Investing for the AI Boom: Masters in Business with Glen Kacher

32:10is 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? 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.

33:06So 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. In addition, when you're 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 utilizing an agent system. 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.

33:56And this is quite a list. Uber, Lyft, Slack, Pinterest, Toast, Harry's, Everlane, Box, BlackBuck, EasyCater. In 2018 at the Ira Zone conference, you presented Palo Alto Networks, far, far cheaper price than where it is today. At a later Zone conference, you presented Farfetched. 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? Sure. In the venture companies that we invest, and even the ones we don't invest in, there's real value into understanding what's happening in the industry.

34:52The 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 an incumbent set of customers that they've been trying to keep happy for, you know, five, 10 years usually. So they're able to be most aggressive in adopting new technology. And so what we learn is, you know, 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, you know, around keeping incumbent their long-term customers happy.

35:43So that's a real advantage. And I think, you know, when we, as in 2022, 2023, as AI was really emerging as a category, when we were talking to some of these early stage firms about, okay, what's, 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. Long before people were talking about it in the mainstream, you're hearing this directly from these clean sheet venture startups. Yeah. There's one great story when I was at Integral.

36:37Bill Joy was a partner at Kleiner Perkins for several years. Previously at Sun, if I remember correctly. One of the four founders, sure. 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 2006 or something like that. And, you know, we, so I always, you know, 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.

37:23So 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 but 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 - They just pivoted? Was that simple for them? 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.

38:11The 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. 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 shellacked in 22. 21 was a rough, you're down 26 % in 21, down 54 % in 22, and then come screaming back in 23, 4, and 5, 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?

39:13But what's it like to live through the regular sort of drawdowns that technology goes through? How much beta are you, how much volatility are you experiencing and how do you manage around that? Yeah, you know, 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, you know, played the COVID market incredibly well. We're short going into COVID emerging. It 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, you know, it was a difficult transition coming out of that for us.

40:04And so it was a really rough time. software really got hit in 22 and over course of a month or two and you know had we had to re-evaluate what we were doing you know and and that was tough you know it's 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 has emerged.

40:56So 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 kind of trades philosophically. Yeah, well, you know, 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 that's, you know, benefiting from this merger of e-commerce and for them and mobile and dominant market share, we'll go along that.

41:52And 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, hey, this is good, this is bad. If only it was that easy, right? 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, if I can say it, that view that software is doomed is sort of a huge simplification. I mean, I think if you look at the history of what happens with incumbent technologies is if they solve a problem really well, they can stick around for a long time.

42:57And 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 gets 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.

43:39So you get these opportunities for bounce backs. And, you know, that's, you know, we're taking advantage of, you know, the doom and gloom as well as the excitement about the new things. That's what we have to do. Really, really interesting. Coming up, we continue our conversation with Glenn 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 of Business on Bloomberg Radio. Some people treat ChatGPT like some kind of smart search engine, and some use it to get work done. ChatGPT Work is a new way of working in ChatGPT that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work.

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45:54The competition in capital markets is a new center of gravity, Dallas. On October 27th, join Bloomberg Invest Dallas as we convene leading exchange operators, wealth managers, private market leaders, and policymakers to examine the trends transforming global finance. Learn from the people shaping what's next in investing, markets, and the economy. Supporting sponsor, NYSE Texas. Learn more at BloombergLive.com slash Invest Dallas Radio. That's BloombergLive.com slash Invest Dallas Radio. I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Glenn 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.

46:45So 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. You know, 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. And going out, talking to people firsthand, working with my team of investors that work at Light Street Capital every day.

47:31And 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 where AI is is, you know, now being cast as sort of this evil empire that is going to one cost people jobs and and to, you know, you know, it's crazy evil people overspending. and it's certain, you know, like it's going to crash and burn eventually. And that's really not the story of AI.

48:23The 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. So that's a productivity story. It's an efficiency story. And obviously it's a profitability story. It's a demand store.

49:14Which kind of raises the question, you're focused on the core AI players. What about everybody else? Forget the Mag-7, the next 493 and the S &P 500. What does this mean to the rest of corporate America? Well, I don't think you can forget the Mag-7. But what does it mean? But we'll 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 bad AI. We need to come up with a plan.

50:00We need to figure out how we're going to harness this tool and make it work for us. And so that is, you know, that's the task at hand. I think it's still early to say, well, this company is doing a great job with AI, so we should buy their stock. You know, that's not, to me, a great thesis today for investing. But, you know, 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. You mentioned demand is really surprising everybody. I want to say it was the second quarter, even Jensen Huang at NVIDIA was surprised for his expectations for the AI infrastructure spend by 2030.

51:00I 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 a coyote step off the cliff and not realize? He's gone a little too far. This is the big question everyone's battling with today. And I think the Mag7 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.

52:06And the question is, how far ahead of demand are they planning? And the reality is they're not ahead today. They're behind. They're playing catch-up now. They're playing catch-up. The negative doomers are expecting them to overinvest, but today that's just not happening. 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're actually, they can only invest so fast.

52:58So today, demand is still running way ahead of supply. And so this doomerism that has grown up around AI, in my mind, is misplaced. 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. It doesn't feel, 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? Well, I think the bottleneck drives the pricing higher than it needs to be today, right?

53:51And so, no offense, but you're probably not paying for your Claude traffic. Bloomberg may be paying for it. 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 crazy. Like, I keep hearing about people 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$100 ,000 a month. It's fairly reasonable for the output you get. 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.

54:46And 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. Are you spending$50 ,000 a month,$100 ,000 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. For programmers, it's not uncommon to spend$100 a day. So it can get expensive. And that can add up quickly. Sure. $30 ,000 a month is not nothing. Yeah. All right. You can spend a lot more than that, too.

55:33Well, 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? Well, that's where this demand for the open source solutions comes in, which are far cheaper. 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 any kind of business. So, you know, being able to do more for less is certainly attractive.

56:21So 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. 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, you know, these computing cycles happen about every, you know, 15 to 25 years since the development of computing. And the way the technology works is completely different.

57:13You know, in the old school of technology is 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 of the industry. So to say that it's going to take multiple decades is not much of a stretch.

58:20So where are we? Are we in year four or five now of a 10 to 15 year first leg? Yeah, 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. You know, your big infrastructure development years take, you know, five to 10 years, let's say. Then year six, 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.

59:09So it's at least a 15 to 20 year cycle that we're looking at. 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? It's a real risk that any bottleneck that slows down the adoption of your technology is a problem. 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.

1:00:01However, we have to bear in mind, you know, is there something, is there an obstacle that's going to get in that way? Today, it's, in our view, you know, 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. you know if you look at northern virginia not far from where i grew up you know that is the data center capital of the world and that opportunity and what's happening what's happened with tax receipts in those communities that that have that have all these large data centers and the demand for blue collar work into in order to build those data centers whether it's electricians and plumbers and construction work.

1:00:57It'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 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. 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.

1:01:57We 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? 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. So run a gas line, set up your own generator, and you're off the grid. And if it's close – 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.

1:02:41And 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. 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?

1:03:39It's an education challenge, yeah. 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, you know, we're bringing our own energy. So it's both a local and a national solution. Really, really interesting. And the Mag 7 keeps coming up. When we met in the spring in San Francisco, you liked Amazon, Google, and NVIDIA.

1:04:22I 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 7 today is that is that still fairly consistent or that's fairly consistent yes you know as i said earlier we're we're we've put microsoft back in our portfolio and and so we're uh i'd say that they're they're back in the good category the challenge for for apple is to get their ai solutions uh 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.

1:05:13And 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, you know, if Apple can get things right, that should accelerate their opportunities or earnings over the next couple of years. They don't have a great history with it.

1:06:02Siri 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 its 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? Potentially, but it's execution-based. Isn't that always the case?

1:06:48It is, but this is a very consistent strategy where they were not the first smartphone, right? They waited. They watched what Nokia did, what BlackBerry did, and RIM BlackBerry. And then they came out with a more elegant solution after those guys established the market. Second mouse gets the cheese. Is that the thinking there? Well, if you have a big bank account and users that really will wait around until you solve the problem in a better way, then it works. 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.

1:07:34What does this technology look like? What's going to define AI for the consumer and business customer in 2031? 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 that you would consume just you know directing ai as you would a search engine and so the ability to have the you know 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 co-workers and teammates.

1:08:36It's incredibly powerful, this technology. 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 got to ask a little more specifically, who were the mentors who shaped your career? Well, you certainly have to look at Julian Robertson and just the example that he set and 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 30s, really, you know, those key years of learning, again, how to run a firm and make great investments.

1:09:34And 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. You mentioned the two Peter Lynch books, One Up on Wall Street and Beat the Street. I know you read a lot of other research. Any other books worth mentioning these days? You know, 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. AC and DC. Yes. And, you know, incredible story. And I think at the end of the day, it was really interesting.

1:10:21And Edison really pushed that AC was dangerous. And to the point where he promoted it for the electric chair because it made AC look bad and dangerous. Didn't one of them electrocute an elephant to show how dangerous it was? Many different animals. Yeah. 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 is already starting to change it. and making it this evil empire.

1:11:08It's pretty fascinating. And I think the other side of that is that, you know, in the end of the day, Westinghouse won out with, you know, steady execution and industrialization of the back end. And you look at the Mag 7 and what's, you know, the opportunity for Amazon and Microsoft and Google to build that back end. And AI is an incredible opportunity for AWS. Which is already the biggest profit center for Amazon. Yes. And so, you know, which is not the, 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.

1:11:52Yeah, 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? Well, entertain. Sure. X is entertaining. You know, all the all the debate around our industry is pretty fascinating. Friends and neighbors is a guilty pleasure. So, you know, that's something I'm streaming regularly. Anything Jon Hamm is and is 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? The number one thing that I tell younger folks is you have all the tools today to make an impact.

1:12:39And so if you want to get in 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. Our final question, what do you know about the world of investing and technology? Today might have been useful back in 1993. when you were first getting started?

1:13:28Yeah, 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. And, you know, 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.

1:14:13And then Palm created the Trio, 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 love that clunkiness and love 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 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.

1:15:19So 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. Glenn, thank you for being so generous with your time. This has been absolutely fascinating. We have been speaking with Glenn Kacher. He is 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.

1:16:06I would be remiss if I didn't thank the crack team that helps put these conversations together each week. Elizabeth Sedrin 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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