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Speaker 1: Bloomberg Audio Studios, podcasts, radio news.

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Speaker 2: This is Master's in Business with Barry red Holds on

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Speaker 2: Bloomberg Radio.

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Speaker 3: This week on the podcast, another extra special guest, David Rue,

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Speaker 3: is chairman of Baypine, a fascinating private equity firm. They

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Speaker 3: are not interested in simply flipping companies or buying firms

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Speaker 3: and then quickly selling them. What they do much more

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Speaker 3: involved than a consulting firm. They are experts at digital

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Speaker 3: transformation across a wide variety of sectors in the investing world,

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Speaker 3: and they essentially take companies as varied as tire manufacturers

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Speaker 3: and industrial producers and re tailors and find intelligent ways

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Speaker 3: to use technology to make these companies more efficient, more productive,

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Speaker 3: more profitable. And they're not again, they're not just consultants.

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Speaker 3: They come in, they take a steak in a company.

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Speaker 3: Sometimes it's a minority state, sometimes it's a larger steak,

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Speaker 3: and they help affect this massive change with great results.

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Speaker 3: They're one of the few companies that specialize in this.

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Speaker 3: Their track record has been very impressive and the approach

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Speaker 3: they bring to transforming old industry companies is absolutely fascinating.

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Speaker 3: Previous to Baypine, David was one of the co founders

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Speaker 3: of Silver Lake Investors, a legendary firm from the nineties

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Speaker 3: and two thousands. With no further ado, my conversation with

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Speaker 3: Baypines David.

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Speaker 1: Rue, Thank you pleasure to be here.

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Speaker 3: It's a pleasure to have you. I've been looking forward

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Speaker 3: to this conversation for quite a while. Let's start out

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Speaker 3: with your background. Bachelor's from Harvard, Masters in philosophy from Cambridge,

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Speaker 3: and then an NBA from Harvard Business School. What was

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Speaker 3: the career plan?

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Speaker 1: You know, I originally wanted to be an architect.

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Speaker 3: Really, I've always wanted to pretend to be an architect's

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Speaker 3: That's an area I'm fascinating And why did you not

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Speaker 3: go into that space?

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Speaker 1: You know, I grew up, you know, building goat karts

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Speaker 1: and treehouses and the like. But I think when I

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Speaker 1: got to school, I found that I could make models,

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Speaker 1: build software, maybe create organizations, and that it was as

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Speaker 1: much fun as building a building.

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Speaker 3: There's a different sense of creating a company versus creating

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Speaker 3: a certain type of space inhabited by people, no doubt

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Speaker 3: about that. So let's talk about some of those companies

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Speaker 3: that you built. You begin at a few tech startups,

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Speaker 3: you found day Techs, which eventually gets acquired by Lotus.

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Speaker 3: What was the startup process? Like, this was mid nineteen eighties,

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Speaker 3: is that about right?

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Speaker 1: Yeah, early eighties. You know, in business school, I realized

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Speaker 1: this is the kind of early PC boom. And I

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Speaker 1: realized from my academic work, you know, there were word processors,

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Speaker 1: there were spreadsheets, but there was not very good database

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Speaker 1: technology for PCs. They didn't have what the mini computers

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Speaker 1: had and the mainframes had. So I saw an opportunity

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Speaker 1: to create some software and also to be able to

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Speaker 1: marry that up with data for people to use on

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Speaker 1: their PC. And that was the idea behind day Text.

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Speaker 3: So day Text gets acquired by Lotus, who eventually acquires Lotus.

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Speaker 1: Lotus is eventually acquired by IBM by coincidence. That was

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Speaker 1: a relationship I managed. So I had a very good

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Speaker 1: kind of ringside seat in all that. They were very

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Speaker 1: interested in the company's suite of primarily communications technologies ccmail,

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Speaker 1: Lotus notes because the sort of networking boom had already

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Speaker 1: started up, and they saw a world where all of

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Speaker 1: these PCs would be interconnected.

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Speaker 3: How did you end up at Oracle?

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Speaker 1: I had met Larry Ellison during my Lotus days I'd

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Speaker 1: done another company, which we sold to Symantec. Larry had

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Speaker 1: contacted me and said, look, we've got a tiger by

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Speaker 1: the tail. The business is growing like crazy. I think

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Speaker 1: there might be some M and A opportunities. We really

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Speaker 1: don't have a corporate development function. Would you be interested

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Speaker 1: to come here and build one. That's how it happened.

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Speaker 3: Oracle, especially in the eighties and nineties, became famous as

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Speaker 3: a serial acquirer of all sorts of pieces, spinouts, roll ups.

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Speaker 3: How long did you stay at Oracle?

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Speaker 1: How was there all through the nineties till nineteen ninety nine.

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Speaker 1: You know, it was really a terrific experience, extremely rapid growth.

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Speaker 1: I ran the venture fund, did all the investing off

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Speaker 1: the balance sheet.

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Speaker 2: I also.

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Speaker 1: Started and managed the M and A programs. So yeah,

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Speaker 1: it was fantastic.

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Speaker 3: Yeah. I can imagine Oracle in the nineties as you

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Speaker 3: were out in California and.

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Speaker 1: Assume right Silicon Valley, right in the heart of things,

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Speaker 1: ground zero, and I got to think Oracle and Ellison

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Speaker 1: like I cut my teeth on them.

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Speaker 3: In the nineties, he seemed to have been everywhere. Oracle

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Speaker 3: was consistently ranked best company to work, Top ten, Fast

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Speaker 3: and growing companies like Oracle. I think people who just

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Speaker 3: came into the market in the past ten twenty years

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Speaker 3: don't know what a powerhouse Oracle was and still is.

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Speaker 1: Yeah, it has a remarkable history, you know, a class

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Speaker 1: of eighty six, meaning that's the same year as Microsoft, Sun, Apple,

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Speaker 1: and so they've been at it and doing a great

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Speaker 1: job for a while. You know, Larry's often thought of

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Speaker 1: as a very aggressive and astute business mind, but I

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Speaker 1: don't think he gets enough credit for his technical chops

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Speaker 1: if you look back, think about it. He has been

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Speaker 1: fearless about betting the company on major new architecture. So

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Speaker 1: you know, he made the original bet around relational databases

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Speaker 1: when everyone else was doing something else. He then made

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Speaker 1: a major bet on Unix when it was a kind

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Speaker 1: of obscure, you know, scientific operating system. He then made

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Speaker 1: a huge bet around enterprise applications, big bet around client server.

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Speaker 1: And then maybe the most courageous bet was in the

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Speaker 1: mid nineties when Netscape had gotten the first browsers out

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Speaker 1: the Internet boom it started. A great story came in

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Speaker 1: one morning after a weekend. We all sat down at

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Speaker 1: our little executive committee call and he says, look, I've

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Speaker 1: been thinking, I think this Internet thing is more important

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Speaker 1: than most people understand. I would like to change one

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Speaker 1: hundred percent of what we're doing in development. I want

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Speaker 1: to stop all of the client server work, and I

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Speaker 1: want to replatform everything that we're doing on a web architecture.

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Speaker 1: Three thousand engineers, dozens hundreds of products, affecting you know,

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Speaker 1: thousands upon thousands of customers. And he very casually said

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Speaker 1: I'd like to do this by the end of the day.

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Speaker 3: Sounds like that's a multi year project.

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Speaker 1: He was he talking, no, no, no, he was talking about

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Speaker 1: I want I want to direct to the turned to

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Speaker 1: the director of engineering and said, I want this done

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Speaker 1: by the end of the day. Just poured it over

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Speaker 1: to its just going to stop. We're not going to

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Speaker 1: write another line of client server code. It's done. This

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Speaker 1: is going to be the new architecture. This is the

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Speaker 1: future of computing. This is what our customers are going

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Speaker 1: to want in two and three, in five years time,

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Speaker 1: so we need to start building it now for it

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Speaker 1: to be ready. Then it was really the thing I mean,

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Speaker 1: I mean, just a incredibly gutsy bet, but a very

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Speaker 1: good sense of his technical prowess and the confidence he

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Speaker 1: had about the kind of what's coming next next part.

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Speaker 3: It sounds like Oracle was quite an experience at the

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Speaker 3: end of the nineteen nineties. You co found silver Lake

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Speaker 3: in nineteen ninety nine. What led to that? You were

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Speaker 3: you were at Oracle. You were like a fifteen year

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Speaker 3: veteran at Oracle. Is that about right?

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Speaker 1: No, not quite. But I'd been there a while and

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Speaker 1: it was, you know, a fantastic experience. I had a

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Speaker 1: great job, really good relationship with Larry, rest of the team.

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Speaker 1: You know, I was my late thirties. I'd kind of

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Speaker 1: come to realize that it was always going to be

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Speaker 1: Larry's business, rightly so, and that I was looking around

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Speaker 1: and I saw what I thought of, as I've come

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Speaker 1: to call it, an OIPSE, which is acronym OIPS for

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Speaker 1: an opportunity and plain sight. And I couldn't understand the following.

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Speaker 1: I couldn't understand why investors were pouring money into venture firms,

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Speaker 1: pouring money into growth equity, and not doing anything to

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Speaker 1: invest in technology using a private equity format. Didn't make

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Speaker 1: sense to me. That it would be a good small company,

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Speaker 1: it would be a good medium sized company, and then

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Speaker 1: all of a sudden it would not be an appropriate

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Speaker 1: place for fiduciary capital. That didn't make sense, and I thought, gosh,

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Speaker 1: that must be a huge opportunity. You know, we were

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Speaker 1: right in the middle of the Internet boom, so tech was,

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Speaker 1: you know, front and center of the news, and yet

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Speaker 1: there was none of the traditional firms were there. In fact,

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Speaker 1: they were actively avoiding it. You know, it was sort

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Speaker 1: of not considered an appropriate place to invest that kind

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Speaker 1: of capital.

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Speaker 3: Why was that? Was it that people were just so

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Speaker 3: distracted by the new hotness, by the dot coms and.

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Speaker 1: The Internet or I don't think so.

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Speaker 3: Well. Were the public markets there for larger companies if

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Speaker 3: they needed capital.

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Speaker 1: No, No, I don't think that. Here's what I think,

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Speaker 1: because this is what they told us. I mean, I

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Speaker 1: asked that exact question. The theory was this, you couldn't

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Speaker 1: go write checks for hundreds of millions of dollars if

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Speaker 1: you couldn't underwrite the technical innovation at the heart of

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Speaker 1: these business models, if you didn't understand how the semiconductor worked,

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Speaker 1: if you didn't understand how the software was built.

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Speaker 3: One.

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Speaker 2: Two.

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Speaker 1: There was a theory that these businesses had volatile cash

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Speaker 1: flows and therefore couldn't be leveraged, which was the you know,

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Speaker 1: the whole point of leverage buyout. And finally that they

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Speaker 1: were companies run by children, you young folks. I was

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Speaker 1: in the business, and when I heard all that, I said,

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Speaker 1: you know, those guys in New York and the skyscrapers,

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Speaker 1: and the guy in London and those people in Munich

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Speaker 1: and Tokyo, I don't think they really know what's going

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Speaker 1: on here. These are actually really good businesses. The cash

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Speaker 1: flows are unbelievable, customer franchises are very, very durable. There's

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Speaker 1: incredible organic growth here. This is a really big and

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Speaker 1: attractive opportunity. I think someone's gonna make a great return

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Speaker 1: by building a business here.

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Speaker 3: And you don't need to underwrite the entire underlying technology.

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Speaker 3: You're really just talking about that transition to whatever makes

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Speaker 3: those companies that much more attractive. Is that a fair assessment?

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Speaker 1: When you're doing what silverl does and what it was

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Speaker 1: built to do, you are making a fundamental bet technology.

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Speaker 1: It's like when people buy technology or enter into an

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Speaker 1: agreement with a company like an Oracle or a Microsoft.

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Speaker 1: You're not buying what they're selling you today. You're buying

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Speaker 1: the promise that they will continue delivering. It's like buying

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Speaker 1: a lot on a river. Now you're not buying the

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Speaker 1: water in front of your house, You're buying the promise

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Speaker 1: that the water will continue to flow. And so you

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Speaker 1: do need to have a point of view about how

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Speaker 1: well positioned these companies are for the future.

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Speaker 3: You co found silver Lake with this is some lineup.

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Speaker 3: Glenn Hutchins, Jim Davidson, Roger mcnamie. Tell us about your

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Speaker 3: silver Lake co founders.

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Speaker 1: Well, look, they're each enormously talented and capable in their

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Speaker 1: own right. You know, we all live near each other,

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Speaker 1: knew each other professionally beforehand. We talked extensively about this

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Speaker 1: opportunity and agreed that it was the next big thing.

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Speaker 1: And I think that, you know, looking back on it,

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Speaker 1: I've been very fortunate at silver Lake and prior companies

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Speaker 1: when I started something to do it with a group

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Speaker 1: of people, and that it's always been great to have

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Speaker 1: folks from different backgrounds, different styles, different professional experience. You know,

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Speaker 1: it's very complimentary and you know it's not for everybody,

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Speaker 1: but for me, it's it's the way I like to

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Speaker 1: do business.

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Speaker 3: They weren't an oracle prior. How did the how.

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Speaker 1: Do we all know each other. Yeah, Glenn and I

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Speaker 1: have been college classmates, tennis partners, fly fishing buddies. He

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Speaker 1: was previously at Blackstone. Jim Davidson and I are both

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Speaker 1: big sports fans and shared season tickets for the Sharks

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Speaker 1: and the Warriors, so we would spend a lot of

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Speaker 1: time together. He was running the H and Q Investment Bank,

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Speaker 1: and then Roger was my next door neighbor and very

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Speaker 1: good friends with Jim. So you know, it was a

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Speaker 1: group of people who already kind of knew each other,

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Speaker 1: had some personal relationships to build on, and you know,

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Speaker 1: came with a different set of experiences.

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Speaker 3: What was silver Lake like in two thousand as the

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Speaker 3: dot COM's all imploded.

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Speaker 1: Well, you know, it was an interesting thing. I would

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Speaker 1: jokingly tell people that we bought high, sold low and

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Speaker 1: made a ton of money, and you know, it was

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Speaker 1: a very challenging economic environment. The NASDAK during that period

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Speaker 1: fell eighty percent right over from the front front end

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Speaker 1: of the fund to the back. The fund itself, in

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Speaker 1: a very fundamental way, was set up as a counterpoint

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Speaker 1: to the mania around the internet. And what we would

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Speaker 1: tell people pointedly, as we say, look, you're completely right

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Speaker 1: to be excited about the technology sector. It's underinvested. It's

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Speaker 1: underappreciated for its scale, it's underappreciated for its growth, it's

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Speaker 1: underappreciated for the strategic value that it plays in the economy.

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Speaker 1: But you're investing in the wrong companies at the wrong price.

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Speaker 1: And I had a little chart that I would show them.

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Speaker 1: So here's a thousand, approximately one thousand public company public

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Speaker 1: tech companies at that time. These ten percent are what's

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Speaker 1: driving the entire valuation. They're trading at ten to thirty

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Speaker 1: plus times revenue, not earnings, not earnings revenue. And I said,

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Speaker 1: I can just tell you that is the wrong price.

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Speaker 1: Not necessarily bad companies, but that's those are the wrong prices.

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Speaker 1: I said, but look at these other ninety percent. If

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Speaker 1: you take the rest of the publicly traded technology companies,

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Speaker 1: they're traded at one time's revenue. It's the same as

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Speaker 1: the S and P at the day of.

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Speaker 3: The day, pretty reasonable, more.

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Speaker 1: Or very reasonable, and they grow twice as fast. So

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Speaker 1: you have an opportunity to buy growth at half price.

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Speaker 1: You've got this situation. There's sort of the fundamental insight

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Speaker 1: at the heart of the silver l like value proposition.

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Speaker 1: Is that technology, the entire tech sector was on sale,

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Speaker 1: even at a time when people thought it was super expensive,

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Speaker 1: because ten percent of the market was super expensive, but

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Speaker 1: most of it was not.

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Speaker 3: How much of what's been going on in the twenty

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Speaker 3: twenties has been a focus on that same top ten

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Speaker 3: percent of tech companies as being overly concentrated and wildly expensive.

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Speaker 3: Do you think the same situation is starting to show

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Speaker 3: up in the modern era?

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Speaker 1: Well, there are some parallels and also some important differences.

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Speaker 1: The parallels are that there is a concentration of interest.

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Speaker 1: The differences those companies are now huge businesses with gigantic

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Speaker 1: levels of profitable unprecedented levels of profitability and growth rates

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Speaker 1: that have never been achieved before by companies at that scale.

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Speaker 1: So that's the part that's really different, Right. A lot

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Speaker 1: of the things in Internet time was highly speculative. The

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Speaker 1: other thing that's different is that today the companies with

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Speaker 1: the most spectacular valuation levels are private. People aren't wrong

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Speaker 1: to say they are a winner the sort of the

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Speaker 1: bed of courses. Are they the only winner?

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Speaker 2: Right?

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Speaker 1: And so you have to believe that there won't be

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Speaker 1: successful competition, you know, I would only point out that

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Speaker 1: forty percent of their sales go to four big vendors,

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Speaker 1: each of whom has their own chip development program. And

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Speaker 1: so I'm not saying they're going to build a better chip,

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Speaker 1: but they're definitely going to build a cheaper chip, and

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Speaker 1: so there'll be some dampening for sure from that.

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Speaker 3: And you know, I'm around long enough to remember when

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Speaker 3: it looked like Intel was impregnable, that they had a

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Speaker 3: position in the ecosystem that nobody could touch, and now

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Speaker 3: it feels like they're and also run well.

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Speaker 1: This goes back to the point that you raised earlier,

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Speaker 1: which is if you're going to do tech investing, you

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Speaker 1: need to have an opinion about the tech. It's not

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Speaker 1: just that you can look at a series of financials

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Speaker 1: and say, oh, they had a good quarter last quarter,

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Speaker 1: they had good year every year numbers I like the

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Speaker 1: three year trend and say fine, you have to separately

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Speaker 1: underwrite the quality of the underlying architecture, what's going on

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Speaker 1: in the industry, and believe that they're going to be

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Speaker 1: able to keep going. And so like, if you like

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Speaker 1: go to end video, let's talk about that. You can

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Speaker 1: look at the financials and say this is fantastic. You know,

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Speaker 1: they're doubling and trebling, and these are incredible numbers and

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Speaker 1: growing blah blah blah. The way they've gotten their improvements

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Speaker 1: has been to go beyond what has been possible in

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Speaker 1: any other chip manufacturer. The other chip manufacturers have gotten

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Speaker 1: their productivity improvements around the physical geometry by making the

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Speaker 1: chip smaller and smaller, more and more transistors.

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Speaker 3: Classic More's law down to ever smaller.

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Speaker 1: Ever smaller. Right, these guys have got have stolen a

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Speaker 1: march on the rest of the industry with their GPU

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Speaker 1: chips by doing other things. They've gotten probably two or

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Speaker 1: two and a half times, which is a lot of improvement.

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Speaker 1: But they're talking about improvements of things that are kind

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Speaker 1: of eight ten, twelve, sixteen times productivity improvement. So they're

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Speaker 1: doing it other ways. They're doing it with algorithms, they're

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Speaker 1: doing it with other approaches, and so you have to

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Speaker 1: form an opinion as an investor about what is the

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Speaker 1: likelihood they can keep doing that because it's been the

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Speaker 1: key driver, and keep doing that for the next three

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Speaker 1: to five years, not just the next quarter. Bingo.

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Speaker 3: So let's talk a little bit about Silver Lake and

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Speaker 3: how that eventually leads to Bay Pine. It feels like,

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Speaker 3: and I don't know if my memory is correct. Silverlake

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Speaker 3: was one of the first buyout shops built around making

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Speaker 3: technology investments or investments in technology companies. Is that a

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Speaker 3: fair description.

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Speaker 1: Several other deals had been done, but they were occasional

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Speaker 1: and they weren't the central focus for anyone.

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Speaker 3: Tell us some of your memorable investments at Silverlake.

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Speaker 1: Well, I think some of the investments that the firm

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Speaker 1: is best known for out of the box. First was Seagate,

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Speaker 1: which was a hard disk drive manufacture. It was at

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Speaker 1: the time the number one producer dis drives. They had

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Speaker 1: the best technology, great management team, very complex, but finally

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Speaker 1: crafted extended supply chain through Asia, and Wall Street hated them.

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Speaker 1: It was hardware in the age of the Internet. So

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Speaker 1: the other thing that traded really really hot back then

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Speaker 1: was any new telecom business, right optical this telecom That

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Speaker 1: one of the insights that we had as a group,

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Speaker 1: and it stemmed from the fact that we were not

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Speaker 1: finance people but industry people with operating backgrounds, is we

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Speaker 1: understood that the entire tech industry is an ecosystem. It

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Speaker 1: operates like your body, right, All the parts sort of

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Speaker 1: need to fit together and they operate inter dependently, and

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Speaker 1: so everybody at that time was talking about the information

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Speaker 1: super Highway and they were buying the highway, right, They're

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Speaker 1: buying the telecom companies, and they were buying all these

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Speaker 1: new applications that you could do on the Internet. But

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Speaker 1: people forgot that you couldn't have an information super highway

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Speaker 1: without parking lots. In other words, the electron the bit

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Speaker 1: had to start somewhere and it needed to end somewhere.

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Speaker 1: So if you believed that broadband was going to explode,

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Speaker 1: then you must also believe that storage is going to explode.

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Speaker 1: And so we were able to look at that kind

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Speaker 1: of systemic arbitrage around the architecture and say, you know,

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Speaker 1: the comm's piece is overpriced. I don't think we should

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Speaker 1: pay ten and fifteen times for a pike when we

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Speaker 1: can pay six times EBITDA, EBITDA. Earnings for the number

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Speaker 1: one storage company in the world, Now, there was a

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Speaker 1: bunch of applications around things they owned, and you know,

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Speaker 1: it was a public company, so it was a leveraged

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Speaker 1: buyout and all the rest of that, but that was

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Speaker 1: really the fundamental insight.

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Speaker 3: So it sounds like a lot of the public market

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Speaker 3: investors had a fundamental misunderstanding about the entire tech sector,

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Speaker 3: the ecosystem as you described it. What are the things

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Speaker 3: did people just not get, not understand, overlook obvious investments

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Speaker 3: in plain sight?

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Speaker 1: Yeah? Well, I think in that case it was a

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Speaker 1: hangover from the very real war of attrition that for

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Speaker 1: the prior twenty years had preceded that moment in time

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Speaker 1: where the industry went from one hundred and twenty disdrive

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Speaker 1: companies to sex or seven.

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Speaker 3: We had an idea who are the winners who were

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Speaker 3: going to be well?

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Speaker 1: And so the question was is there going to be

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Speaker 1: more blood in the water or have we arrived at

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Speaker 1: an industry structure where everybody is going to do okay

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Speaker 1: and the number one player is probably going to do

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Speaker 1: better than most. That was one. The second issue is

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Speaker 1: could anyone figure out a way to At the time,

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Speaker 1: Seagate owned some shares and other software companies, and it

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Speaker 1: wasn't clear to the market how they could sell those

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Speaker 1: in a tax efficient way. And that's one of the

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Speaker 1: things that with structuring we were able to figure out.

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Speaker 1: I'll give you another one where we bought a vago

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Speaker 1: Eulett Packard's semiconductor division in this TI frame, the early

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Speaker 1: two thousands, it was very much the fashion to be

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Speaker 1: out of semi. Semis were out of fashion, right. The

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Speaker 1: world was infatuated with the other end of the stack,

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Speaker 1: not the you know, I didn't want to hear it.

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Speaker 1: People didn't want to hear about semis. They didn't want

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Speaker 1: to hear about subassemblies, they didn't want to hear about components.

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Speaker 1: They didn't want to hear about computers. They wanted to

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Speaker 1: hear about all the sexy, high margin, no cost of goods,

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Speaker 1: no capital equipment, software services, internet applications sounded wonderful. You know,

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Speaker 1: this is the age of pets, dot com, that thing.

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Speaker 1: And so it wasn't wrong to say that software was good,

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Speaker 1: but it didn't automatically follow that hardware's bad. And so

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Speaker 1: people had this idea, almost like a dialectic, which is

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Speaker 1: that you couldn't believe in something that you like, that

403
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Speaker 1: the other must be bad. And so semis were completely

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Speaker 1: out of fashion. Semens spun theirs off, HP, spun theirs off, IBM,

405
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Speaker 1: either closed or spun theirs off. You know, just all

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Speaker 1: these peoplehood who had very significant capabilities and fabs that

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Speaker 1: today would be worth fortunes leave aside the intellectual property

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Speaker 1: and the skill sets and the trained labor force you know,

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Speaker 1: you know, all went off the back of the truck.

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Speaker 1: So we bought this from HP, hired a great manager

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Speaker 1: CEO named hot Tan, and built this up into a

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Speaker 1: kind of highly specialized and others. We didn't buy it

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Speaker 1: with the idea that we're going to go compete with

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Speaker 1: Intel and try to dis lodge them from the PC market,

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Speaker 1: but rather with the idea that everything was going to

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Speaker 1: have a processor, cars, kids, toys, you know, your kitchen appliances,

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Speaker 1: and that somebody was going to have to make all

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Speaker 1: those processors. And so there was an exploding rest of

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Speaker 1: market opportunity that Intel wasn't focused on that people like Evago.

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Speaker 3: Could be Today. I think automobiles are the second biggest

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Speaker 3: consumer of semiconductors. I don't know if that's still true,

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Speaker 3: that was true a few years ago.

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Speaker 1: I think that's right. In fact, the Stata i Avn'

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Speaker 1: quote for people is that the semiconductor content in a

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Speaker 1: car is more valuable than all of the metal, than

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Speaker 1: the all the steel and all the aluminum. And maybe

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Speaker 1: more importantly, it is increasingly the case that what the

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Speaker 1: semiconductors enable the navigation, the.

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Speaker 3: Abs, departure worn, All the features that really give a

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Speaker 3: car kind of its identity are increasingly denominated by the

431
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Speaker 3: digital capabilities. What about the rest of the world outside

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Speaker 3: of PCs and automobiles. It was incredibly for looking to

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Speaker 3: say in the early two thousands, by the way, they're

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Speaker 3: going to be chips in everything, not just dishwashers and refrigerators,

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Speaker 3: but toys and electric bikes and you name it. It's

436
00:27:54,160 --> 00:27:57,160
Speaker 3: going to need a chip that was a decade ahead

437
00:27:57,160 --> 00:27:57,640
Speaker 3: of its time.

438
00:27:57,840 --> 00:28:01,480
Speaker 1: You would have been very immune. Is when we raised

439
00:28:01,480 --> 00:28:05,359
Speaker 1: our first fund You may remember the Ferbie doll, sure,

440
00:28:05,440 --> 00:28:10,440
Speaker 1: of course, which was a hot hot productry hot kids product,

441
00:28:10,600 --> 00:28:14,480
Speaker 1: little furry thing, eyes bad and it had in it

442
00:28:14,640 --> 00:28:18,080
Speaker 1: a digital signal processing chip. They would allow it to

443
00:28:18,560 --> 00:28:21,399
Speaker 1: make a little noise, cuddly noises, and you know, whittle

444
00:28:21,440 --> 00:28:23,320
Speaker 1: its legs. And I used to bring it with me

445
00:28:23,760 --> 00:28:27,119
Speaker 1: to all of our fundraising meetings. I wouldn't say a

446
00:28:27,119 --> 00:28:28,679
Speaker 1: word about it. I'd simply take it out of my

447
00:28:28,960 --> 00:28:31,920
Speaker 1: briefcase and I would put it on the desk between

448
00:28:31,920 --> 00:28:34,720
Speaker 1: myself and the prospective investor, and I wouldn't say a

449
00:28:34,720 --> 00:28:37,080
Speaker 1: word about it. You know, I'd launch into my talk

450
00:28:37,160 --> 00:28:41,960
Speaker 1: about semiconductors and hardware and the evolution of the sector

451
00:28:42,040 --> 00:28:44,840
Speaker 1: and so forth. And finally, sometimes it would be five minutes,

452
00:28:44,880 --> 00:28:47,880
Speaker 1: sometimes ten, but it was never more than thirty minutes.

453
00:28:48,040 --> 00:28:50,880
Speaker 1: Right the investor would say, David, what is that doll?

454
00:28:51,400 --> 00:28:53,120
Speaker 1: Why do you have that doll there? I go, oh,

455
00:28:53,280 --> 00:28:55,840
Speaker 1: I'm so sorry. I forgot to mention it. That's a

456
00:28:55,880 --> 00:28:59,920
Speaker 1: Ferbie doll. And I brought that for you because I

457
00:29:00,160 --> 00:29:03,960
Speaker 1: wanted to illustrate in a simple way how the march

458
00:29:04,040 --> 00:29:08,480
Speaker 1: of technology is going to go. I said that Ferbie

459
00:29:08,520 --> 00:29:12,640
Speaker 1: doll has more processing power than the lunar land.

460
00:29:12,800 --> 00:29:13,880
Speaker 3: I knew you're going to go that way.

461
00:29:14,080 --> 00:29:17,400
Speaker 1: And I said, we're looking at a world where all

462
00:29:17,440 --> 00:29:19,840
Speaker 1: of music is going to be digital, all of film

463
00:29:19,920 --> 00:29:22,760
Speaker 1: is going to be digital, Television is going to be digital.

464
00:29:23,040 --> 00:29:25,000
Speaker 1: The way you do your phone is going to be digital.

465
00:29:25,160 --> 00:29:28,160
Speaker 1: I said, So all of these analog things, as they

466
00:29:28,200 --> 00:29:34,200
Speaker 1: become more digital, need this technology. And if you understand

467
00:29:34,280 --> 00:29:39,280
Speaker 1: how the technology works, you will not because you're kind

468
00:29:39,320 --> 00:29:42,880
Speaker 1: of a big brain genius, but because you've played the

469
00:29:42,920 --> 00:29:45,800
Speaker 1: game before and you understand what all the pieces do,

470
00:29:46,360 --> 00:29:49,320
Speaker 1: you'll be in a really good position to identify those

471
00:29:49,520 --> 00:29:53,600
Speaker 1: opportunities going forward. I'll give you another great example. The

472
00:29:53,640 --> 00:29:56,120
Speaker 1: part that and the little wrinkle that I think gave

473
00:29:56,200 --> 00:29:57,920
Speaker 1: us a lot of credibility, and by the way, gives

474
00:29:58,000 --> 00:30:02,480
Speaker 1: us credibility now is to say, let's own the right technology,

475
00:30:03,160 --> 00:30:05,600
Speaker 1: put it in the right companies, and the key part

476
00:30:05,720 --> 00:30:09,600
Speaker 1: is at the right price. It is bringing an investment

477
00:30:10,640 --> 00:30:16,480
Speaker 1: sensibility and financial discipline to the work that we do. Right.

478
00:30:16,520 --> 00:30:20,280
Speaker 1: We're not like technology zealots and I want to just

479
00:30:20,360 --> 00:30:22,520
Speaker 1: own it. To own it right. It's not a prize,

480
00:30:22,560 --> 00:30:26,560
Speaker 1: it's not a trophy. It's you know, would this be

481
00:30:26,720 --> 00:30:30,040
Speaker 1: useful and would somebody else be interested. I'll give another example.

482
00:30:30,360 --> 00:30:34,160
Speaker 1: eBay came out of the blocks super hot. They bought PayPal,

483
00:30:34,920 --> 00:30:37,560
Speaker 1: and then they bought this thing called Skype, and Skype

484
00:30:37,680 --> 00:30:44,680
Speaker 1: was the first software based peer to peer video conferencing capability,

485
00:30:44,800 --> 00:30:48,800
Speaker 1: so long before Zoom. Twenty thirty million people on at

486
00:30:48,800 --> 00:30:51,960
Speaker 1: the same time, which was an amazing technical feat. Wasn't

487
00:30:51,960 --> 00:30:54,400
Speaker 1: exactly here what it had to do with auctions, even

488
00:30:54,440 --> 00:30:57,520
Speaker 1: less clear what it had to do with PayPal. But

489
00:30:57,880 --> 00:31:02,000
Speaker 1: eBay bought Skype and it kind of noodled along in

490
00:31:02,040 --> 00:31:06,160
Speaker 1: the Skype portfolio in the early two thousands for a year.

491
00:31:06,880 --> 00:31:10,920
Speaker 1: Two years, no one paid any attention to it at all.

492
00:31:11,160 --> 00:31:14,640
Speaker 1: We said, my gosh, look they've had three CEOs in

493
00:31:14,720 --> 00:31:17,520
Speaker 1: two years, they're spread out all over the place, they

494
00:31:17,520 --> 00:31:20,800
Speaker 1: haven't upgraded the product in two years. Maybe they'd be

495
00:31:20,800 --> 00:31:24,400
Speaker 1: willing to sell it. Contact with them once, contact them twice.

496
00:31:24,440 --> 00:31:26,680
Speaker 1: Eventually they said, yeah, we'd be willing to talk to

497
00:31:26,680 --> 00:31:28,760
Speaker 1: you about that, because we had a point of view

498
00:31:28,840 --> 00:31:33,320
Speaker 1: about this is a really exciting market, but no one's

499
00:31:33,320 --> 00:31:35,920
Speaker 1: paying any attention. So if we could carve that out,

500
00:31:35,600 --> 00:31:38,880
Speaker 1: we want to. Eban said, keep it as much of

501
00:31:38,880 --> 00:31:40,800
Speaker 1: this as you want. We'll buy the rest of it

502
00:31:40,840 --> 00:31:44,920
Speaker 1: at a full valuation, which we did. And it was

503
00:31:44,920 --> 00:31:49,000
Speaker 1: a business that had nice growth despite really being a

504
00:31:49,040 --> 00:31:52,920
Speaker 1: feral child, right and we said, look, get paid twice.

505
00:31:53,320 --> 00:31:56,120
Speaker 1: We'll pay you once what it's worth, and we're going

506
00:31:56,160 --> 00:31:59,080
Speaker 1: to make this way more valuable than you possibly could

507
00:31:59,400 --> 00:32:01,920
Speaker 1: because we can focus on it and make a bunch

508
00:32:01,960 --> 00:32:04,080
Speaker 1: of changes. And it was. It was sort of a troubled,

509
00:32:04,760 --> 00:32:07,040
Speaker 1: was complicated asset where you know, there was so much

510
00:32:07,040 --> 00:32:10,560
Speaker 1: staying in litigation. They hadn't upgraded it for a long time.

511
00:32:11,680 --> 00:32:14,080
Speaker 1: Apple had just announced that they were going to be

512
00:32:14,360 --> 00:32:19,360
Speaker 1: offering a you know, video video face time FaceTime. So

513
00:32:19,400 --> 00:32:21,440
Speaker 1: there was you know, Microsoft said they wanted to be

514
00:32:21,440 --> 00:32:21,960
Speaker 1: in the business.

515
00:32:21,960 --> 00:32:23,680
Speaker 3: So there was a lot of companies.

516
00:32:23,680 --> 00:32:27,960
Speaker 1: There was competition from very credible large players.

517
00:32:27,840 --> 00:32:31,720
Speaker 3: And if I recall correctly, around that time, all of

518
00:32:31,760 --> 00:32:36,800
Speaker 3: the fat pipes and broad bandwidth that had come public

519
00:32:36,840 --> 00:32:39,960
Speaker 3: in the late nineties early two thousands were coming back

520
00:32:40,040 --> 00:32:43,560
Speaker 3: up around pennies on the dollar. I recall Global Crossing,

521
00:32:43,560 --> 00:32:46,360
Speaker 3: a Mentro Media, Fiber and all these companies. So the

522
00:32:46,400 --> 00:32:49,880
Speaker 3: bandwidth was coming a line at a cheap price that

523
00:32:49,960 --> 00:32:52,400
Speaker 3: didn't exist that way in the nineties, which is very

524
00:32:52,520 --> 00:32:54,840
Speaker 3: much right into the sweet spot of Skype.

525
00:32:55,120 --> 00:32:57,680
Speaker 1: Yeah, and by the way, not so just similar from

526
00:32:58,280 --> 00:33:01,720
Speaker 1: the AI process sink crunch that we have today, where

527
00:33:01,760 --> 00:33:05,280
Speaker 1: people are pouring a huge amount of super expensive stuff

528
00:33:05,880 --> 00:33:08,880
Speaker 1: which you do need, but which will be available three

529
00:33:08,960 --> 00:33:10,920
Speaker 1: years and five years and ten years from now.

530
00:33:11,120 --> 00:33:14,320
Speaker 3: Mut's really really different pricing you end up buying them,

531
00:33:14,360 --> 00:33:18,280
Speaker 3: if I recall directly, not much long after that did

532
00:33:18,440 --> 00:33:20,840
Speaker 3: Microsoft come along and scoop them up from you.

533
00:33:20,960 --> 00:33:24,480
Speaker 1: Well, what happened is is that we bought it, completely,

534
00:33:24,560 --> 00:33:30,360
Speaker 1: upgraded the software, changed out the entire management team, developed

535
00:33:30,400 --> 00:33:34,200
Speaker 1: a series of partnerships, built a business side of it

536
00:33:34,240 --> 00:33:36,760
Speaker 1: because it had been very much kind of B two

537
00:33:36,760 --> 00:33:40,240
Speaker 1: C phenomenon, try to really opened up a product line

538
00:33:40,240 --> 00:33:43,520
Speaker 1: around B to B and it ended up being very

539
00:33:43,560 --> 00:33:47,240
Speaker 1: attractive for Microsoft sold it to them, you know, one

540
00:33:47,240 --> 00:33:50,160
Speaker 1: of the foundation elements in what his teams today, and

541
00:33:50,400 --> 00:33:52,200
Speaker 1: really helped them. I think it was a great It

542
00:33:52,200 --> 00:33:54,800
Speaker 1: turned out to be a good deal for silver Lake,

543
00:33:54,880 --> 00:33:57,120
Speaker 1: but it also I think, as all deals should be

544
00:33:57,440 --> 00:33:59,240
Speaker 1: a very good deal for the acquirers.

545
00:33:59,520 --> 00:34:02,720
Speaker 3: Any other any other memorable civil like deals.

546
00:34:02,520 --> 00:34:06,040
Speaker 1: Were oftentimes you know, I think the maybe two others

547
00:34:06,040 --> 00:34:10,480
Speaker 1: that we are well known for. We're the largest investor

548
00:34:10,480 --> 00:34:11,120
Speaker 1: in Ali.

549
00:34:10,880 --> 00:34:13,080
Speaker 3: Baba before it went really before it.

550
00:34:13,000 --> 00:34:17,000
Speaker 1: Went public, and that was a you know, explosive that

551
00:34:17,040 --> 00:34:19,800
Speaker 1: was that was explosive, but it was a scary investment.

552
00:34:19,800 --> 00:34:24,200
Speaker 1: It was a minority investment in a Chinese e commerce company,

553
00:34:24,640 --> 00:34:26,680
Speaker 1: you know, located on the other side of the world.

554
00:34:27,400 --> 00:34:31,080
Speaker 3: Who's also your co your co investor is the People's

555
00:34:31,080 --> 00:34:36,320
Speaker 3: Republic of China, right they own ultimate regulator for sure

556
00:34:37,239 --> 00:34:39,120
Speaker 3: owner slash regulator.

557
00:34:38,760 --> 00:34:43,480
Speaker 1: And you know, Massason and soft Bank are already large investors.

558
00:34:43,880 --> 00:34:48,080
Speaker 1: But we liked the management team we love the story,

559
00:34:48,120 --> 00:34:50,520
Speaker 1: and that turned out to be a you know, very

560
00:34:50,560 --> 00:34:53,600
Speaker 1: good that was a very very good investment. And then

561
00:34:53,640 --> 00:34:56,960
Speaker 1: the last one and really still very much in the

562
00:34:56,960 --> 00:35:03,040
Speaker 1: news was Dell, big well known public company, you know,

563
00:35:03,160 --> 00:35:07,960
Speaker 1: eponymously named for its CEO who'd left kind of like

564
00:35:08,480 --> 00:35:12,400
Speaker 1: you know, Charles Schultz left, came back first, went private

565
00:35:12,680 --> 00:35:17,439
Speaker 1: where Michael rolled essentially all of his ownership into it.

566
00:35:18,040 --> 00:35:21,040
Speaker 1: Made a very large personal bet. So it was a

567
00:35:21,040 --> 00:35:24,560
Speaker 1: gutsy bet because it was at a time again, this

568
00:35:24,600 --> 00:35:27,680
Speaker 1: is a place where the conventional wisdom was the PC

569
00:35:27,880 --> 00:35:30,200
Speaker 1: was going away, we were going to use our phones,

570
00:35:30,239 --> 00:35:34,040
Speaker 1: We're going to use iPads. Somehow it was going to

571
00:35:34,040 --> 00:35:36,600
Speaker 1: go away. We didn't think it was going to go away.

572
00:35:36,760 --> 00:35:43,719
Speaker 1: And we thought that the market hadn't really appreciated how

573
00:35:43,800 --> 00:35:49,160
Speaker 1: much work Michael had done building up a store of

574
00:35:49,200 --> 00:35:58,040
Speaker 1: intellectual property around next generation computing, whether it's cyber cloud computing,

575
00:35:58,960 --> 00:36:02,560
Speaker 1: and you know, maybe it's like maybe a basketball franchise

576
00:36:02,600 --> 00:36:05,560
Speaker 1: that has a bunch of draft picks, you know what

577
00:36:05,560 --> 00:36:08,400
Speaker 1: I'm saying, kind of young talent, which we thought was

578
00:36:08,440 --> 00:36:11,000
Speaker 1: going to be very valuable because we had a point

579
00:36:11,040 --> 00:36:13,600
Speaker 1: of view about the importance of cloud. We had a

580
00:36:13,600 --> 00:36:16,080
Speaker 1: point of view about the importance of cyber and we

581
00:36:16,120 --> 00:36:21,160
Speaker 1: thought that those assets were undervalued because the whole of

582
00:36:21,200 --> 00:36:23,600
Speaker 1: the company was getting valued like it was a commodity

583
00:36:23,640 --> 00:36:24,360
Speaker 1: PC vendor.

584
00:36:24,640 --> 00:36:27,920
Speaker 3: So let's talk about what did you do post silver

585
00:36:28,000 --> 00:36:29,760
Speaker 3: Lake in the twenty tens.

586
00:36:30,000 --> 00:36:33,360
Speaker 1: I'm a starter and a builder. I like backing social

587
00:36:33,600 --> 00:36:41,240
Speaker 1: entrepreneurs and feel particularly passionate about conservation, biomedical research and education.

588
00:36:41,880 --> 00:36:46,040
Speaker 1: We took our foundation resources and focused it first on

589
00:36:46,080 --> 00:36:49,879
Speaker 1: a thing out in Seattle called the Institute for Health

590
00:36:49,920 --> 00:36:54,400
Speaker 1: Metrics and Evaluation. Stood that up. Bill Gates blessedly is

591
00:36:54,760 --> 00:36:57,719
Speaker 1: doing most of the support now, but that's now you know,

592
00:36:57,800 --> 00:37:03,520
Speaker 1: five hundred researchers, and they focus on understanding in detail

593
00:37:04,239 --> 00:37:08,160
Speaker 1: the global burden of disease, so that we know how

594
00:37:08,200 --> 00:37:11,520
Speaker 1: healthy or sick you know every country is, and you

595
00:37:11,520 --> 00:37:15,080
Speaker 1: know where to allocate our healthcare dollars. On the biomedical

596
00:37:15,120 --> 00:37:20,000
Speaker 1: research side, became very active as the chairman of Jackson Laboratories,

597
00:37:20,800 --> 00:37:25,000
Speaker 1: one of the largest independent institutes in the country, focused

598
00:37:25,040 --> 00:37:28,799
Speaker 1: on kind of the genetic causes of rare diseases. We

599
00:37:28,840 --> 00:37:32,080
Speaker 1: were able to double the size of that quadruple that endowment,

600
00:37:32,880 --> 00:37:37,960
Speaker 1: and then more recently in the education space. I had

601
00:37:37,960 --> 00:37:43,640
Speaker 1: this view that we were not appreciating how big artificial

602
00:37:43,680 --> 00:37:47,000
Speaker 1: intelligence was going to be, and that as a consequence,

603
00:37:47,160 --> 00:37:52,360
Speaker 1: as a nation, we are underinvested in advanced computer science

604
00:37:52,360 --> 00:37:56,760
Speaker 1: and others. We've got programs at all the best universities,

605
00:37:57,080 --> 00:38:00,000
Speaker 1: but they graduating hundreds of people, tens of thousands, maybe

606
00:38:00,080 --> 00:38:03,480
Speaker 1: even hundreds of thousands that we need. Really yeah, because

607
00:38:03,560 --> 00:38:07,280
Speaker 1: what's happened is that the academics are focused on building

608
00:38:07,280 --> 00:38:11,000
Speaker 1: the new platform, the so called large language models. When

609
00:38:11,320 --> 00:38:14,719
Speaker 1: think about that, like it's a nuclear power plant, you know,

610
00:38:15,000 --> 00:38:20,120
Speaker 1: complicated high science, but we now it now works, and

611
00:38:20,160 --> 00:38:22,279
Speaker 1: it'll work a little better, and they'll keep refining it

612
00:38:22,320 --> 00:38:25,040
Speaker 1: and so forth, but it works, and that what we

613
00:38:25,080 --> 00:38:29,480
Speaker 1: need next are application engineers. We need electricians. We need

614
00:38:29,480 --> 00:38:32,799
Speaker 1: people to design appliances. We need to run wires, we

615
00:38:32,880 --> 00:38:36,799
Speaker 1: need to change from steam to electricity, and we don't

616
00:38:36,800 --> 00:38:40,560
Speaker 1: have those people. And so we already know what we need.

617
00:38:41,040 --> 00:38:42,680
Speaker 1: It's going to by the way, it'll be twenty or

618
00:38:42,719 --> 00:38:47,040
Speaker 1: thirty years of implementation ahead of us. So these will

619
00:38:47,080 --> 00:38:50,600
Speaker 1: be great jobs for a lot of people. So we've

620
00:38:50,719 --> 00:38:53,680
Speaker 1: built the first school. We've spent a few years getting

621
00:38:53,680 --> 00:38:57,919
Speaker 1: that organized, opened it in twenty we're now, I guess

622
00:38:58,000 --> 00:39:01,400
Speaker 1: four years in we've got a thousands to two hundred

623
00:39:01,440 --> 00:39:08,600
Speaker 1: corporate partners and started or accelerated ninety four companies, four

624
00:39:08,680 --> 00:39:10,800
Speaker 1: hundred jobs. You know, really exciting.

625
00:39:11,000 --> 00:39:13,359
Speaker 3: You're doing this for a couple of years. You're standing up.

626
00:39:13,640 --> 00:39:17,160
Speaker 1: I'm happy as a clam. Right, I'm making things, I'm

627
00:39:17,320 --> 00:39:18,040
Speaker 1: helping people.

628
00:39:18,320 --> 00:39:21,760
Speaker 3: Right, you're running the Rue Family Foundation, the Ru Institute.

629
00:39:22,120 --> 00:39:28,120
Speaker 1: You're basically I was not looking to start a new business,

630
00:39:28,760 --> 00:39:33,520
Speaker 1: much less an investment firm. Right. What happened is is

631
00:39:33,560 --> 00:39:37,359
Speaker 1: that my very good friend Anjin Mukherjee, we were talking

632
00:39:37,400 --> 00:39:40,000
Speaker 1: about the future of private equity. During that conversation, we're saying,

633
00:39:40,040 --> 00:39:43,680
Speaker 1: you know, this next generation of private act needs to

634
00:39:43,760 --> 00:39:48,680
Speaker 1: do something different if we want to continue producing the

635
00:39:49,520 --> 00:39:52,840
Speaker 1: super normal levels of profit that we've seen from the

636
00:39:52,880 --> 00:39:58,200
Speaker 1: asset class. Because there's more competition, prices are higher, credits

637
00:39:58,200 --> 00:40:03,040
Speaker 1: more difficult. You can't count a multiple expansion, So you're

638
00:40:03,040 --> 00:40:06,240
Speaker 1: going to have to make the business you buy better

639
00:40:06,880 --> 00:40:09,200
Speaker 1: during the pendency of your ownership. There's only so much

640
00:40:09,239 --> 00:40:13,319
Speaker 1: procurement improvements available. You can only upgrade management so far.

641
00:40:14,520 --> 00:40:17,279
Speaker 1: My observation was this, which is that I said, you

642
00:40:17,280 --> 00:40:21,000
Speaker 1: know this tech thing, it's only ten percent of the economy.

643
00:40:21,120 --> 00:40:23,600
Speaker 1: When you take all of GDP, When you take all

644
00:40:23,640 --> 00:40:27,680
Speaker 1: of semis, all of computing, all of networking, all of software,

645
00:40:27,840 --> 00:40:31,439
Speaker 1: all of social media, it's ten percent of GDP. I said,

646
00:40:31,440 --> 00:40:34,320
Speaker 1: what's going on right now is the other ninety percent

647
00:40:34,760 --> 00:40:39,239
Speaker 1: of the economy is being digitized. Huge opportunity. Now, big

648
00:40:39,280 --> 00:40:44,799
Speaker 1: difference is that now the nature of technology is that

649
00:40:44,840 --> 00:40:48,759
Speaker 1: it's the only capital good that really kind of decreases

650
00:40:48,800 --> 00:40:50,880
Speaker 1: in price and makes itself smaller. Right, So you think

651
00:40:50,920 --> 00:40:53,080
Speaker 1: about what's the difference between now and twenty years ago.

652
00:40:53,800 --> 00:40:58,000
Speaker 1: Now the technology is much smaller, it's much more ubiquitous,

653
00:40:58,200 --> 00:41:01,040
Speaker 1: it's much less expensive, and it's much easier to use.

654
00:41:01,680 --> 00:41:05,480
Speaker 1: All of those things. Mean, it's going to go everywhere.

655
00:41:05,719 --> 00:41:08,200
Speaker 1: So we're talking about this and we're getting ourselves lathered

656
00:41:08,320 --> 00:41:12,759
Speaker 1: up about the fact that all of these analog companies,

657
00:41:13,280 --> 00:41:18,879
Speaker 1: industrial firms, consumer firms, healthcare firms, services companies, they all

658
00:41:18,920 --> 00:41:21,960
Speaker 1: need to adopt more technology, but none of them know

659
00:41:22,040 --> 00:41:25,719
Speaker 1: how opportunity. In plain sight, it's dead obvious that they're

660
00:41:25,760 --> 00:41:29,200
Speaker 1: going to do this, Right, you think about the companies

661
00:41:29,239 --> 00:41:33,600
Speaker 1: that you know in those kind of sectors, that are

662
00:41:33,640 --> 00:41:37,200
Speaker 1: doing well are almost always those that have adopted the

663
00:41:37,200 --> 00:41:41,160
Speaker 1: technology earlier, right, you know, JP Morgan and Finance, or

664
00:41:41,200 --> 00:41:44,719
Speaker 1: Walmart in retail. You know, those companies that get there

665
00:41:44,840 --> 00:41:48,480
Speaker 1: early get a big leg up on their analog competitors.

666
00:41:48,800 --> 00:41:53,160
Speaker 1: We said we could do. We could build an investment

667
00:41:53,280 --> 00:41:58,759
Speaker 1: firm that not only could write a check, but could

668
00:41:58,840 --> 00:42:02,600
Speaker 1: be your technology part partner in helping you architect a

669
00:42:02,640 --> 00:42:08,200
Speaker 1: business model future that would allow you to grow your

670
00:42:08,200 --> 00:42:12,239
Speaker 1: company faster, perform better, you know, produce more profits you know,

671
00:42:12,320 --> 00:42:13,040
Speaker 1: and drive value.

672
00:42:13,120 --> 00:42:16,000
Speaker 3: So let me push back again against one thing you said,

673
00:42:16,040 --> 00:42:19,439
Speaker 3: just a little bit. Please, this opportunity in plain sight.

674
00:42:20,080 --> 00:42:23,759
Speaker 3: If it was really in such plain sight, everybody would

675
00:42:23,800 --> 00:42:26,440
Speaker 3: be doing it. But instead it takes a couple of

676
00:42:26,440 --> 00:42:28,680
Speaker 3: guys with a lot of technology experience, a lot of

677
00:42:28,800 --> 00:42:33,760
Speaker 3: operational experience, and financial experience to make this real.

678
00:42:34,880 --> 00:42:38,399
Speaker 1: I partially agree. Okay, all right, here's the partial part.

679
00:42:38,480 --> 00:42:42,360
Speaker 1: The partial part is is that I think the opportunity

680
00:42:42,960 --> 00:42:46,479
Speaker 1: is easy to see. I think the execution is hard

681
00:42:46,560 --> 00:42:49,759
Speaker 1: is the challenge. So the way I oftentimes say it

682
00:42:49,560 --> 00:42:53,520
Speaker 1: is that it's easy to describe. It's just really hard

683
00:42:53,560 --> 00:42:56,480
Speaker 1: to do. And it's hard to do because you need

684
00:42:56,520 --> 00:43:01,800
Speaker 1: to understand the technology itself. You need to know the vendors,

685
00:43:02,480 --> 00:43:06,319
Speaker 1: you need to be able to set priorities, you need

686
00:43:06,360 --> 00:43:09,120
Speaker 1: to have a realistic sense of time, and you need

687
00:43:09,160 --> 00:43:14,279
Speaker 1: to know how to weave this new technology into the

688
00:43:14,320 --> 00:43:18,200
Speaker 1: processes that already exists. It's not like these companies have

689
00:43:18,239 --> 00:43:22,600
Speaker 1: no tech. Everyone, any company of any scale, has an

690
00:43:22,800 --> 00:43:27,360
Speaker 1: ERP system. They have a bunch of databases. There's compliance issues,

691
00:43:27,440 --> 00:43:31,880
Speaker 1: there's cyber there's all kinds of things, so that you

692
00:43:32,040 --> 00:43:34,360
Speaker 1: have to integrate into what's already there.

693
00:43:34,800 --> 00:43:38,040
Speaker 3: So when I think of private equity, at least from

694
00:43:38,160 --> 00:43:42,359
Speaker 3: the nineties, two thousands, even the twenty tens, I think

695
00:43:42,360 --> 00:43:47,080
Speaker 3: of them as a form of financial engineering to unlock value.

696
00:43:47,200 --> 00:43:52,640
Speaker 3: What you're really describing is digital transformative capital, to steal

697
00:43:53,000 --> 00:43:56,680
Speaker 3: a phrase from your website. So this insight is, hey,

698
00:43:56,719 --> 00:44:00,399
Speaker 3: we don't need to just do financial engineering. You get

699
00:44:00,400 --> 00:44:03,600
Speaker 3: these companies to adapt the latest greatest tech in a

700
00:44:03,640 --> 00:44:07,360
Speaker 3: way that's useful and productive, we can really unlock a

701
00:44:07,400 --> 00:44:11,360
Speaker 3: lot of value. Is that what led to bapine getting

702
00:44:11,400 --> 00:44:14,200
Speaker 3: launched and you kind of coming out of retirement to

703
00:44:15,719 --> 00:44:16,360
Speaker 3: try it again.

704
00:44:16,600 --> 00:44:19,160
Speaker 1: Yes, I mean that was sort of engine calling for

705
00:44:19,200 --> 00:44:20,760
Speaker 1: the lefty from the bull ten.

706
00:44:21,080 --> 00:44:23,240
Speaker 3: Right, right, let's get the lefty.

707
00:44:23,440 --> 00:44:28,480
Speaker 1: Yeah. So it started innocently enough where it was really

708
00:44:28,760 --> 00:44:32,240
Speaker 1: a conversation between two friends with a lot of mutual

709
00:44:32,280 --> 00:44:37,160
Speaker 1: express you know respect where we had a similar you know,

710
00:44:37,280 --> 00:44:40,640
Speaker 1: fifteen twenty year runs in private equity. So we were

711
00:44:40,960 --> 00:44:45,799
Speaker 1: very current, highly topical understanding of what was going on,

712
00:44:47,120 --> 00:44:53,080
Speaker 1: and we realized that we could take and put in

713
00:44:53,200 --> 00:44:57,560
Speaker 1: one place. Really, it's like a binary weapon, right where

714
00:44:58,320 --> 00:45:06,640
Speaker 1: a mooker g quality, world class private equity firm with

715
00:45:07,280 --> 00:45:14,600
Speaker 1: fabulous diligence, great structuring, really thoughtful modeling, you know, great

716
00:45:14,680 --> 00:45:18,040
Speaker 1: financial engineering. We don't want to throw that away. You know,

717
00:45:18,080 --> 00:45:22,879
Speaker 1: those are all valuable lessons. But compline it with the

718
00:45:22,920 --> 00:45:29,279
Speaker 1: operating prowess, tech insight, and extended personal network of relationships

719
00:45:29,760 --> 00:45:33,880
Speaker 1: that would allow us to do things for and on

720
00:45:34,000 --> 00:45:39,160
Speaker 1: behalf of our portfolio companies that simply wouldn't be possible, practical,

721
00:45:39,280 --> 00:45:42,759
Speaker 1: or maybe even imagined by our competitors.

722
00:45:43,239 --> 00:45:49,080
Speaker 3: It sounds like your competitors are the consulting firms who

723
00:45:49,120 --> 00:45:52,600
Speaker 3: come in and you know, kind of seagull an event.

724
00:45:52,680 --> 00:45:55,320
Speaker 3: They come in and they eat everything, they grap all everything,

725
00:45:55,360 --> 00:45:58,479
Speaker 3: they fly away as opposed to you guys not only

726
00:45:58,480 --> 00:46:03,000
Speaker 3: coming in with technology experts, these operational consperitse, but capital

727
00:46:03,080 --> 00:46:07,160
Speaker 3: writing a check. That's a very different relationship than paying

728
00:46:07,160 --> 00:46:07,880
Speaker 3: a consultant.

729
00:46:08,400 --> 00:46:12,239
Speaker 1: Yeah, you know, it's interesting. The consultants actually play a

730
00:46:12,480 --> 00:46:16,520
Speaker 1: very important role, and I wouldn't want to diminish it. Okay,

731
00:46:18,040 --> 00:46:25,160
Speaker 1: around awareness building, and when we go into talk to

732
00:46:25,320 --> 00:46:34,480
Speaker 1: a management team, they almost always have had a consulting encounter, right,

733
00:46:34,840 --> 00:46:38,600
Speaker 1: and they'll have a stack of PowerPoint slides which they'll

734
00:46:38,680 --> 00:46:41,239
Speaker 1: kind of run to their office to show us. That

735
00:46:41,400 --> 00:46:44,400
Speaker 1: says the consultant told me, there are sixteen things that

736
00:46:44,480 --> 00:46:51,120
Speaker 1: I can do with technology, but I don't know which

737
00:46:51,160 --> 00:46:54,560
Speaker 1: one I should do. I don't know what I should

738
00:46:54,600 --> 00:46:56,719
Speaker 1: do first. I don't know who should do it for me.

739
00:46:56,760 --> 00:46:58,880
Speaker 1: I don't know how much should it should cost that

740
00:46:59,280 --> 00:47:01,960
Speaker 1: to take, I don't know how it integrates with what

741
00:47:02,000 --> 00:47:04,400
Speaker 1: I've already got, and I particularly don't know what to

742
00:47:04,440 --> 00:47:07,239
Speaker 1: do if anything goes wrong. Right, And so it's the

743
00:47:07,360 --> 00:47:10,759
Speaker 1: it goes back to the implementation part. And so what

744
00:47:10,800 --> 00:47:14,359
Speaker 1: we like to see is a management team that has

745
00:47:14,920 --> 00:47:22,640
Speaker 1: self awareness and enthusiasm but are not themselves technically fluent.

746
00:47:23,320 --> 00:47:27,040
Speaker 1: Where you know, we can bring that to the party

747
00:47:27,960 --> 00:47:36,160
Speaker 1: in a way that can be catalytic for the management

748
00:47:36,200 --> 00:47:40,120
Speaker 1: team to give them confidence because they have a willingness

749
00:47:40,440 --> 00:47:43,279
Speaker 1: to act. They're just not sure what to do and

750
00:47:43,320 --> 00:47:44,680
Speaker 1: they don't want to do any harmful. They want to

751
00:47:44,719 --> 00:47:48,600
Speaker 1: do something harmful, and so having somebody who's done it before,

752
00:47:48,719 --> 00:47:51,520
Speaker 1: been there, you know, is super useful.

753
00:47:51,640 --> 00:47:54,640
Speaker 3: So, so let's talk about some of your portfolio companies

754
00:47:54,680 --> 00:48:00,640
Speaker 3: and how they're engaging in digital transformation. We're talking about

755
00:48:00,680 --> 00:48:05,320
Speaker 3: AI earlier. How are you guys looking at AI to

756
00:48:05,480 --> 00:48:10,480
Speaker 3: facilitate taking some existing companies and making them more productive.

757
00:48:10,920 --> 00:48:15,720
Speaker 1: Yeah, well, first thing, we could spend a whole session

758
00:48:16,640 --> 00:48:21,560
Speaker 1: on AI. But here's what I would say. First, we

759
00:48:21,680 --> 00:48:27,640
Speaker 1: believe it is actually, despite all the height and notwithstanding

760
00:48:27,719 --> 00:48:31,120
Speaker 1: all the attention, it's already received bigger than most people think.

761
00:48:31,600 --> 00:48:35,000
Speaker 3: Yeah, I'm with you on that. I'll give you a

762
00:48:35,000 --> 00:48:37,720
Speaker 3: funny example please. So I'm in the midst of putting

763
00:48:37,719 --> 00:48:41,799
Speaker 3: together a manuscript and the publisher they're not keen on

764
00:48:42,120 --> 00:48:45,399
Speaker 3: doing an index. Takes a couple of months. You're paying

765
00:48:45,400 --> 00:48:48,560
Speaker 3: a person all this time to look up every name, everything,

766
00:48:48,640 --> 00:48:51,440
Speaker 3: every that for a couple of hundred bucks. There's an

767
00:48:51,480 --> 00:48:57,600
Speaker 3: AI pdf indexer that will identify every proper name in

768
00:48:58,000 --> 00:49:01,640
Speaker 3: four hundred pages and creating an deck relative to and

769
00:49:02,200 --> 00:49:07,920
Speaker 3: I'm just imagining reproducing that sort of dumb mechanical work

770
00:49:08,520 --> 00:49:10,520
Speaker 3: over and over and over again. And I know I'm

771
00:49:10,560 --> 00:49:12,160
Speaker 3: just scratching the surface here.

772
00:49:12,880 --> 00:49:16,759
Speaker 1: It's a great it's a great example. I think that

773
00:49:16,960 --> 00:49:24,000
Speaker 1: right now, most people's experience of AI maybe is a chatbot, right,

774
00:49:24,200 --> 00:49:26,279
Speaker 1: you know, chat GPT or you know.

775
00:49:26,560 --> 00:49:30,279
Speaker 3: Or any any go to any car company, you get

776
00:49:30,280 --> 00:49:32,839
Speaker 3: that pop up and you know that's not a live

777
00:49:32,920 --> 00:49:33,919
Speaker 3: person until the morning.

778
00:49:34,000 --> 00:49:37,800
Speaker 1: What I always say is, just imagine all the best

779
00:49:38,440 --> 00:49:42,200
Speaker 1: AI current ones today. And by the way, the ones

780
00:49:42,239 --> 00:49:44,720
Speaker 1: that you're seeing today are the worst that you will.

781
00:49:44,600 --> 00:49:47,120
Speaker 3: Ever see a little better every day.

782
00:49:47,360 --> 00:49:52,000
Speaker 1: Worst you will ever see. They they read, they write,

783
00:49:52,760 --> 00:49:59,400
Speaker 1: they hear, they see, They can compose poetry, music, in

784
00:49:59,480 --> 00:50:06,080
Speaker 1: any job, photorealistic images, they can create video. All of

785
00:50:06,120 --> 00:50:12,359
Speaker 1: this today, right right, this is all available today. They

786
00:50:12,400 --> 00:50:19,680
Speaker 1: also write computer code as well or better than most programmers.

787
00:50:20,080 --> 00:50:22,920
Speaker 1: They can do complex mathematics, they can solve puzzles, they

788
00:50:22,960 --> 00:50:26,080
Speaker 1: can play games, they can run factories, they can drive cars.

789
00:50:27,400 --> 00:50:37,160
Speaker 1: It is really hard to overestimate what's possible, and we

790
00:50:37,200 --> 00:50:40,520
Speaker 1: are standing really for the first time after decades of

791
00:50:41,000 --> 00:50:45,680
Speaker 1: discussion about it on a you know, on the brink

792
00:50:45,920 --> 00:50:52,520
Speaker 1: of real white collar dramatic white collar productivity games, really dramatic.

793
00:50:53,080 --> 00:50:55,880
Speaker 1: Best example that I would use for you to to

794
00:50:55,960 --> 00:50:58,600
Speaker 1: kind of give you a framework for it, is that

795
00:50:59,120 --> 00:51:01,000
Speaker 1: you're going to see a lot of AI show up

796
00:51:01,200 --> 00:51:05,360
Speaker 1: as features in products that you already use, like you know,

797
00:51:05,400 --> 00:51:09,600
Speaker 1: all your Apple products, right, will have it soon. The

798
00:51:09,800 --> 00:51:11,640
Speaker 1: first thing you get with is probably a product. It

799
00:51:11,680 --> 00:51:14,160
Speaker 1: will be agents, you know, something that works with you,

800
00:51:14,200 --> 00:51:16,440
Speaker 1: like a partner, right, like a writing partner that you

801
00:51:16,480 --> 00:51:18,759
Speaker 1: would use right, sort of a you know, think about

802
00:51:18,760 --> 00:51:21,680
Speaker 1: it's a more advanced version of what you were just describing.

803
00:51:22,400 --> 00:51:25,360
Speaker 1: The best thing out there right now. To illustrate that

804
00:51:25,440 --> 00:51:30,719
Speaker 1: as a product called Copilot from Microsoft, which works with

805
00:51:32,239 --> 00:51:34,880
Speaker 1: a software engineer. You have it running on your machine

806
00:51:35,200 --> 00:51:39,040
Speaker 1: and it's basically a programming buddy that will help you

807
00:51:39,480 --> 00:51:44,000
Speaker 1: write code, suggest different options, you know, help you debug

808
00:51:44,160 --> 00:51:49,800
Speaker 1: track blah blah blah, and it typically improves productivity twenty

809
00:51:49,840 --> 00:51:52,799
Speaker 1: five to fifty percent out of the box, amazing and

810
00:51:52,800 --> 00:51:54,760
Speaker 1: then just and can be up to one hundred percent.

811
00:51:54,960 --> 00:51:55,080
Speaker 3: Right.

812
00:51:56,640 --> 00:52:02,160
Speaker 1: It all by itself has dampen the demand for computer

813
00:52:02,239 --> 00:52:06,120
Speaker 1: programmers really because it's made the ones that we have

814
00:52:06,520 --> 00:52:07,239
Speaker 1: so much better.

815
00:52:07,280 --> 00:52:12,239
Speaker 3: You've just done you've you've doubled the effective productivity up to.

816
00:52:12,400 --> 00:52:16,560
Speaker 1: But think about it as very dramatic, right, you know,

817
00:52:16,640 --> 00:52:19,040
Speaker 1: if you had five, maybe you need four if you

818
00:52:19,040 --> 00:52:26,520
Speaker 1: you know, it's just a really significant, uh improvement, which

819
00:52:27,080 --> 00:52:32,000
Speaker 1: makes it practical to imagine that you're going to be

820
00:52:32,000 --> 00:52:35,400
Speaker 1: able to do this in law firms and accounting firms

821
00:52:35,440 --> 00:52:38,920
Speaker 1: and consulting firms, where you take your average employee and

822
00:52:38,920 --> 00:52:40,080
Speaker 1: make them as good as your best.

823
00:52:40,320 --> 00:52:44,680
Speaker 3: So let's take an old economy company that's not traditionally

824
00:52:44,840 --> 00:52:50,680
Speaker 3: tech oriented. You guys own Mavis Tire Express Services. How

825
00:52:50,680 --> 00:52:55,440
Speaker 3: does a consumer service business like that get digitally transformed?

826
00:52:55,480 --> 00:52:59,839
Speaker 1: How do you model? Walks into our office and said,

827
00:52:59,840 --> 00:53:02,600
Speaker 1: I know everything in the world about tires. I know

828
00:53:02,640 --> 00:53:05,680
Speaker 1: where to buy them, not to store them, not to

829
00:53:05,719 --> 00:53:08,120
Speaker 1: put them on, how to rotate them, I know how

830
00:53:08,120 --> 00:53:11,560
Speaker 1: to balance them, I know how to align them nothing.

831
00:53:11,640 --> 00:53:14,080
Speaker 1: I know everything about tires. I know anything about technology.

832
00:53:14,239 --> 00:53:18,000
Speaker 1: But I have a very strong opinion that technology could

833
00:53:18,040 --> 00:53:20,000
Speaker 1: help my business, and I just don't know where to start.

834
00:53:21,160 --> 00:53:23,360
Speaker 1: I've got He had talked to a bunch of consultants.

835
00:53:23,800 --> 00:53:25,960
Speaker 1: He had lots and lots of ideas, and.

836
00:53:25,960 --> 00:53:28,720
Speaker 3: There were hundreds of these Mayavis stores right.

837
00:53:28,600 --> 00:53:31,680
Speaker 1: On thousands, thousands. There were a thousand Mayvis stores when

838
00:53:31,680 --> 00:53:36,680
Speaker 1: we first started chatting three years ago. So it's a

839
00:53:36,719 --> 00:53:40,200
Speaker 1: you know, it's a good size, it's a good size business,

840
00:53:41,320 --> 00:53:44,719
Speaker 1: very well run, nice growth, profitable. So it wasn't not

841
00:53:44,760 --> 00:53:48,800
Speaker 1: a business that's broken, but a business where the management

842
00:53:48,800 --> 00:53:55,000
Speaker 1: team had a felt need around the opportunity to make

843
00:53:55,000 --> 00:53:58,319
Speaker 1: it better and and and really steal a march on

844
00:53:58,400 --> 00:54:01,480
Speaker 1: their competitors. And so what we did is sit down

845
00:54:01,480 --> 00:54:03,839
Speaker 1: with them and say, look, here are six different use

846
00:54:03,880 --> 00:54:07,360
Speaker 1: cases that you know you might want to think about.

847
00:54:07,800 --> 00:54:12,360
Speaker 1: Here's a way around you know, digital marketing. Here's a

848
00:54:12,400 --> 00:54:17,279
Speaker 1: better customer experience. Here's what you can do around inventory management.

849
00:54:17,800 --> 00:54:26,480
Speaker 1: Here's labor productivity and capacity utilization planning, here's dynamic pricing.

850
00:54:26,760 --> 00:54:30,160
Speaker 1: And we went through an entire kind of you know,

851
00:54:30,360 --> 00:54:35,239
Speaker 1: brainstorming session around that produced a whole plan. So you know,

852
00:54:35,360 --> 00:54:38,480
Speaker 1: usually when you do a new investment, you'll do an underwriting,

853
00:54:39,160 --> 00:54:41,520
Speaker 1: and we do a normal financial underwrite, and like everyone else,

854
00:54:41,600 --> 00:54:45,240
Speaker 1: what's different is we also do in addition a separate

855
00:54:45,680 --> 00:54:49,680
Speaker 1: digital underwriting where we talk with the management team to create,

856
00:54:49,800 --> 00:54:54,719
Speaker 1: you know, a technology roadmap for the enterprise that integrates

857
00:54:54,719 --> 00:54:58,600
Speaker 1: with their business model and extends it to create performance improvements.

858
00:54:59,400 --> 00:55:01,560
Speaker 1: And what we did with them sat down. We got

859
00:55:02,280 --> 00:55:06,479
Speaker 1: better digital marketing so that the search engines optimized for

860
00:55:06,880 --> 00:55:09,120
Speaker 1: if you're calling and writing in I've got I got

861
00:55:09,160 --> 00:55:12,120
Speaker 1: a flat tire and I'm in Poughkeepsie, then here's where

862
00:55:12,160 --> 00:55:18,080
Speaker 1: you go. Improve the customer experience so you know, you

863
00:55:18,120 --> 00:55:21,680
Speaker 1: know when to bring your car in, limit weight times,

864
00:55:22,200 --> 00:55:26,680
Speaker 1: Accurate estimates of how long it is going to take,

865
00:55:27,160 --> 00:55:31,080
Speaker 1: what it's going to cost, what your options are. Dramatically

866
00:55:31,080 --> 00:55:37,960
Speaker 1: improved kind of labor utilization in the shops, capacity utilization,

867
00:55:39,800 --> 00:55:43,839
Speaker 1: got the pricing right so that we manage margins and

868
00:55:43,960 --> 00:55:49,680
Speaker 1: customer expectations appropriately. All of that, some of it we

869
00:55:49,719 --> 00:55:51,800
Speaker 1: could we could get done in two days or two weeks,

870
00:55:51,920 --> 00:55:54,080
Speaker 1: but some of those things has taken us two years

871
00:55:54,840 --> 00:55:58,759
Speaker 1: to put up. The end result, though, is that the

872
00:55:58,800 --> 00:56:03,320
Speaker 1: business is now more than twice as big huh, roughly

873
00:56:03,360 --> 00:56:09,760
Speaker 1: twice as profitable. And that's not all due to the digital,

874
00:56:10,080 --> 00:56:13,880
Speaker 1: but the digital is very fundamentally enabling of that growth

875
00:56:13,880 --> 00:56:15,799
Speaker 1: as you might imagine if you're opening new stores, it's

876
00:56:15,840 --> 00:56:17,520
Speaker 1: a lot easier to do if you do the same

877
00:56:17,520 --> 00:56:18,560
Speaker 1: thing in every single store.

878
00:56:18,880 --> 00:56:22,680
Speaker 3: So let's do it. Talk about another portfolio company, Pollywood

879
00:56:23,120 --> 00:56:27,840
Speaker 3: I Density Palathene outdoor furniture. How can technology improve that?

880
00:56:30,040 --> 00:56:34,400
Speaker 1: You know, it's an interesting business. It's a specialty manufacturing

881
00:56:34,480 --> 00:56:42,040
Speaker 1: company that builds kind of very high quality. It feels

882
00:56:42,080 --> 00:56:49,760
Speaker 1: like wood outdoor furniture, very durable, colorful, but doesn't chip,

883
00:56:50,280 --> 00:56:52,520
Speaker 1: doesn't fade, doesn't need to be painted, doesn't need to

884
00:56:52,520 --> 00:56:54,439
Speaker 1: be painted, you have to take it in during the winter,

885
00:56:54,719 --> 00:56:56,560
Speaker 1: any of those things. So that's sort of the fundamental

886
00:56:56,640 --> 00:57:01,040
Speaker 1: value proposition of the thing. But here's the difference, which

887
00:57:01,120 --> 00:57:05,080
Speaker 1: is that we said, look, you guys are manufacturing guys

888
00:57:05,280 --> 00:57:07,880
Speaker 1: people who built it, and they're really good at that

889
00:57:07,920 --> 00:57:12,279
Speaker 1: because they use recycled plastics, so it's incredibly sustainable. You know,

890
00:57:12,280 --> 00:57:14,080
Speaker 1: they drill the holes, they do the trimming, they just

891
00:57:14,120 --> 00:57:16,160
Speaker 1: take the plastic waste put it back on the top.

892
00:57:16,240 --> 00:57:21,800
Speaker 1: So it's a zero waste, highly sustainable, fantastic story. During COVID,

893
00:57:22,320 --> 00:57:26,440
Speaker 1: they grew their online business a lot. They're not marketing people, right,

894
00:57:26,760 --> 00:57:31,640
Speaker 1: so we're able to show them how to significantly improve

895
00:57:31,920 --> 00:57:37,440
Speaker 1: yield on their online the e commerce side of the

896
00:57:37,440 --> 00:57:40,520
Speaker 1: business we're to and we're able to do that, by

897
00:57:40,520 --> 00:57:44,680
Speaker 1: the way, very quickly, almost instantly around that, able to

898
00:57:45,160 --> 00:57:49,520
Speaker 1: see how to get to new adjacent market areas based

899
00:57:49,520 --> 00:57:52,840
Speaker 1: on finding more people like the ones who are you

900
00:57:52,880 --> 00:57:54,440
Speaker 1: know already buying.

901
00:57:54,560 --> 00:57:56,600
Speaker 3: Once you identify a customer you want to be able

902
00:57:56,600 --> 00:57:57,520
Speaker 3: to identify.

903
00:57:57,120 --> 00:57:59,720
Speaker 1: Once you identify them electronically, then it's a lot easier

904
00:57:59,720 --> 00:58:03,600
Speaker 1: to find that electronic signature similar people and go look

905
00:58:03,680 --> 00:58:07,960
Speaker 1: for it online rather than waiting for people to find you.

906
00:58:08,640 --> 00:58:11,680
Speaker 1: The other thing that we're doing there is that we

907
00:58:11,800 --> 00:58:17,520
Speaker 1: have highly automated manufacturing and so that we can we

908
00:58:17,560 --> 00:58:22,520
Speaker 1: can take the manufacturing and instead of manufacturing twenty or

909
00:58:22,520 --> 00:58:25,600
Speaker 1: two hundred chairs, putting them in a warehouse, sending them

910
00:58:25,640 --> 00:58:28,960
Speaker 1: to a distribution center a store, and hoping somebody buys them,

911
00:58:29,280 --> 00:58:32,959
Speaker 1: we can instead take an order, build the chair, send

912
00:58:32,960 --> 00:58:37,040
Speaker 1: it to them. So it's not just just in time,

913
00:58:37,280 --> 00:58:41,760
Speaker 1: but it's real time. That creates pulls, so that dramatic

914
00:58:41,840 --> 00:58:45,280
Speaker 1: improvements and efficiency, but it also makes it hard easier

915
00:58:45,280 --> 00:58:48,880
Speaker 1: to do custom things. Improves turnaround time, you get your

916
00:58:48,880 --> 00:58:51,320
Speaker 1: furniture much faster. Those would be good examples.

917
00:58:51,400 --> 00:58:54,439
Speaker 3: Huh, really interesting. I only have you for a few

918
00:58:54,440 --> 00:58:57,120
Speaker 3: more minutes, so before I get to my favorite questions,

919
00:58:57,480 --> 00:58:59,640
Speaker 3: let me just ask you one last question. We talked

920
00:58:59,640 --> 00:59:04,200
Speaker 3: about the Real Family Foundation and institute. Briefly, tell us

921
00:59:04,200 --> 00:59:06,520
Speaker 3: a little bit about what you focus on with the

922
00:59:06,560 --> 00:59:07,720
Speaker 3: Real Family Foundation.

923
00:59:09,400 --> 00:59:14,720
Speaker 1: What we like to do is find social entrepreneurs, folks

924
00:59:14,800 --> 00:59:24,720
Speaker 1: who are looking to make scale impact in education, particularly

925
00:59:24,800 --> 00:59:31,640
Speaker 1: educational access, conservation, you know, kind of environmental things, biomedical research,

926
00:59:32,080 --> 00:59:35,600
Speaker 1: and then a particular focus of mind is around helping

927
00:59:36,520 --> 00:59:38,680
Speaker 1: support veterans in their families.

928
00:59:38,880 --> 00:59:41,880
Speaker 3: Really really good stuff. All right, So this will be

929
00:59:41,960 --> 00:59:44,600
Speaker 3: our speed round. I have about four minutes five minutes

930
00:59:44,960 --> 00:59:47,880
Speaker 3: to get through five questions. Let's just do this quickly.

931
00:59:48,920 --> 00:59:51,240
Speaker 3: What's keeping you entertained these days? What are you watching

932
00:59:51,480 --> 00:59:52,240
Speaker 3: or listening to?

933
00:59:54,200 --> 00:59:57,600
Speaker 1: Right now? My wife and I are watching The Lioness

934
00:59:57,640 --> 00:59:58,520
Speaker 1: and The Diplomat.

935
01:00:00,160 --> 01:00:03,560
Speaker 3: We're about halfway through The Diplomat, so no spoilers. Season

936
01:00:03,600 --> 01:00:04,480
Speaker 3: two good.

937
01:00:05,160 --> 01:00:11,200
Speaker 1: We loved The Crown, and I am waiting anxiously for

938
01:00:11,320 --> 01:00:16,600
Speaker 1: season two of wolf Hall. The Henry the Eighth and

939
01:00:16,680 --> 01:00:22,560
Speaker 1: Thomas Cromwell story. In podcast land, my current favorite is

940
01:00:22,680 --> 01:00:25,640
Speaker 1: Fall of Civilizations by Paul Cooper.

941
01:00:26,160 --> 01:00:29,680
Speaker 3: Interesting. Tell us about your mentors who helped to shape

942
01:00:29,720 --> 01:00:30,280
Speaker 3: your career.

943
01:00:31,600 --> 01:00:34,040
Speaker 1: You know, I've had a couple. I've been very fortunate.

944
01:00:34,920 --> 01:00:36,760
Speaker 1: Early guy was a guy named Chuck Glover is a

945
01:00:36,800 --> 01:00:41,560
Speaker 1: newspaper guy who ended up running Cox Enterprises, the media

946
01:00:41,680 --> 01:00:47,840
Speaker 1: company he funded my Cox funded my first company. He

947
01:00:48,040 --> 01:00:53,080
Speaker 1: was a newspaper guy, and the key lesson from him was, look,

948
01:00:53,120 --> 01:00:55,680
Speaker 1: I had to put out a product every day, and

949
01:00:55,760 --> 01:00:59,520
Speaker 1: so just getting in the habit of putting one foot

950
01:00:59,520 --> 01:01:02,000
Speaker 1: in front of the other, making a little bit of

951
01:01:02,040 --> 01:01:06,640
Speaker 1: progress every day and just keep going was really valuable.

952
01:01:06,640 --> 01:01:09,440
Speaker 1: The other guy who was great for me was doctor

953
01:01:09,520 --> 01:01:14,360
Speaker 1: Frank King. What I learned from Frank he was the

954
01:01:14,400 --> 01:01:17,160
Speaker 1: head of engineering at Lotus and it had a similar

955
01:01:17,240 --> 01:01:20,520
Speaker 1: job at IBM before that. What I learned from him

956
01:01:20,720 --> 01:01:22,960
Speaker 1: was that the people were more important than the.

957
01:01:22,880 --> 01:01:30,480
Speaker 2: Products, and that building your organization primacy of people and

958
01:01:30,560 --> 01:01:35,640
Speaker 2: particularly always being recruit you know, always recruiting, being kind

959
01:01:35,680 --> 01:01:39,520
Speaker 2: of on the pro all the time was super valuable.

960
01:01:39,680 --> 01:01:41,760
Speaker 3: Let's talk about books. What are some of your favorites

961
01:01:41,760 --> 01:01:42,680
Speaker 3: and what are you reading? Carl.

962
01:01:44,240 --> 01:01:49,720
Speaker 1: I'm a Mark Halpern fan. I loved Paris in the

963
01:01:49,760 --> 01:01:55,920
Speaker 1: present tense. I like Don Winslow. City on Fire is

964
01:01:55,960 --> 01:01:58,960
Speaker 1: one of my favorite books, first in a trilogy with

965
01:01:59,400 --> 01:02:03,400
Speaker 1: City of Design, City of City in Ruins. I like

966
01:02:03,480 --> 01:02:10,200
Speaker 1: Anything by Dennis Lahane, Anything by Elizabeth Strout, and I'm

967
01:02:10,280 --> 01:02:16,000
Speaker 1: currently just finished The Magician by Edmundavaal Just Reading The

968
01:02:16,080 --> 01:02:20,200
Speaker 1: Hair with amberis also by Edmund de vaud Interesting.

969
01:02:20,880 --> 01:02:23,680
Speaker 3: Our final two questions what sort of advice would you

970
01:02:23,680 --> 01:02:26,640
Speaker 3: give a recent college grad interested in a career in

971
01:02:26,760 --> 01:02:29,480
Speaker 3: either private equity or technology.

972
01:02:30,960 --> 01:02:32,440
Speaker 1: You know, I always tell them the same thing. I

973
01:02:32,480 --> 01:02:37,200
Speaker 1: always tell them to do something else first. And I

974
01:02:37,240 --> 01:02:41,680
Speaker 1: say that because I'm a great believer in domain expertise,

975
01:02:43,280 --> 01:02:46,800
Speaker 1: and so I usually counsel younger folks coming out of

976
01:02:46,840 --> 01:02:52,040
Speaker 1: school to go learn an industry and or learn a craft.

977
01:02:52,120 --> 01:02:54,600
Speaker 1: Learn a skill, you know, be good at marketing, be

978
01:02:54,680 --> 01:02:58,959
Speaker 1: good at sales, you know, be good at finance. Pick

979
01:02:59,120 --> 01:03:02,040
Speaker 1: something where you're really good at it, because it gives

980
01:03:02,080 --> 01:03:07,040
Speaker 1: you a cachet and a standing that you don't otherwise have.

981
01:03:07,280 --> 01:03:10,120
Speaker 3: H And our final question, what do you know about

982
01:03:10,160 --> 01:03:13,040
Speaker 3: the world of private equity investing today that might have

983
01:03:13,120 --> 01:03:15,880
Speaker 3: been helpful back in nineteen ninety nine when you were

984
01:03:15,880 --> 01:03:17,280
Speaker 3: first standing up Silver.

985
01:03:17,160 --> 01:03:23,720
Speaker 1: Lake I wish I knew how important it was to

986
01:03:23,800 --> 01:03:24,480
Speaker 1: be first.

987
01:03:25,240 --> 01:03:28,000
Speaker 3: Really, huh, that was interesting.

988
01:03:28,080 --> 01:03:34,840
Speaker 1: I think as an operating person, I probably intuited it

989
01:03:35,040 --> 01:03:38,040
Speaker 1: and understood it because I kind of saw it around me.

990
01:03:38,560 --> 01:03:41,560
Speaker 1: The advantages that a crew to a you know, a

991
01:03:41,600 --> 01:03:45,200
Speaker 1: category leader, you just don't need to be as good,

992
01:03:45,640 --> 01:03:50,280
Speaker 1: you know, think about think about Elon Musk. You know,

993
01:03:50,440 --> 01:03:54,760
Speaker 1: his first electric car was a bundle of borrowed parts

994
01:03:54,760 --> 01:03:59,640
Speaker 1: and components. It barely worked, It was hugely expensive.

995
01:04:00,120 --> 01:04:03,080
Speaker 3: Literally a lotus alon with laptop batteries.

996
01:04:02,800 --> 01:04:06,000
Speaker 1: With laptop batteries in it, wired together with you know,

997
01:04:06,040 --> 01:04:10,520
Speaker 1: with soldering wire, cost of fortune, incredibly uncomfortable to drive,

998
01:04:10,680 --> 01:04:12,560
Speaker 1: totally unreliable.

999
01:04:13,080 --> 01:04:14,120
Speaker 3: Got to start somewhere.

1000
01:04:14,400 --> 01:04:17,200
Speaker 1: But he was able to do that for years and

1001
01:04:17,320 --> 01:04:21,640
Speaker 1: years and years and learn and learn and develop, you know,

1002
01:04:21,760 --> 01:04:25,800
Speaker 1: and expertise and some skills. Same thing's true for if

1003
01:04:25,800 --> 01:04:28,480
Speaker 1: you think about it, Jeff Bezos right, selling books that

1004
01:04:28,520 --> 01:04:33,120
Speaker 1: no one wanted, losing money hand over fist for a decade,

1005
01:04:34,080 --> 01:04:40,640
Speaker 1: but building infrastructure, building experience, learning lessons, you know, creating

1006
01:04:40,680 --> 01:04:44,520
Speaker 1: a team that became the basis for you know, both

1007
01:04:44,560 --> 01:04:47,320
Speaker 1: of those things didn't work until they did, and boy,

1008
01:04:47,320 --> 01:04:48,680
Speaker 1: when they worked, They really worked great.

1009
01:04:48,760 --> 01:04:52,120
Speaker 3: They really were. Thank you, David for being so generous

1010
01:04:52,160 --> 01:04:55,320
Speaker 3: with your time. We have been speaking with David Route.

1011
01:04:55,640 --> 01:04:59,720
Speaker 3: He is the executive chairman of Baypine, a private equity

1012
01:04:59,720 --> 01:05:04,720
Speaker 3: firm focused on digital transformation. If you enjoy this conversation,

1013
01:05:04,880 --> 01:05:07,640
Speaker 3: well check out any of the five hundred plus discussions

1014
01:05:07,640 --> 01:05:10,600
Speaker 3: we've had over the past ten years. You can find

1015
01:05:10,600 --> 01:05:15,040
Speaker 3: those at iTunes, Spotify, YouTube, Bloomberg, wherever you find your

1016
01:05:15,080 --> 01:05:19,760
Speaker 3: favorite podcasts, and check out my new podcast At the Money,

1017
01:05:20,240 --> 01:05:26,080
Speaker 3: short discussions with experts on specific topics involving your money,

1018
01:05:26,720 --> 01:05:30,160
Speaker 3: earning it, spending it, and most importantly, investing in it.

1019
01:05:30,640 --> 01:05:34,040
Speaker 3: At the Money, wherever you find your favorite podcasts, and

1020
01:05:34,200 --> 01:05:38,120
Speaker 3: in the Masters in Business feed I would be romiss

1021
01:05:38,120 --> 01:05:39,720
Speaker 3: if I do not thank the crack team that helps

1022
01:05:39,760 --> 01:05:43,520
Speaker 3: with these conversations together each week. Anna Luke is my producer,

1023
01:05:43,800 --> 01:05:48,000
Speaker 3: Sean Russo is my researcher. Sage Bauman is the head

1024
01:05:48,000 --> 01:05:51,680
Speaker 3: of podcasts here at Bloomberg. I'm Barry Retults. You've been

1025
01:05:51,720 --> 01:06:01,160
Speaker 3: listening to Masters and Business on Bloomberg Radio