A growth equities investor makes the long-term case for AI.
Our guest this week is Ankur Crawford. Ankur is a senior vice president and portfolio manager at Fred Alger Management, based in New York City. Ankur comanages the Alger Capital Appreciation Fund, which is highly regarded by Morningstar’s Manager Research Team, among other US large-cap growth equity strategies. She’s also the sole manager in the Alger Concentrated Equity Strategy, which is available as both an ETF and a traditional mutual fund. Prior to joining Alger in 2004, Ankur worked as an engineer at Intel and earned a PhD in material science and engineering from Stanford.
Background
Alger Capital Appreciation Fund
Alger Concentrated Equity Strategy
Market Volatility
“Bad News Is Good News for the Market Again, Says Alger’s Ankur Crawford,” Closing Bell interview, cnbc.com, May 14, 2024.
“The ‘Magnificent 7’ Should Have Another Good Earnings Season, Says Alger’s Ankur Crawford,” Closing Bell interview, cnbc.com, June 20, 2024.
Artificial Intelligence
“AI and the Declining Cost to Create,” by Ankur Crawford and Patrick Kelly, alger.com.
“Empowering Intelligence,” by Ankur Crawford and Patrick Kelly, alger.com.
“Still Very Early on in the AI Megacycle, Says Alger’s Crawford,” Squawk on the Street interview, youtube.com, Feb. 15, 2024.
“AI Adoption Looks Like ‘Structural Imperative’ for all Companies, Says Alger’s Ankur Crawford,” Closing Bell interview, youtube.com.
Other
Dan Lefkovitz: Hi, and welcome to The Long View. I’m Dan Lefkovitz, strategist for Morningstar Indexes.
Christine Benz: And I’m Christine Benz, director of personal finance and retirement planning for Morningstar.
Lefkovitz: Our guest this week is Ankur Crawford. Ankur is a senior vice president and portfolio manager at Fred Alger Management, based in New York City. Ankur comanages the Alger Capital Appreciation Fund, which is highly regarded by Morningstar’s Manager Research Team, among other US large-cap growth equity strategies. She’s also the sole manager in the Alger Concentrated Equity Strategy, which is available as both an ETF and a traditional mutual fund. Prior to joining Alger in 2004, Ankur worked as an engineer at Intel and earned a PhD in material science and engineering from Stanford. Ankur, thanks so much for joining us on The Long View.
Ankur Crawford: It’s great to be here, Christine and Dan.
Lefkovitz: Happy to have you. You have a very interesting, unusual background, and you took atypical routes to becoming a portfolio manager. Maybe you can start by talking a little bit about your upbringing and also your journey from working at Intel and getting a PhD to working in investment management.
Crawford: I think I do have a somewhat unusual background and that I grew up all over the world, really. I was born in Manhattan, Kansas, and when I was five, my family moved to the Middle East, and when they were in the Middle East, they sent me to a boarding school in India, to a convent in the Himalayas. So, it wasn’t just a boarding school, it was this little convent that was nestled in the Himalayas. And when I was 12, I moved to Buffalo, New York. I think by the time I was 12 or 13, I had been to eight different schools and had been to 25 different countries. And that’s how I grew up, exploring the world and questioning all different kinds of things that I was seeing and experiencing, which played a big part in what I decided to do for college. I got a bachelor’s in mechanical engineering and material science from Berkeley. And the impetus for that was really, I just liked to figure out how things worked. I liked to understand the fundamental ways of mechanics and materials and physics. And I fell in love with material science and became a master’s and PhD student at Stanford University after I graduated from Berkeley, and I got a PhD from there in 2004. So, I think that’s a somewhat unusual background, but I think tied together because it explains why I did what I did through the course of my education career at least.
Benz: We want to ask how you leverage your engineering background and academic work as an investor. And I’m also curious whether finance recruited you or whether you got interested in it your own volition?
Crawford: I’ll start with the second one first. I had a curiosity about finance. I had worked as a summer intern with Merrill Lynch and had turned me on to Wall Street and finance and it was actually working with emerging-markets bonds that summer. And so, I knew that I had this interest in it, but really had already committed to getting a PhD. So, at the end of my PhD, I figured I’m going to go try it out again and see if I love it or not. And I so happen to have loved it and I’m still here. I would say I pursued finance versus finance recruiting me. And your first question, Christine, was how has my education influenced me as an investor? I don’t know if my education influenced me, or this is more so just a personality trait. I tend to be highly curious, and I question a lot, and I just want to understand things to a deeper level. And I guess my education allowed me to do that in that I understood how a car works or how an engine works, how a material is made, what the atomic structure means for the physical properties of something. But I think the biggest influence my engineering background probably had was in the linearity of thinking.
Engineers usually have to have a very logical framework by which they attack a problem. And when I look at businesses today, oftentimes there’s a very logical framework by which you understand a business. And oftentimes I’m working with our teams on building these frameworks so we can better understand a business. And those frameworks tend to be somewhat linear in their thinking, even if the big picture is not as linear and it’s more expansive than just linearity. But putting the problem together and solving the problem, even for a business in Excel, oftentimes tends to be half the battle for an investor.
Lefkovitz: Well, as we’re talking to you here in August of 2024, we’re seeing some real market volatility and it’s hitting some of your top holdings hard. You’ve got significant exposure to several of the Mag 7 companies, many of which are connected to AI, which we’ll talk about. But first I just wanted to ask, in general terms, how you think about market volatility and significant share price fluctuations in your holdings? It’s obviously not the first time we’ve seen volatility like this, but does it shake your confidence? Does it open up opportunities?
Crawford: I think different volatility is different, Dan. There could be volatility that is fear-based. There’s volatility that is actually steeped in fundamentals where the market should go down because we’re going to get earnings revisions lower. And so, I think there’s not one answer to what you’re asking because I think different points in time require a different response for a portfolio manager. What I would say is if we are to focus just on what is happening today or what happened over the last two weeks, this volatility was caused by a slew of different things coming together, whether it’s the yen strengthening and therefore causing the perturbations because the yen carry trade. We have a lot of geopolitical saber rattling occurring. We have domestically surprising election issues with Trump having gotten shot and Biden having stepped down. We got a jobs report and unemployment report that was—it showed a continuing weakening of our economy. So, all of these tied together were ratcheting up the level of a fear in the market. And then on top of that, you got the beginnings or the discussion in the market of, are we in an AI bubble?
And with Sequoia writing or doing a podcast on this or a blog on it and with Goldman Sachs having written a note on it. And this was all front of mind. And I think the first response that many investors had was to push the sell button. I fundamentally believe that—first of all, it shook out some weak hands, which, it’s a healthy correction, in my opinion. I think there are some parts of the fear that we just saw that I think we need to heed and other parts that we can say, you know what, this is just fear for now, which is the AI portion and the fear around the AI—overspending AI is just a bubble. And I think that’s more in the fear camp. So, I’ll tell you how we responded in these last few weeks. We are a fundamentally based firm where we look for change. And if you think about what is happening in our world today, the world is changing at a pace that is going to astonish us as we look back a decade from now.
And we are in very early stages of this. So, on Saturday and Sunday, I think it was last weekend in the beginning of August, my team and I were working all weekend trying to figure out, well, where do these stocks find a floor, because we knew that Monday, the following Monday, was going to be a very messy day. And in part because there was an information article about Nvidia that was going to scare everyone. And we started looking at where should these stocks find a floor on cash flow. Where should they find a floor based on a negative outlook on earnings and cash flows and growth. And what we found is that a lot of the stocks when they opened on Monday were within 5% to 10% of what we had perceived as great risk reward. And so. we didn’t sell, we didn’t get scared, we in fact leaned into that because we thought that it was fear that was really driving a lot of that stock.
Lefkovitz: I want to just ask—you mentioned there are some parts of the fear that we do need to heed. I’d be curious to hear what that is.
Crawford: I think definitively we are seeing a slowing in our economy. It’s a slowing I think that has been the most anticipated slowing the last two years. I believe everyone thought that our consumer was going to roll over. Two years ago, and one year ago, and our consumer has remained relatively resilient. This is the first time I think that you’ve started to see consumer companies really call out a weakening consumer. And you’re seeing it across the board with—Visa had lower volume growth and they guided to lower transaction growth; J.P. Morgan called out—Jamie Dimon—called out a weaker consumer. Across the consumer space whether it’s Chipotle calling out a weaker consumer to, even CPG companies are calling out more value-based consumers and consumers wanting a deal. Or they have this value-seeking behavior. They’re buying less units. So, all of these are indicators that the consumer is coming under pressure. One can say this is the low-end consumer. It’s a midtier consumer. It doesn’t affect the high-end consumers.
But net, net we’re starting to see it in the numbers of the companies that are reporting. And that is a slightly negative change and it’s worth monitoring, especially as you see unemployment rates going up. We do have to heed some of these signals and say three months ago, no one was talking about a landing. Everyone said no landing, which is why the market had this steady pace upward. And the mentality has shifted from a no landing to a good scenario, or Goldilocks almost, to a which is it? A soft landing or a hard landing? And so again, you have to follow the data to figure out where exactly we’re going to land. And at this point, I think it’s too hard to say we’re going to have a hard landing versus a soft landing because the data is, it’s inconclusive as of now. But we’re definitively slowing.
Benz: We know that you’re a bottom-up firm, very company-by-company in terms of your work. But how does that sort of view that the consumer is obviously slowing down, and certain types of the consumer space are getting hit harder than others—how does that find its way into the bottom-up work that you do on the companies in your portfolio?
Crawford: The framework which we use, we do this bottom-up work to give us confidence in the numbers and where we think a business can go relative to its market. But we also have to consider how big the market is, what is the growth rate of the market, because they reside inside of that the same context, that is a company’s revenue is derived from the market which they service. So, let’s take the digital advertising market, for example. Digital advertising is a function of consumption. And if consumption slows, then the digital advertising, or the dollars available for digital advertising, is naturally going to flex downward or flex upward if consumption is greater than you think. So that is a toggle in the thinking that you have to toggle down in an environment where you think the consumer is starting to slow because consumption will slow. Underneath that, you can say, well, I believe that Meta is taking share because they’re using AI more effectively. Or AppLovin is using AI effectively and therefore their outcomes for their customers are greater, therefore they will take more share. And that drives the top line. However, the overall market itself is slower. So, it might shave off some units of growth or some percentage of growth as you look forward in the business models relative to what we had modeled previously.
Lefkovitz: We wanted to zoom out a little bit and ask you about AI. You mentioned that you think we’re in the early stages of a long-term transformation. For many of us, ChatGPT, when it launched in late 2022 was a real a-ha moment in terms of gen AI and what it’s capable of. It certainly seems to be a turning point for markets. Were you as surprised as the rest of us by ChatGPT? Did you see it as a significant milestone?
Crawford: Yes, I was as surprised about ChatGPT. But maybe for different reasons than the market was surprised by it. So, given that our philosophy is to look for change, we’ve been looking at AI, and I would argue at that point it was machine learning since the 2015 and 2016, and we started talking and thinking about digital transformation. We’ve done many videos and thought pieces about AI. AI is nothing to fear in 2017 and ’18. In part because we knew that AI was going to be a structural imperative once it came onto the scene. The reason that ChatGPT was really interesting and exciting is because ChatGPT isn’t just like LLM like Chatbot. What it does and what all of these large language models do is they democratize compute. And one of the reasons that AI and INTERNET OF THINGS, and all of these innovations, didn’t take hold over the last 10 years has been because the compute costs were so expensive that it was almost too expensive to really implement these kinds of technologies. However, when ChatGPT burst onto the scene and you got these large language models that have basically ingested all of the data that they can get their hands on, it changes the equation because effectively what you’ve done is what I said before, you’ve democratized compute. And as you democratize compute and that compute itself continues to get cheaper and cheaper because the innovations that the chip manufacturers are currently producing, you end up getting products and businesses that end up being able to use and be more productive and it doesn’t cost them an arm and a leg to deploy the technology. So, the productivity gain is greater than the cost that you have to incur to deploy the product or to deploy AI. And I think that’s what’s really exciting about gen AI. It has made the cost of this compute accessible for all. And that is the key, the golden key, for why a decade from now our world will look so incredibly different than it looks today because the cost structures are the ROI on deploying AI is going to be vastly positive, especially over the next two to five years.
Benz: We’ve heard lots of historical parallels invoked to help make sense of AI, the internet, railroads, electricity, even the discovery of fire. So, do you find any of these historical parallels to be useful?
Crawford: I do. Actually, I find one in particular very useful, which is the steam engine. And I came about this simply because I was helping my, at the time, 13-year-old study the industrial revolution in Europe and what had happened over the course of really 100 years. And it’s really interesting because when the steam engine burst onto the scene—it actually didn’t burst; it was a very slow ramp. And at the time, I believe that Spain was the major economic force in the world. And India, they were exporting a lot of cloth and spices to the West. However, what’s interesting is that the UK was the first to adopt the steam engine and in part because at the time, the cost of labor in UK was significantly higher than in India and China. So, for them, the ROI of deploying a steam engine into a factory and making their own cloth was quite positive. And what it did, it allowed them to start manufacturing cloth. They started buying raw materials from India. And they started to manufacture cloth on their own and effectively disrupted the entire Indian economy that was based on the creation of fabric.
Which gave UK enough both money and might to then become an imperial power over the next 200 years. So, that was one really interesting aspect of the industrial revolution with the steam engine that I found very interesting and has some parallels to today. The other aspect is clearly, what it did to the labor force. England was largely an agrarian society. And they had to transition to a more industrial society where a lot of the farmers then had to figure out how to work in factories and things like labor laws, child labor laws had to get put in place. And so there was a great disruption to the labor force that took about a generation for the country to adjust to this new kind of labor need. And as you think through what’s about to happen with AI, I feel like this might happen at a pace that’s faster than a generation. I think it might happen in half a generation because the adoption rates for this and the productivity gains are going to be significant. So, I think that there are interesting parallels to what we’re going to have to do in order to train and retrain our labor force as we move from a non-AI world to a more AI-based world.
Lefkovitz: Earlier you referenced some of the skepticism that’s out there about AI. Some of the skepticism surrounds the cost of the technology, the environmental impact. There have been questions about how much value it can add, like what huge problem it’s solving. How do you how do you think about some of those questions?
Crawford: Oh, gosh, there’s a lot there. And what I’m going to tell you now, I can tell you is it might be wrong. Because at this point, we have to simply think about the implications of a new world that is using a highly productive workforce. And what the implications are for the environment or for labor, and what I will tell you—and I feel highly confident of—is that the productivity gains will be significant. And let me give you an example of a company that we own called Cadence. Cadence Design Systems is a company that basically makes software that allows engineers to make chips, and you can’t make a semiconductor without using a piece of software like Cadence. Cadence is basically a duopoly-type market, incredible management team, incredible products. And they have seen that in their own usage of AI, they’ve taken their product cycle for their hardware that they make, and they design from six years to three years. So, they cut the product design timeline by half. It’s hard to quantify what that’s worth to them, but it’s significant. Their own engineers are anywhere from 20% to 30% more efficient as they’re designing chips. A typical chip design engineer, a fully loaded cost, maybe from $300,000 to $500,000. If an engineer is 30% more productive, there is a dollar value that’s associated with that productivity.
That’s very tangible. So, I think there are many examples of productivity gains or benefits of using AI. I think as the cost of that compute declines over time, the number of different productivity use cases that open up just become more and more significant from a consumer-based AI app that I expect Apple will introduce eventually or from enterprise-based apps that will eventually show productivity. Everyone complains about Copilot, for example. And they’re like, well, Microsoft’s Copilot, it’s only 10% productive. I will remind everyone that, ChatGPT burst onto the scene, as you had mentioned Dan, only two years ago, approximately. And so, we are in the very, very early innings of this adventure. And you can’t expect the whole world to change inside of two years. But the trajectory of where we’re going is really exciting because when software begins to write software, innovation becomes exponential. It is no longer linear and limited by humans. And I think that’s the big takeaway that when software begins to write software, innovation is exponential.
Benz: We wanted to ask about potential legal and regulatory challenges. We’re told that you know something about intellectual property. You hold some patents. And it’s been pointed out that AI models are trained on data and IP without necessarily getting permission for it. You earlier pointed out all of the data that ChatGPT ingests. Do you see that as a risk?
Crawford: I think, Christine, that it is something that we have to watch. As any technology matures over time there are puts and takes and just like we had to put in child labor laws back in the industrial revolution when children were being put to work. In the same way, we have to put guardrails around any technology that bursts onto the scene like this. So, is there going to be some IP issues around using data? There will be, and we’ll have to solve them as we progress. I think these are all issues. The regulatory environment—I’m a firm believer that if you regulate today, it will A) stop innovation. But also, the environment is changing so fast. So, if you say, “OK well, I’m going to regulate a model that has 500 billion parameters.” OK, well the models are moving faster than we can actually put regulatory rules in. And so, I fundamentally believe if we regulate too fast on this gen AI, we’ll probably regulate to the wrong metric because the world is moving too fast, and we haven’t yet matured to a point where we know where we sit. And so, what are we regulating to? It’s a tricky period because you want to protect things like IP, but our government can’t be too heavy-handed either.
Lefkovitz: Homing in on investment implications a little bit more—so far, Nvidia has been a big winner, semiconductor stocks are picks-and-shovels suppliers. How are you thinking about winners of AI beyond the corporate capital-expenditure beneficiaries?
Crawford: I think there are many different winners. I was actually explaining to my kids that—I often use them to see if I can explain it in the way that is understandable. And I’m explaining that like there’s dominoes. So, we have this, if you line up our dominoes, there’s so many different implications of AI and the cost of compute declining and the idea that we’re going to start using, all of these chips to be more productive. It changes our data centers. It changes what we need in our grid. And what companies you may want to invest in in the industrial space. I think it changes certain business models to a point where there might be wide swathes of the market you don’t want to necessarily invest in. And there’s two papers that we wrote, over the last year. Actually, we wrote them about a year ago. One called Empowering Intelligence that talks about the need for electricity from data centers and how that’s growing. And we think it’s growing from 2% of the US market to about 10% by 2030. And the second one I feel like is a really seminal paper on what happens when the cost to create declines and the implications for entire subsectors like software. And what the risks are as gen AI starts writing, when software writes software, what happens to a software company that resides on code? Is that good or bad for margins? And our conclusion is that over time the margin structure for software businesses will for the most part be under pressure.
So, the implications are very, very broad of having this kind of revolution, I think even in terms of our portfolio construction. As I think out over the next decade, it changes evaluation parameters that we’re going to really lean on. And an example I love to use is Macy’s. Macy’s traded from a 16 to 20 multiple free Amazon. And as Amazon started to make way into their markets and the market started to realize that this business is under structural decline or secular decline, the multiple has slowly come into four to six multiple. And it took a decade for that to happen as people started to really understand that the terminal value of this business isn’t going to go up. It’s actually on a downward bias. And I think some of that starts to happen again where businesses that can’t be AI’d end up getting significantly higher multiples than you might think. And businesses that are going to see margin pressure because of AI. And, at multiples lower than you might think. And I would also argue that a lot of investors today, have made their careers owning software. And I structurally believe that software is going to have to prove itself in this new world. So, what does that do to the multiple? The ramifications across the market, I feel like, are going to be very significant, whether that’s in the cyclical industrials or the AI-based industrials. You’ve seen companies like Vertiv, which is cooling for data centers. And the stock has, I think, tripled over the last year and a half to two years; or even some of the utilities, like Constellation Energy, which is a nuclear utility. Again, I think the valuation parameters that we’ve used historically, I think we need to rethink all of them.
Benz: I wanted to ask, you piqued my interest when you were talking about businesses and sectors that can’t be AI’d. And I think as workers, we all think about that. You want to be a worker where you aren’t vulnerable to AI, where that’s not going to come and get your job. So, can you talk about, when you look at this from a big-picture view, what are the industries and types of businesses that cannot be AI’d?
Crawford: It’s a great question, Christine. I think, industries that have tangible value, that is, we own a company called Martin Marietta. And effectively, they basically go, they have stone quarries, and they take stone, and they sell it to construction sites. They provide asphalt for streets to the government. So, you can’t AI that aspect. You still need a stone quarry, and you need to go get the stone, and you need to transport it. And a majority of that business cannot be AI’d. What’s exciting is that they could probably make their business more efficient, in that, could they change their routes or could they have a bot or eventually a humanoid go and dig up the stone instead of a human. So, that’s a business that can benefit from AI and really enhance their operating margins or at least maintain their operating margins. I would argue any kind of job that is more… I actually tell my daughter that—if you don’t want to get AI’d, you should probably go become a nuclear engineer. Because, you’re going to need that, we have no nuclear engineers, and you really can’t AI out a nuclear engineer, especially as we build more and more nuclear around the world. So, there’s going to be spots in the market that won’t be able to get be AI’d mostly on the construction-type areas that I’ve been thinking about. I think many of the white-collar functions over time, AI will add productivity, and now the question is, will it take away jobs? Or will we just be simply more productive and be producing at a rate that is astonishing? And I think it’s probably going to be a little bit of both on that front.
Lefkovitz: I wanted to ask you about market leadership by investment style, growth versus value. Alger is obviously a growth equity shop, you’re a growth manager. Growth has had a long run of dominance in the market, though 2022 was a down year for growth stocks. Do you have a view on growth versus value and whether we might see a rotation by investment style?
Crawford: So, Dan, I think growth and value are—I hate to bucket everything into growth or value, in part because there’s value that is cyclical value and there’s value whose terminal value is being questioned, in part because of the big changes that we’re seeing in society. So, I think you have to bifurcate what is value because not all value is the same. And I gave this example of Macy’s, Macy’s one would argue is a value stock. Historically, what happens with value is that the reason you buy value at a very low multiple is because effectively they’re underearning and the cyclical forces eventually allow you to make a lot of money on these stocks as you get a cyclical rebound and you go from underearning to overearning. And the multiple goes up as the earnings are going up. What I would argue right now for value is that there’s a lot of value that’s under pressure secularly. And so that mean-reversion tendency is being called to question. And if that’s what is occurring, then you’re not really in value, you’re in a value trap because you’re just going to, bleed, bleed, and bleed down until you get to like a zero-dollar terminal value or a terminal value that is significantly lower.
I think we have to distinguish between like value and traditional cyclical value. I think there’s other value like materials or even oil. At this point, one can question the secular growth of fossil fuels as we make our way into an economy that is more efficient, and we’re using a lot more EVs. And what does that do to oil demand? It’s definitively not going to accelerate. So, I would say that, is oil really interesting as a value play? I would say at this point in time, probably not. So, I think we have to really distinguish between the different kinds of values. There could be certain materials that are very interesting, like uranium might be very interesting—that looks more value-esque, perhaps, and you can play that for mean-reversion play. So, I think you have to really understand the market. And I don’t think they’ll all trade as one big chunk of value. Oh, it’s value. So, it’s going to go up.
Benz: We want to ask about the role of interest rates in all of this. When growth stocks crashed in 2022, everyone was blaming that on interest rates, but now growth stocks have thrived in a higher-rate environment in 2023 and the first half of 2024. So, do you have a view on the impact of rates on equity valuations and on growth stock equity valuations in particular?
Crawford: As we came out of ’21 into ’22 when we saw this rising interest-rate environment, all the duration, you have to pull in duration because that rising interest-rate environment really called to question growth, and how much growth would be allowed in the economy as these interest rates were rising, as it dampened growth. And you had to pull in the time horizon on how you were thinking about businesses and their growth characteristics. And that was like end of ’21 into ’22. The last rate hike was middle of 2023 sometime, I think July of ’23. And what you saw is that growth started to work again four to six months ahead of that. And that’s to be expected because, as we approached a landing on the peak interest rate where the Fed was going to stop, we could look to a more stabilization of the economy and a starting point for growth again, and I think that’s what you’ve seen. Clearly, you’ve seen these growth businesses take off over the last year, year and a half. And now we’re at another inflection point where the Fed is going to reduce interest rates, oftentimes that will be good for duration.
And I would argue, a lot of people who have been parked in bonds and getting a 5% interest rate, might some of that money, as that interest rate comes down, might some of that capital end up in the equity markets, as they reach for a higher return. So, at the same time, I think we have to be aware of the fact that if the Fed takes down interest rates at a very fast clip, it’s because the economy is worsening or weakening at a rate that will be surprising to us. And it may cause another growth scare and it may cause perturbations in the market. So, there’s two sides to the Fed taking down interest rates. And I’m going to cite a Goldman piece that I read recently that, when the Fed starts to take down interest rates, if you go into recession, the market is down 10% to 15% in the subsequent year. If you end up with a no-landing scenario, the market goes up 10% to 15%. So, if the Fed takes down the interest rates and we don’t go into recession, I think everything will, especially growth, will end up, back on a great trajectory. And I think if we do end up with a recession, we could end up with a growth scare again, one that you’re going to want to buy.
Lefkovitz: I wanted to ask about market concentration. The US equity market has obviously become very concentrated. And as a growth investor, you are managing to a benchmark that’s very top-heavy. Microsoft, Apple, and Nvidia are over 30% of the benchmark weight together. How do you manage to that kind of concentration?
Crawford: It’s been a challenge. And in part, I went back and looked at what the benchmark compositions were when I started in this business, which was 2004. And there was no entity that was greater than like 3% of the benchmark and 3% felt egregious. So, if you told me at the time, there would be 10% positions at the benchmark, I would have balked at you. And here we are. I think it makes it really challenging and in part because, the benchmarks themselves are not diversified. And portfolio managers today are fighting this SEC diversification rule, which basically says that if you have any stock, if you sum up all your stocks over 5% and they’re over 25% of the portfolio, you can’t buy any more of those stocks. So, as a larger group, we are fighting against this rule that was really incepted a long time ago before we had a benchmark, which itself wasn’t diversified.
And I would argue the inception of this was to protect customers and to protect the public from having too much concentration risk. But given how we have grown with these platform companies really dominating the top end of the benchmark, it’s actually hurting. It’s really hurting the public and investors in active management. So, I think that we have to figure out a solution for these diversification rules and whether that’s moving from a diversified portfolio to nondiversified actively managed portfolios. That’s been our answer is really starting to manage more nondiversified portfolios that are not bound by that rule. But there’s relatively few of them, because we have to effectively start a new fund to do that, which is what we are doing.
Benz: We wanted to ask about that because earlier this year, you launched a concentrated equity strategy, which has an ETF version. I wonder if you can talk about, and I guess you sort of have, why the new strategy? And also, maybe you can focus on what’s different about running a fully transparent ETF versus running a traditional mutual fund strategy?
Crawford: Great. Thanks for asking about that, Christine. So, we launched a concentrated equity strategy as an ETF, a fund, and as an SMA, the ticker is CNEQ if you’re interested in looking at it. Really, the, the reason we launched this was because we went on a listening tour of our clients, and they actually asked for it. They told us they wanted a lower fee, more concentrated, nondiversified portfolio that had a little bit more flexibility on the top end of the portfolio. So, there’s also a more scientific reason for launching a concentrated portfolio. This particular portfolio can hold 20 to 30 holdings and really the probability of success from the tail end of a portfolio reduces dramatically relative to the benchmark for most active managers. And so, our viewpoint is that if you have fewer number of holdings with the tail end of the portfolio actually being much more active and you have high conviction in those names and you’re a good stock-picker—which Alger is, we are very good stock-pickers—you can generate more alpha in a more concentrated setting with a more graceful way by which to approach portfolio management. In terms of running a transparent portfolio, I will give hats off to our back office and they make it completely seamless to run a fully transparent portfolio relative to anything else that I run. So, there might be a lot of like logistics in the back, Christine. I’m just not aware of it.
Lefkovitz: Do you feel like in any way you’re giving away your secret sauce, through the full transparency.
Crawford: No. I think, if someone wants to track the portfolio every day and make the same changes I do, they earned the 55 bit. That’s how I feel.
Crawford: So, the part I didn’t answer, you said, in growth versus value, I think one of the reasons that growth has done so well is because the innovation cycles that we are seeing are shrinking. And so, we started this decade with the tech bubble. And then immediately you started to see advertising go from more like newspapers and an analog kind of advertising to shift to digital advertising as we started to have distributed compute. Followed by mobile compute, which enabled an enormous number of business models, which was great change. Think about Uber, or would Starbucks be as successful if they didn’t have their preorder button? And which is made for a mobile market. Or Netflix, being able to watch, to shop, to do anything anywhere, enabled many, many different business models and disrupted a whole wide swath of other different business models. And I think that’s why you’ve seen this bifurcation between growth and value because there are these very long product cycles that keep layering on top of themselves as we are going through the last couple of decades. And now the icing on the cake is AI that carries us probably for another decade.
Lefkovitz: Well, we’re almost out of time, but I did want to ask you about Alger because it has a unique history, sort of a tragic history. The capital appreciation fund that you comanaged was previously run by David Alger, who was killed in 9/11. It was before you came to the firm, but curious how Alger’s history affects the company today and its workforce.
Crawford: I wasn’t at the firm at the time. However, the sentiment of when you walk into our offices, there’s actually a series of paintings of candles. And each candle represents a person that worked at Alger that died in 9/11. We don’t talk about it on a regular basis. I think it is intrinsic that our success was built on the shoulders of many people on that team, including David Alger. And I feel like there is a desire just out of respect for each of them to succeed. And there’s a commitment that we all have to do our best and be our best. Because, without them, again, whose shoulders would we be standing on today? And I think that kind of fundamental view on philosophy is something that’s intrinsic to everyone that works here. I’ll leave it at that.
Lefkovitz: Well, Ankur, thanks so much for joining us on The Long View. It’s been great.
Crawford: Well, thank you so much. And thanks for your great questions, Christine and Dan.
Benz: Thank you so much, Ankur.
Lefkovitz: Thank you for joining us on The Long View. If you could, please take a moment to subscribe to and rate the podcast on Apple, Spotify, or wherever you get your podcasts.
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Benz: And @Christine_Benz on X or Christine Benz on LinkedIn.
Lefkovitz: George Castady is our engineer for the podcast and Kari Greczek produces the show notes each week.
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