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

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

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  Speaker 1: Bloomberg Audio Studios. Podcasts, radio, news.
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  Speaker 2: This week on the podcast, my extra special guest is
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  Speaker 2: Glenn Kacher. He is the founder and chief investment officer
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  Speaker 2: at Light Street Capital. He's got really a fascinating background
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  Speaker 2: and a great track record. He worked at Julian Robertson's
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  Speaker 2: Tiger Management, eventually ended up at Roger McNamee's Integral Capital Partners.
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  Speaker 2: He's put together really a fascinating focus and track record.
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  Speaker 2: One of the few hedge funds located right in the
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  Speaker 2: middle of Silicon Valley, focused on AI and technology. I
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  Speaker 2: found this conversation to be absolutely fascinating. And I think
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  Speaker 2: you will also, with no further ado, my conversation with
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  Speaker 2: Light Street Capital's Glenn Kacher. Glenn Kacher, welcome to Bloomberg.
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  Speaker 1: Thank you.
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  Speaker 2: Before we get into Light Street, let's talk a little
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  Speaker 2: bit about your background. You graduate from University of Virginia
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  Speaker 2: School of Commerce with a bachelor's in commerce and eventually
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  Speaker 2: get an MBA from Stanford. Was investing always the career plan?
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  Speaker 1: It was. I started really looking into that industry. I
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  Speaker 1: read a book by Peter Lynch called while I was
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  Speaker 1: in college, one up on Wall Street. Or beating the street.
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  Speaker 1: It could have been the first book, actually. And I
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  Speaker 1: was just caught by this idea of the search for
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  Speaker 1: great companies, great ideas. And the way he told the
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  Speaker 1: story of finding these companies and researching them, it was
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  Speaker 1: really a journey of a detective trying to figure out
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  Speaker 1: what would matter in the future. And that really captivated
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  Speaker 1: me and my interest in becoming an investor.
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  Speaker 2: So in between UVA and getting your MBA at Stanford,
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  Speaker 2: you work at Julian Roberts Tiger Management. How do you
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  Speaker 2: get to Tiger at 22?
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  Speaker 1: A very fortunate opportunity. So one of the teachers or
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  Speaker 1: instructors at McIntyre School of Commerce at UVA, was a
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  Speaker 1: former Tiger management partner in Michael Bills. And Michael taught
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  Speaker 1: several finance classes there for a couple of years. He
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  Speaker 1: had taken some years off from Wall Street after working
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  Speaker 1: at Tiger and then before starting a fund-to-funds business that
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  Speaker 1: he's run very successfully. And he suggested that I take
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  Speaker 1: a look at it. I certainly knew of Tiger. Tiger
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  Speaker 1: was It seemed about half of the investment staff actually
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  Speaker 1: at one point or another attended UVA. And so a
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  Speaker 1: lot of the guys there, you know, sort of knew
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  Speaker 1: what we were capable of as young guys coming out
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  Speaker 1: with finance degrees from UVA.
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  Speaker 2: And Robertson was legendary in 93. Was he still running
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  Speaker 2: the ship? Oh, yeah. Very much in charge.
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  Speaker 1: Very much in charge, yes. You know, I was there
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  Speaker 1: from 93 to 96 full-time, and then still when I
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  Speaker 1: went to Stanford for a year, I worked for Tiger
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  Speaker 1: as well, and Julian would occasionally wake me up with
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  Speaker 1: a 6 a.m. phone call when I was in business school.
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  Speaker 2: 6 a.m.
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  Speaker 1: East Coast. No, 6 a.m. my time, 9 a.m. his time,
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  Speaker 1: just a half hour before the market. So he had
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  Speaker 1: some discretion there, but we had some great times. learning
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  Speaker 1: and talking through the technology industry at the time, investing
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  Speaker 1: in companies like Dell, Microsoft, Compaq, and Cisco were some
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  Speaker 1: of the.
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  Speaker 2: So really, right out of college, you're full-on technology? Did
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  Speaker 2: you look at other spaces?
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  Speaker 1: I worked briefly looking at financial institutions with Rob Pitts there,
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  Speaker 1: and we had a great time doing that, but I
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  Speaker 1: was certainly more interested in technology and I had really
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  Speaker 1: studied that industry prior to going to New York. And
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  Speaker 1: so it was a better fit for me following that industry. And,
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  Speaker 1: you know, I think two or three months into the job,
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  Speaker 1: I ended up sitting two chairs away from Bill Gates
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  Speaker 1: at an analyst meeting at the sort of after the
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  Speaker 1: meeting dinner. And at that point, I knew I was
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  Speaker 1: in the right spot.
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  Speaker 2: To say the very least. So after Stanford, you end
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  Speaker 2: up at Roger McNamee's Integral Capital, and you stay for
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  Speaker 2: 13 years, and you're really less of a public markets
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  Speaker 2: analyst and more of a venture sort of banker. You
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  Speaker 2: either lead or co-lead venture investments, and the list is
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  Speaker 2: pretty impressive. Agile, ArcSight, Blue Nile, Epiphany, Extensity, Fortify, Interwoven.
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  Speaker 2: log me in, OpenTableOvertureGoTo.com. What's the common thread? Is it just, hey,
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  Speaker 2: that's what was hot in the late 90s? Or what
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  Speaker 2: tied that list together?
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  Speaker 1: Well, the amazing thing about Roger was he really focused
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  Speaker 1: on saying, look, we can't cover every company in this industry.
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  Speaker 1: We were a small team, much like at Tiger, there
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  Speaker 1: were two or three of us looking at tech at
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  Speaker 1: any one time. And At Integral, even though we were
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  Speaker 1: a tech-focused firm, but we had four or five people total.
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  Speaker 1: But even with that number, you can't cover the entire industry.
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  Speaker 1: So you have to focus in when you're investing and say,
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  Speaker 1: where is the change really happening most quickly? Where is
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  Speaker 1: it most dramatic? That disruption equals opportunity as an investor.
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  Speaker 2: That's a theme that comes up over and over in
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  Speaker 2: your career. Identify the disruption and get in front of it.
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  Speaker 2: before the existing companies realize what's coming down the pike.
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  Speaker 1: It's great to be early, but not too early, right?
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  Speaker 1: I mean, that's also an important part of it. Right.
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  Speaker 2: I started on a desk, and early was equal to wrong,
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  Speaker 2: at least when you're trading public equities. Not only do
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  Speaker 2: you do all of these privates where you've co-led, is
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  Speaker 2: this right, about 46 deals? 46 deals at Integral over
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  Speaker 2: 13 years. I read something you had said about that,
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  Speaker 2: And you said, you know, the takeaway from all these
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  Speaker 2: private venture investments is you don't buy the second or
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  Speaker 2: third best company in the space. You always buy the
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  Speaker 2: best company you can. Give us a little details on that.
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  Speaker 2: What's the thinking behind it?
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  Speaker 1: Well, experience, right? I mean, you see the movie over
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  Speaker 1: and over again, whether it's private investment or in the
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  Speaker 1: public markets, that, you know, the old saying was, you know,
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  Speaker 1: the number one player is going to get two-thirds of
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  Speaker 1: the market. number two player might get 20%, 25% tops,
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  Speaker 1: and everyone else fights for the scraps, right? And, you know,
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  Speaker 1: there's the ability to make higher margins and have the
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  Speaker 1: dominant market share is just so dramatic. And I think
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  Speaker 1: in technology, we've seen the power of that, you know,
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  Speaker 1: the ability to sort of compound that lead is definitely there. Now,
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  Speaker 1: You also see in technology that you can get disrupted, right?
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  Speaker 1: Incumbents are the real innovation, and these disruptive changes tend
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  Speaker 1: not to come from the big companies but the smaller companies.
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  Speaker 1: There are exceptions to that, and we can talk through that.
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  Speaker 1: AI is kind of an interesting test case and the
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  Speaker 1: semiconductors behind AI, but there's real power into compounding that lead.
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  Speaker 2: So let's talk about those moats and the winner-take-all situation,
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  Speaker 2: is that primarily a technology phenomena? Is it a modern
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  Speaker 2: era phenomena? Or is this companies that develop a unique
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  Speaker 2: moat regardless of the space they're in get to capture
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  Speaker 2: most of the market share?
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  Speaker 1: Well, I think you've seen in mature industries, whether you
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  Speaker 1: look back at, you know, GE and Coca-Cola, you know,
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  Speaker 1: you've seen certainly there's advantages to having that dominant distribution
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  Speaker 1: and market share. But in technology, I think it's more
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  Speaker 1: a story of getting in front of your competitors and
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  Speaker 1: investing more. You have more dollars to invest back in
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  Speaker 1: the technology and to grow that lead and that compounding
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  Speaker 1: of advantages or compounding of innovation at the early part
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  Speaker 1: of a market's development can be incredibly powerful. And then
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  Speaker 1: that gives you the opportunity to put in place other
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  Speaker 1: kinds of moats that do kind of block your competitors
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  Speaker 1: from coming along. You know, there's a lot of discussion
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  Speaker 1: today around NVIDIA. A lot of people sort of assume
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  Speaker 1: NVIDIA is going to lose their massive market share in
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  Speaker 1: AI accelerators, which is roughly 85%. And certainly, I think
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  Speaker 1: the move to inference is an opportunity for competitors to
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  Speaker 1: change what's going on there. But I think people right
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  Speaker 1: now are, for instance, underestimating NVIDIA's opportunity to innovate as well.
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  Speaker 2: So let's define some terms for some of the lay
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  Speaker 2: people that might be listening. Compute and inference. Explain what
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  Speaker 2: those are. Explain how they're investable themes.
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  Speaker 1: Sure. So the training compute or the chips, the AI
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  Speaker 1: accelerator chips and Today, NVIDIA dominates that still with their
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  Speaker 1: graphics processor chips. And that, you know, those chips originally
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  Speaker 1: were made for gaming, for doing very rapid mathematics that
• 10:05
  Speaker 1: have to do with calculating physics and lighting, shading in
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  Speaker 1: video games. It turns out that the same kind of
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  Speaker 1: mathematics are incredibly well positioned to do the math around AI.
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  Speaker 1: And then, so you're training a model, an AI model
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  Speaker 1: that will be able to make judgments. And then when
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  Speaker 1: you're actually using that model to ask questions and, you know,
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  Speaker 1: or have it solve problems and actually execute those problems,
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  Speaker 1: that's called inferencing, right? And so inferencing can be done
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  Speaker 1: on a more simple chip. So people have kind of
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  Speaker 1: used a phrase XPU to X out the graphics and
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  Speaker 1: let's say this is the next generation of chips that
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  Speaker 1: can be used to actually solve the problems with those
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  Speaker 1: models that are built.
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  Speaker 2: Meaning the compute and the inference are going to be
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  Speaker 2: on the same chip.
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  Speaker 1: They can be done with the same chip, but you
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  Speaker 1: can have a more specialized lower-cost chip, usually an inference
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  Speaker 1: with more memory, for instance. And there's different approaches and
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  Speaker 1: software to execute that with a lower-cost chip.
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  Speaker 2: So it sounds like our alphabetical evolution has been CPUs,
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  Speaker 2: then FPUs, GPUs, and now XPUs. What's beyond that?
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  Speaker 1: Well, I think that's why we use the term X.
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  Speaker 1: You know, there's TPUs, you know, Google's version of their
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  Speaker 1: AI chip. We've got Tranium, et cetera, and others, competitors.
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  Speaker 1: So there's lots of flavors. You know, you also saw,
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  Speaker 1: for instance, NVIDIA by Grok, which was another approach to inference.
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  Speaker 1: So there will be many flavors and many opportunities and
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  Speaker 1: ways to innovate in inference. Because ultimately that will be
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  Speaker 1: a larger market than the training market.
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  Speaker 2: Really interesting. So I usually save my mentor question towards
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  Speaker 2: the end of our conversation, but your list of people
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  Speaker 2: you've worked with and worked for is just so incredible.
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  Speaker 2: I want to get it out early. In addition to
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  Speaker 2: Julian Robertson and Roger McNamee, there was Philip LaFont, Steve Mandel,
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  Speaker 2: Chip Morris, who was, I think, at Blue Ridge. Alger, Viking,
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  Speaker 2: Lone Pine, Impala, Matrix, KOTU. That's like a murderer's row
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  Speaker 2: of modern investing names. What did all these legends have
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  Speaker 2: in common and how were they each different?
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  Speaker 1: Well, I think the focus for, you know, we had
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  Speaker 1: a great team there at Tiger Management and so many
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  Speaker 1: of us went on to start our own firms and
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  Speaker 1: many of them in sort of modeled by what we
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  Speaker 1: experienced at Tiger and seeing how Julian did it. I
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  Speaker 1: think Julian was just such a inspirational leader and was
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  Speaker 1: so values-driven and really focused on, hey, we want to
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  Speaker 1: work with the best people. That doesn't just mean the
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  Speaker 1: people around the table with you and your investment staff.
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  Speaker 1: That also means the CEOs that we backed and the
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  Speaker 1: CFOs of those companies. We looked for people that we
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  Speaker 1: thought were of high integrity and if there was any
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  Speaker 1: question about the integrity of those CEOs and CFOs, we
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  Speaker 1: were out. We just weren't interested in that company. And
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  Speaker 1: then Julian was, there were no shortcuts, right? It was,
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  Speaker 1: you got to do the work, explain to me why
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  Speaker 1: and how we got to the conclusion that this company
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  Speaker 1: is one, positioned incredibly well, and two, it has a
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  Speaker 1: real opportunity. There's something fundamentally changing in their industry or
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  Speaker 1: in their product set that's going to change their trajectory.
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  Speaker 1: And then the last one was, Hey, let's, let's use
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  Speaker 1: our power and, and, uh, success to help other people.
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  Speaker 2: Right.
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  Speaker 1: And so the combination of that, uh, of those, um,
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  Speaker 1: principles was very powerful. We, we all, I think many
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  Speaker 1: of us wanted to, uh, see if we could do,
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  Speaker 1: do something similar. And, and that was really powerful. And
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  Speaker 1: then I was lucky to go on to work with Roger, uh,
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  Speaker 1: and John Powell at, at integral capital and chip Morris,
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  Speaker 1: all three of those guys came out of T-Row price.
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  Speaker 1: And we worked with Kleiner Perkins. We were in their building.
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  Speaker 1: So we were surrounded by some other incredible investors that
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  Speaker 1: just saw things early and really invested in great entrepreneurs,
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  Speaker 1: people like Jeff Bezos and the founders of Google. I
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  Speaker 1: was lucky that I was able to see so many
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  Speaker 1: inspirational people and things happen early in my career and
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  Speaker 1: just wanted to try to do it on my own.
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  Speaker 2: Really, really fascinating. Coming up, we continue our conversation with
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  Speaker 2: Glenn Kacher, founder and CIO of Lightstreet Capital, discussing the
• 15:08
  Speaker 2: firm's founding and launch. I'm Barry Ritholtz. You're listening to
• 15:13
  Speaker 2: Masters in Business on Bloomberg Radio. I'm Barry Ritholtz. You're
• 15:18
  Speaker 2: listening to Masters in Business on Bloomberg Radio. My extra
• 15:22
  Speaker 2: special guest this week is Glenn Kacher. He is founder
• 15:25
  Speaker 2: and chief investment officer of Lightstreet Capital. The firm is
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  Speaker 2: a technology-focused hedge fund and private investment firm located in
• 15:36
  Speaker 2: Palo Alto, which is a good place to start. You
• 15:40
  Speaker 2: launch in 2010. The great financial crisis is still dominating
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  Speaker 2: the news flow. What was the original pitch to investors? Sure.
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  Speaker 1: The original pitch was, look, we've The game board had
• 15:58
  Speaker 1: kind of been reset in terms of making money. Multiples
• 16:02
  Speaker 1: were low. And we saw the emergence of kind of
• 16:06
  Speaker 1: four things. Mobile, with the smartphone really growing. At that point,
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  Speaker 1: it was becoming a dominant platform. Social media, most of
• 16:18
  Speaker 1: it was private, but we saw Facebook emerging and Twitter
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  Speaker 1: really redefining media. Cloud, the development of taking the internet
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  Speaker 1: technology and using it for business and the ability to
• 16:34
  Speaker 1: propagate applications everywhere that the internet was available was incredibly powerful.
• 16:43
  Speaker 1: And e-commerce, the ability to sell goods anywhere at a
• 16:48
  Speaker 1: very low cost and with the back end that Amazon
• 16:52
  Speaker 1: and others had built. to get products delivered within a
• 16:56
  Speaker 1: day or two to many locations in the globe. Those
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  Speaker 1: four things were incredibly powerful. And then ultimately we saw
• 17:05
  Speaker 1: things like the sharing economy come out of that. You
• 17:08
  Speaker 1: couldn't have had Uber and Lyft and DoorDash without having
• 17:13
  Speaker 1: e-commerce and the mobile phone and the ability to get
• 17:18
  Speaker 1: those companies distributed through the mobile universe. So there was
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  Speaker 1: a real emergence of these four powerful things, mobile, social, cloud,
• 17:26
  Speaker 1: and e-commerce. And it was really redefining what we could
• 17:29
  Speaker 1: do as consumers and business people.
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  Speaker 2: I love how you described the firm. We are the
• 17:35
  Speaker 2: Silicon Valley home team, one of the few hedge funds
• 17:38
  Speaker 2: living and working at the center of the technology universe
• 17:41
  Speaker 2: in Palo Alto, 100% focused on tech opportunities. The first
• 17:47
  Speaker 2: time I read that, I was like, that can't be right.
• 17:50
  Speaker 2: There has to be tons of hedge funds out there,
• 17:53
  Speaker 2: like not many hedge funds in, in the center of
• 17:57
  Speaker 2: the VC universe. Cause all of those successful venture investments
• 18:01
  Speaker 2: eventually go public.
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  Speaker 1: Yeah. There's a relatively small number of public market, uh,
• 18:07
  Speaker 1: managers out there and, and a good number of, of, uh,
• 18:10
  Speaker 1: you know, you've seen Philippe, what he's done at, at
• 18:12
  Speaker 1: Coto has been amazing. And, and, um, at Tiger Global,
• 18:17
  Speaker 1: Chase has done incredibly well. Uh, and, and, uh, Whale
• 18:21
  Speaker 1: Rock out of Boston with Alex. And so you've just
• 18:24
  Speaker 1: seen the success of those guys. So I'm not saying
• 18:26
  Speaker 1: it can't be done, right, by any means. But there
• 18:29
  Speaker 1: is a real advantage to living and working in the
• 18:32
  Speaker 1: place where the innovation is centered. And I think when
• 18:38
  Speaker 1: you see this fundamental innovation like we're seeing now with AI,
• 18:42
  Speaker 1: it really draws that advantage of geography back to Silicon Valley.
• 18:51
  Speaker 1: I think there's a small number of great AI entrepreneurs,
• 18:55
  Speaker 1: and they want to be in the same community with
• 18:58
  Speaker 1: one another. And so that's a real advantage for us.
• 19:01
  Speaker 2: Yeah, I kept hearing that San Francisco was over. It's dead.
• 19:04
  Speaker 2: The city is, you know, on its last gasp. We
• 19:07
  Speaker 2: were there in the spring, and the city is just,
• 19:11
  Speaker 2: it's a boom town. Like, I know there's a little
• 19:14
  Speaker 2: bit of a boom and bust, West Coast, gold rush mentality.
• 19:18
  Speaker 2: And each new cycle of technology kinda works its way
• 19:23
  Speaker 2: through but it's to anybody who steps foot we were
• 19:26
  Speaker 2: down by the embarcadero the city is just absolutely on
• 19:30
  Speaker 2: fire what's it like is this feel like the late
• 19:34
  Speaker 2: nineties in terms of the amount of uh... human capital
• 19:38
  Speaker 2: intellectual capital and actual money sloshing through That's.
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  Speaker 1: A great question. I'd say more in the mid-90s, probably.
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  Speaker 1: I think that we're at a point where this is
• 19:51
  Speaker 1: very fundamental, low-level technology. We've seen something of a renaissance
• 19:58
  Speaker 1: in the hardware industry, and that hardware innovation really matters
• 20:04
  Speaker 1: when you're trying to scale at the rate.
• 20:08
  Speaker 2: Meaning semiconductors or everything around it or the whole ecosystem?
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  Speaker 1: Semiconductors, networking, down to printed circuit boards. You have to
• 20:19
  Speaker 1: innovate at sort of every level of the stack in
• 20:22
  Speaker 1: order to grow at a 10x, 100x rate. And the
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  Speaker 1: acceleration required in order to provide AI cycles at a
• 20:34
  Speaker 1: competitive price is huge. incredibly challenging. And, you know, the
• 20:40
  Speaker 1: amount of demand that's out there is incredible. So the
• 20:43
  Speaker 1: need to scale is back. And I think, you know,
• 20:47
  Speaker 1: it's pretty interesting, you know, what we've seen, you know,
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  Speaker 1: I think in the early 2000s and the semiconductor industry
• 20:56
  Speaker 1: was allowed to consolidate and the capital was provided to
• 21:01
  Speaker 1: do that. And you saw a company like Avago and
• 21:05
  Speaker 1: and Hoctan, you know, really organize the industry and do
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  Speaker 1: some horse trading of properties to other semiconductor firms and
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  Speaker 1: really rationalize that industry. And so, as AI has emerged,
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  Speaker 1: what it's done is it's really taken advantage of the
• 21:27
  Speaker 1: fact that there are a small number of companies that
• 21:30
  Speaker 1: compete for a massive market. So, AMD and Broadcom and
• 21:36
  Speaker 1: NVIDIA and TSMC, of course, in Taiwan on the back end,
• 21:42
  Speaker 1: and then, of course, the semiconductor capital equipment companies like ASML,
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  Speaker 1: those companies just have very large market share and have
• 21:51
  Speaker 1: huge demand and huge moats and advantages.
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  Speaker 2: So let's talk about the first four companies you mentioned,
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  Speaker 2: Taiwan Semi, NVIDIA, Broadcom, and AMD. That's about 40% of
• 22:03
  Speaker 2: the public portion of your portfolio, or at least it
• 22:07
  Speaker 2: was a few filings ago. I know you're not a
• 22:11
  Speaker 2: big fan of revealing too much of your portfolios, but
• 22:16
  Speaker 2: that's a fairly concentrated portfolio. Tell us the thinking behind
• 22:20
  Speaker 2: having such a dominant emphasis on those four semiconductor companies.
• 22:25
  Speaker 1: Sure. Well, it goes back to what I was saying earlier.
• 22:29
  Speaker 1: You want to focus your capital in the place where
• 22:32
  Speaker 1: you see the most innovation. And And right now, that's
• 22:35
  Speaker 1: at the core of accelerating computing in order to do AI.
• 22:41
  Speaker 1: And right now, NVIDIA's got 80-plus percent market share in
• 22:47
  Speaker 1: the network GPU market. AMD is certainly coming up in that.
• 22:52
  Speaker 1: And as we move to agentic AI, which is a
• 22:55
  Speaker 1: very important innovation that's happening in AI and is really
• 22:59
  Speaker 1: driving that next leg of growth, there's certain advantages that
• 23:05
  Speaker 1: AMD has, because they also are one of the two
• 23:09
  Speaker 1: major players in the CPU market for desktops and servers.
• 23:14
  Speaker 1: So that explains why AMD matters a lot. And Broadcom
• 23:19
  Speaker 1: and what they've done with Google with their TPU over
• 23:23
  Speaker 1: the years is incredibly impressive and has gotten them now
• 23:27
  Speaker 1: opportunities with OpenAI and some of the other major AI players.
• 23:33
  Speaker 1: So that's certainly great exposure and then TSMC makes the
• 23:37
  Speaker 1: chips for all three of those companies and the ability
• 23:42
  Speaker 1: to Kind of win no matter who wins is and
• 23:46
  Speaker 1: and really have a massive Oligopoly monopoly almost for TSMC
• 23:55
  Speaker 1: We certainly want to back that company as well.
• 23:57
  Speaker 2: So those four companies plus Microsoft you described in 2024
• 24:03
  Speaker 2: as the AI5, and while everybody was focused on the Mag7,
• 24:08
  Speaker 2: the AI5 significantly outperformed the Mag7 that year. Is it
• 24:13
  Speaker 2: still a concentrated holding, all five, and how does that
• 24:18
  Speaker 2: thesis hold up today?
• 24:19
  Speaker 1: It's a great question, yeah. I'd say the company that's
• 24:22
  Speaker 1: kind of been in and out of our portfolio, mostly out,
• 24:25
  Speaker 1: has been Microsoft. And their early lead with OpenAI, they,
• 24:33
  Speaker 1: in our opinion, kind of fumbled that.
• 24:36
  Speaker 2: And hence, giving an opening to Anthropic?
• 24:40
  Speaker 1: Yes, for sure. And so the uptake of Microsoft's AI
• 24:46
  Speaker 1: that was somewhat powered by OpenAI really didn't work that well.
• 24:53
  Speaker 1: And that was a real miss for them. And ultimately,
• 24:57
  Speaker 1: they pulled back on on their development and funding of
• 25:02
  Speaker 1: their AI efforts. And I think they're now back in
• 25:04
  Speaker 1: the game. But at the same time, what we're seeing
• 25:07
  Speaker 1: now is, you know, for instance, Microsoft is the largest
• 25:16
  Speaker 1: security company in the world. And one of the things
• 25:18
  Speaker 1: that we've learned is that AI creates a lot of
• 25:22
  Speaker 1: security vulnerabilities for businesses. So any business, you know, is
• 25:28
  Speaker 1: going to need to invest more aggressively in their cybersecurity defenses.
• 25:33
  Speaker 1: And so that will be a big benefit for Microsoft.
• 25:37
  Speaker 1: So that's a huge advantage for them. But I think
• 25:40
  Speaker 1: what they've done and the repositioning that they've done on
• 25:43
  Speaker 1: the Azure side of their business has been very impressive.
• 25:46
  Speaker 1: They've also rationalized some of the spending that wasn't going
• 25:50
  Speaker 1: as well in their gaming business and are sort of
• 25:53
  Speaker 1: pulling back there. So I think they're repositioning the company
• 25:56
  Speaker 1: well after they sort of blinked on AI and, um,
• 26:01
  Speaker 1: you know, we're, we're, it's back in our portfolio at this.
• 26:04
  Speaker 2: So when we talk about agentic and we talk about
• 26:08
  Speaker 2: the major AI players, is this going to be a duopoly?
• 26:12
  Speaker 2: Is this going to be anthropic and open AI or
• 26:15
  Speaker 2: is it going to be a little more wide open
• 26:17
  Speaker 2: than that? Yeah.
• 26:18
  Speaker 1: It's, I think, uh, in this battle's happening in real
• 26:22
  Speaker 1: time between those two, uh, leading companies and, um, Google
• 26:28
  Speaker 1: certainly still a player with Gemini and their advantage in
• 26:32
  Speaker 1: distribution with their massive success, of course, in the search
• 26:38
  Speaker 1: engine business. And they've also now they're backing Apple's AI
• 26:44
  Speaker 1: efforts as well. So they have a real distribution advantage.
• 26:48
  Speaker 1: So I wouldn't count Google out and they still have
• 26:50
  Speaker 1: great technology.
• 26:51
  Speaker 2: By the way, their notebook LLM is outstanding. If you
• 26:55
  Speaker 2: want to upload a giant a file, a book, or anything,
• 26:59
  Speaker 2: it's unbelievably accurate and fast. I've been really impressed with that.
• 27:06
  Speaker 1: Yeah. Their ability to innovate is stunning. But the real
• 27:12
  Speaker 1: battle that's emerging today is open source models that, one,
• 27:19
  Speaker 1: are cheaper than the closed, anthropic, and open AI models.
• 27:26
  Speaker 1: because they're free. You can download them for free and
• 27:30
  Speaker 1: run them on local hardware, or you can engage with
• 27:33
  Speaker 1: them on others' commodity hardware in the sky. And those
• 27:40
  Speaker 1: open-source solutions are really battling these more expensive frontier models
• 27:44
  Speaker 1: from the two big companies. So we will see. I
• 27:49
  Speaker 1: think the early signals are that... There's a place for
• 27:56
  Speaker 1: both of these solutions broadly defined. There's also some regulatory questions,
• 28:02
  Speaker 1: open models. You really can't regulate very well because you
• 28:05
  Speaker 1: can install them on your own software. You can adjust
• 28:07
  Speaker 1: them to work how you want. There's questions about how
• 28:11
  Speaker 1: to make sure these are engaged safely and in the wild.
• 28:15
  Speaker 1: But there's also not a lot of choices around for regulators, too,
• 28:19
  Speaker 1: because those are in the wild.
• 28:21
  Speaker 2: So I want to combine what you've said about Microsoft
• 28:25
  Speaker 2: and security and open source. Is it fair to say
• 28:30
  Speaker 2: that security-aware enterprises are going to be steering clear of
• 28:36
  Speaker 2: open source because of the various security problems and the
• 28:41
  Speaker 2: duopoly of Anthropic and OpenAI is going to be where
• 28:46
  Speaker 2: the big players are going to end up, if for
• 28:48
  Speaker 2: no other reason, if there's a hack, it's a defendable decision?
• 28:53
  Speaker 1: Well, there's two questions. There's using AI within your four
• 28:57
  Speaker 1: walls and being able to provide the proper controls to
• 29:03
  Speaker 1: make sure that it doesn't get to your data that
• 29:10
  Speaker 1: is sensitive and that it doesn't somehow leak that or
• 29:14
  Speaker 1: distribute that. The second is what a bad actor can
• 29:19
  Speaker 1: do with an open source technology from outside of your
• 29:23
  Speaker 1: firm trying to break into your firm. So those are
• 29:26
  Speaker 1: the two things that you have to, you know, account
• 29:30
  Speaker 1: for with your cybersecurity spend. And so there's lots of
• 29:34
  Speaker 1: opportunity for whether it's CrowdStrike or Palo Alto and Microsoft,
• 29:39
  Speaker 1: as we talked about. But you've got to protect those. And,
• 29:42
  Speaker 1: you know, in addition, when it's your internal to your organization,
• 29:48
  Speaker 1: understanding what the roles are of the user of that
• 29:53
  Speaker 1: technology or the open source technology and what they can
• 29:55
  Speaker 1: access as a user, you have to make sure you...
• 29:59
  Speaker 1: that you honor those restrictions as utilizing an agent system.
• 30:06
  Speaker 2: So we're talking a lot about public companies. Let's just
• 30:10
  Speaker 2: look at some of the private venture investments Lightstreet has
• 30:13
  Speaker 2: made over the years. And this is quite a list. Uber, Lyft, Slack, Pinterest, Toast, Harry's, Everlane, Box, Blackbuck, EasyCater.
• 30:26
  Speaker 2: In 2018, at the IRISONE conference, you presented Palo Alto Networks, far,
• 30:32
  Speaker 2: far cheaper price than where it is today. At a
• 30:35
  Speaker 2: later IRISONE conference, you presented Farfetch'd. All of these have
• 30:40
  Speaker 2: become giant winners. The key question I have to ask is,
• 30:46
  Speaker 2: what does investing in VC teach you about public companies
• 30:52
  Speaker 2: and vice versa? What do you learn about public companies
• 30:55
  Speaker 2: that are useful when evaluating a venture opportunity?
• 30:59
  Speaker 1: Sure. In the venture companies that we invest in, and
• 31:04
  Speaker 1: even the ones we don't invest in, there's real value
• 31:06
  Speaker 1: into understanding what's happening in the industry. The advantage for
• 31:13
  Speaker 1: us as an investor is that when we meet a
• 31:16
  Speaker 1: CEO or founder of a company and try to understand
• 31:20
  Speaker 1: how they're solving a problem, they're starting with a blank
• 31:24
  Speaker 1: sheet of paper. They don't have ties to some incumbent
• 31:29
  Speaker 1: solution and incumbent set of customers that they've been trying
• 31:35
  Speaker 1: to keep happy for five, 10 years usually. So they're
• 31:39
  Speaker 1: able to be most aggressive in adopting new technology. And
• 31:43
  Speaker 1: so what we learn is that we can apply in
• 31:47
  Speaker 1: our private investing and our public investing is what matters
• 31:51
  Speaker 1: to them, what what technologies can solve the problem with
• 31:55
  Speaker 1: no constraints, you know, around keeping their long-term customers happy.
• 32:01
  Speaker 1: So that's a real advantage. And I think, you know,
• 32:04
  Speaker 1: when we, as in 2022, 2023, as AI was really
• 32:10
  Speaker 1: emerging as a category, when we were talking to some
• 32:13
  Speaker 1: of these early stage firms about, okay, how are you
• 32:19
  Speaker 1: developing your AI solutions and which semiconductors and infrastructure and
• 32:26
  Speaker 1: service providers are you using. That gave us a real
• 32:30
  Speaker 1: insight into NVIDIA and AMD and Broadcom and Marvell as
• 32:38
  Speaker 1: potential investments for our public side.
• 32:41
  Speaker 2: Long before people were talking about it in the mainstream,
• 32:45
  Speaker 2: you're hearing this directly from these clean sheet venture startups. Yeah.
• 32:50
  Speaker 1: I mean, there's one great story when I was at Integral.
• 32:56
  Speaker 1: Bill Joy was a partner at Kleiner Perkins for several years.
• 32:59
  Speaker 2: Previously at Sun, if I remember correctly.
• 33:01
  Speaker 1: One of the four founders, sure. And Bill, I can't
• 33:05
  Speaker 1: remember the exact year. It was early to mid-2000s. And
• 33:10
  Speaker 1: he was talking about this group of engineers that he
• 33:14
  Speaker 1: came across. I think it was at Caltech that were
• 33:17
  Speaker 1: utilizing the GPU technology. to do early AI calculations. And
• 33:24
  Speaker 1: so this was 2005 or 2006 or something like that. And,
• 33:27
  Speaker 1: you know, we... So I always... And, you know, the
• 33:30
  Speaker 1: conclusion of that team and of Bill himself, one of
• 33:34
  Speaker 1: the great pioneers of Silicon Valley, was that GPUs would
• 33:38
  Speaker 1: be the best chip architecture to do AI calculations. So
• 33:43
  Speaker 1: I always had that in the back of my mind.
• 33:44
  Speaker 1: And over the years when we would visit with NVIDIA
• 33:48
  Speaker 1: we would ask about AI and Jensen would talk about it.
• 33:52
  Speaker 1: And it was a tiny, tiny product and solution or
• 33:56
  Speaker 1: end market for them. And at that time, crypto mattered
• 34:00
  Speaker 1: a heck of a lot more. But it was very
• 34:03
  Speaker 1: fortunate in the back half of 22, crypto crashed at
• 34:10
  Speaker 1: the same time as AI was taking off. And so that.
• 34:13
  Speaker 2: Gave us- It was that simple.
• 34:15
  Speaker 1: For them or- They were always working on these things, right?
• 34:20
  Speaker 1: And it's really about market adoption more so than they're
• 34:23
  Speaker 1: addressing it, right? And, you know, it just so happens
• 34:27
  Speaker 1: that these things coincided. The stock market was much more
• 34:30
  Speaker 1: focused on what was happening with crypto that drove the
• 34:33
  Speaker 1: stock down and not as focused on this emerging opportunity
• 34:38
  Speaker 1: in AI. And so as AI took off in the
• 34:42
  Speaker 1: back half of 22... we were able to build a
• 34:46
  Speaker 1: great position in NVIDIA.
• 34:47
  Speaker 2: So let's talk about that run following 22. You guys
• 34:56
  Speaker 2: had one of the best three-year runs of any hedge
• 34:59
  Speaker 2: fund in recent memory. I'm looking for my exact numbers.
• 35:03
  Speaker 2: 21 and 22, the whole market got slacked in 22.
• 35:06
  Speaker 2: 21 was a rough, you're down 26% in 21, down 54%
• 35:07
  Speaker 2: in 22. and then come screaming back in 23, 4,
• 35:18
  Speaker 2: and 5, you're up 46%, 59%, and 37%. First of all,
• 35:26
  Speaker 2: how much are you just holding on for dear life
• 35:29
  Speaker 2: when you see numbers like that? What's it like to
• 35:33
  Speaker 2: live through the regular sort of drawdowns that technology goes through?
• 35:40
  Speaker 2: How much beta are you How much volatility are you
• 35:44
  Speaker 2: experiencing and how do you manage around that?
• 35:46
  Speaker 1: Yeah, it's very challenging. I mean, I think it was
• 35:49
  Speaker 1: a very frustrating time, obviously, for us in 21 and 22.
• 35:53
  Speaker 1: We came off an incredible 2020 where we played the
• 35:58
  Speaker 1: COVID market incredibly well. We're short going into COVID emerging.
• 36:03
  Speaker 1: It got very short, the market, and then had a
• 36:07
  Speaker 1: tremendous run backing SaaS and e-commerce through that period of
• 36:14
  Speaker 1: the world being in kind of a quarantine. And, you know,
• 36:18
  Speaker 1: it was a difficult transition coming out of that for us.
• 36:22
  Speaker 1: And so it was a really rough time. Software really
• 36:27
  Speaker 1: got hit in 22 over the course of a month
• 36:30
  Speaker 1: or two. And, you know, we had to reevaluate what
• 36:35
  Speaker 1: we were doing, you know, And that was tough, you know,
• 36:38
  Speaker 1: it's a tough time. And so I think the ability
• 36:41
  Speaker 1: to step back and say, okay, AI is emerging and
• 36:46
  Speaker 1: these are the incredible companies that are, you know, very
• 36:51
  Speaker 1: well positioned for it. You know, I think, and they
• 36:54
  Speaker 1: were trading it, what we thought were attractive valuations. And
• 36:57
  Speaker 1: so we've just been, you know, solving for looking forward
• 37:01
  Speaker 1: over the next, you know, six, 12, 18, 24 months.
• 37:06
  Speaker 1: since then, and it's been very fortunate that we've been
• 37:11
  Speaker 1: in the right place as AI has emerged.
• 37:14
  Speaker 2: So let's talk a little bit about that philosophical look,
• 37:21
  Speaker 2: and obviously AI and software is a perfect example of
• 37:25
  Speaker 2: what you've described as long the disruptor, short the incumbent.
• 37:30
  Speaker 2: And it's not just SaaS versus AI anymore. You could
• 37:33
  Speaker 2: be long Uber, was an example I saw you discuss once,
• 37:38
  Speaker 2: and short rental car companies. Walk us through those kind
• 37:42
  Speaker 2: of trades philosophically.
• 37:44
  Speaker 1: Yeah, well, you know, we don't necessarily do paired trades,
• 37:47
  Speaker 1: but if we think there's a well-positioned solution like Uber
• 37:54
  Speaker 1: at a certain period of time and think that's, you know,
• 37:58
  Speaker 1: benefiting from this merger of trades, of e-commerce and for
• 38:04
  Speaker 1: them and mobile and dominant market share, you know, we'll
• 38:09
  Speaker 1: go along that. And if we see a company out
• 38:12
  Speaker 1: there that's getting displaced or substituted, you know, there's short opportunities.
• 38:16
  Speaker 1: We look at them as independent opportunities, frankly. So I
• 38:21
  Speaker 1: think sometimes the market or the press around the stock
• 38:26
  Speaker 1: market tries to simplify things into a, hey, this is good,
• 38:30
  Speaker 1: this is bad.
• 38:32
  Speaker 2: Um, you know, if only it was that easy, right? Yeah.
• 38:34
  Speaker 1: I think, you know, I think, I think sometimes, uh, that,
• 38:37
  Speaker 1: that leads to things getting overdone. I think software just
• 38:41
  Speaker 1: in the last, uh, month or two has really had
• 38:44
  Speaker 1: an incredible, uh, bounce back. The, I think people, the
• 38:49
  Speaker 1: SAS POC cop SAS POC ellipse, if I can say it, um,
• 38:54
  Speaker 1: you know, that, uh, that view that software is, is
• 38:59
  Speaker 1: doomed as sort of a, a huge simplification, right? I mean,
• 39:04
  Speaker 1: I think if you look at the history of what
• 39:08
  Speaker 1: happens with incumbent technologies is if they solve a problem
• 39:12
  Speaker 1: really well, they can stick around for a long time.
• 39:15
  Speaker 1: And I think until very recently, many brokerage firms and
• 39:20
  Speaker 1: banks are running mainframe solutions still because it works. And
• 39:26
  Speaker 1: you know, when you get a new technology, you want
• 39:29
  Speaker 1: to take that new technology and you want to apply
• 39:31
  Speaker 1: it to do new things that really get you an
• 39:34
  Speaker 1: advantage versus your competitors. You don't want to take a
• 39:39
  Speaker 1: new technology and say, what's the boring business process that
• 39:44
  Speaker 1: we've automated and it really works really well that we
• 39:46
  Speaker 1: can apply this new technology to? No one does that, right?
• 39:50
  Speaker 1: That would be a waste of innovation in a lot
• 39:53
  Speaker 1: of ways. So those core systems don't tend to get
• 39:57
  Speaker 1: swapped out. So you get these opportunities for bounce backs.
• 40:00
  Speaker 1: And we're taking advantage of the doom and gloom as
• 40:06
  Speaker 1: well as the excitement about the new things. That's what
• 40:09
  Speaker 1: we have to do.
• 40:10
  Speaker 2: Really interesting. Coming up, we continue our conversation with Glenn Kacher,
• 40:15
  Speaker 2: founder and chief investment officer at LightStreetCapital.com. discussing the current
• 40:19
  Speaker 2: environment for AI and beyond. I'm Barry Ritholtz. You're listening
• 40:24
  Speaker 2: to Masters in Business on Bloomberg Radio. I'm Barry Ritholtz.
• 40:29
  Speaker 2: You're listening to Masters in Business on Bloomberg Radio. My
• 40:33
  Speaker 2: extra special guest this week is Glenn Kacher, the founder
• 40:37
  Speaker 2: and chief investment officer of Lightstreet Capital, a technology-focused hedge
• 40:42
  Speaker 2: fund located right in the heart of Silicon Valley, in
• 40:47
  Speaker 2: palo alto so i have so many great quotes of
• 40:51
  Speaker 2: yours uh... i want to uh... through by you i'm
• 40:55
  Speaker 2: gonna start with variant perception we look for a mismatch
• 41:00
  Speaker 2: in perception reality timing matters but there must be a
• 41:04
  Speaker 2: thesis about when and how the mismatch resolves itself also
• 41:10
  Speaker 2: that sounds pretty easy that's all you have to do
• 41:12
  Speaker 2: tell us a little bit about identifying that variant perception.
• 41:16
  Speaker 1: You know it uh... I started this by talking a
• 41:18
  Speaker 1: little bit about why I got excited to be an
• 41:20
  Speaker 1: investor from the beginning. And part of it is being
• 41:24
  Speaker 1: a detective, right? And going out, talking to people firsthand,
• 41:29
  Speaker 1: working with my team of investors that work at Light
• 41:34
  Speaker 1: Street Capital every day. And we all operate in the
• 41:36
  Speaker 1: same way as Roger would say, everybody goes out for
• 41:39
  Speaker 1: a pass. And go out, talk to the people that matter,
• 41:44
  Speaker 1: talk to the customers, talk to the suppliers. talk to
• 41:47
  Speaker 1: the innovators themselves. And that's how we try to get
• 41:52
  Speaker 1: it done and get the real story. I think we're
• 41:55
  Speaker 1: in a situation today where AI is, you know, now
• 42:02
  Speaker 1: being cast as sort of this evil empire that is
• 42:07
  Speaker 1: going to, one, cost people jobs and, two, you know,
• 42:15
  Speaker 1: It's crazy evil people overspending and it's certain you know
• 42:19
  Speaker 1: like it's it's it's gonna crash and burn eventually and
• 42:25
  Speaker 1: that's really not the story of AI the story of
• 42:28
  Speaker 1: AI is that the end users are Self-selecting every day
• 42:34
  Speaker 1: in their browser or now with agent software or or
• 42:38
  Speaker 1: their development tool to build more software and And they're saying,
• 42:43
  Speaker 1: this is how I can get more done quickly and
• 42:47
  Speaker 1: well with these tools. And that's what's driving the demand
• 42:52
  Speaker 1: that's creating the capacity build of AI compute. And so
• 43:00
  Speaker 1: we look at that and say, you know, there's a
• 43:03
  Speaker 1: mismatch in the way AI is being perceived today. And
• 43:07
  Speaker 1: that will reverse, but you have to figure out when.
• 43:10
  Speaker 2: So that's a productivity story, it's an efficiency story, and
• 43:14
  Speaker 2: obviously it's a profitability story.
• 43:16
  Speaker 1: It's a demand story.
• 43:18
  Speaker 2: Which kind of raises the question, you're focused on the
• 43:22
  Speaker 2: core AI players. What about everybody else? Forget the Mag7,
• 43:27
  Speaker 2: the next 493 and the S & P 500. What
• 43:30
  Speaker 2: does this mean to the rest of corporate America?
• 43:35
  Speaker 1: Well, it... I don't think you can forget the Mag7.
• 43:39
  Speaker 1: But what does it mean? But we'll put that aside.
• 43:43
  Speaker 1: You know, what does it mean for the rest of
• 43:44
  Speaker 1: corporate America? I think it's gotten their attention. It got
• 43:48
  Speaker 1: their attention pretty quickly. And, you know, I think if
• 43:51
  Speaker 1: you talk to anyone on the board of directors of
• 43:53
  Speaker 1: a public company or the CEO and top managers, they're saying, gosh,
• 43:58
  Speaker 1: you know, we hear bad AI. We need to come
• 44:03
  Speaker 1: up with a plan. We need to figure out how
• 44:06
  Speaker 1: we're going to harness this tool and make it work
• 44:10
  Speaker 1: for us. And so that's the task at hand. I
• 44:18
  Speaker 1: think it's still early to say, well, this company is
• 44:21
  Speaker 1: doing a great job with AI, so we should buy
• 44:23
  Speaker 1: their stock. That's not, to me, a great.
• 44:31
  Speaker 1: You know, thesis for today for investing. But, you know,
• 44:35
  Speaker 1: I think that everyone that I talk to in corporate
• 44:38
  Speaker 1: America is very focused on, hey, we've got to take
• 44:41
  Speaker 1: advantage of this tool.
• 44:43
  Speaker 2: You mentioned demand is really surprising everybody. I want to
• 44:51
  Speaker 2: say it was the second quarter, even Jensen Huang at
• 44:55
  Speaker 2: NVIDIA was surprised for his expectations for the AI infrastructure
• 45:02
  Speaker 2: spend by 2030. I think he bumped from $ 1 trillion
• 45:06
  Speaker 2: to $ 4 trillion. That's just a Forex giant set of numbers.
• 45:14
  Speaker 2: Are we running the risk of over-allocating to AI the
• 45:17
  Speaker 2: way we did for things like fiber and go down
• 45:20
  Speaker 2: the list of every new technology that seems to get over-allocated?
• 45:25
  Speaker 2: At what point does this become, is this explosive upside
• 45:29
  Speaker 2: demand going to, when does a coyote step off the
• 45:33
  Speaker 2: cliff and not realize he's gone a little too far?
• 45:36
  Speaker 1: This is the big question everyone's battling with today. And
• 45:40
  Speaker 1: I think, you know, the MAG-7, we mentioned a minute
• 45:45
  Speaker 1: or two ago, they have really become the key partner.
• 45:50
  Speaker 1: I think if you look at Amazon, you look at Microsoft, Google,
• 45:55
  Speaker 1: Those companies are partnering with Anthropic and OpenAI in order
• 46:02
  Speaker 1: to fulfill on building this compute stack and the infrastructure
• 46:08
  Speaker 1: to run AI. And the question is, how far ahead
• 46:13
  Speaker 1: of demand are they planning? And the reality is they're
• 46:20
  Speaker 1: not ahead today. They're behind.
• 46:23
  Speaker 2: They're playing catch-up now.
• 46:25
  Speaker 1: They're playing catch-up. The negative doomers are expecting them to over-invest,
• 46:34
  Speaker 1: but today that's just not happening. There are bottlenecks, right?
• 46:39
  Speaker 1: There are real bottlenecks, and it's quite well discussed, that
• 46:45
  Speaker 1: have slowed down the ability to build. And you've got
• 46:49
  Speaker 1: companies that are in control of some of those bottlenecks,
• 46:52
  Speaker 1: whether it's memory... companies, which we like as well, or
• 46:58
  Speaker 1: whether it's Taiwan Semiconductor, they're actually, they can only invest
• 47:01
  Speaker 1: so fast. So today, demand is still running way ahead
• 47:05
  Speaker 1: of supply. And so this doomerism that has grown up
• 47:10
  Speaker 1: around AI, in my mind, is misplaced.
• 47:15
  Speaker 2: Let's talk a little bit about the bottlenecks. I use Gemini,
• 47:19
  Speaker 2: I use Notebook, I use Chad, I use Perplexity, but really,
• 47:23
  Speaker 2: Claude Pro has become my favorite way to engage. And
• 47:30
  Speaker 2: I've noticed just going from Opus to Fable, like an
• 47:35
  Speaker 2: order of magnitude, faster, deeper, better. It doesn't feel, and
• 47:41
  Speaker 2: these are coming along like every few weeks, it doesn't
• 47:44
  Speaker 2: feel like this much of a bottleneck. When you say bottleneck,
• 47:47
  Speaker 2: what are you referring to?
• 47:49
  Speaker 1: Well, I think the bottleneck drives the pricing higher. than
• 47:53
  Speaker 1: it needs to be today, right? And so, no offense,
• 47:58
  Speaker 1: but you're probably not paying for your clawed traffic. Bloomberg
• 48:03
  Speaker 1: may be paying for it. No, I'm paying.
• 48:05
  Speaker 2: Well, my firm is paying, and it's $ 200 a month,
• 48:08
  Speaker 2: and then we just did a whole enterprise thing, and
• 48:14
  Speaker 2: it hasn't been crazy. Like, I keep hearing about people
• 48:18
  Speaker 2: just going crazy on credits and spending a year's worth
• 48:21
  Speaker 2: of credits in a month. We're pretty reasonable and a
• 48:25
  Speaker 2: little aware of our spending, but it's not like it's
• 48:30
  Speaker 2: $ 100, 000 a month. It's fairly reasonable for the output you get. Right.
• 48:36
  Speaker 1: I've been surprised. We have our own software product and
• 48:43
  Speaker 1: stack that we have developed on for 15 years where
• 48:47
  Speaker 1: we run our entire research process. And so we're constantly
• 48:51
  Speaker 1: improving that. We're also doing analysis and sentiment tracking, et cetera,
• 48:59
  Speaker 1: of sources of data that we buy. And it's not
• 49:05
  Speaker 1: cheap to do that.
• 49:06
  Speaker 2: Are you spending $ 50, 000 a month, $ 100, 000 a month? What
• 49:10
  Speaker 2: does it look like? What is a typical hedge fund
• 49:13
  Speaker 2: in the tech space? Not necessarily yours, but what do
• 49:16
  Speaker 2: you think people are spending? I know I'm only scratching
• 49:19
  Speaker 2: the surface for what I'm doing.
• 49:20
  Speaker 1: Well, for programmers, it's not uncommon to spend $ 100 a day.
• 49:27
  Speaker 1: It can get expensive, you know, and that can add
• 49:30
  Speaker 1: up quickly. Sure.
• 49:31
  Speaker 2: $ 30, 000 a month is not nothing.
• 49:33
  Speaker 1: Yeah.
• 49:33
  Speaker 2: All right.
• 49:35
  Speaker 1: You can spend a lot more than that, too.
• 49:36
  Speaker 2: Well, you know, a couple of months ago there were
• 49:38
  Speaker 2: stories about, wait, we had a whole budget for a
• 49:40
  Speaker 2: year and it's gone in four weeks. Is that the bottleneck,
• 49:44
  Speaker 2: being able to service the super clients, the hyper users
• 49:49
  Speaker 2: like that?
• 49:50
  Speaker 1: Well, that's where this demand for the open source solutions
• 49:55
  Speaker 1: comes in, which are far cheaper, right? And so the
• 49:59
  Speaker 1: ability to load it up on your own hardware and
• 50:01
  Speaker 1: have it run and be able to also adjust the
• 50:05
  Speaker 1: weightings of the model and, you know, train it on
• 50:08
  Speaker 1: your own data, those are all very powerful opportunities for
• 50:14
  Speaker 1: investors or, you know, just any kind of business. So, yeah.
• 50:20
  Speaker 1: You know, being able to do more for less is
• 50:23
  Speaker 1: certainly attractive.
• 50:24
  Speaker 2: So another quote of yours, you were talking about the
• 50:27
  Speaker 2: AI build-out, and you said, it's a 10-year cycle of demand.
• 50:32
  Speaker 2: The bear case is that CapEx gets cut the moment
• 50:36
  Speaker 2: returns disappoint. Tell us a little bit about why you
• 50:39
  Speaker 2: think this demand cycle is going to go a full decade. Yeah.
• 50:45
  Speaker 1: Well, I mean, I think we're changing the entire stack
• 50:49
  Speaker 1: of computing. The only thing that looks like this that
• 50:53
  Speaker 1: we've experienced before is the move to the Internet architecture
• 50:59
  Speaker 1: from client-server. And, you know, these computing cycles happen about every,
• 51:05
  Speaker 1: you know, 15 to 25 years since, you know, since
• 51:09
  Speaker 1: the development of computing. And the way the technology works...
• 51:15
  Speaker 1: is completely different. You know, in the old school of
• 51:19
  Speaker 1: technology is search and retrieve or create, search and then
• 51:24
  Speaker 1: retrieve model where you stored things in databases. And here
• 51:29
  Speaker 1: in the AI world, the technology is essentially creating a
• 51:35
  Speaker 1: custom solution, custom to your question, custom to your data
• 51:43
  Speaker 1: every single time you use it. It's just a much
• 51:47
  Speaker 1: more complex and compute-intensive model. The ability to have custom
• 51:59
  Speaker 1: solutions and custom answers every single time you need data
• 52:06
  Speaker 1: is so much more powerful. If we follow history, these
• 52:11
  Speaker 1: things tend to to 15 years to become a quarter
• 52:17
  Speaker 1: of the total capacity of the industry. So to say
• 52:20
  Speaker 1: that it's going to take multiple decades is not much
• 52:23
  Speaker 1: of a stretch.
• 52:24
  Speaker 2: So where are we? Are we in year four or
• 52:26
  Speaker 2: five now of a 10 to 15 year first leg?
• 52:30
  Speaker 1: Yeah, exactly. We're kind of a third of the way
• 52:33
  Speaker 1: through the first leg. I mean, if you look at
• 52:35
  Speaker 1: the way technology develops, it sort of goes in three cycles.
• 52:38
  Speaker 1: You know, your big infrastructure development years take, you know,
• 52:42
  Speaker 1: five to 10 years, let's say. Then year six, year
• 52:47
  Speaker 1: six through 16, let's say, that's when your platform or
• 52:52
  Speaker 1: OS really gets developed and put into place. And then
• 52:57
  Speaker 1: the applications kind of come in years 11 through 21,
• 53:03
  Speaker 1: and applications become the dominant place where businesses invest and
• 53:11
  Speaker 1: the innovation happens. It's at least a 15 to 20
• 53:15
  Speaker 1: year cycle that we're looking at.
• 53:18
  Speaker 2: I'm kind of fascinated by the energy demands and the
• 53:22
  Speaker 2: build out of these giant data centers. And I'm curious,
• 53:26
  Speaker 2: what are your thoughts to the political pushback to where
• 53:31
  Speaker 2: these are located? A couple of states have already banned them.
• 53:36
  Speaker 2: I never saw the politics against tech morphing this way.
• 53:41
  Speaker 2: How do you look at that as a investment risk?
• 53:45
  Speaker 1: It's a real risk that, you know, any bottleneck that
• 53:48
  Speaker 1: slows down the adoption of your technology is a problem, right?
• 53:52
  Speaker 1: We're investing in NVIDIA or Taiwan Semiconductor saying, okay, here's
• 53:57
  Speaker 1: what we expect. And in our view, the numbers are
• 54:01
  Speaker 1: still significantly better than Wall Street's looking for. However, we
• 54:06
  Speaker 1: have to bear in mind, you know, Is there an
• 54:10
  Speaker 1: obstacle that's going to get in that way? Today, it's,
• 54:12
  Speaker 1: in our view, not a big enough problem, but it's
• 54:18
  Speaker 1: an emerging problem. And I think the way, as an industry,
• 54:21
  Speaker 1: we have to get around this is that we have
• 54:24
  Speaker 1: to explain the places that invest most heavily and most aggressively.
• 54:31
  Speaker 1: You know, if you look at Northern Virginia, not far
• 54:34
  Speaker 1: from where I grew up, you know, that is the
• 54:38
  Speaker 1: data center capital of the world. And that opportunity and
• 54:42
  Speaker 1: what's happening, what's happened with tax receipts in those communities
• 54:47
  Speaker 1: that have all these large data centers and the demand
• 54:51
  Speaker 1: for blue collar work in order to build those data centers,
• 54:55
  Speaker 1: whether it's electricians and plumbers and construction work. It's a,
• 55:01
  Speaker 1: it's a massive, uh, you know, shot in the arm
• 55:04
  Speaker 1: for those economies. And then the, the tax revenue is,
• 55:08
  Speaker 1: is an ongoing, uh, ongoing, uh, payment that happens over
• 55:13
  Speaker 1: many years. So I think it's, uh, it's a little bit, uh,
• 55:18
  Speaker 1: it's sad that, that, uh, some of these communities are
• 55:21
  Speaker 1: not as, um, positive about, uh, about the opportunities. I
• 55:26
  Speaker 1: think they're just not well-educated by uh... their elected officials uh.
• 55:32
  Speaker 2: i'm not surprised that it's in virginia or new york uh...
• 55:35
  Speaker 2: i'm enormously surprised when you see push back in places
• 55:39
  Speaker 2: like texas which is big enough that you can stick
• 55:44
  Speaker 2: a data center out wherever this juice and and nobody
• 55:49
  Speaker 2: has to see it here it be concerned about it uh...
• 55:53
  Speaker 2: but it it it keeps raising the question of cost
• 55:57
  Speaker 2: of electricity and People seem to be concerned. We let
• 56:01
  Speaker 2: a data center in here, our electrical costs are going
• 56:04
  Speaker 2: to go up. How should we as a tech-savvy nation
• 56:10
  Speaker 2: of investors respond to that concern about electricity?
• 56:13
  Speaker 1: Yeah, absolutely. The source of electricity needs to be behind
• 56:18
  Speaker 1: the meter, right? So the firm that creates the data center,
• 56:24
  Speaker 1: if there's not enough existing energy, then they have to
• 56:29
  Speaker 1: provide the energy.
• 56:30
  Speaker 2: So run a gas line, set up your own generator,
• 56:34
  Speaker 2: and you're off the grid.
• 56:36
  Speaker 1: And if it's close, look, if it's close to a
• 56:38
  Speaker 1: residential area, there's actually a data center that's being contemplated
• 56:41
  Speaker 1: in San Mateo, California, not far from Palo Alto. And
• 56:47
  Speaker 1: their solution is to put Bloom Energy servers behind, which
• 56:53
  Speaker 1: are powered with natural gas. with almost no emissions. And
• 57:00
  Speaker 1: they're incredibly quiet, almost no audible sound. And you can
• 57:05
  Speaker 1: put a Bloom Energy fuel cell behind the meter. And
• 57:11
  Speaker 1: even though that's the plan, residents have rallied against it
• 57:17
  Speaker 1: because they've heard data centers are bad. They're just not
• 57:21
  Speaker 1: educated on what the solution is and how it will
• 57:25
  Speaker 1: not impact their energy prices. And there will be no
• 57:27
  Speaker 1: emissions and no noise.
• 57:29
  Speaker 2: And, and we have midterms coming up in November. Is
• 57:33
  Speaker 2: this the sort of thing that once we get past
• 57:35
  Speaker 2: the next group of elections, this will fade or is
• 57:38
  Speaker 2: this really an ongoing challenge for the industry?
• 57:42
  Speaker 1: It's an education challenge. Yeah.
• 57:44
  Speaker 2: Yeah.
• 57:44
  Speaker 1: We've got it. We've got it. And it's from local
• 57:48
  Speaker 1: to national, right? Each project has to explain this is the,
• 57:52
  Speaker 1: This is the decision we're making around procuring this energy.
• 57:57
  Speaker 1: These are the number of jobs it's going to create.
• 57:59
  Speaker 1: These are the tax revenues it's going to generate. Here's
• 58:02
  Speaker 1: our existing energy situation. This can go on the grid
• 58:06
  Speaker 1: without much of an impact, or we're bringing our own energy.
• 58:11
  Speaker 1: So it's both a local and a national solution.
• 58:14
  Speaker 2: Really interesting. And the MAG-7 keeps coming up. When we
• 58:19
  Speaker 2: met in the spring in San Francisco— You liked Amazon, Google,
• 58:25
  Speaker 2: and NVIDIA. I don't recall what your thoughts were on Microsoft.
• 58:29
  Speaker 2: You weren't a big fan of Meta, Tesla, and Apple.
• 58:33
  Speaker 2: How do you see the Mag 7 today? Is that
• 58:36
  Speaker 2: still fairly consistent?
• 58:38
  Speaker 1: That's fairly consistent, yes. As I said earlier, we've put
• 58:44
  Speaker 1: Microsoft back in our portfolio, and so I'd say that
• 58:48
  Speaker 1: they're back in the good category. The challenge for Apple
• 58:52
  Speaker 1: is to get their AI solutions tuned up and working
• 58:57
  Speaker 1: well for the consumer. If you think about your mobile phone,
• 59:01
  Speaker 1: it's in a very unique position. It has both your
• 59:05
  Speaker 1: personal and your business data to the extent that you're
• 59:10
  Speaker 1: not a small business person. And the security is there
• 59:14
  Speaker 1: to separate those two things. And so that device has
• 59:19
  Speaker 1: the ability to optimize and recommend actions or solutions. to
• 59:27
  Speaker 1: you as a consumer that address both your business life
• 59:31
  Speaker 1: and your personal life. And that's a very unique position
• 59:34
  Speaker 1: that Apple's in. And obviously, you carry it around and
• 59:37
  Speaker 1: it's on most of the time, if not all the time.
• 59:41
  Speaker 1: And they have a real opportunity to bring AI solutions
• 59:45
  Speaker 1: to democratize them for consumers in a very complicated but
• 59:52
  Speaker 1: elegant way. And so, If Apple can get things right,
• 59:57
  Speaker 1: that should accelerate their opportunities or earnings over the next
• 1:00:03
  Speaker 1: couple of years.
• 1:00:04
  Speaker 2: They don't have a great history with it. Siri has
• 1:00:06
  Speaker 2: been nothing less than a total embarrassment for a decade.
• 1:00:10
  Speaker 2: I mean, I'm not revealing any secrets here. Everybody knows
• 1:00:14
  Speaker 2: it's garbage. And there was some criticism of Apple for
• 1:00:19
  Speaker 2: not jumping in with both feet. to become a hyperscale
• 1:00:24
  Speaker 2: and spend tens billions dollars what they've done with google
• 1:00:29
  Speaker 2: is worked out great for both companies hey what's a
• 1:00:32
  Speaker 2: couple of billion dollars a year to apple and to
• 1:00:35
  Speaker 2: google it's pure profit is the same sort of setup
• 1:00:39
  Speaker 2: teeing up where it's a win-win for apple to integrate
• 1:00:44
  Speaker 2: google's technology into the.
• 1:00:46
  Speaker 1: Iphone potentially but it's execution based Mm-hmm.
• 1:00:50
  Speaker 2: Isn't that always the case?
• 1:00:52
  Speaker 1: It is, but their strategy, this is a very consistent
• 1:00:55
  Speaker 1: strategy where they are the, you know, they were not
• 1:00:59
  Speaker 1: the first smartphone, right? They waited. They watched what Nokia did,
• 1:01:04
  Speaker 1: what BlackBerry did, and RIM BlackBerry, and then they came
• 1:01:10
  Speaker 1: out with a more elegant solution after those guys established
• 1:01:14
  Speaker 1: the market.
• 1:01:16
  Speaker 2: Second, mouse gets the cheese. Is that the thinking there?
• 1:01:19
  Speaker 1: Well, If you have a big bank account and users
• 1:01:23
  Speaker 1: that really will wait around until you solve the problem
• 1:01:26
  Speaker 1: in a better way, then it works.
• 1:01:28
  Speaker 2: Last question before we get to our favorite questions. So
• 1:01:32
  Speaker 2: I'm not going to ask you about 20 years out
• 1:01:34
  Speaker 2: or 10 years out, but five years out, what does
• 1:01:38
  Speaker 2: this technology look like? What's going to define AI for
• 1:01:42
  Speaker 2: the consumer and business customer in 2031? Agents. Agents.
• 1:01:48
  Speaker 1: The ability to have the technology working on problems when
• 1:01:54
  Speaker 1: you're not directing it. That is incredibly powerful. It leads
• 1:02:01
  Speaker 1: to users consuming 5X the tokens that you would consume
• 1:02:09
  Speaker 1: just directing AI as you would a search engine. And
• 1:02:14
  Speaker 1: so the ability to have your agent... or agents working
• 1:02:20
  Speaker 1: on your personal life and solving problems as they come
• 1:02:23
  Speaker 1: into your inbox or into your messaging solutions with your
• 1:02:27
  Speaker 1: family and friends. And then on the business side, the
• 1:02:31
  Speaker 1: same thing, solving problems for you, solving problems with your
• 1:02:37
  Speaker 1: coworkers and teammates. It's incredibly powerful, this technology.
• 1:02:44
  Speaker 2: To say the very least. All right, let's jump to
• 1:02:46
  Speaker 2: our favorite questions. that we ask all of our guests
• 1:02:49
  Speaker 2: starting with, and I already asked, but I got to
• 1:02:52
  Speaker 2: ask a little more specifically who were the mentors who
• 1:02:56
  Speaker 2: shaped your career?
• 1:02:58
  Speaker 1: Well, you certainly have to look at Julian, uh, Robertson and, and, um,
• 1:03:03
  Speaker 1: you know, he just has his, the, the example that
• 1:03:07
  Speaker 1: he set and how to run a, you know, an
• 1:03:09
  Speaker 1: investment business with integrity and, um, but you know, and, and,
• 1:03:15
  Speaker 1: and intellectual honesty and, principles. And so that was incredible.
• 1:03:20
  Speaker 1: Roger and John at Integral Capital Partners were just great
• 1:03:25
  Speaker 1: as I got out of business school and was in
• 1:03:27
  Speaker 1: my early 30s, really, you know, those key years of learning, again,
• 1:03:34
  Speaker 1: how to run a firm and make great investments. And
• 1:03:37
  Speaker 1: they gave me the opportunity to both succeed and fail
• 1:03:41
  Speaker 1: in some of those private investments that I made. Those
• 1:03:46
  Speaker 1: are going to be the key people that really shaped
• 1:03:50
  Speaker 1: my career.
• 1:03:51
  Speaker 2: You mentioned the two Peter Lynch books, One Up on
• 1:03:54
  Speaker 2: Wall Street and Beat the Street. I know you read
• 1:03:56
  Speaker 2: a lot of other research. Any other books worth mentioning
• 1:04:00
  Speaker 2: these days?
• 1:04:01
  Speaker 1: You know, I pulled a book off the shelf recently,
• 1:04:05
  Speaker 1: Empires of Light, which tells the story of the propagation
• 1:04:10
  Speaker 1: of electricity and the battle between Edison, General Electric, Tesla, Westinghouse.
• 1:04:17
  Speaker 2: AC and DC.
• 1:04:18
  Speaker 1: Yes. And, you know, incredible story. And I think at
• 1:04:22
  Speaker 1: the end of the day, it was really interesting. And
• 1:04:25
  Speaker 1: Edison really pushed that AC was dangerous. And to the
• 1:04:31
  Speaker 1: point where he promoted it for the electric chair, because
• 1:04:37
  Speaker 1: it made it made AC look bad and dangerous.
• 1:04:40
  Speaker 2: Didn't one of them electrocute an elephant to show how
• 1:04:43
  Speaker 2: dangerous it was?
• 1:04:43
  Speaker 1: Many different animals. Yeah. And a prisoner. And it didn't
• 1:04:48
  Speaker 1: go so well, actually. The first electric chair didn't work
• 1:04:52
  Speaker 1: extremely well. So to scare people and say AC is bad,
• 1:04:56
  Speaker 1: and you look at what's happening today with AI and
• 1:05:00
  Speaker 1: people taking this incredibly powerful technology that is going to
• 1:05:05
  Speaker 1: change the world and is already starting to change it,
• 1:05:09
  Speaker 1: and making it this evil empire. It's pretty fascinating. And
• 1:05:13
  Speaker 1: I think the other side of that is that, you know,
• 1:05:17
  Speaker 1: in the end of the day, Westinghouse won out with,
• 1:05:19
  Speaker 1: you know, steady execution and industrialization of the back end.
• 1:05:24
  Speaker 1: And you look at the Mag 7 and what's, you know,
• 1:05:28
  Speaker 1: the opportunity for Amazon and Microsoft and Google to build
• 1:05:35
  Speaker 1: that back end And AI is an incredible opportunity for AWS.
• 1:05:42
  Speaker 2: Which is already the biggest profit center for Amazon.
• 1:05:45
  Speaker 1: Yes. And so, you know, which is not the, if
• 1:05:48
  Speaker 1: you say Amazon, everyone thinks about e-commerce and they don't
• 1:05:51
  Speaker 1: first think about AWS. But AWS is the more important
• 1:05:55
  Speaker 1: part of the company.
• 1:05:55
  Speaker 2: Yeah, to say the least. What are you streaming these days?
• 1:05:59
  Speaker 2: I know you're on a plane pretty regularly. What are
• 1:06:01
  Speaker 2: you listening to or watching to keep yourself entertained?
• 1:06:06
  Speaker 1: Well, entertain, I mean, sure. X is entertaining. You know,
• 1:06:09
  Speaker 1: all the debate around our industry is pretty fascinating. Friends
• 1:06:14
  Speaker 1: and neighbors is a guilty pleasure. So, you know, that's
• 1:06:19
  Speaker 1: something I'm streaming regularly.
• 1:06:22
  Speaker 2: Anything Jon Hamm is always worth watching. Final two questions.
• 1:06:27
  Speaker 2: What sort of advice would you give to a recent
• 1:06:29
  Speaker 2: college grad interested in a career in either investing or technology?
• 1:06:34
  Speaker 1: The number one thing that I tell younger folks is
• 1:06:38
  Speaker 1: you have all the tools today to make an impact.
• 1:06:42
  Speaker 1: And so if you want to get in the investment business, one,
• 1:06:47
  Speaker 1: of course, start investing. But two, do your research, go online,
• 1:06:54
  Speaker 1: and then publish your research. Put it on X. Interact
• 1:06:57
  Speaker 1: with people like you, people like me. And if you
• 1:07:00
  Speaker 1: can uncover... the story behind a stock and make some
• 1:07:08
  Speaker 1: great recommendations, you're trying out for the world in real time.
• 1:07:15
  Speaker 1: And if you have the courage to do that and
• 1:07:18
  Speaker 1: you do it well, it's a no-brainer to hire that person.
• 1:07:21
  Speaker 2: Our final question, what do you know about the world
• 1:07:25
  Speaker 2: of investing and technology today? Might have been useful back
• 1:07:28
  Speaker 2: in 1993 when you were first getting started.
• 1:07:31
  Speaker 1: Yeah, I think early on and for investors coming to
• 1:07:36
  Speaker 1: our market, there's this perception that things happen very fast
• 1:07:40
  Speaker 1: and no doubt they're changing rapidly. But at the same time,
• 1:07:45
  Speaker 1: there's this reality that things do take time. And we
• 1:07:52
  Speaker 1: talked about the emergence of the smartphone. The first smartphone-like
• 1:07:57
  Speaker 1: device that came out was the Newton. And it didn't
• 1:08:01
  Speaker 1: really work that well. And then General Magic had a
• 1:08:04
  Speaker 1: solution that also didn't really work that well. And then
• 1:08:08
  Speaker 1: Palm created the first thing that actually got some adoption,
• 1:08:11
  Speaker 1: but it didn't do any email or messaging. And it
• 1:08:15
  Speaker 1: certainly wasn't a phone. And then Palm created the Trio, right?
• 1:08:19
  Speaker 1: And then I'd say in some ways, RIM was the
• 1:08:23
  Speaker 1: real first, you know, RIM BlackBerry was the first real
• 1:08:28
  Speaker 1: working smartphone. but it was somewhat clunky. And some people
• 1:08:31
  Speaker 1: love that clunkiness, right? And love that keyboard. But then
• 1:08:36
  Speaker 1: ultimately got to Apple. And so while things happen fast,
• 1:08:45
  Speaker 1: it also takes years for things to really develop. And
• 1:08:48
  Speaker 1: so I think if we apply that today, AI can
• 1:08:53
  Speaker 1: do some incredible things, but it's going to do way
• 1:08:57
  Speaker 1: more in a few years. And there are some obstacles
• 1:09:04
  Speaker 1: other than the ones we mentioned to adoption, right? Data
• 1:09:07
  Speaker 1: security and comfort of your coworkers and your superiors in
• 1:09:14
  Speaker 1: terms of giving access to data to an AI agent.
• 1:09:22
  Speaker 1: So it will take time in order to see, you know,
• 1:09:28
  Speaker 1: ultimately what it can deliver. And so I think we're
• 1:09:31
  Speaker 1: just scratching the surface, even though, as I mentioned, there's
• 1:09:34
  Speaker 1: a lot of battles between open source, for instance, and
• 1:09:37
  Speaker 1: the closed frontier models. But there's way more to go here.
• 1:09:42
  Speaker 2: Glenn, thank you for being so generous with your time.
• 1:09:44
  Speaker 2: This has been absolutely fascinating. We have been speaking with
• 1:09:48
  Speaker 2: Glenn Kacher. He is the founder and chief investment officer
• 1:09:52
  Speaker 2: of Light Street Capital. If you enjoy this conversation, check
• 1:09:56
  Speaker 2: out any of the 651 previous discussions we've done over
• 1:10:01
  Speaker 2: the past 12 years. You can find those at Bloomberg, iTunes, Spotify, YouTube,
• 1:10:06
  Speaker 2: or wherever you get your favorite podcasts. I would be
• 1:10:10
  Speaker 2: remiss if I didn't thank the crack team that helps
• 1:10:12
  Speaker 2: put these conversations together each week. Elizabeth Sedrin is my
• 1:10:16
  Speaker 2: video producer. Anna Luke is my podcast producer. Sean Russo
• 1:10:21
  Speaker 2: is my researcher. I'm Barry Ritholtz. You've been listening to
• 1:10:24
  Speaker 2: Masters in Business on Bloomberg Radio.