I
Speaker 3: understand what the issues are in fabricating a chip, and
Speaker 3: you know how hard it is to fabricate a chip,
Speaker 3: so you know, it just gives you a little bit
Speaker 3: of a edge on the conceptual understanding and where the
Speaker 3: industry is going, so you know early I remember in
Speaker 3: twenty thirty, eleven, twelve or thirteen, one of those years,
Speaker 3: I put together a presentation about how we're at the
Speaker 3: end of Moore's Law and what will happen if we're
Speaker 3: at the end of Moore's law. And I sent the
Speaker 3: presentation out to all of the companies that I covered,
Speaker 3: and I said, I would like your feedback and tell
Speaker 3: me why I'm wrong.
Speaker 1: But that was thinking kind of eight nine.
Speaker 3: Years ahead because it had implications for the entire sector.
Speaker 3: And so those kinds of insights I think are easier,
Speaker 3: not that any everyone can have them, they just come
Speaker 3: probably a little easier because I understand the technology.
Speaker 2: So I see the advantage of having the technical background
Speaker 2: as an analyst. I'm curious what made you leave the
Speaker 2: technical field being an engineer and working with Semis to
Speaker 2: becoming an analyst in the space and working on the
Speaker 2: financing of semis.
Speaker 3: Yeah, you know, I had gone through my graduate career
Speaker 3: and really I had set some goals for myself. I
Speaker 3: want to write this many papers, I want to you know, present,
Speaker 3: I want you know, I want.
Speaker 1: To be useful to society.
Speaker 3: And at the end of it, I felt like I
Speaker 3: had kind of achieved all those but I wasn't happy.
Speaker 3: I just wasn't content and happy. And I thought to myself,
Speaker 3: my gosh, if I have achieved everything that I set
Speaker 3: out to do and yet I'm still not happy. What
Speaker 3: happens if I become a professor and i'm you know,
Speaker 3: and we just go through a tough spot on you know,
Speaker 3: raising money or whatever it might be like in the research,
Speaker 3: Will I be able to Will I be even unhappier?
Speaker 3: And I think that self awareness made me realize I
Speaker 3: needed to go look somewhere else. And when I came
Speaker 3: to alger it was really like I was thinking I'd
Speaker 3: be here for two years and then go back and
Speaker 3: do a postdoc somewhere and be a professor, and I
Speaker 3: never left.
Speaker 2: Huh, really really interesting. One of the complaints I've heard
Speaker 2: from people who are technologists or engineers or what have you,
Speaker 2: is everything has become so increasingly specialized and narrow that
Speaker 2: you get put in to a silo, you have no
Speaker 2: idea what's going on in any of the adjacent science
Speaker 2: is more or less even within your field, everybody gets
Speaker 2: too specific.
Speaker 3: Was that a concern, Oh, for sure, And that's a
Speaker 3: great insight it is. Actually, you know, I was in
Speaker 3: a room this is probably at you know, fifteen by
Speaker 3: fifteen room. I spent you know, three and a half
Speaker 3: years in the basement of a building at Stanford taking care.
Speaker 1: Of a tool that was about this big.
Speaker 3: I was the plumber and the electrician and carrying out
Speaker 3: cryopumps and fixing them.
Speaker 1: And it was a very narrow, lonely.
Speaker 2: Experience I can imagine.
Speaker 3: And my advisor was fantastic, but you know, just that
Speaker 3: process required. It was very narrow and I'm very proud
Speaker 3: of the work that we did, but it was very,
Speaker 3: very niche.
Speaker 2: So you moved from a field governed by the laws
Speaker 2: of physics and nature to another field kind of governed
Speaker 2: by the eccentricities of human behavior. What are the challenges
Speaker 2: in that transition?
Speaker 3: You know, I don't really I didn't know anything when
Speaker 3: I started in this business. I knew a lot about
Speaker 3: atoms and materials and magnets and how to how to
Speaker 3: make a chip. But I really didn't know very much
Speaker 3: about investing, so honestly it was all new to me.
Speaker 3: So the challenge was really understanding like it. I was
Speaker 3: always asking why, well, why does this happen? Or why
Speaker 3: does the stock go up on this? Or why does
Speaker 3: stock not go up on this? And understanding that human
Speaker 3: behavior aspect was more a fascination versus a challenge because
Speaker 3: this idea of expectations versus the truth. You know, I
Speaker 3: grew up in a world where there is a single
Speaker 3: answer right where you write an equation and there is
Speaker 3: a way to do it, versus you know, people can
Speaker 3: skin the cat in so many different ways. In what
Speaker 3: we do, you can get to the same result in
Speaker 3: you know, an infinite number of ways. But so that
Speaker 3: was I suppose that was the challenge of understanding that
Speaker 3: there isn't just one way of doing it, but perhaps
Speaker 3: you have to understand the different ways than adopt your
Speaker 3: own way of approaching the problem.
Speaker 2: I love the Richard Feynman quote, imagine how much harder
Speaker 2: physics would be if electrons had feelings. It always always
Speaker 2: cracks me up at the intersection of science and investing.
Speaker 2: So you answer a recruiting ad from Alger despite knowing
Speaker 2: nothing about investing. What made you think your skills might
Speaker 2: get you through the door at a shop like Alger.
Speaker 1: I didn't, really, I really didn't. I was reading a book.
Speaker 3: It was written by a bunch of Mackenzie consultants at
Speaker 3: the time, and I forgot the name of the book,
Speaker 3: but it was all about you know, profit and loss
Speaker 3: and just businesses, how businesses are run. And I really
Speaker 3: didn't know, honestly, Berry, what I was applying for. I
Speaker 3: knew that I needed to do something else. I had
Speaker 3: worked at Merrill Lynch for a summer before I had
Speaker 3: started graduate school, and I loved it. It was on
Speaker 3: like kind of the emerging markets debt desk, and.
Speaker 1: I was like, let me give this a go again.
Speaker 3: And when I applied to Alger, I knew that I
Speaker 3: was curious enough that I would be able to cross
Speaker 3: the chasm and I would be able to you know,
Speaker 3: learn and give back to our company.
Speaker 2: Huh.
Speaker 1: Really, really, I really did know.
Speaker 2: Well, that's really fascinating. We'll explore that more coming up.
Speaker 2: We continue our conversation with Encore Crawford CopM of the
Speaker 2: Large Cap Strategy at Alger and PM of the Concentrated
Speaker 2: Portfolio ETF. Talking about her career at Alger. I'm Barry Results.
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm
Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.
Speaker 2: My extra special guest this week is doctor OnCore Crawford.
Speaker 2: She is portfolio manager at Alger, where she copms the
Speaker 2: large Capital Appreciation strategy as well as running the Concentrated ETF.
Speaker 2: So we were talking earlier about you answered an and
Speaker 2: that Alger had had put up to hire people. I
Speaker 2: read a story that Alger's CEO, Dan Chung hired you
Speaker 2: right on the spot. That's kind of unusual in this space.
Speaker 2: Tell us about what happened there.
Speaker 3: It was funny. I actually walked into this meeting. I
Speaker 3: had just come back from Tahoe. I'm a big skier,
Speaker 3: and I was really frustrated because it was pouring outside
Speaker 3: and I walked in like drenched and really like upset,
Speaker 3: and I was like, the only good thing about this
Speaker 3: is that it's snowing in Tahoe.
Speaker 1: And Dan happens to be a skier.
Speaker 3: I didn't know that, and so we started this conversation
Speaker 3: talking about, you know, our mutual love of skiing. After that,
Speaker 3: I think he realized I didn't know very much at
Speaker 3: all about investing, and he asked me my opinion of
Speaker 3: Intel versus AMD.
Speaker 1: And this was two thousand and three, two thousand.
Speaker 2: And four and peak Intel.
Speaker 1: It was peak Intel.
Speaker 3: And I remember saying, you know, I worked at Intel,
Speaker 3: and you know, I think I prefer AMD versus Intel
Speaker 3: because this is you know, kind of what I'm seeing
Speaker 3: inside of Intel, not inside information, but more the culture
Speaker 3: that had developed. And we had this long discussion about it.
Speaker 3: That evening, they hosted a a kind of a get
Speaker 3: together for all of the applicants, and Dan and I
Speaker 3: got into an argument about nanded versus hard dist drives and.
Speaker 2: You were on the nan side, right.
Speaker 3: Well, he was telling me that all hard to strives
Speaker 3: were going to go to zero.
Speaker 2: And he was eventually right.
Speaker 3: And he will be eventually right. And you know what's
Speaker 3: so funny. I just had this discussion with him yesterday.
Speaker 3: I was like, Dan, we had this discussion twenty two
Speaker 3: years ago.
Speaker 2: It only took you two decades to be right. You know,
Speaker 2: in trading is the same as wrong.
Speaker 3: Well, it was great because we had this Really it
Speaker 3: wasn't it wasn't a heated conversation, but it was definitely
Speaker 3: kind of looking at this problem in two different ways.
Speaker 1: And as we were walking out, he was like, you're hired.
Speaker 2: Just like that, Just like that. I need someone who's
Speaker 2: not afraid of me, will stand up and make me
Speaker 2: think of this problem from multiple angles.
Speaker 3: Well, I think it's a little bit of the culture
Speaker 3: that we have at ALGA of it's always better to
Speaker 3: have different perspectives versus go along with the norm and
Speaker 3: be consensus, and to always encourage that debate. So, you know,
Speaker 3: and one thing I am for sure is especially because
Speaker 3: again I come from a place of we're always trying
Speaker 3: to find the truth.
Speaker 1: There is an answer.
Speaker 3: I do bring that to the table here too, and
Speaker 3: that there is an answer, right whether or not you
Speaker 3: look at it from one angle versus the other. There
Speaker 3: is an answer, like the earnings are the answer, the
Speaker 3: trajectory of earnings are the answer, and getting that right
Speaker 3: is you know, it can be a topic of debate
Speaker 3: and how you get there. We can debate it to
Speaker 3: make sure that we're getting to the truth.
Speaker 2: So you start in the analyst training program at alger
Speaker 2: you advance to a research associate, then an analyst, and
Speaker 2: a tech sector head and ultimately a portfolio manager. What
Speaker 2: transition was the most challenging? What changed the way you
Speaker 2: thought about the job.
Speaker 3: I would say that the transition from kind of being
Speaker 3: an analyst to a portfolio manager, and even as a
Speaker 3: tech sector head, it was I kind of had my
Speaker 3: fingers in everything, and I was my little OCD tendencies
Speaker 3: were still able to play out a little bit. That
Speaker 3: transition to portfolio manager, however, required a different skill set,
Speaker 3: which was allowing for other people to do the thinking
Speaker 3: and or the detailed work which I loved to do,
Speaker 3: and kind of taking a much more macro perspective and
Speaker 3: a bigger picture perspective, where you know, it was a
Speaker 3: much more socratic methodology of questioning and asking the right
Speaker 3: questions to guide the analysts in the right direction, so.
Speaker 1: You know, and that was like I.
Speaker 3: Used to do that with the companies, right, I would
Speaker 3: ask all these questions of the companies, But doing it
Speaker 3: with your peer set and people that work with you
Speaker 3: is a little bit different, and so that was a
Speaker 3: kind of a tough transition for me.
Speaker 2: So you have a PhD, but not an MBA. I'm
Speaker 2: curious the apprenticeship you went through going through all those
Speaker 2: steps at alger What do you think you learned through
Speaker 2: that process that, hey, a green NBA right at a
Speaker 2: school is going to take them a couple of years
Speaker 2: to figure out.
Speaker 3: You know, when you are on the hook for real
Speaker 3: performance for real clients and you make a wrong decision,
Speaker 3: it isn't like doing poorly on a test, right, It's
Speaker 3: simply not equivalent because you actually feel the pain of
Speaker 3: having made that decision that impacted someone else. So, you know,
Speaker 3: I think learned abilities that are experiential just have a
Speaker 3: different impact than when you're sitting in a classroom because
Speaker 3: I think in a classroom that the consequences are just
Speaker 3: lower than they are when it's when you're really investing
Speaker 3: other people's money.
Speaker 2: That is the classic academia versus real life. All Right,
Speaker 2: I didn't get one hundred, I got a ninety six.
Speaker 2: Isn't the same as this one position is ruining all
Speaker 2: of my performance for the quarter.
Speaker 1: That's right.
Speaker 3: It's very different, and I think what you learn from
Speaker 3: it is I mean, there are certain I have this
Speaker 3: book where I used to write down and not so
Speaker 3: much anymore. But I have all my learnings from when
Speaker 3: I was a kid in the business where to remind
Speaker 3: myself not to make those same mistakes again.
Speaker 1: And I haven't looked at it in a while.
Speaker 3: I probably should go back and see how I developed,
Speaker 3: because there were so many learnings that I would carry
Speaker 3: with me and have shaped who I am today.
Speaker 2: I think that's how Ray Dalio wrote Principles, just get
Speaker 2: writing all his mistakes and what he learned from them.
Speaker 3: Oh really, I love Principles. I actually have his book
Speaker 3: for kids.
Speaker 2: Oh really, I don't real but he's described Principles as
Speaker 2: just every mistake he's made, every adjustment he's made, and
Speaker 2: ultimately how to turn errors into better performance. It's really
Speaker 2: very insightful, especially from a time when Wall Street didn't
Speaker 2: love to admit they ever got anything wrong. It's kind
Speaker 2: of fascinating. So you ran Alger's tech sector and then
Speaker 2: took over with your colleague the capital appreciation strategies, being
Speaker 2: hyper focused in one sector versus broad capital appreciation. What's
Speaker 2: that transition like? That sounds like a really big leap
Speaker 2: from something you're very comfortable with to gee, there's a
Speaker 2: lot of risk and a lot of uncertainty around that
Speaker 2: sort of new job description.
Speaker 1: Yeah.
Speaker 3: Absolutely, But look, there's things that rhyme. And I think
Speaker 3: that the sector I struggled with most was healthcare because
Speaker 3: it is so incredibly esotary.
Speaker 2: And why why is healthcare so esoteric? You come out
Speaker 2: with a drug, you sell a few billion dollars worth,
Speaker 2: everybody's happy.
Speaker 3: Yeah, but it doesn't quite work that way all the
Speaker 3: time now it does it.
Speaker 4: So and there's all this like legislative you know, overhang,
Speaker 4: there's regulatory stuff that's happening, there's subsidies that come and go,
Speaker 4: There's there's a political backdrop that you have to always
Speaker 4: be aware of for healthcare.
Speaker 1: So, you know, healthcare.
Speaker 3: Actually was a part of the market where it didn't
Speaker 3: really rhyme with anything that I had done before. But
Speaker 3: if you think about industrials and financials, those were cyclicals,
Speaker 3: cyclicals of a different nature. You know, some were long
Speaker 3: kind of longtime cyclicals versus semis. You know, financials were
Speaker 3: also cyclicals tied to the economy. The emphasis more on
Speaker 3: the macro was something I started to incorporate more in
Speaker 3: my thinking. But you know, for someone who has a
Speaker 3: very curious like I'm always curious and I'm always asking
Speaker 3: questions to me. It was it was kind of a
Speaker 3: breath of fresh air to kind of expand my purview
Speaker 3: to understand and synthesize how the world works. But it
Speaker 3: was I quite enjoy having that broader perspective. And you
Speaker 3: know what, Elsa is super interesting and I've only had
Speaker 3: this appreciation probably in the last decade, is how history rhymes.
Speaker 3: So I have become a bit of a you know,
Speaker 3: a history fan. And in part because I got I
Speaker 3: started when I was I was working with my now
Speaker 3: eighteen year old and doing history homework with her, and
Speaker 3: all of a sudden, I started to realize, like, there
Speaker 3: is so much that is similar that is going on
Speaker 3: today as has been, as it happened before. So you know,
Speaker 3: I think that is also really fascinating as you start
Speaker 3: to pull the big picture together, because it just gives
Speaker 3: you a different perspective on sectors and how to invest.
Speaker 2: Really really interesting. So you also are the sole manager
Speaker 2: of the alger concentrated equity strategy, which is now inn
Speaker 2: etf form. When I think of concentrated portfolios, we're talking
Speaker 2: fifteen twenty twenty five names. How many names are concentrated
Speaker 2: and then how do you size them? Are they all
Speaker 2: equal weight or you know what does that look like?
Speaker 3: Yeah, so this this portfolio is twenty to thirty stocks.
Speaker 3: It is an actively managed, fully transparent ETF. And you know,
Speaker 3: when we think about sizing for the portfolio, and look,
Speaker 3: the concept of this portfolio is to just invest in
Speaker 3: the best businesses that are going to have the greatest
Speaker 3: change and have the most kind of have the best
Speaker 3: risk reward at any given point in time.
Speaker 2: So tell us the full name of the ETF and.
Speaker 3: It's the Concentrated Equity Portfolio and the tickers cn EQ.
Speaker 3: So the idea here is we want to invest in
Speaker 3: the best companies that are going to be benefited by
Speaker 3: you know, the major the best growing trends in the
Speaker 3: market and the compounding nature of earnings should drive the
Speaker 3: portfolio and drive the companies that are within that portfolio. So,
Speaker 3: you know, position sizing is just like any other portfolio.
Speaker 3: The risk reward dictates how big the companies are in
Speaker 3: the portfolio. And there are some that you know, in
Speaker 3: Nvidia is currently at thirteen and a half percent position
Speaker 3: in the portfolio, whereas there's other companies that were waiting
Speaker 3: at the bottom of the portfolio, kind of like figure
Speaker 3: technologies that is smaller and waiting to see when the
Speaker 3: true traction in their market starts in the overhang of
Speaker 3: some of the selling and to take it up.
Speaker 1: But each of the businesses.
Speaker 3: That are owned in this portfolio have large opportunity and
Speaker 3: big TAM.
Speaker 2: Total addressable market yes, c N eq R right, I'm
Speaker 2: going to make a note of that. So I know
Speaker 2: alger back when it was Algera Capital Growth or alge
Speaker 2: Capital Management launched in nineteen sixty four, what does growth
Speaker 2: investing mean an Algea, because there are definitions that seem
Speaker 2: to be different from place to place. What are you
Speaker 2: looking for that Perhaps the market hasn't priced correctly, so.
Speaker 1: You know, I don't.
Speaker 3: I think what makes us interesting as growth investors is
Speaker 3: that the fundamental thing we look for is not necessarily growth.
Speaker 1: It is change.
Speaker 3: And the change begets the growth right, So the growth
Speaker 3: is an output of the change, and I think that's
Speaker 3: an important differentiator because it's not just expressed as let's
Speaker 3: do a screen and find the companies that are growing
Speaker 3: the fastest. It is let us look for the change,
Speaker 3: because where there is change, there is often unidentified opportunity
Speaker 3: and because of that, we will get the growth. So
Speaker 3: we have a significant research team that is always looking
Speaker 3: for change. Now, the way that Fred had initially incepted
Speaker 3: this concept of change was to look at two different pillars.
Speaker 3: The first is what we call high unit volume growth,
Speaker 3: and that is typical kind of company that that is
Speaker 3: growing their top line, they become market dominant or have
Speaker 3: a positioning that is they're taking a lot of share,
Speaker 3: very forward thinking and you know, expresses itself as high
Speaker 3: top line and growing bottom line. It could be small
Speaker 3: in mid cab companies, it could be larger companies. It
Speaker 3: really spans the gamut of of change and growth. So
Speaker 3: that would be more of a typical growth company, traditional
Speaker 3: kind of a traditional growth company. The other side, which
Speaker 3: I think makes us really unique, is what we call
Speaker 3: life cycle change. And oftentimes we like to show this
Speaker 3: this it's almost like an S curve of you know,
Speaker 3: we like to invest in the companies that are early
Speaker 3: on in the S curve and companies that have already
Speaker 3: gone through the S curve. They're kind of saturated out
Speaker 3: of their markets and they're starting to question who.
Speaker 2: They are saturated as in fully priced or saturated as in, hey,
Speaker 2: that's as big as their market share.
Speaker 3: Yeah, that's that's as big as their market is going
Speaker 3: to get. So what you see is oftentimes companies that
Speaker 3: are you know, their growth, their growth, they were great
Speaker 3: growth companies and all of a sudden their growth has
Speaker 3: stabilized or growth is starting to kind of approach GDP.
Speaker 3: I remember kind of more mature, right, so kind of
Speaker 3: a more mature company. And then the management has a
Speaker 3: decision to make am I still a growth company? Or
Speaker 3: am I going to just milk what we have? And
Speaker 3: oftentimes that that gets change. So a new management comes
Speaker 3: in and the sides, we're going to kind of jettison
Speaker 3: all our low growth businesses and start buying higher growth businesses,
Speaker 3: and it changes the profile of the business. It could
Speaker 3: be a regulatory change that makes the company a little
Speaker 3: bit more growthy than it was historically. It could be
Speaker 3: m and A that again reaccelerates top line growth. It
Speaker 3: could be a technology technological change that they really embrace.
Speaker 3: And this was Microsoft in its early days back into
Speaker 3: you know, when there was a when Satya Nadella first
Speaker 3: came to the helm. So it's almost as if the
Speaker 3: company had a decision to make and we're looking for
Speaker 3: changes that the decision from here is to get onto
Speaker 3: a growth trajectory and then study how they execute such
Speaker 3: that it drives both top line and bottom line growth,
Speaker 3: so that you know, oftentimes when we buy companies that
Speaker 3: are on that side of the ledger, people will think
Speaker 3: they're value names, and they're not really value names. They're
Speaker 3: actually unidentified and misunderstood growth and that's how we think
Speaker 3: of them. And a great example of this is you
Speaker 3: know what's happening to the hard to drive companies right now,
Speaker 3: where you know, they were trading at single digit multiples,
Speaker 3: but in an era of AI, all of a sudden,
Speaker 3: you need a lot more data and you need to
Speaker 3: store all that data. So you know, hard tos drives
Speaker 3: all of a sudden became you know, in shortage, and
Speaker 3: now they're taking pricing and their their their earnings power
Speaker 3: has you know, gone up three four fivefold over over
Speaker 3: the last few years.
Speaker 2: Even though people thought it was a at the tail
Speaker 2: end of their useful life cycle, they found a second.
Speaker 3: Life, that's right, and so so that is a that
Speaker 3: is also a change, and it happens to be a
Speaker 3: change in the market broadly, right, So So.
Speaker 2: That raises a really fascinating question. I have to ask you.
Speaker 2: There are companies that appear to be on the back
Speaker 2: end of their of their life cycle. Their growth has plateaued.
Speaker 2: Maybe they're not gaining market share, maybe the market itself
Speaker 2: isn't growing. How can you identify when something is legitimately
Speaker 2: fading or potentially at the start? Like I know IBM
Speaker 2: just had a rough quarter, But how many times has
Speaker 2: that company reinvented itself and been left for dead only
Speaker 2: to surprise everybody. And there's a bunch of Microsoft you
Speaker 2: brought up as another example. What were they thirty forty
Speaker 2: years old when Nadella came in? That that's a huge
Speaker 2: turnaround story. So how do you identify when, hey, these
Speaker 2: guys are never going to be what they once were
Speaker 2: or no, there's something real happening.
Speaker 3: Okay, So there is this publicly traded fintech company that
Speaker 3: you know was just struggling in part because they had
Speaker 3: saturated their markets and there was nowhere for them to
Speaker 3: grow and it was becoming a lot more competitive.
Speaker 1: CEO and CF so leave new.
Speaker 3: Management comes in, you know, put together a brand new
Speaker 3: strategy that is fantastic. Our team looks at it like promising. However,
Speaker 3: the core issues of their business have not been resolved.
Speaker 3: Right do you go from a four and five percent
Speaker 3: grower to a ten twelve to fifteen percent grower With
Speaker 3: the strategy, We couldn't really you know, resolve that they
Speaker 3: would be able to get there because the pressures in
Speaker 3: their markets were so significant. Competitive mature, competitive maturity, the
Speaker 3: you know, they were just fighting to kind of stay
Speaker 3: alive or stay stay at that like three four five
Speaker 3: percent type growth.
Speaker 1: So that was one that we looked at.
Speaker 3: The catalyst was a new CEO, a new management team,
Speaker 3: like the entire management team was different, but to us
Speaker 3: it wasn't really logical that it would change or they
Speaker 3: could change the trajectory of the business. Microsoft a completely
Speaker 3: different story because Satya comes in, he says, we're going
Speaker 3: to turn the ship, We're going to develop cloud and
Speaker 3: then we started to understand what it meant to go
Speaker 3: to a SaaS based business. Gosh, it can be you know,
Speaker 3: in the near term it would be depressing their earnings,
Speaker 3: but longer term it's really interesting, right, and they can
Speaker 3: get to a mid teens type growth again, which they
Speaker 3: did get to. I mean Microsoft, if you remember everyone
Speaker 3: thought Google was going to take over Google Sheets was
Speaker 3: going to take over Excel, and like, why do we
Speaker 3: all need Microsoft?
Speaker 2: I asked myself that question every time I launch and
Speaker 2: look at the annoying new ribbon that they changed this
Speaker 2: decade ago. But I use both, but you use both?
Speaker 3: Ye, and after all these years, and I assume in
Speaker 3: ten years we'll still be using Microsoft. So you know,
Speaker 3: Satya then pivoted this ship and got into the cloud
Speaker 3: business with Azure, and so we watched the actions as well,
Speaker 3: so we can dream the dream and then test the
Speaker 3: hypothesis and see whether or not they're executing against it.
Speaker 2: Really really interesting. Let me reverse the question to you
Speaker 2: and say what leads you when you're running a concentrated
Speaker 2: portfolio to say I'm going to sell this? Is it
Speaker 2: the fundamentals deteriorating the thesis not working out sometimes is
Speaker 2: based on valuation or is it simply we only have
Speaker 2: room for X number of companies and this opportunity is here,
Speaker 2: and that opportunity is all the way up here.
Speaker 1: It's all of the above, right.
Speaker 3: There is examples of selling a company because there's a
Speaker 3: better opportunity and you don't want to take you don't
Speaker 3: want to take double the risks so to the same
Speaker 3: end market, yet the upside of one is greater than
Speaker 3: the upside in the other. There are examples of you know,
Speaker 3: you sell or at least trim because the price target
Speaker 3: has been achieved, and maybe beyond the price target has
Speaker 3: been achieved, so their risk reward is simply different. There
Speaker 3: are examples of disappointments, you know, companies that disappoint relative
Speaker 3: to our expectations and they didn't deliver on what we
Speaker 3: expected them to do and the hypothesis didn't play out.
Speaker 1: So I think there's.
Speaker 3: All of the above, and every sale has a different reason.
Speaker 2: Really really interesting. Coming up, we continue our conversation with
Speaker 2: doctor Encore Crawford, executive vice president and portfolio manager at Alger,
Speaker 2: diving in to her AI thesis. I'm Barry Richards. You're
Speaker 2: listening to Masters in Business on Bloomberg Radio. I'm Barry Ridults.
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. My
Speaker 2: extra special guest today is doctor Encore Crawford. She's portfolio
Speaker 2: manager at al where she copms the large capital appreciation
Speaker 2: strategy and runs the concentrated ETF for the firm. So
Speaker 2: we are legally obligated to discuss artificial intelligence, but you're
Speaker 2: the perfect person to have this conversation with. There's a
Speaker 2: quote of yours that I found fascinating. You said, when
Speaker 2: software begins to write software, innovation becomes exponential. That's already happening.
Speaker 2: Walk us through what this means for earning powers for
Speaker 2: the semiconductors, for the hyperscalers, and then for the rest
Speaker 2: of the S and P five hundred.
Speaker 1: Okay, so that is a very big question.
Speaker 3: Look, I think we're at this I mean, elon, don't
Speaker 3: call it a singularity, or we're at this point in
Speaker 3: time where we have never seen this kind of innovation.
Speaker 1: And imagine everything that.
Speaker 3: You know, let's take to it's easiest to describe with software.
Speaker 3: We used to sit in code software, right and we
Speaker 3: had to understand the coding, We had to debug it.
Speaker 3: It would take a long time. Well, when software begins
Speaker 3: to write software, that whole process is truncated. And imagine
Speaker 3: what can be done in our largely digital world when
Speaker 3: software begins to code, decode, and create.
Speaker 2: So let me push you a little bit there. The
Speaker 2: large language models that are out there give AI the
Speaker 2: ability to effectively cut and paste everything that's been done before.
Speaker 2: How good is AI at creatively innovating code that's never
Speaker 2: been written before.
Speaker 3: So look, I am not a coder, so I can't
Speaker 3: tell you whether the code is elegant or you know,
Speaker 3: can be taken to production. I will tell you that
Speaker 3: I was able to build a pretty interesting app inside
Speaker 3: of a few months. And this is just doing it
Speaker 3: on the weekends, occasionally on the weekends, not even you know,
Speaker 3: every weekend, so.
Speaker 1: You know, and that was all VIBE coded.
Speaker 3: So it is adding this technology that is highly viable.
Speaker 3: You talk to coders, they are using it ninety percent
Speaker 3: of the time and are now just instructing and have
Speaker 3: to have this the logical framework of how to use
Speaker 3: the code. And I think that the big picture here
Speaker 3: is that once the code begins to write the code,
Speaker 3: then it's not going to necessarily be creating. The creation
Speaker 3: still has to come from you, the insight still has
Speaker 3: to come from you, but it can actually innovate, right,
Speaker 3: the innovation curve for you is significantly higher.
Speaker 1: So that's what we're seeing today where.
Speaker 3: These digital assets are becoming more innovative, or they're allowing
Speaker 3: us to be more innovative and we've hit that point
Speaker 3: in time where we're getting exponential innovation and we've never
Speaker 3: really seen anything like this before. You know, humanity hasn't
Speaker 3: seen this before in such a short period of time.
Speaker 3: You know, if you look at previous industrial revolutions, they
Speaker 3: would be you know, over generations.
Speaker 1: It wouldn't be coming in the span of five years.
Speaker 3: And so this is what makes it really interesting because
Speaker 3: you ask like, how good is it for semiconductors, and
Speaker 3: how good is it for you know, the rest of
Speaker 3: the S and P and the hyperscalers. The impact on
Speaker 3: all of these differs. So, you know, software is that
Speaker 3: the you know, we wrote a paper three years ago
Speaker 3: called aim the Declining Cost to Create, and it was
Speaker 3: all about how when software begins to write software, the
Speaker 3: cost to create software goes to zero. And what happens
Speaker 3: to the incumbents when the cost to create software is zero? Right,
Speaker 3: one of the modes goes away, and that necessarily means
Speaker 3: that the operating profit of businesses must change. Not that
Speaker 3: software is dead, it's just that the operating profile of
Speaker 3: all of the companies must change because it becomes more competitive.
Speaker 1: Right, and where does that value go?
Speaker 3: We had five and a half trillion dollars of spending,
Speaker 3: now six trillion dollars of IT spending. Fifty percent of
Speaker 3: that was IT services and software, and our contention was
Speaker 3: that the value would go from IT services and software
Speaker 3: into hardware networking, because that is really what is driving
Speaker 3: this innovation curve. So, you know, there's entire sectors that
Speaker 3: have been kind of grown a lot, and others that
Speaker 3: are facing their own pressures. I would say the same
Speaker 3: thing for any sector in the market. You know, we
Speaker 3: spoke about healthcare earlier. How can a United Healthcare actually
Speaker 3: use AI to bend the cost of care? And can
Speaker 3: there be incumbents that cross the chasm or there might
Speaker 3: be some that can't cross the chasm, and there are
Speaker 3: new companies that begin to use AI to bend the
Speaker 3: cost of care.
Speaker 2: So I'm glad you brought up healthcare. I've been fascinated
Speaker 2: not so much by bending the curve of cost from
Speaker 2: somebody like United, but all of the small biotechs and
Speaker 2: new molecules and huge well of existing chemistry and pharmaceuticals
Speaker 2: and studies we've done that nobody's really had the ability
Speaker 2: to go back to and say, hey, maybe something's in
Speaker 2: here that we've missed.
Speaker 4: You know.
Speaker 2: The most cliched example is I'd never pronounce it right.
Speaker 2: Sildnfl Viagra was supposed to be a heart treatment and
Speaker 2: had this unusual side effect and now it's a multi
Speaker 2: billion dollar met the same thing with GLPS and originally
Speaker 2: for diabetes. But hey, everyone's losing a lot of weight
Speaker 2: on these. I'm curious, not so much on the cost side,
Speaker 2: but there's this giant body of un excavated research that
Speaker 2: just seems like it's waiting for AI to attack it.
Speaker 3: Yeah, and so you know, recently I met with the
Speaker 3: I was actually on a panel where I was the
Speaker 3: moderator for a company and I've forgotten I've forgotten the
Speaker 3: name of the CEO and the company, but they're basically
Speaker 3: a new AI company that is taking this compendium of
Speaker 3: knowledge and taking it to companies and saying, marry it
Speaker 3: with the data that you have, and can we start
Speaker 3: finding not only the solutions, but you know, for your targets,
Speaker 3: but use this history to get there faster. So there's
Speaker 3: lots of efforts being made on this right now. I
Speaker 3: do think that we will accelerate drug discovery and the
Speaker 3: impact it will have to healthcare. I mean Look, the
Speaker 3: holy grail is staff personalized healthcare at some point.
Speaker 2: Well wasn't wasn't DNA testing supposed to give us that
Speaker 2: couple of years ago?
Speaker 3: Well, DNA, yes, But DNA testing used to cost a
Speaker 3: million dollars a year, a million dollars per sample, and
Speaker 3: today it's one hundred. So we're getting to the point
Speaker 3: where we can actually look at our individual DNA and
Speaker 3: it just takes time, and at some point can.
Speaker 1: We marry it with some AI insights.
Speaker 3: So look, I think healthcare is going to be greatly impacted.
Speaker 3: I think that I'm most excited actually to see how
Speaker 3: we can democratize healthcare because really our healthcare system here
Speaker 3: is kind of broken, not kind of to be polite,
Speaker 3: and how can we take down that cost of care?
Speaker 3: And really, like I would love to have universal health care,
Speaker 3: it just can't be done in the construct of healthcare
Speaker 3: as it is today. So, you know, can we use
Speaker 3: AI to provide universal health care?
Speaker 1: I think we can.
Speaker 3: It will take a few years, maybe a decade, but
Speaker 3: I think we can. And this is a global statement,
Speaker 3: it's not necessarily just the US. It's bringing the cost
Speaker 3: of care down enough such that you know, anyone on
Speaker 3: this planet will have at access to healthcare.
Speaker 2: So I'm going to assume that you think all of
Speaker 2: the AI bubble talk is wildly overblown.
Speaker 3: Yes, I do think it's wildly overblown. I think they're look.
Speaker 3: I think the trade has gotten a bit harder, you know,
Speaker 3: and in part because the first two to three years
Speaker 3: of the trade was oh, you just have to buy
Speaker 3: the GPUs and anything that the GPU touched was gold.
Speaker 3: And then it became more nuanced. Well, agents use CPUs
Speaker 3: and we have a memory shortage, and memory has now
Speaker 3: gone up four x in price, So Capex budgets are
Speaker 3: going up. So that question of ROI is coming to
Speaker 3: the fore and how much does Capex have to go
Speaker 3: up to accommodate the supply chains being as tight as
Speaker 3: they are, And there's technological differences between a CPU versus
Speaker 3: a GPU and how they're used, and the Chinese might
Speaker 3: be coming and right. So there's there is like a
Speaker 3: lot of different aspects that have made it a little
Speaker 3: bit harder. Where you know, open versus closed source debate,
Speaker 3: like the open model versus the closed model.
Speaker 1: That's another debate.
Speaker 3: The debt and the CDs spreads widening, that's another day.
Speaker 3: So all of a sudden, we've gone from a relatively
Speaker 3: simple we're going to need AI, We're going to need
Speaker 3: compute two.
Speaker 1: There's a slew of.
Speaker 3: Different narratives that one can press on for the barecase. Now,
Speaker 3: I structurally believe that we just talked about healthcare and
Speaker 3: the innovation curve in healthcare and what that can give
Speaker 3: back to society.
Speaker 1: That is true value.
Speaker 3: Right, If we can bend the cost of care from
Speaker 3: x to X minus, that is value that's created for humanity,
Speaker 3: and we will pay for that value.
Speaker 1: You know.
Speaker 3: The other day there's been this big debate about token maxing.
Speaker 2: And there was to find that for the lay listener.
Speaker 3: Yeah, token maxing was this behavior that companies were encouraging
Speaker 3: their engineers to basically have leader boards of who can
Speaker 3: use the most tokens, which sounds insane, right. It would
Speaker 3: almost be like, you know, telling your employees to see
Speaker 3: how much they can spend on lunch, and whoever spends
Speaker 3: the most on lunch gets an award, right.
Speaker 2: Well, I imagine if you're a FedEx driver and the
Speaker 2: company holds a competition to who's going to go through
Speaker 2: the most amount of gas and tires, meaning making the
Speaker 2: most deliveries not a bad thing for the company.
Speaker 3: Not necessarily a bad thing. But you know, in this case,
Speaker 3: the tokens that were being used or what was being
Speaker 3: used is actually not necessarily tied to deliveries. It was
Speaker 3: just use the most tokens as you can. It didn't
Speaker 3: kind of matter what you built with it, right, So
Speaker 3: or there wasn't as much scrutiny as to how many
Speaker 3: quote unquote deliveries you made. You just burned through your tires.
Speaker 1: So so it was kind of inefficient.
Speaker 3: But you know, they came out and they said, you know,
Speaker 3: we blew through our entire budget in a quarter for
Speaker 3: the year, for the year, the entire budget for the
Speaker 3: year in a quarter, and you know, and we haven't
Speaker 3: gotten an ROI.
Speaker 1: Well, no kidding.
Speaker 3: Well, they turned around last week and they laid off
Speaker 3: ten percent of the people that worked for the company
Speaker 3: because of AI. Well, you know, somewhere along the way,
Speaker 3: the use of artificial intelligence allowed them to kind of
Speaker 3: refine their workforce.
Speaker 2: That sounds like they didn't lay off people because of AI.
Speaker 2: They sound like they laid off people because management was
Speaker 2: kind of misincentivizing the employees.
Speaker 3: Well, I mean they said that they lay the laid
Speaker 3: off people because of AI.
Speaker 1: There's been many companies like Jack Dorsey at x y Z.
Speaker 3: You know also, you know he cut forty percent of
Speaker 3: the staff blaming AI. Who knows really what the real
Speaker 3: reason is. It could be AI, or it could be
Speaker 3: they just over hired.
Speaker 2: Which he has a history of.
Speaker 1: Which he has a history of.
Speaker 2: If you've tracked him over various contents.
Speaker 3: And many of these companies did right, So I can't
Speaker 3: deconvolve that. However, you know, Uber in particular said it
Speaker 3: was because of AID they had kind of they have
Speaker 3: been very front foot forward on the use of AI
Speaker 3: and now they're able to increase productivity enough that they
Speaker 3: can titrate down their workforce.
Speaker 1: So, you know, I do think that there is.
Speaker 3: Value that is being created because of AI. I think
Speaker 3: that it is not necessarily a technology that's plug and
Speaker 3: play into an enterprise, and there has to be some
Speaker 3: learnings and before you can get to that ROI, and.
Speaker 2: We're seeing those stumbles in that learning car.
Speaker 3: It doesn't mean that it's never going to work. And
Speaker 3: my viewpoint is that where there is value, we work
Speaker 3: in a system of rewarding value. So if you can
Speaker 3: create value, I believe that whoever uses that system that
Speaker 3: creates the value, they will pay for it.
Speaker 2: So you've described the demand for computers insatiable. What would
Speaker 2: have to happen for you to say, all right, we're
Speaker 2: getting to saturation or satiation. What does the top of
Speaker 2: the cycle look like? Or is it so far off
Speaker 2: in the future that we can't even think about it.
Speaker 3: What I would say is that this is not a
Speaker 3: question that can I can say, oh, we'll never like
Speaker 3: in twenty thirty we won't need compute, or I think
Speaker 3: it's a function of how much we put into the ground, Right,
Speaker 3: it's a delicate balance of you know, if we put
Speaker 3: X into the ground today we put You know that
Speaker 3: the hyperscalers are spending six hundred and fifty billion dollars
Speaker 3: or whatever that that number is.
Speaker 2: It's circular, exists. It's that we've heard these complaints for
Speaker 2: two years.
Speaker 3: But six hundred and fifty billion dollars seemed like a
Speaker 3: really big number. Yet we are still short compute, right,
Speaker 3: you're hearing from the hyperscalers we do not have enough.
Speaker 3: The neo clouds are telling you that there are four
Speaker 3: times as many asks for compute as they have capacity
Speaker 3: so if one says that we are short compute today,
Speaker 3: I don't really understand the logic. Now, let's fast forward
Speaker 3: two and three years. If we put three trillion dollars
Speaker 3: into the ground next year or the year after, which
Speaker 3: we cannot do today because we are short power, we
Speaker 3: are short people, we are short capacity, we are short
Speaker 3: you know, we can't make the those chips. But let's
Speaker 3: hypothetically say we put in three trillion dollars of compute
Speaker 3: into the ground in twenty twenty eight. I would say
Speaker 3: that that is overcapacity. But we can't do it because
Speaker 3: there is almost a natural, a natural limiter to the
Speaker 3: growth of this market in we don't have the chips,
Speaker 3: we don't have the people, we don't have the power, right,
Speaker 3: and so the market is being capped. If all normal
Speaker 3: forces and if we had an infinite supply of everything,
Speaker 3: I think we would be in overcapacity today because it's such.
Speaker 1: A big market.
Speaker 3: Everyone would be building at a pace that you know
Speaker 3: they wanted to be that they would want to be first.
Speaker 3: But the fact is, it's actually a blessing that the
Speaker 3: market is being capped by all of these supply change shortages.
Speaker 3: The fact that we don't have plumbers and electricians to
Speaker 3: actually work in their data centers. Is capping the growth
Speaker 3: of data centers, and so it is allowing for duration
Speaker 3: versus kind of having a one time growth pop and
Speaker 3: which you were not going to pay a high multiple for.
Speaker 3: So I think that oversupply is a function of how
Speaker 3: much we put into the ground and how we use it.
Speaker 2: So let's unpack some of that. In the beginning of
Speaker 2: twenty five, when deep Seek first Kinda was released and
Speaker 2: everyone was startled, and the initial reaction was, oh, we've overbuilt.
Speaker 2: We don't need this many GPUs. We don't need all
Speaker 2: these giant data centers. We just need slightly clever software
Speaker 2: that can do more with less. Didn't take long before
Speaker 2: that just was overrun with No, we need horsepower. We
Speaker 2: really need the ability for big problems to not come
Speaker 2: up with clever little workarounds, but we need the firepower.
Speaker 2: And then again, more recently, we've seen a number of
Speaker 2: open source models out of China that seem to be
Speaker 2: doing a whole lot more with less. At what point
Speaker 2: does it begin to become, Hey, do we really need
Speaker 2: a three trillion dollars worth of capacity? Don't we just
Speaker 2: need to take a little bit of that working out
Speaker 2: of the constraints we have the way the Chinese models have.
Speaker 3: Yeah, so one of the things that I think is
Speaker 3: well understood is that the Chinese models didn't do this
Speaker 3: on their own. So the way I like to think
Speaker 3: of it is, you know, you have like these almost
Speaker 3: like like an animal world.
Speaker 1: Right. I just went on on on safari to Kenya.
Speaker 3: And you know, giraffes almost always have like birds sitting
Speaker 3: on their necks, and those birds are you know, it's
Speaker 3: a mutually symbiotic relationship. I suppose it's not that symbiotic
Speaker 3: to the giraffe, but you know, the bird gets to
Speaker 3: rest on the durrafs deck and benefits from the fact
Speaker 3: that the.
Speaker 1: Giraffe is walking around. So similarly, I suppose the bird
Speaker 1: might like keep the bugs away, keep the bugs away, or.
Speaker 3: Like eat at the ticks on the giraffe. I don't know,
Speaker 3: but similarly, you know, the Kimmi model is a little
Speaker 3: bit like the bird on the giraffe. Whereas look, and
Speaker 3: I think that they are incredibly like the Chinese are
Speaker 3: are very innovative in their own right. I think they're
Speaker 3: very good fast followers. However, they need the giraffe, which
Speaker 3: is our l MS in order to survive. And so,
Speaker 3: you know, I think there are many different ways to
Speaker 3: address what is happening. You know, the scenario that I
Speaker 3: think is actually most logical, which I'm not quite sure
Speaker 3: that you know, the large language models will do is
Speaker 3: basically to hold the N and N minus one model
Speaker 3: internal and allow for you know, certain businesses, certain companies,
Speaker 3: the US government, other governments who are not going to
Speaker 3: distill this model and kind of feed a Kimney type
Speaker 3: model and allow for them use of that model, and
Speaker 3: only make public the N minus two model. And that
Speaker 3: way it keeps any of the distillation at Bay. Now,
Speaker 3: in order for that to happen, all of the frontier
Speaker 3: models will have to agree to do this, because if
Speaker 3: there's any frontier model that you know is equivalently is good,
Speaker 3: then it kind of breaks the breaks the ecosystem that
Speaker 3: I'm describing. But so so, I think the point is
Speaker 3: that you need to spend the capex for the training
Speaker 3: in order to get that output so that Kimmy can
Speaker 3: train on that output.
Speaker 2: So these opening on the output, not on the not
Speaker 2: creating their.
Speaker 3: Own LLM, Well, Kimmy has created their own LLLM by
Speaker 3: feeding off the it's called distilling, feeding off the output
Speaker 3: from the large language models. So you know, but a
Speaker 3: lot of the spending that is happening is actually coming
Speaker 3: from the use of the compute like so from the
Speaker 3: inference aspect. So you train and then you have to infer.
Speaker 3: So the inference is what we experience as consumers, and
Speaker 3: so that inference is driving a majority of the spend.
Speaker 3: And you look at the revenues of open ai Anthropic,
Speaker 3: they're kind of like, I've never seen growth like this.
Speaker 3: I don't think we as ever have seen growth that
Speaker 3: is as significant as what we're seeing today.
Speaker 2: You know, people frequently make a comparison to the dot coms,
Speaker 2: and I always feel feel like that's a terrible comparison
Speaker 2: because these are real companies with real revenue, real potential profits,
Speaker 2: like almost they're not clicks and eyeballs. But the one
Speaker 2: thing some of the skeptics have pointed out that almost
Speaker 2: resonate is, you know, during the Internet era, we have
Speaker 2: this huge boom where most of that value ended up
Speaker 2: landing in the consumer's laps, not the investors lapse, because
Speaker 2: so many of those companies crashed and burned. How similar
Speaker 2: or different? Is this environment to that?
Speaker 3: So I think it's quite different. Look, there were there
Speaker 3: may be parallels at some point, I e. Do we
Speaker 3: overbuild and how long does it take to actually eep
Speaker 3: through that overbuild? So you think about two thousands. One
Speaker 3: of the reasons we overbuilt is because we had dreamed
Speaker 3: the stream of what the Internet would be, and we
Speaker 3: had you know, pets dot com was actually a brilliant idea,
Speaker 3: just a little it was chally now it's chewy, but
Speaker 3: but it's but chewey became a significant business. Amazon has built,
Speaker 3: you know, a many trillion dollar business off the back
Speaker 3: of consumers buying on the Internet. But we didn't have
Speaker 3: the internet, right, we had dial up. Right dial up
Speaker 3: is not good enough to increase productivity back then.
Speaker 1: What I would argue today is that we actually have
Speaker 1: the tools. All we needed.
Speaker 3: We had the Internet, we had the the productivity, or
Speaker 3: we had the kind of the the infrastructure that was
Speaker 3: needed for ubiquitous intelligence. All we needed was the chips, right,
Speaker 3: We need the data centers and the chips, and that's
Speaker 3: what is happening today. And so if we actually need
Speaker 3: ubiquitous intelligence and infinite intelligence to some extent, like if
Speaker 3: we overbuild, we will eat through that overbuild as well.
Speaker 4: So what do you.
Speaker 2: Think the skeptics misunderstand about AI? Is it the scale,
Speaker 2: the economics, how durable the investment cycle is? What are
Speaker 2: the bears getting wrong here?
Speaker 3: I think it's the duration, I definitively think I think
Speaker 3: it's the I think maybe it's all of the above, really,
Speaker 3: but it's duration, it's the scale, it's the economics. All
Speaker 3: three of those is where I think they're pushing on
Speaker 3: the wrong on the wrong thread.
Speaker 2: So so last question before I get to all of
Speaker 2: my favorite questions, I asked, well, my guests, what do
Speaker 2: you think investors aren't talking about or thinking about that?
Speaker 2: Perhaps they should be? What what is getting overlooked here?
Speaker 2: And it could be any asset, geography, policy, whatever, But
Speaker 2: what aren't people talking about?
Speaker 3: But should Yeah, I think that people aren't really talking about,
Speaker 3: you know, the net positive benefits to humanity from AI.
Speaker 3: And you know, we talked about healthcare and how we
Speaker 3: can make healthcare kind of available to any human on
Speaker 3: this planet.
Speaker 1: The same goes for education.
Speaker 3: There's no reason why any child should be quote unquote
Speaker 3: left behind. I mean, I've been shocked at you know,
Speaker 3: what I've been reading recently is kids going to college
Speaker 3: and they can't read. Right, That is a failure of
Speaker 3: our education system that can be solved using artificial intelligence.
Speaker 3: You know, this is and this is again, it's a
Speaker 3: global issue. It is not a local issue. This is
Speaker 3: something that we can There's no one that should not
Speaker 3: be educated. And like the AI kind of the anti
Speaker 3: AIS a climate change right, I mean, I do think
Speaker 3: that using AI will we be able to solve the
Speaker 3: problems that we have with climate change? Will we be
Speaker 3: able to engineer things that will help with the rapid
Speaker 3: rate of climate change. And a lot of the AI
Speaker 3: AI kind of doomers or AI naysayers who don't want
Speaker 3: the data center built in their backyard or data center
Speaker 3: built anywhere, are ignoring the fact that there are many
Speaker 3: different aspects of AI that will be good for humanity.
Speaker 1: And does it require great change? And is change scary?
Speaker 3: It is, and it will require change, It will require
Speaker 3: change from all of us. But the end point is
Speaker 3: actually quite beautiful.
Speaker 2: I like that you're such a techno optimist. All right,
Speaker 2: let's jump to our favorite questions, starting with who your
Speaker 2: mentors who helped shape your career?
Speaker 3: Oh gosh, I think that's that's a pretty easy one.
Speaker 3: Our CEO, Dan Chung has been pivotal in in my career,
Speaker 3: in my career growth, and you know, I told you
Speaker 3: like he hired me from Stanford without knowing anything and
Speaker 3: without I really knew nothing about this business, and he
Speaker 3: recognized that, you know, why not take a shot on
Speaker 3: someone who's non traditional And he himself is a non
Speaker 3: traditional thinker. He was a lawyer, and you know is
Speaker 3: thinks very much outside of the box. So you know,
Speaker 3: over the years he's challenged me in ways that have
Speaker 3: been sometimes frustrating, but I learned from. He pushes me
Speaker 3: in ways that sometimes I don't under stand, but again
Speaker 3: I learn from and grow from.
Speaker 1: So yeah, I think Dan is Dan's like my number
Speaker 1: one mentor.
Speaker 2: Let's talk about books. What are some of your favorites?
Speaker 2: What are you reading currently?
Speaker 3: So my favorite book is a book called Think Again.
Speaker 3: It's by Adam Grant, who is an organizational psychologist. And
Speaker 3: I know it's an odd us I think he was
Speaker 3: har Wharton, I think, and I know it's an odd
Speaker 3: book to have be a favorite book of mine, but
Speaker 3: in context of business, it definitively is and in.
Speaker 1: Part it's because.
Speaker 3: It talks about how you can have a hypothesis, but
Speaker 3: you have to be humble enough to understand that you must.
Speaker 3: You can also change your hypothesis, but you have to
Speaker 3: have the confidence enough to hold a hypothesis. And really
Speaker 3: intelligence is about the ability two to morph and be
Speaker 3: be nimble without without and and it's not about arrogance,
Speaker 3: like it's not about our business requires a constant questioning
Speaker 3: of what you think right and those that become very
Speaker 3: uh tied to a thesis and uh, I think on
Speaker 3: the wrong side of a lot of trades. And so
Speaker 3: I just loved the book because the way he writes
Speaker 3: about intelligence and the humility of questioning and of holding
Speaker 3: conversations with people. And I think this is true for
Speaker 3: society in general right now, of having conversations where you
Speaker 3: may not you may not agree, but to hear other
Speaker 3: people out even if they don't agree with you.
Speaker 2: And any of the books anything you're reading currently.
Speaker 3: The last book I read was, uh, the the recent
Speaker 3: one by Brad Jacobs, which was how to Make a
Speaker 3: Few More Billions. Brad Jacobs is the CEO of QXO
Speaker 3: and he wrote his first book, How to Make a
Speaker 3: Few Billion and then he wrote How to Make a
Speaker 3: Few More Billions. And what I thought was so interesting
Speaker 3: about the book, as the first two chapters, is about
Speaker 3: how he centers himself. And he's an incredibly successful entrepreneur,
Speaker 3: has built many businesses really from scratch. He's a self
Speaker 3: made billionaire, and he starts every morning meditating, right, and
Speaker 3: how he finds that center, And it just like to me, it's,
Speaker 3: you know, we often don't talk about that aspect of
Speaker 3: you know, investing in business. It feels sometimes really transactional.
Speaker 3: But hearing that aspect of Brad, you know, it only
Speaker 3: puts him in even higher regard for me.
Speaker 2: Really interesting. What are you streaming these days? What are
Speaker 2: you either listening to or watch it?
Speaker 1: Oh, gosh, I don't. I don't watch much.
Speaker 3: I don't have that much time, and usually when I
Speaker 3: do watch something with my kids, I fall asleep.
Speaker 1: So but but.
Speaker 3: I am a runner, and so i have a lot
Speaker 3: of time that I spend running and I'm constantly listening
Speaker 3: to podcasts. My favorite ones happen to be Macro Voices.
Speaker 3: I love the Knowledge Project.
Speaker 2: Oh Shane Pash, Yeah, yeah, he's that's a regular on
Speaker 2: Sunday mornings.
Speaker 4: For me.
Speaker 3: Yeah, and so I mean the variety of conversations that
Speaker 3: he has with different people from you know, from wellness
Speaker 3: and well being and I've saw I was listening to
Speaker 3: one about the Alpha school and how how education should
Speaker 3: be reshaped. There's just like an awesome amount of diversity
Speaker 3: of thought the circuit which is all about semiconductors and
Speaker 3: and chips. I think of other ones that I listened
Speaker 3: to regularly. That's all that comes up.
Speaker 2: That's a nice list to start with. Our final two questions,
Speaker 2: what sort of advice would you give to a recent
Speaker 2: college grad interested in a career in either engineering, material
Speaker 2: science or investing.
Speaker 3: Oh wow, well, look, I think for any college grad,
Speaker 3: make sure that you do something that you love right,
Speaker 3: and it doesn't have to be you love it every day,
Speaker 3: but you spend a lot of your time at work.
Speaker 3: Your life a third, more than a third of your
Speaker 3: life is going to be spent from here on out
Speaker 3: at work. Make sure that you do something that you
Speaker 3: believe in that gives you great gratification, that you feel
Speaker 3: like you're contributing to society. Don't just do it because
Speaker 3: you're on a treadmill of you know, I'm going to
Speaker 3: go do this because I set out to do this past,
Speaker 3: then you know, I just have to go trotting along,
Speaker 3: allow yourself the grace to change and to change your mind.
Speaker 3: I did, and it was probably the best risk that
Speaker 3: I ever took. So the best gamble that I ever
Speaker 3: took on was completely pivoting in my career. So you know,
Speaker 3: allow yourself to explore because you change over time as well.
Speaker 3: You know, what you want today maybe different what you
Speaker 3: want in five and ten years, but definitively make sure
Speaker 3: that you love what you do because once you know
Speaker 3: that you love what you do, you will be the
Speaker 3: best at it.
Speaker 2: Huh. And our final question, what do you know about
Speaker 2: the world of investing today? Might have been useful twenty
Speaker 2: or so years ago when you were first starting out.
Speaker 3: So you told me this question would stump me, and it.
Speaker 2: Stump me because you know the answers that I'm not
Speaker 2: looking for. Are you know buy Amazon in O two
Speaker 2: when it was seven dollars? Yeah, it's it's what insight
Speaker 2: might have been useful way back when? What have you
Speaker 2: be what have you learned? What expensive lessons came along
Speaker 2: that you know I could have saved myself a lot
Speaker 2: of headache? Yeah, had I figured this out sooner.
Speaker 3: You know what, Barry, I don't think I would. I
Speaker 3: would in my way back machine. I wouldn't go tell
Speaker 3: myself anything.
Speaker 2: Uh huh. So it's the path and not necessary.
Speaker 1: Yeah, it's the journey.
Speaker 3: Like my most painful moments as an investor have been
Speaker 3: the biggest learning moments for me that have had like
Speaker 3: like they've branded me in some way with that, with
Speaker 3: that experience, and so I wouldn't want to shortcut that
Speaker 3: because it has shaped me. And every single time I've
Speaker 3: fallen on my face, it has shaped me, and it
Speaker 3: has reminded me of you know, the perils of not
Speaker 3: paying attention to X, Y or Z or I won't
Speaker 3: make that same mistake again because again, it is so
Speaker 3: like it goes back to that first question you asked
Speaker 3: me about like academic versus learning on the job.
Speaker 2: You need the real experience, you need the scars.
Speaker 3: You need the scars. And it's a little bit like
Speaker 3: your kids. All Right, you can tell your kids, don't
Speaker 3: do that.
Speaker 1: You're going to get hurt. Don't do that, you're gonna
Speaker 1: get hurt.
Speaker 3: Well, sometimes they just have to fall down and get
Speaker 3: hurt to realize they're going to get hurt, so it.
Speaker 2: Makes a ton of sense. Encore, thank you so much
Speaker 2: for being so generous with your time. We have been
Speaker 2: speaking with Encore Crawford, portfolio manager at Alger. If you
Speaker 2: enjoy this conversation, well, be sure and check out any
Speaker 2: of the six hundred and fifty three we've done over
Speaker 2: the past twelve years. We launched July twenty fourteen. You
Speaker 2: can find those at iTunes, Spot, YouTube, Bloomberg, wherever you
Speaker 2: find your favorite podcasts. I would be remiss if I
Speaker 2: and thank the crack team that helps put these conversations
Speaker 2: together each week. Alexis Noriega is my video producer. Jean
Speaker 2: Russo is my researcher. Anna Luke is my producer. I'm
Speaker 2: Barry Ritoltz. You've been listening to Masters in Business on
Speaker 2: Bloomberg Radio.