The transcript from this week’s MiB: Glen Kacher, CIO of Light Street Capital, is below.
You can stream and download our full conversation, including any podcast extras, on Apple Podcasts, Spotify, YouTube (video), YouTube (audio), and Bloomberg. All of our earlier podcasts on your favorite pod hosts can be found here.
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MASTERS IN BUSINESS: Glen Kacher
Founder & Chief Investment Officer, Light Street Capital
Bloomberg Radio — Transcript
ANNOUNCER (00:00:02): Bloomberg Audio Studios. Podcasts. Radio. News.
BARRY RITHOLTZ (00:00:07): This week on the podcast, my extra special guest is Glen Kacher. He is the founder and Chief Investment Officer at Light Street Capital. He’s got really a fascinating background and a great track record. He worked at Julian Robertson’s Tiger Management, eventually ended up at Roger McNamee’s Integral Capital Partners.
He’s put together really a fascinating focus and track record, one of the few hedge funds located right in the middle of Silicon Valley, focused on AI and technology. I found this conversation to be absolutely fascinating, and I think you will also. With no further ado, my conversation with Light Street Capital’s Glen Kacher.
Glen Kacher, welcome to Bloomberg.
GLEN KACHER (00:01:07): Thank you.
BARRY RITHOLTZ (00:01:08): Before we get into Light Street, let’s talk a little bit about your background. You graduate from University of Virginia School of Commerce with a bachelor’s in commerce, and eventually getting an MBA from Stanford. Was investing always the career plan?
GLEN KACHER (00:01:25): It was. I started really looking into that industry. I read a book by Peter Lynch while I was in college, One Up on Wall Street —
BARRY RITHOLTZ (00:01:33): Sure.
GLEN KACHER (00:01:34): — or Beating the Street. It could have been the first book, actually.
And I was just caught by this idea of the search for great companies, great ideas. And the way he told the story of finding these companies and researching them, it was really a journey, and of a detective trying to figure out what would matter in the future. And that really captivated me and my interest in becoming an investor.
BARRY RITHOLTZ (00:02:04): So in between UVA and getting your MBA at Stanford, you work at Julian Robertson’s Tiger Management. How do you get to Tiger at 22?
GLEN KACHER (00:02:17): Oh, very fortunate opportunity. So one of the teachers, or instructors, at McIntire School of Commerce at UVA was a former Tiger Management partner, Michael Bills.
And Michael taught finance, several finance classes there for a couple of years. He had taken some years off from Wall Street after working at Tiger and then before starting a fund of funds business that he has run very successfully. And he suggested that I take a look at it. I certainly knew of Tiger.
Tiger was — it seemed about half of the investment staff, actually, at one point or another attended UVA. And so a lot of the guys there sort of knew what we were capable of as young guys coming out with finance degrees from UVA.
BARRY RITHOLTZ (00:03:11): And Robertson was legendary. In ’93, was he still running the ship?
GLEN KACHER (00:03:16): Oh yeah, very much in charge. Very much in charge, yes. I was there from ’93 to ’96 full time. And then still, when I went to Stanford for a year, I worked for Tiger as well, and Julian would occasionally wake me up with a 6:00 AM phone call when I was in business school.
BARRY RITHOLTZ (00:03:35): 6:00 AM East Coast?
GLEN KACHER (00:03:37): No, 6:00 AM my time. Okay, 9:00 AM his time, just a half hour before the market. So he had some discretion there, but we had some great times.
Learning and talking through the technology industry at the time, investing in companies like Dell, Microsoft, Compaq, and Cisco were some of the —
BARRY RITHOLTZ (00:03:59): So really right out of college, you are full on technology. Did you look at other spaces?
GLEN KACHER (00:04:04): I worked briefly in looking at financial institutions with Rob Pitts there. And we had a great time doing that, but I was certainly more interested in technology. I’d really studied that industry prior to going to New York. And so it was a better fit for me following that industry.
And I think two or three months into the job, I ended up sitting two chairs away from Bill Gates at an analyst meeting, at the sort of after-the-meeting dinner. And at that point I knew I was in the right spot. That was —
BARRY RITHOLTZ (00:04:41): To say the very least. So after Stanford, you end up at Roger McNamee’s Integral Capital, and you stay for 13 years, and you’re really less of a public markets analyst and more of a venture sort of banker. You either lead or co-lead venture investments, and the list is pretty impressive: Agile, ArcSight, Blue Nile, E.piphany, Extensity, Fortify, Interwoven, LogMeIn, OpenTable, Overture, GoTo.com.
What’s the common thread? Is it just, hey, that’s what was hot in the late nineties? Or what tied that list together?
GLEN KACHER (00:05:21): Well, the amazing thing about Roger was he really focused on saying, look, we can’t cover every company in this industry. We were a small team, much like at Tiger, there were two or three of us looking at tech at any one time. And at Integral, even though we were a tech-focused firm, we had four or five people total. But even with that number, you can’t cover the entire industry.
So you have to focus in when you’re investing and say, where is the change really happening most quickly? Where is it most dramatic? That disruption equals opportunity as an investor.
BARRY RITHOLTZ (00:05:57): That’s a theme that comes up over and over in your career.
GLEN KACHER (00:06:00): Yeah.
BARRY RITHOLTZ (00:06:01): Identify the disruption and get in front of it before the existing companies realize what’s coming down the pike.
GLEN KACHER (00:06:09): It’s great to be early, but not too early.
BARRY RITHOLTZ (00:06:11): Right.
GLEN KACHER (00:06:12): I mean, that’s also an important part of it.
BARRY RITHOLTZ (00:06:13): Right. I started on a desk, and early was equal to wrong, at least when you’re trading public equities. Not only do you do all of these privates where you’ve co-led — is this right? About 46 deals, is that right?
GLEN KACHER (00:06:28): 46 deals at Integral over 13 years.
BARRY RITHOLTZ (00:06:30): I read something you had said about that, and you said the takeaway from all these private venture investments is you don’t buy the second or third best company in the space. You always buy the best company. Can you give us a little details on that? What’s the thinking behind it?
GLEN KACHER (00:06:48): Well, experience, right? I mean, you see the movie over and over again, whether it’s private investment or in the public markets. The old saying was, the number one player’s going to get two thirds of the market, number two player might get 20%, 25% tops, and everyone else fights for the scraps, right? And the ability to make higher margins and have the dominant market share is just so dramatic.
And I think in technology, we’ve seen the power of that. The ability to sort of compound that lead is definitely there. Now, you also see in technology that you can get disrupted, right? The real innovation in these disruptive changes tends not to come from the big companies, but the smaller companies. There are exceptions to that, and we can talk through that.
AI is kind of an interesting test case, and the semiconductors behind AI. But there’s real power into compounding that lead.
BARRY RITHOLTZ (00:07:56): So let’s talk about those moats and the winner-take-all situation. Is that primarily a technology phenomenon? Is it a modern-era phenomenon? Or is this companies that develop a unique moat, regardless of the space they’re in, get to capture most of the market share?
GLEN KACHER (00:08:15): Well, I think you’ve seen in mature industries, whether you look back at GE and Coca-Cola, you’ve seen, certainly, there’s advantages to having that dominant distribution and market share. But in technology, I think it’s more a story of getting in front of your competitors and investing more. You have more dollars to invest back in the technology and to grow that lead, and that compounding of advantages, or compounding of innovation, at the early part of a market’s development can be incredibly powerful. And then that gives you the opportunity to put in place other kinds of moats that do kind of block your competitors from coming along.
There’s a lot of discussion today around Nvidia, that a lot of people sort of assume Nvidia’s going to lose their massive market share in AI accelerators, which is roughly 85%. And certainly I think the move to inference is an opportunity for competitors to change what’s going on there. But I think people right now are, for instance, underestimating Nvidia’s opportunity to innovate as well.
BARRY RITHOLTZ (00:09:30): So let’s define some terms for some of the lay people that might be listening: compute and inference. Explain what those are. Explain how they’re investible themes.
GLEN KACHER (00:09:43): Sure. So the training compute, or the chips, the AI accelerator chips — and today Nvidia dominates that still with their graphics processor chips. And those chips originally were made for gaming, for doing very rapid mathematics that have to do with calculating physics and lighting, shading in video games. It turns out that the same kind of mathematics are incredibly well positioned to do the math around AI.
And so you’re training a model, an AI model, that will be able to make judgments. And then when you’re actually using that model to ask questions, or have it solve problems and actually execute those problems, that’s called inferencing, right? And so inferencing can be done on a more simple chip. So people have kind of used a phrase, XPU, to X out the graphics and say, this is the next generation of chips that can be used to actually solve the problems with those models that are built.
BARRY RITHOLTZ (00:11:05): Meaning the compute and the inference are all going to be on the same chip?
GLEN KACHER (00:11:08): They can be done with the same chip, but you can have a more specialized, lower-cost chip, usually in inference with more memory, for instance. And there’s different approaches in software to execute that with a lower-cost chip.
BARRY RITHOLTZ (00:11:23): So it sounds like our alphabetical evolution has been CPUs, then FPUs, GPUs, and now XPUs. What’s beyond that?
GLEN KACHER (00:11:33): Well, I think that’s why we use the term X. There’s TPUs, Google’s version of their AI chip. We’ve got Trainium, et cetera, and other competitors. So there’s lots of flavors. You also saw, for instance, Nvidia buy Groq, which is another approach to inference. So there will be many flavors and many opportunities and ways to innovate in inference, because ultimately that will be a larger market than the training market.
BARRY RITHOLTZ (00:12:08): Hmm. Really, really interesting. So I usually save my mentor question towards the end of our conversation, but your list of people you’ve worked with and worked for is just so incredible, I wanted to get it out early.
In addition to Julian Robertson and Roger McNamee, there was Philippe Laffont, Steve Mandel, Chip Morris, who was, I think, at — Blue Ridge, Alger, Viking, Lone Pine, Impala, Matrix, Coatue. That’s like a murderer’s row of modern investing names. What did all these legends have in common, and how were they each different?
GLEN KACHER (00:12:49): Well, I think the focus for — we had a great team there at Tiger Management, and so many of us went on to start our own firms, and many of them sort of modeled by what we experienced at Tiger and seeing how Julian did it. I think Julian was just such an inspirational leader, and was so values-driven, and really focused on, hey, we want to work with the best people. That doesn’t just mean the people around the table with you on your investment staff. That also means the CEOs that we backed and the CFOs of those companies.
We looked for people that we thought were of high integrity. And if there was any question about the integrity of those CEOs and CFOs, we were out. We just weren’t interested in that company. And then, with Julian, there were no shortcuts, right?
It was, you’ve got to do the work. Explain to me why and how we got to the conclusion that this company is, one, positioned incredibly well, and two, it has a real opportunity. There’s something fundamentally changing in their industry or in their product set that’s going to change their trajectory.
And then the last one was, hey, let’s use our power and success to help other people, right? And so the combination of those principles was very powerful. I think many of us wanted to see if we could do something similar, and that was really powerful. And then I was lucky to go on to work with Roger and John Powell at Integral Capital, and Chip Morris.
All three of those guys came out of T. Rowe Price, and we worked with Kleiner Perkins. We were in their building. So we were surrounded by some other incredible investors that just saw things early and really invested in great entrepreneurs, people like Jeff Bezos and the founders of Google. And I was lucky that I was able to see so many inspirational people and things happen early in my career, and just wanted to try to do it on my own.
BARRY RITHOLTZ (00:14:58): Huh. Really, really fascinating. Coming up, we continue our conversation with Glen Kacher, founder and CIO of Light Street Capital, discussing the firm’s founding and launch. I’m Barry Ritholtz, you’re listening to Masters in Business on Bloomberg Radio.
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BARRY RITHOLTZ (00:15:17): I’m Barry Ritholtz, you’re listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Glen Kacher. He is founder and Chief Investment Officer of Light Street Capital. The firm is a technology-focused hedge fund and private investment firm located in Palo Alto, which is a good place to start.
You launch in 2010. The great financial crisis is still dominating the news flow. What was the original pitch to investors?
GLEN KACHER (00:15:52): Sure. The original pitch was, look, the game board had kind of been reset in terms of making money. Multiples were low. And we saw the emergence of kind of four things. Mobile, with the smartphone really growing at that point.
It was becoming a dominant platform. Social media — most of it was still private, but we saw Facebook emerging, and Twitter, and really redefining media. Cloud: the development of taking the internet technology and using it for the business, and the ability to propagate applications everywhere that the internet was available was incredibly powerful. And e-commerce, the ability to sell goods anywhere at a very low cost.
And with the back end that Amazon and others had built to get products delivered within a day or two to many locations in the globe, those four things were incredibly powerful. And then ultimately we saw things like the sharing economy come out of that. You couldn’t have had Uber and Lyft and DoorDash without having e-commerce and the mobile phone and the ability to get those companies distributed through the mobile universe. So there was a real emergence of these four powerful things.
Mobile, social, cloud and e-commerce. And it was really redefining what we could do as consumers and business people.
BARRY RITHOLTZ (00:17:31): I love how you described the firm: “We are the Silicon Valley home team, one of the few hedge funds living and working at the center of the technology universe in Palo Alto, 100% focused on tech opportunities.” The first time I read that I was like, that can’t be right. There has to be tons of hedge funds out there. Like, not many hedge funds in the center of the VC universe?
Because all of those successful venture investments eventually go public.
GLEN KACHER (00:18:03): Yeah. There’s a relatively small number of public market managers out there.
BARRY RITHOLTZ (00:18:08): Huh.
GLEN KACHER (00:18:09): And a good number of — you’ve seen Philippe, what he’s done at Coatue has been amazing.
And at Tiger Global, Chase has done incredibly well, and Whale Rock out of Boston with Alex. And so you’ve just seen the success of those guys. So I’m not saying it can’t be done by any means, but there is a real advantage to living and working in the place where the innovation is centered. And I think when you see this fundamental innovation like we’re seeing now with AI, it really draws that advantage of geography back to Silicon Valley. I think there’s a small number of great AI entrepreneurs, and they want to be in the same community with one another.
And so that’s a real advantage for us.
BARRY RITHOLTZ (00:19:01): Yeah, I kept hearing that San Francisco was over, it’s dead, the city is on its last gasp. We were there in the spring, and the city is just — it’s a boomtown. Like, I know there’s a little bit of a boom-and-bust West Coast gold rush mentality, and each new cycle of technology kind of works its way through, but to anybody who steps foot — we were down by the Embarcadero. The city is just absolutely on fire.
What’s it like? Does this feel like the late nineties in terms of the amount of human capital, intellectual capital and actual money sloshing through?
GLEN KACHER (00:19:42): That’s a great question. I’d say more in the mid-nineties, probably. I think that we’re at a point where this is very fundamental, low-level technology. We’ve seen something of a renaissance in the hardware industry.
And that hardware innovation really matters when you’re trying to scale. When you’re trying to scale at the rate —
BARRY RITHOLTZ (00:20:07): Meaning semiconductors, or everything around it, or the —
GLEN KACHER (00:20:11): The whole — semiconductors, networking, down to printed circuit boards. You have to innovate at sort of every level of the stack in order to grow at a 10x, a 100x rate. And the acceleration required in order to provide AI cycles at a competitive price is incredibly challenging. And the amount of demand that’s out there is incredible. So the need to scale is back.
And I think it’s pretty interesting, what we’ve seen. I think in the early 2000s, the semiconductor industry was allowed to consolidate, and the capital was provided to do that. And you saw a company like Avago and Hock Tan really organize the industry and do some horse trading of properties to other semiconductor firms and really rationalize that industry. And so as AI has emerged, what it’s done is it’s really taken advantage of the fact that there are a small number of companies that compete for a massive market.
So AMD and Broadcom and Nvidia, and TSMC, of course, in Taiwan on the back end. And then of course the semiconductor capital equipment companies like ASML. Those companies just have very large market share and have huge demand and huge moats and advantages.
BARRY RITHOLTZ (00:21:55): So let’s talk about the first four companies you mentioned: Taiwan Semi, Nvidia, Broadcom, and AMD. That’s about 40% of the public portion of your portfolio, or at least it was a few filings ago. I know you’re not a big fan of revealing too much of your portfolios, but that’s a fairly concentrated portfolio. Tell us the thinking behind having such a dominant emphasis on those four semiconductor companies.
GLEN KACHER (00:22:25): Sure. Well, it goes back to what I was saying earlier. You want to focus your capital in the place where you see the most innovation. And right now that’s at the core of accelerating computing in order to do AI.
And right now Nvidia’s got 80-plus percent market share in the network GPU market. AMD is certainly coming up in that. And as we move to agentic AI, which is a very important innovation that’s happening in AI and is really driving that next leg of growth, there’s certain advantages that AMD has, because they also are one of the two major players in the CPU market for desktops and servers. So that explains why AMD matters a lot. And Broadcom, what they’ve done with Google, with their TPU over the years, is incredibly impressive, and it’s gotten them now opportunities with OpenAI and some of the other major AI players.
So that’s certainly great exposure. And then TSMC makes the chips for all three of those companies, and the ability to kind of win no matter who wins, and really have a massive oligopoly — monopoly, almost — for TSMC, we certainly want to back that company as well.
BARRY RITHOLTZ (00:23:57): So those four companies plus Microsoft you described in 2024 as the AI Five, and while everybody was focused on the Mag Seven, the AI Five significantly outperformed the Mag Seven that year. Is it still a concentrated holding, all five? And how does that thesis hold up today?
GLEN KACHER (00:24:19): That’s a great question. Yeah, I’d say the company that’s kind of been in and out of our portfolio, mostly out, has been Microsoft, and their early lead with OpenAI. They, in our opinion, kind of fumbled that and —
BARRY RITHOLTZ (00:24:36): And hence giving an opening to Anthropic.
GLEN KACHER (00:24:40): Yes, for sure. And so the uptake of Microsoft’s AI that was somewhat powered by OpenAI really didn’t work that well. And that was a real miss for them. And ultimately they pulled back on their development and funding of their AI efforts.
And I think they’re now back in the game. But at the same time, what we’re seeing now is — for instance, Microsoft is the largest security company in the world, and one of the things that we’ve learned is that AI creates a lot of security vulnerabilities for businesses. So any business is going to need to invest more aggressively in their cybersecurity defenses. And so that will be a big benefit for Microsoft.
So that’s a huge advantage for them. But I think what they’ve done, and the repositioning that they’ve done on the Azure side of their business, has been very impressive. They’ve also rationalized some of the spending that wasn’t going as well in their gaming business, sort of pulling back there. So I think they’re repositioning the company well after they sort of blinked on AI, and it’s back in our portfolio at this —
BARRY RITHOLTZ (00:26:04): So when we talk about agentic and we talk about the major AI players, is this going to be a duopoly? Is this going to be Anthropic and OpenAI, or is it going to be a little more wide open than that?
GLEN KACHER (00:26:18): Yeah, I think this battle’s happening in real time between those two leading companies, and Google’s certainly still a player with Gemini and their advantage in distribution with their massive success, of course, in the search engine business. And now they’re backing Apple’s AI efforts as well. So they have a real distribution advantage. So I wouldn’t count Google out, and they still have great technology.
BARRY RITHOLTZ (00:26:51): By the way, their NotebookLM is outstanding. If you want to upload a giant file, a book or anything, it’s unbelievably accurate and fast. I’ve been really impressed with that.
GLEN KACHER (00:27:04): Yeah, their ability to innovate is stunning. But the real battle that’s emerging today is open source models that, one, are cheaper than the closed Anthropic and OpenAI models, because they’re free — you can download them for free and run them on local hardware, or you can engage with them on other commodity hardware in the sky. And those open source solutions are really battling these more expensive frontier models from the two big companies. So we will see. I think the early signals are that there’s a place for both of these solutions, broadly defined.
There’s also some regulatory questions. Open models you really can’t regulate very well, because you can install them on your own software, you can adjust them to work how you want. So there’s questions about how to make sure these are engaged safely in the wild, but there’s also not a lot of choices around for regulators, too, because those are in the wild.
BARRY RITHOLTZ (00:28:21): So I want to combine what you’ve said about Microsoft and security —
GLEN KACHER (00:28:27): Yes.
BARRY RITHOLTZ (00:28:28): — and open source. Is it fair to say that security-aware enterprises are going to be steering clear of open source because of the various security problems, and the duopoly of Anthropic and OpenAI is going to be where the big players are going to end up, if for no other reason, if there’s a hack, it’s a defendable decision?
GLEN KACHER (00:28:52): Well, there’s two questions. There’s using AI within your four walls and being able to provide the proper controls to make sure that it doesn’t get to your data that is sensitive, and that it doesn’t somehow leak that or distribute that. The second is what a bad actor can do with an open source technology from outside of your firm, trying to break into your firm. So those are the two things that you have to account for with your cybersecurity spend.
And so there’s lots of opportunity for, whether it’s CrowdStrike or Palo Alto, and Microsoft, as we talked about. But you’ve got to protect those. And in addition, when it’s internal to your organization, understanding what the roles are of the user of that technology, or the open source technology, and what they can access as a user — you have to make sure that you honor those restrictions as you’re utilizing the agent system.
BARRY RITHOLTZ (00:30:06): So we’re talking a lot about public companies. Let’s just look at some of the private venture investments Light Street has made over the years, and this is quite a list: Uber, Lyft, Slack, Pinterest, Toast, Harry’s, Everlane, Box, BlackBuck, ezCater. In 2018 at the Ira Sohn Conference, you presented Palo Alto Networks at a far, far cheaper price than where it is today. At a later Sohn conference you presented Farfetch. All of these have become giant winners.
The key question I have to ask is, what does investing in VC teach you about public companies, and vice versa? What do you learn about public companies that are useful when evaluating a venture opportunity?
GLEN KACHER (00:30:59): Sure. In the venture companies that we invest in, and even the ones we don’t invest in, there’s real value into understanding what’s happening in the industry. The advantage for us as an investor is that when we meet a CEO or founder of a company, and trying to understand how they’re solving a problem, they’re starting with a blank sheet of paper. They don’t have ties to some incumbent solution and incumbent set of customers that they’ve been trying to keep happy for five, 10 years, usually. So they’re able to be most aggressive in adopting new technology.
And so what we learn, that we can apply in our private investing, in our public investing, is what matters to them. What technologies can solve the problem with no constraints around keeping their long-term customers happy. So that’s a real advantage. And I think in 2022, 2023, as AI was really emerging as a category, when we were talking to some of these early stage firms about, okay, how are you developing your AI solutions, and which semiconductors and infrastructure and service providers are you using?
That gave us a real insight into Nvidia and AMD and Broadcom and Marvell as potential investments for our public side.
BARRY RITHOLTZ (00:32:41): Long before people were talking about it in the mainstream, you’re hearing this directly from these clean-sheet venture startups?
GLEN KACHER (00:32:49): Yeah, I mean, there’s one great story. When I was at Integral, Bill Joy was a partner at Kleiner Perkins for several years —
BARRY RITHOLTZ (00:32:59): Previously at Sun, if I remember correctly, right?
GLEN KACHER (00:33:01): One of the four founders, sure. Yeah. And Bill — I can’t remember the exact year. It was early to mid 2000s.
And he was talking about this group of engineers that he came across, I think it was at Caltech, that were utilizing the GPU to do early AI calculations. And so this was 2005 or ’06 or something like that. And the conclusion of that team and of Bill himself, one of the great pioneers of Silicon Valley, was that GPUs would be the best chip architecture to do AI calculations. So I always had that in the back of my mind.
And over the years when we would visit with Nvidia, we would ask about AI, and Jensen would talk about it, and it was a tiny, tiny product and solution, or end market, for them. And at that time, crypto mattered a heck of a lot more. But it was very fortunate: in the back half of ’22, crypto crashed at the same time as AI was taking off. And so that gave us —
BARRY RITHOLTZ (00:34:13): They just pivoted? Was that simple for them, or —
GLEN KACHER (00:34:16): Well, they were always working on these things, right? And it’s really about market adoption more so than they’re addressing it, right? And it just so happens that these things coincided. The stock market was much more focused on what was happening with crypto, that drove the stock down, and not as focused on this emerging opportunity in AI.
And so as AI took off in the back half of ’22, we were able to build a great position in Nvidia.
BARRY RITHOLTZ (00:34:47): So let’s talk about that run following ’22. You guys had one of the best three-year runs of any hedge fund in recent memory. I’m looking for my exact numbers. ’21 and ’22 — the whole market got whacked in ’22, ’21 was rough. You’re down 26% in ’21, down 54% in ’22, and then come screaming back in ’23, ’24 and ’25: you’re up 46%, 59% and 37%. First of all, how much are you just holding on for dear life?
When you see numbers like that, what’s it like to live through the regular sort of drawdowns that technology goes through? How much beta, how much volatility are you experiencing, and how do you manage around that?
GLEN KACHER (00:35:46): Yeah, it’s very challenging. I mean, I think it was a very frustrating time, obviously, for us, in ’21 and ’22. We came off an incredible 2020 where we played the COVID market incredibly well. We were short going into COVID emerging. Got very short the market and then had a tremendous run backing SaaS and e-commerce through that period of the world being in kind of a quarantine.
And it was a difficult transition coming out of that for us. And so it was a really rough time. Software really got hit in ’22, over a course of a month or two, and we had to reevaluate what we were doing, and that was tough. It was a tough time. And so I think the ability to step back and say, okay, AI is emerging, and these are the incredible companies that are very well positioned for it — and they were trading at what we thought were attractive valuations. And so we’ve just been solving for looking forward over the next 6, 12, 18, 24 months since then.
And it’s been very fortunate that we’ve been in the right place as AI’s emerged.
BARRY RITHOLTZ (00:37:14): So let’s talk a little bit about that philosophical look, and obviously AI and software is a perfect example of what you’ve described as long the disruptor, short the incumbent. And it’s not just SaaS versus AI. You could be long Uber, was an example I saw you discuss once, and short rental car companies. Walk us through those kinds of trades philosophically.
GLEN KACHER (00:37:44): Yeah. Well, we don’t necessarily do paired trades, but if we think there’s a well-positioned solution like Uber at a certain period of time, and think it’s benefiting from this merger of e-commerce, for them, and mobile, and dominant market share, we’ll go long that. And if we see a company out there that’s getting displaced or substituted, there’s short opportunities. We look at them as independent opportunities, frankly.
So I think sometimes the market, or the press around the stock market, tries to simplify things into a this-is-good, this-is-bad war —
BARRY RITHOLTZ (00:38:33): If only it was that easy, right?
GLEN KACHER (00:38:34): Yeah. I think sometimes that leads to things getting overdone. I think software just in the last month or two has really had an incredible bounce back. I think people — the SaaSpocalypse, SaaS apocalypse, if I can say it — that view that software is doomed is sort of a huge simplification, right?
I mean, I think if you look at the history of what happens with incumbent technologies, if they solve a problem really well, they can stick around for a long time. And I think until very recently, many brokerage firms and banks are running mainframe solutions still, because it works. And when you get a new technology, you want to take that new technology and you want to apply it to do new things that really get you an advantage versus your competitors. You don’t want to take a new technology and say, what’s the boring business process that we’ve automated?
And it really works really well, that we can apply this new technology to? No one does that, right? That would be a waste of innovation in a lot of ways. So those core systems don’t tend to get swapped out. So you get these opportunities for bounce backs, and we’re taking advantage of the doom and gloom as well as the excitement about the new things.
And that’s what we have to do.
BARRY RITHOLTZ (00:40:09): Huh. Really, really interesting. Coming up, we continue our conversation with Glen Kacher, founder and Chief Investment Officer at Light Street Capital, discussing the current environment for AI and beyond. I’m Barry Ritholtz, you’re listening to Masters in Business on Bloomberg Radio.
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BARRY RITHOLTZ (00:40:28): I’m Barry Ritholtz. You are listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Glen Kacher, the founder and Chief Investment Officer of Light Street Capital, a technology-focused hedge fund located right in the heart of Silicon Valley in Palo Alto. So I have so many great quotes of yours I want to throw by you. I’m going to start with variant perception.
“We look for a mismatch in perception and reality. Timing matters, but there must be a thesis about when and how the mismatch resolves itself.” Oh, so that sounds pretty easy. That’s all you have to do.
Tell us a little bit about identifying that variant perception.
GLEN KACHER (00:41:16): I started this by talking a little bit about why I got excited to be an investor from the beginning. And part of it is being a detective, right? And going out, talking to people firsthand, working with my team of investors that work at Light Street Capital every day. And we all operate in the same way. As Roger would say, everybody goes out for a pass. And go out, talk to the people that matter, talk to the customers, talk to the suppliers, talk to the innovators themselves.
And that’s how we try to get it done and get the real story. I think we’re in a situation today where AI is now being cast as sort of this evil empire that is going to, one, cost people jobs. And two, it’s crazy, evil people overspending, and it’s going to crash and burn eventually. And that’s really not the story of AI.
The story of AI is that the end users are self-selecting every day in their browser, or now with agent software, or their development tool to build more software. And they’re saying, this is how I can get more done quickly and well with these tools. And that’s what’s driving the demand. That’s creating the capacity build of AI compute.
And so we look at that and say, there’s a mismatch in the way AI is being perceived today, and that will reverse, but you have to figure out when.
BARRY RITHOLTZ (00:43:10): So that’s a productivity story, it’s an efficiency story, and obviously it’s a profitability story.
GLEN KACHER (00:43:16): It’s a demand story.
BARRY RITHOLTZ (00:43:17): Which kind of raises the question: your focus on the core AI players. What about everybody else? Forget the Mag Seven, the next 493 in the S&P 500. What does this mean to the rest of corporate America?
GLEN KACHER (00:43:34): Well, I don’t think you can forget the Mag Seven, but what does it mean — but we will put that aside. What does it mean for the rest of corporate America? I think it’s gotten their attention. It got their attention pretty quickly. And I think if you talk to anyone on the board of directors of a public company, or the CEO and top managers, they’re saying, gosh, we hear about AI, we need to come up with a plan.
We need to figure out how we’re going to harness this tool and make it work for us. And so that’s the task at hand. I think it’s still early to say, well, this company’s doing a great job with AI, so we should buy their stock. That’s not, to me, a great thesis for today for investing.
But I think that everyone that I talk to in corporate America is very focused on, hey, we’ve got to take advantage of this tool.
BARRY RITHOLTZ (00:44:42): You mentioned demand is really surprising everybody. I want to say it was the second quarter, even Jensen Huang at Nvidia was surprised — his expectations for the AI infrastructure spend by 2030, I think he bumped from 1 trillion to 4 trillion. That’s just a 4x, giant set of numbers. Are we running the risk of over-allocating to AI the way we did for things like fiber, and go down the list of every new technology that seems to get over-allocated?
At what point does this become — is this explosive upside demand going to — when does the coyote step off the cliff and not realize he’s gone a little too far?
GLEN KACHER (00:45:25): This is the big question everyone’s battling with today. And I think the Mag Seven we mentioned a minute or two ago, they have really become the key partner. I think if you look at Amazon, you look at Microsoft, Google, those companies are partnering with Anthropic and OpenAI in order to fulfill on building this compute stack and the infrastructure to run AI. And the question is, how far ahead of demand are they planning?
And the reality is they’re not ahead today, they’re behind.
BARRY RITHOLTZ (00:46:23): They’re playing catch-up now.
GLEN KACHER (00:46:25): They’re playing catch-up. The negative doomers are expecting them to overinvest, but today that’s just not happening. I mean, there are bottlenecks, right? There are real bottlenecks, and it’s quite well discussed, that have slowed down the ability to build.
And you’ve got companies that are in control of some of those bottlenecks, whether it’s memory companies, which we like as well, or whether it’s Taiwan Semiconductor. They can only invest so fast. So today, demand is still running way ahead of supply. And so this doomerism that has grown up around AI, in my mind, is misplaced.
BARRY RITHOLTZ (00:47:15): Let’s talk a little bit about the bottlenecks. I use Gemini, I use Notebook, I use Chat, I use Perplexity. But really Claude Pro has become my favorite way to engage.
And I’ve noticed just going from Opus to Fable, like an order of magnitude faster, deeper, better. And these are coming along like every few weeks. It doesn’t feel like there’s much of a bottleneck. When you say bottleneck, what are you referring to?
GLEN KACHER (00:47:48): Well, I think the bottleneck drives the pricing higher than it needs to be today, right? And so, no offense, but you’re probably not paying for your Claude traffic. Bloomberg may be paying for it.
BARRY RITHOLTZ (00:48:05): No, I’m paying. Well, my firm is paying, and it’s 200 a month, and then we just did a whole enterprise thing, and it hasn’t been — like, I keep hearing about people, right, just going crazy on credits and spending a year’s worth of credits in a month.
We’re pretty reasonable and a little aware of our spending, but it’s not like it’s a hundred thousand dollars a month. It’s fairly reasonable for the output you get.
GLEN KACHER (00:48:34): Right. I’ve been surprised. We have our own software product and stack that we have developed on for 15 years, where we run our entire research process. And so we’re constantly improving that. We’re also doing analysis and sentiment tracking, et cetera, of sources of data that we buy.
And it’s not cheap to do that.
BARRY RITHOLTZ (00:49:06): Well, are you spending 50,000 a month? A hundred thousand a month? What does it look like?
What is a typical hedge fund in the tech space — not necessarily yours, but what do you think people are spending? I know I’m only scratching the surface for what I’m doing.
GLEN KACHER (00:49:20): Well, for programmers, it’s not uncommon to spend a hundred dollars a day. So it can get expensive, and that adds up. That can add up quickly.
BARRY RITHOLTZ (00:49:31): Sure. 30 grand a month is not nothing.
GLEN KACHER (00:49:33): Yeah.
BARRY RITHOLTZ (00:49:34): All right.
GLEN KACHER (00:49:35): You can spend a lot more than that, too.
BARRY RITHOLTZ (00:49:36): Well, a couple of months ago there were stories about, wait, we had a whole budget for a year and it’s gone in four weeks. Is that the bottleneck, being able to service the super clients, the hyper users like that?
GLEN KACHER (00:49:49): Well, that’s where this demand for the open source solutions comes in, that are far, far cheaper, right? And so the ability to load it up on your own hardware and have it run, and be able to also adjust the weightings of the model and train it on your own data, those are all very powerful opportunities for investors, or just general, any kind of business. So being able to do more for less is certainly attractive.
BARRY RITHOLTZ (00:50:24): So another quote of yours. You were talking about the AI build-out, and you said, “It’s a 10-year cycle of demand. The bear case is that CapEx gets cut the moment returns disappoint.” Tell us a little bit about why you think this demand cycle is going to go a full decade.
GLEN KACHER (00:50:44): Yeah. Well, I mean, I think we’re changing the entire stack of computing. The only thing that looks like this that we’ve experienced before is the move to the internet architecture from client-server. And these computing cycles happen about every 15 to 25 years.
So since the development of computing — and the way the technology works is completely different. The old school of technology is a search and retrieve, or create, search and then retrieve model, where you stored things in databases. And here in the AI world, the technology is essentially creating a custom solution, custom to your question, custom to your data, every single time you use it. It’s just a much more complex and compute-intensive model.
And the ability to have custom solutions and custom answers every single time you need data is so much more powerful. And if we follow history, these things take 10 to 15 years to become a quarter of the total capacity in the industry. So to say that it’s going to take multiple decades is not much of a stretch.
BARRY RITHOLTZ (00:52:24): So where are we? Are we in year four or five now —
GLEN KACHER (00:52:28): Yeah.
BARRY RITHOLTZ (00:52:29): — of a 10-to-15-year first leg?
GLEN KACHER (00:52:30): Yeah, we’re exactly — we’re kind of a third of the way through the first leg. I mean, if you look at the way technology develops, it sort of goes in three cycles. Your big infrastructure development years take five to 10 years, let’s say. Then year six through 16, let’s say, that’s when your platform or OS really gets developed and put into place. And then the applications kind of come in years 11 through 21.
And applications become the dominant place where businesses invest and the innovation happens. So it’s at least a 15-to-20-year cycle that we’re looking at.
BARRY RITHOLTZ (00:53:18): I’m kind of fascinated by the energy demands and the build-out of these giant data centers. And I’m curious, what are your thoughts to the political pushback to where these are located? A couple of states have already banned them. I never saw the politics against tech morphing this way.
How do you look at that as an investment risk?
GLEN KACHER (00:53:44): It’s a real risk. Any bottleneck that slows down the adoption of your technology is a problem, right? We’re investing in Nvidia or Taiwan Semiconductor saying, okay, here’s what we expect. And in our view, the numbers are still significantly better than Wall Street’s looking for. However, we have to bear in mind, is there an obstacle that’s going to get in that way? Today, it’s, in our view, not a big enough problem, but it’s an emerging problem.
And I think the way, as an industry, we have to get around this is that we have to explain the places that invest most heavily and most aggressively. If you look at Northern Virginia, not far from where I grew up, that is the data center capital of the world. And that opportunity, and what’s happened with tax receipts in those communities that have all these large data centers, and the demand for blue collar work in order to build those data centers, whether it’s electricians and plumbers and construction work, it’s a massive shot in the arm for those economies. And then the tax revenue is an ongoing payment that happens over many years.
So I think it’s a little bit sad that some of these communities are not as positive about the opportunities. I think they’re just not well educated by their elected officials.
BARRY RITHOLTZ (00:55:31): I’m not surprised that it’s in Virginia or New York. I’m enormously surprised when you see pushback in places like Texas, which is big enough that you can stick a data center out wherever there’s juice and nobody has to see it, hear it, be concerned about it. But it keeps raising the question of cost of electricity. And people seem to be concerned: we let a data center in here, our electrical costs are going to go up. How should we, as a tech-savvy nation of investors, respond to that concern about electricity?
GLEN KACHER (00:56:13): Yeah, absolutely. The source of electricity needs to be behind the meter, right? So the firm that creates the data center, if there’s not enough existing energy, then they have to provide the energy.
BARRY RITHOLTZ (00:56:30): So run a gas line, natural gas, set up your own generator, and you’re off the grid.
GLEN KACHER (00:56:35): And look, if it’s close to a residential area — there’s actually a data center that’s being contemplated in San Mateo, California, not far from Palo Alto. And their solution is to put Bloom Energy servers behind, which are powered with natural gas, with almost no emissions. And they’re incredibly quiet, almost no audible sound. And you can put a Bloom Energy fuel cell behind the meter.
And even though that’s the plan, residents have rallied against it because they’ve heard data centers are bad. They’re just not educated on what the solution is and how it will not impact their energy prices. And there will be no emissions and no noise.
BARRY RITHOLTZ (00:57:29): And we have midterms coming up in November. Is this the sort of thing that once we get past the next group of elections, this will fade? Or is this really an ongoing challenge for the industry?
GLEN KACHER (00:57:42): It’s an education challenge. Yeah. We’ve got to — and it’s from local to national, right?
Each project has to explain, this is the decision we’re making around procuring this energy. These are the number of jobs it’s going to create. These are the tax revenues it’s going to generate. Here’s our existing energy situation.
This can go on the grid without much of an impact. Or, we’re bringing our own energy. So it’s both a local and a national solution.
BARRY RITHOLTZ (00:58:14): Huh. Really, really interesting. And the Mag Seven keeps coming up. When we met in the spring in San Francisco, you liked Amazon, Google, and Nvidia. I don’t recall what your thoughts were on Microsoft. You weren’t a big fan of Meta, Tesla and Apple.
How do you see the Mag Seven today? Is that still fairly consistent, or —
GLEN KACHER (00:58:38): That’s fairly consistent, yes. As I said earlier, we’ve put Microsoft back in our portfolio, and so I’d say that they’re back in the good category. The challenge for Apple is to get their AI solutions tuned up and working well for the consumer.
If you think about your mobile phone, it’s in a very unique position. It has both your personal and your business data, to the extent that you’re not a small business person. And the security is there to separate those two things. And so that device has the ability to optimize and recommend actions or solutions to you as a consumer that address both your business life and your personal life.
And that’s a very unique position that Apple’s in. And obviously you carry it around, and it’s on most of the time, if not all the time. And they have a real opportunity to bring AI solutions, to democratize them for consumers, in a very complicated but elegant way. And so if Apple can get things right, that should accelerate their opportunities, or earnings, over the next couple of years.
BARRY RITHOLTZ (01:00:04): They don’t have a great history with it. Siri has been nothing less than a total embarrassment for a decade. I mean, I’m not revealing any secrets here. Everybody knows it’s garbage.
And there was some criticism of Apple for not jumping in with both feet to become a hyperscaler and spend tens of billions of dollars. What they’ve done with Google has worked out great for both companies. Hey, what’s a couple of billion dollars a year to Apple? And to Google, it’s pure profit. Is the same sort of setup teeing up, where it’s a win-win for Apple to integrate Google’s technology into the iPhone?
GLEN KACHER (01:00:47): Potentially. But it’s execution-based.
BARRY RITHOLTZ (01:00:50): Isn’t that always the case?
GLEN KACHER (01:00:52): It is, but their strategy — this is a very consistent strategy, where they were not the first smartphone, right? They waited. They watched what Nokia did, what BlackBerry did, RIM BlackBerry, and then they came out with a more elegant solution after those guys established the market.
BARRY RITHOLTZ (01:01:15): Second mouse gets the cheese. Is that the thinking there?
GLEN KACHER (01:01:20): Well, if you have a big bank account and users that really will wait around till you solve the problem in a better way, then it works.
BARRY RITHOLTZ (01:01:28): Last question before we get to our favorite questions. So I’m not going to ask you about 20 years out or 10 years out, but five years out, what does this technology look like? What’s going to define AI for the consumer and business customer in 2031?
GLEN KACHER (01:01:47): Agents. The ability to have the technology working on problems when you’re not directing it, that is incredibly powerful. It leads to users consuming 5x the tokens that you would consume just directing AI as you would a search engine. And so the ability to have your agent or agents working on your personal life and solving problems as they come into your inbox or into your messaging solutions with your family and friends.
And then on the business side, the same thing. Solving problems for you, solving problems with your coworkers and teammates. It’s incredibly powerful, this technology —
BARRY RITHOLTZ (01:02:44): To say the very least. All right, let’s jump to our favorite questions that we ask all of our guests, starting with — and I already asked, but I’ve got to ask a little more specifically — who were the mentors who shaped your career?
GLEN KACHER (01:02:58): Well, you certainly have to look at Julian Robertson, and the example that he set in how to run an investment business with integrity and intellectual honesty and principles. And so that was incredible. Roger and John at Integral Capital Partners were just great as I got out of business school and was in my early thirties — really those key years of learning, again, how to run a firm and make great investments. And they gave me the opportunity to both succeed and fail in some of those private investments that I made. Those are going to be the key people that really shaped my career.
BARRY RITHOLTZ (01:03:51): You mentioned the two Peter Lynch books, One Up on Wall Street and Beating the Street. I know you read a lot of other research. Any other books worth mentioning these days?
GLEN KACHER (01:04:01): I pulled a book off the shelf recently, Empires of Light, which tells the story of the propagation of electricity and the battle between Edison, General Electric, Tesla, Westinghouse —
BARRY RITHOLTZ (01:04:17): AC and DC.
GLEN KACHER (01:04:18): Yes. And incredible story. And I think at the end of the day it was really interesting that Edison really pushed that AC was dangerous, to the point where he promoted it for the electric chair, because it made AC look bad and dangerous.
BARRY RITHOLTZ (01:04:40): Didn’t one of them electrocute an elephant to show how dangerous it was?
GLEN KACHER (01:04:43): Many different animals. Yeah. And a prisoner, and it didn’t go so well.
Actually, the first electric chair didn’t work extremely well. So, to scare people and say AC is bad — and you look at what’s happening today with AI, and people taking this incredibly powerful technology that is going to change the world, and it’s already starting to change it, and making it this evil empire. It’s pretty fascinating. And I think the other side of that is that, at the end of the day, Westinghouse won out with steady execution and industrialization of the back end.
And you look at the Mag Seven, and the opportunity for Amazon and Microsoft and Google to build that back end. And AWS — AI is an incredible opportunity for AWS, and —
BARRY RITHOLTZ (01:05:42): Which is already the biggest profit center for Amazon.
GLEN KACHER (01:05:45): Yes. And so — if you say Amazon, everyone thinks about e-commerce, and they don’t first think about AWS, but AWS is the more important part of the company.
BARRY RITHOLTZ (01:05:55): Yeah. To say the least. What are you streaming these days?
I know you’re on a plane pretty regularly. What are you listening to or watching to keep yourself entertained?
GLEN KACHER (01:06:06): Well, entertaining — I mean, sure, X is entertaining. All the debate around our industry is pretty fascinating. Friends and Neighbors is a guilty pleasure.
So that’s something I’m streaming regularly.
BARRY RITHOLTZ (01:06:22): Anything Jon Hamm is in is always worth watching. Final two questions. What sort of advice would you give to a recent college grad interested in a career in either investing or technology?
GLEN KACHER (01:06:34): The number one thing that I tell younger folks is, you have all the tools today to make an impact. And so if you want to get into the investment business, one, of course, start investing. But two, do your research, go online, and then publish your research. Put it on X, interact with people like you, people like me. And if you can uncover the story behind a stock and make some great recommendations, you’re trying out for the world in real time.
And if you have the courage to do that and you do it well, it’s a no-brainer to hire that person.
BARRY RITHOLTZ (01:07:21): Our final question: what do you know about the world of investing and technology today that might have been useful back in 1993 when you were first getting started?
GLEN KACHER (01:07:32): Yeah, I think early on, and for investors coming to our market, there’s this perception that things happen very fast, and no doubt they’re changing rapidly, but at the same time, there’s this reality that things do take time. We talked about the emergence of the smartphone. The first smartphone-like device that came out was the Newton, and it didn’t really work that well. And then General Magic had a solution that also didn’t really work that well.
And then Palm created the first thing that actually got some adoption, but it didn’t do any email or messaging, and it certainly wasn’t a phone. And then Palm created the Treo, right? And then, I’d say in some ways RIM was the real first — RIM BlackBerry was the first real working smartphone, but it was somewhat clunky, and some people loved that clunkiness, right?
And loved that keyboard. But then ultimately got to Apple. And so while things happen fast, it also takes years for things to really develop. And so I think if we apply that today, AI can do some incredible things, but it’s going to do way more in a few years. And there are some obstacles, other than the ones we’ve mentioned, to adoption, right?
Data security, and comfort of your coworkers and your superiors in terms of giving access to data to an AI agent. So it will take time in order to see ultimately what it can deliver. And so I think we’re just scratching the surface, even though, as I mentioned, there’s a lot of battles between open source, for instance, and the closed frontier models. But there’s way more to go here.
BARRY RITHOLTZ (01:09:42): Glen, thank you for being so generous with your time. This has been absolutely fascinating. We have been speaking with Glen Kacher. He’s the founder and Chief Investment Officer of Light Street Capital.
If you enjoy this conversation, check out any of the 651 previous discussions we’ve done over the past 12 years. You can find those at Bloomberg, iTunes, Spotify, YouTube, or wherever you get your favorite podcasts. I would be remiss if I didn’t thank the crack team that helps put these conversations together each week. Elizabeth Srin is my video producer. Anna Luke is my podcast producer.
Sean Russo is my researcher. I’m Barry Ritholtz. You’ve been listening to Masters in Business on Bloomberg Radio.
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