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Speaker 1: Masters in Business is brought to you by the American

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Speaker 1: Arbitration Association. Business disputes are inevitable, resolve faster with the

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Speaker 1: American Arbitration Association, the global leader in alternative dispute resolution

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Speaker 1: for over ninety years. Learn more at a d R

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Speaker 1: dot org. My special guest today is Richard Barton. He

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Speaker 1: has quite the curriculum vita. He is the founder of

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Speaker 1: numerous and highly regarded startups, including Expedia, Zillo, and glass Door.

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Speaker 1: He is on the board of directors at Netflix, and

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Speaker 1: he is a partner at the venture capital firm Benchmark Partners.

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Speaker 1: Richard Barton, Welcome to Bloomberg. Thank you very much for

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Speaker 1: having me on. So I've been looking forward to chatting

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Speaker 1: with you about so many different things. I have to

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Speaker 1: start though, there's such a Your background is so start

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Speaker 1: up oriented, and yet you graduate Stanford at eighty nine

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Speaker 1: with a degree in engineering you end up at Microsoft.

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Speaker 1: What was it like working in Microsoft in the midst

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Speaker 1: of the nineties when everything was on five? You know

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Speaker 1: the power we with which we view Facebook and Google

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Speaker 1: and Apple today, Imagine all that power wrapped up into

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Speaker 1: one company and one company. That's what Microsoft was with

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Speaker 1: one person at the helm, who really was running the show,

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Speaker 1: who really was running the show involved deeply. When I

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Speaker 1: showed up, there were about three thousand employees, so it

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Speaker 1: was relatively relatively small company. Um. Now, tech wasn't as

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Speaker 1: pervasive as it is today, but Microsoft was by far

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Speaker 1: the most important company in the technology world and basically

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Speaker 1: building the future. It was quite exciting that, that's right

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Speaker 1: at the process as tech became more pervasive meant that

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Speaker 1: a ton of money was flowing into their coffers. And

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Speaker 1: before the iPhone there was really nothing ever like Microsoft before.

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Speaker 1: That's right, I mean IBM. You could argue em had

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Speaker 1: a pretty good position prior to Microsoft, not on the

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Speaker 1: consumer side, but never on the business. And it wasn't

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Speaker 1: as big a business, right, It was a much smaller business.

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Speaker 1: And it was whether we're talking typewriters or mainframes, there

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Speaker 1: was still an aspect. It wasn't the dominant technology of

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Speaker 1: the day. So so what did you do with Microsoft?

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Speaker 1: My first job at Microsoft was product manager of MS

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Speaker 1: DOS five. So you're the person I should have been

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Speaker 1: sending those nasty letters. Do you even do? Do you

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Speaker 1: know what the big feature for MS DOS five? I

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Speaker 1: do that much of a geek. Uh was this pre shell?

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Speaker 1: Is that what we're talking? Well, there was a shell

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Speaker 1: that we have this weird shell. There was no Windows,

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Speaker 1: way before there was no Windows. But I feel like

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Speaker 1: three one as we're really that's that's Windows three point one,

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Speaker 1: which was a couple of years after Horrible that. I

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Speaker 1: remember that well, it was a big event. These were

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Speaker 1: consumer ending events. And MS DOS five upgrade was the

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Speaker 1: first operating system that Microsoft sold at retail as an upgrade,

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Speaker 1: and they did it to break the six forty K barrier,

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Speaker 1: which they are about five percent of your listeners right

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Speaker 1: now that the computer was using and got it. I'm

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Speaker 1: m dot sis. And what this enabled was these older

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Speaker 1: PCs that were getting obsolete to load up this this

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Speaker 1: new upgrade, which was a very hard process to do,

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Speaker 1: kind of imagine lifting a house, pouring a new foundation

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Speaker 1: and dropping the house back down onto It was hard,

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Speaker 1: but it allowed them to run bigger programs faster, and

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Speaker 1: people lined up around the block at midnight outside of Egghead,

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Speaker 1: So I remember that. I mean, it was nothing like

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Speaker 1: when you know the Rolling Stones. Start Me Up was

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Speaker 1: the theme song, and it just went utterly crazy. I

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Speaker 1: was the lonely Mac guy during that era and actually

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Speaker 1: thrilled when when the deal was cut in ninety eight,

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Speaker 1: jobs came back and Gates essentially, I think intelligently made

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Speaker 1: the bet that hey, if Apples around, we're less of

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Speaker 1: a monopoly because look, there's a credible consumer competitor, and

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Speaker 1: that's why that deal was made. That investment. Yeah, a

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Speaker 1: guy named Greg Maffei who I'm on the board of

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Speaker 1: Liberty Interactive as well, and Greg is on my board

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Speaker 1: at Zillo. He was my first chairman at Expedia. He's

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Speaker 1: the guy that led that investment, uh, for Microsoft and Apple.

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Speaker 1: You know, it's too bad Microsoft sold that position. It

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Speaker 1: would be worth um quite a large time. I'm told

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Speaker 1: they did well. Microsoft. I'm told they were accounting reasons.

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Speaker 1: Why once it just starts becoming an outsized portion of

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Speaker 1: the balance sheet, it's all right, this is problematic. Let's

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Speaker 1: just liquid dated. Not that they needed the money. They've

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Speaker 1: been a cash machine from day one. So, so you've

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Speaker 1: been a CEO at a public company. What is that

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Speaker 1: process like and and why are so many companies reluctant

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Speaker 1: to go public these days. I'm one of these guys

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Speaker 1: who thinks when you take a company public, you're making

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Speaker 1: it to the majors. It's like going from the minors

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Speaker 1: to the majors. And you know, I grew up in

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Speaker 1: a time when that was the dream. Uh. Plus, your

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Speaker 1: backers would like to eventually. And I actually feel a

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Speaker 1: responsibility to the people that actually invested in my companies

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Speaker 1: and by the way, my employees, who I've paid in

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Speaker 1: stock options in part. And so I believe it's my

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Speaker 1: responsibility as an entrepreneur to figure out how to get

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Speaker 1: a company public so that you know, to reward these people.

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Speaker 1: But I also believe it creates optionality. And when you

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Speaker 1: have optionality to buy companies with stock, to issue stock,

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Speaker 1: and to to raise cash, it gives it creates value.

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Speaker 1: Optionality creates value. And so I'm a big fan of

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Speaker 1: being public. I also believe that this quarterly reporting stuff

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Speaker 1: is great discipline and having auditors in your operation. You know,

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Speaker 1: even me as a CEO, I'm happy to have auditors

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Speaker 1: and they're just to kind of be digging through and

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Speaker 1: looking for stuff. And and now I understand why you've

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Speaker 1: been the IT of three successful companies, because you understand

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Speaker 1: things that other people seem to just complain about. When

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Speaker 1: I when we see companies startups say it's too onerous,

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Speaker 1: it's too invasive to go public. What's your response to that.

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Speaker 1: I mean, can you imagine Tom Brady in college? Where

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Speaker 1: was he in Michigan? I think saying I don't want

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Speaker 1: to go pro. You know, you know, it's too much pressure. Everybody.

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Speaker 1: They're writing about me. There are people on the radio

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Speaker 1: making fun of me and how I played over the week.

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Speaker 1: You imagine that anyway, that's kind of the pervasive attitude

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Speaker 1: right now. Our vcs, ore vcs encouraging that attitude or

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Speaker 1: do they want to go public as much as the

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Speaker 1: next well earlier stage vcs absolutely want their companies to

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Speaker 1: be public so that they can get liquid their business. Right.

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Speaker 1: The the issue is there's been in the world's a

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Speaker 1: Washington capital as as you and your listeners well now,

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Speaker 1: and there's been a flood money into late stage private investing, okay,

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Speaker 1: and those people don't want this IPO problem to go

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Speaker 1: away because they're getting to invest that they're getting to

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Speaker 1: write hundreds of millions of dollars, if not billions of

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Speaker 1: dollars checks and companies like Uber to get into the

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Speaker 1: cap table before the public does. And so that flood

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Speaker 1: of capital into late stage private has made it easy

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Speaker 1: for an entrepreneur to say, uh, I'm not you know,

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Speaker 1: I'm not gonna go public. I'm gonna stay private. They're

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Speaker 1: letting them cash stock out their founders that in these

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Speaker 1: late stage deals they get to sell stock, and so, well,

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Speaker 1: what incentive do I have? You know, if the CEO

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Speaker 1: is is getting liquid prior to being public, then what

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Speaker 1: incentive does the CEO have to actually get public? So

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Speaker 1: there's a there are a myriad of problems. I guess

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Speaker 1: I would also it could be an interesting conversation depending

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Speaker 1: on how you feel about it. But I feel I

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Speaker 1: feel that the the the regulatory burden is and the

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Speaker 1: legal burden is high. Okay. Now, any one regulation, if

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Speaker 1: you looked at it on its own, UM makes sense. Okay,

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Speaker 1: any of these regulations, but in totality it is quite crushing. Uh.

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Speaker 1: And the lawsuits that that that ensued because of these

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Speaker 1: regulations as well, that is quite time consuming. And a

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Speaker 1: lot of people look at that and they say, well,

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Speaker 1: I'm not gonna be public. Michael Mobison, who I know

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Speaker 1: you've had on published paper. He's terrific. He published a

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Speaker 1: paper six or eight months ago, Uh, basically saying they're

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Speaker 1: they're less than half of the companies. They're less than

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Speaker 1: half the number of public companies today as there were

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Speaker 1: fifteen years ago. I may I may have that wrong,

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Speaker 1: but some okay, Um, we have all the public exactly okay.

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Speaker 1: And so they I think that the the industry if

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Speaker 1: they're lobbying for more shareholder activism and more ability to

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Speaker 1: put people onto boards. Uh. And as as as this

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Speaker 1: money floods into activist funds, what they're doing actually is

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Speaker 1: kind of killing the golden goose. They're they're driving companies

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Speaker 1: out of the out of the public markets. So let's

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Speaker 1: talk a little bit about Zello, which I find to

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Speaker 1: be absolutely fascinating. First, what inspired this, right, I mean,

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Speaker 1: it's a segment too. You're not the only one that

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Speaker 1: finds it fascinating. Very We get over one hundred and

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Speaker 1: seventy million visitors a month to our portfolio of sites,

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Speaker 1: and we we don't only truly a street Easy here

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Speaker 1: in New York City, which was a really really great

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Speaker 1: acquisition nobody noticed. I mean, you don't have multiple listing

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Speaker 1: services in big cities. It's a very closely held thing,

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Speaker 1: and they're one of the few entities that track big city.

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Speaker 1: I know they started in New York. They did. Then

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Speaker 1: they nailed this vertical living, yeah, really did. But they

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Speaker 1: had trouble expanding me on New York. Uh, and so

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Speaker 1: we met up with them. We got along with um.

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Speaker 1: The founders wanted to get some liquidity and we and

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Speaker 1: we bought it maybe five years ago to that was

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Speaker 1: a really wonderful thing. And there's another brand called Hot

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Speaker 1: Pads and Naked Apartments that we own, so we have

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Speaker 1: a portfolio of brands. What got it going, however, was

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Speaker 1: my personal frustration with not being able. I was shopping

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Speaker 1: for a home my family. I just had twins. You

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Speaker 1: live where no part of the country. I live in Seattle, Washington,

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Speaker 1: lovely part of the from the world, one kid to three,

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Speaker 1: and I was shopping my my co founder and I

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Speaker 1: were shopping for homes together. We both just left Expedia

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Speaker 1: after having a really successful leadership, and so we're looking

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Speaker 1: for homes and we can't believe. And this is like

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Speaker 1: two thousand four, two five modern era. It's like the

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Speaker 1: web has been around for at least ten years at

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Speaker 1: this point, and so where's all the information? What where

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Speaker 1: is it? It's like it's like it's like it was

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Speaker 1: when we started expediing it. It's like, I can't access

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Speaker 1: that the information that I want to see prices, I

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Speaker 1: want to see schedules. I want to do it myself.

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Speaker 1: I want to take control. People want control. I want

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Speaker 1: to control. My partner, Lloyd wanted control. And we said,

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Speaker 1: all right, well this is broken. You know there's something

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Speaker 1: going on here that's broken because this information, if you

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Speaker 1: dig hard enough, is available. And so we built spreadsheets,

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Speaker 1: We went to the county websites. We started doing dollars

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Speaker 1: per square foot. So we're just trying to figure out

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Speaker 1: what we should pay for a house. And so Zillo

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Speaker 1: was born of this, this frustration and this desire for

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Speaker 1: to to be empowered with information. And once we got

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Speaker 1: it all together and we figured it out, we're like, well,

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Speaker 1: why should you have to be a spreadsheet geek like us,

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Speaker 1: uh to be able to get this and we said

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Speaker 1: we kind of slapped our heads. We found this estimate.

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Speaker 1: We kind of stumbled into this estimate UM. And once

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Speaker 1: we stumbled into thet this estimate is an estimated market

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Speaker 1: value of every home in the country that we update

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Speaker 1: just about every night. UM. We kind of had on

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Speaker 1: the whiteboard we had a stock a stock chart. So

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Speaker 1: this is this isn't this isn't a time when the

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Speaker 1: real estate business was going, you know, the real and

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Speaker 1: everybody's most significant asset if they owned a home, was

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Speaker 1: their home, and people were wanted to know what their

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Speaker 1: home was worth. So they're watching the market, and so

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Speaker 1: we thought, well, why shouldn't you track the value of

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Speaker 1: a home like you like, like you track your portfolio.

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Speaker 1: But you guys pivoted complete letting. His estimate is still

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Speaker 1: part of the ZILO. But I would say it's it's

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Speaker 1: now probably the least significant daily usage. That a fair

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Speaker 1: way to describe it. I would say that is a

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Speaker 1: fair way to describe it. It's mostly people who are

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Speaker 1: shopping for rentals and mortgages and homes and people who

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Speaker 1: are selling those properties. But but we envisioned right from

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Speaker 1: the start that this estimate, which was a missing key

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Speaker 1: piece of marketplace data, could in fact be at the

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Speaker 1: core of a new real estate marketplace, a digital one.

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Speaker 1: So the question, I have so many questions about this subject.

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Speaker 1: The data that you wanted was clearly in existence, but

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Speaker 1: it was control by the National Association of Realtors. I

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Speaker 1: believe they're they own the multiple listing service, or they're

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Speaker 1: affiliated with it. Not really, actually it's quite separate. In fact,

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Speaker 1: there is not even one MLS. There are nine in

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Speaker 1: their own by broke regional brokerages generally speaking. But the

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Speaker 1: n A R has clearly has influence, and you guys

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Speaker 1: eventually tapped into all of that. UM, we eventually got

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Speaker 1: all that data. But what we did was we we

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Speaker 1: didn't show up to the pot luck with just a fork.

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Speaker 1: Dave Linegar, the founder of Remax, is a colorful fellow.

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Speaker 1: Uh upbraided me publicly early on by telling me I

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Speaker 1: had shown up to the pot luck with just a fork,

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Speaker 1: because if I could come into his business and just

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Speaker 1: use his data and then get users and then turn

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Speaker 1: around and charge charge him for advertising. But that's not

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Speaker 1: the case. We brought pot roast and marshmallow jello salad.

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Speaker 1: We brought this estimate, and we brought this huge database

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Speaker 1: of all homes, not just the small number it's for

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Speaker 1: sale right now, but a database of all homes. And

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Speaker 1: it was a living one so people could add to it.

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Speaker 1: A new data kept coming in and finally, UH the industry,

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Speaker 1: in a very complementary way, realized that this data was

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Speaker 1: really important to buyers and sellers and agents alike, and

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Speaker 1: eventually everybody ended up participating. So the Zilla, I'm not exaggerating.

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Speaker 1: The Zillo app is literally on the home screen one.

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Speaker 1: Is it screen one? A screen too? No, that is

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Speaker 1: screen one. There you go. It's um like Zilo, son

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Speaker 1: nos and uh sky something I don't remember it is

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Speaker 1: um So let me three of I don't EXPEDI is

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Speaker 1: sort of before my time. I know that sounds ridiculous.

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Speaker 1: We'll talk about Expedia soon. The three ways I use

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Speaker 1: Zillo constantly constantly. First, any time you're driving in a

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Speaker 1: neighborhood and you see a nice house or something right,

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Speaker 1: and I love to just you know, my mother was

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Speaker 1: a real estate agent. We own a couple of homes.

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Speaker 1: I'm very much your target real estate junkie audience. So

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Speaker 1: outcomes the app. And as long as there's some connectivity,

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Speaker 1: UM mobile telecom in United States is an embarrassment, but

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Speaker 1: we'll save that for another podcast. Hey, they're all the

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Speaker 1: houses that are for sale recently sold, and you could

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Speaker 1: you could see the prices and what have you. It's

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Speaker 1: really fun to do that on a boat out in

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Speaker 1: harbor because you could actually see the houses that you

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Speaker 1: can't see from the road, like they're down driveways, behind edges,

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Speaker 1: they're invisible. That's a ton of fun. How do you

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Speaker 1: find people generally use the app? And what I mean

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Speaker 1: by that The obvious is I'm looking for a house.

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Speaker 1: What other ways have come up? That might have been

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Speaker 1: someone unexpected? H The development people and universities and philanthropies

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Speaker 1: use it to target to target people. That's interesting, divorce attorneys,

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Speaker 1: I'm I'm kind of joking. Uh. There are so many

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Speaker 1: uses for Zilla, both as an entertainment, as as information

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Speaker 1: and as entertainment and to inform a shopping process, be

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Speaker 1: it a casual shopping process or a or a serious

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Speaker 1: shopping process. Not to mention, we're the largest rental site

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Speaker 1: network of rental sites now, so you know most most

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Speaker 1: of the growth in households right now is going to rentals,

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Speaker 1: and so rentals has become quite a big business. Always

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Speaker 1: has been UM and mortgage shopping people do. But it

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Speaker 1: turns out that our homes are not only where we

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Speaker 1: lay our head, they are our entertainment. Look no further

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Speaker 1: than HGTV and all these worm shows on they are entertainment.

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Speaker 1: We love to track the value of it too, because

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Speaker 1: it's for many of us, our our biggest asset. And

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Speaker 1: so this this kind of intertwining of emotion and financial importance.

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Speaker 1: I kind of view it as a year and a yang.

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Speaker 1: This is really quite a sweet spot in consumer products

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Speaker 1: that I look for. When you have both emotion and

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Speaker 1: financial significance, you have a potential huge idea. So we

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Speaker 1: moved a couple of years ago, and we when we

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Speaker 1: were selling the previous house, we brought in two different

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Speaker 1: real estate agents and we surprisingly received wildly disparate estimates

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Speaker 1: from each of them. And again, my mom was real

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Speaker 1: estate agent. I grew up with dinner table conversation about

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Speaker 1: what terrible people some agents um, what terrible behaviors some

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Speaker 1: agents exhibit. I mean that was literally dinner conversation. And

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Speaker 1: so when we got these different estimates, I said, let

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Speaker 1: me play with Zello a little bit and see what

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Speaker 1: I can do. And the first thing I did was

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Speaker 1: come up with a I searched for our house number

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Speaker 1: of bedrooms. Bathrooms were a kitchen deck proximity to the water.

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Speaker 1: I tried to make it as close as humanly possible

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Speaker 1: as I could, and then that generated two lists. It

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Speaker 1: generated the list of homes that were for sale in

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Speaker 1: my neighborhood and surrounding towns within my price point. But

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Speaker 1: it also generated a second list of homes that were

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Speaker 1: sold with the same characteristics. And so you get these

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Speaker 1: two data sets, and then you look at the overlap

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Speaker 1: and lo and behold, if you want to sell your

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Speaker 1: price at them, the get the maximum return on the

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Speaker 1: sale in the fastest amount of time. You could wait

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Speaker 1: six months and maybe get another ten or twenty grand.

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Speaker 1: But if you want to be right in the sweet spot,

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Speaker 1: and we literally sold the house within ten thousand dollars

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Speaker 1: of ask in three weeks. And I have to give

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Speaker 1: thanks to you, guys, because that would not have happened

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Speaker 1: without you, guys. Very that scenario is exactly why we

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Speaker 1: built Solo. That's exactly it. You were fully empowered to

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Speaker 1: look at all that data yourself in a way that

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Speaker 1: you could understand it and didn't exist a few years

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Speaker 1: you weren't able to access that information before. That is

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Speaker 1: power to the people. I love, love, love that we

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Speaker 1: really have this you know, this ethic at this think

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Speaker 1: at Zillo and all my companies, where we want to

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Speaker 1: empower regular people with data and tools so that they

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Speaker 1: can make much better decisions about big, important financial stuff.

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Speaker 1: Let's talk a little bit about Expedia, because that's a

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Speaker 1: fascinating story. You're working at Microsoft. They are not exactly

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Speaker 1: a startup culture by that point. They're they're the eight

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Speaker 1: pound guerrilla. How did Expedia come about? I always had

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Speaker 1: the itch to have my own thing. I always wanted

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Speaker 1: to start my own company, and I was going to

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Speaker 1: do it somewhere um and the Internet was just happening.

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Speaker 1: The Internet hadn't quite the graphical Web hadn't happened yet

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Speaker 1: pre Netscape, so this is pre nat scape. But post

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Speaker 1: Prodigy and a O l okay so compy survey, well Prodigy,

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Speaker 1: these were the big services, and I was fascinated by them.

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Speaker 1: I really thought, Wow, these are empowering, this is something

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Speaker 1: some thing interesting is going to happen here, and this

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Speaker 1: tool here could lead to some new industrial revolution. I

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Speaker 1: was thinking pretty grandly, like what could happen to the

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Speaker 1: travel industry? Uh? If people had access to all this data?

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Speaker 1: What could happen to real estate? I was even thinking

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Speaker 1: about real estate back then. I wrote a plan to

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Speaker 1: to have an electronic stockbroker because I like to trade.

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Speaker 1: Trade and investor is pre e trade. This is pre

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Speaker 1: e trade, but but e trade didn't even exist. But

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Speaker 1: I was a Schwab customer, and I was one of

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Speaker 1: those Schwab guys that used telebroker. I would trade with

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Speaker 1: my finger, you know, on the on the keypad, because

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Speaker 1: I don't really want to talk to somebody. I knew

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Speaker 1: how to do it myself. I wanted control anyway, So

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Speaker 1: I envisioned that this online thing might turn into something

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Speaker 1: pretty interesting. I wanted to be an entrepreneur. I was

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Speaker 1: especially frustrated as a traveling young businessperson with the interactions

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Speaker 1: I was having with my Microsoft corporate travel agent. I

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Speaker 1: could someone in house who would book travel for in

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Speaker 1: house or working for American Express, but basically assigned to

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Speaker 1: the Microsoft account, and I'd call her up. Usually it

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Speaker 1: was it was a woman, but there were, you know,

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Speaker 1: many different people, and I'd hear the keyboard clicking, you know,

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Speaker 1: as I was asking questions and Seattle, then Denver, and

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Speaker 1: then I gotta go up to Chicago, back to Seattle,

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Speaker 1: and I'd hear these keyboard clicking. I'm like, you know,

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Speaker 1: she answered me with one choice, and I'd be like,

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Speaker 1: well that it can't be the only choice. You've got

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Speaker 1: a screen full of stuff in front of you. So

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Speaker 1: I wanted to jump through the phone line, turned the

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Speaker 1: screen towards me and take control of myself. Okay, So

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Speaker 1: that was the inception of me being going to UH

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Speaker 1: to Bill Gates, who I view as my first venture capitalist,

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Speaker 1: and Steve Bomber was in the room too, UH and

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Speaker 1: pitching the idea for for what became Expedient. So tell

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Speaker 1: us about that meeting. That has to be a seminal

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Speaker 1: moment in your career. It was. It was not you know,

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Speaker 1: I did you realize that at the time, or was

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Speaker 1: it like I just gotta get these guys to sign off? No? No, no,

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Speaker 1: it felt special to me. I knew I had a

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Speaker 1: I knew I had a potential tiger at the tail

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Speaker 1: I did, and and I actually wanted to convince them

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Speaker 1: to that it wasn't going to really be a software business.

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Speaker 1: It was going to be a travel business. And I

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Speaker 1: wanted to build a fund me on the outside to

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Speaker 1: do my own thing, because at this point Microsoft was

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Speaker 1: getting bigger and the stuff I was working on I

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Speaker 1: was paid in stock options. But the stuff I was

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Speaker 1: working on, you know, it wasn't Windows. At the time,

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Speaker 1: and so really it would only matter how windows in office,

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Speaker 1: did you know, versus two relative to my compensation. So

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Speaker 1: I was thinking, well, if I can do this on

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Speaker 1: the outside, if I if I can build a team

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Speaker 1: and do really well with this, then I can do

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Speaker 1: well too. Anyway, I pitched them on that idea right

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Speaker 1: from the start. But Bill laughed and he said, who

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Speaker 1: are you going to hire? We don't do that, but look,

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Speaker 1: grow it internally and if it, you know, if it

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Speaker 1: needs to be independent from Microsoft, will take a look.

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Speaker 1: Did he give you a budget to build this out?

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Speaker 1: Or yeah, it's a budget and some headcount. And I

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Speaker 1: was able to hire the most entrepreneurial people at Microsoft,

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Speaker 1: which was the place to be if you were intact,

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Speaker 1: like the only place to be. So I got really

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Speaker 1: great people, established a network of incredible people who were

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Speaker 1: entrepreneurially minded, because I said, come to Expedia, we're gonna

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Speaker 1: build this thing. We're gonna be the largest seller travel

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Speaker 1: in the world. We're building a new travel marketplace, and

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Speaker 1: we're gonna spin out if it's good. Okay, we're gonna

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Speaker 1: spin out of Microsoft. And so I got really incredible

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Speaker 1: people to Bill and Steve's credit. I was working for

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Speaker 1: Balmer when we when we spun out to their credit.

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Speaker 1: I went to Steve and I asked him for one

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Speaker 1: hundred million dollars to spend on marketing on Expedia. And

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Speaker 1: he laughed, like he have you met Steve. He's a

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Speaker 1: I haven't met him, but I've heard him laugh. He's

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Speaker 1: like he's aggressive and he's big and he makes noise.

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Speaker 1: And he did. He laughed and got kind of red

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Speaker 1: in the face that we're not gonna do that. And

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Speaker 1: I said, well, Steve, but YEA anchored him at a

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Speaker 1: hundred million. I wanted a hundred million, and I said it,

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Speaker 1: but I need a hundred million on any tv ods.

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Speaker 1: This thing could be huge. We're half the size of

429
00:23:55,640 --> 00:23:57,840
Speaker 1: the number one guy right now, but we need we

430
00:23:57,880 --> 00:24:00,320
Speaker 1: need this jet fuel. We got a better prod sucked.

431
00:24:00,480 --> 00:24:02,640
Speaker 1: We got to do it. He said no, and I said,

432
00:24:02,640 --> 00:24:06,760
Speaker 1: the public markets will do it. It's pets, pet food,

433
00:24:06,800 --> 00:24:10,360
Speaker 1: dot com, food com. Everything got funded and I said, look,

434
00:24:10,440 --> 00:24:12,440
Speaker 1: let's spin it out. Let's take the public. We don't

435
00:24:12,440 --> 00:24:14,119
Speaker 1: have to sell much of the company. We got a

436
00:24:14,200 --> 00:24:16,600
Speaker 1: hundred million dollars and we'll give it a shot. And

437
00:24:16,640 --> 00:24:20,000
Speaker 1: to Steve's credit and Bill's credit. They said, alright, let's

438
00:24:20,000 --> 00:24:22,280
Speaker 1: do an hr experiment. Let's let's try to do this thing.

439
00:24:22,320 --> 00:24:25,200
Speaker 1: And so I took a hundred and fifty people out

440
00:24:25,240 --> 00:24:27,640
Speaker 1: of the hottest company in the world to work out

441
00:24:28,000 --> 00:24:32,879
Speaker 1: where they already had stocks, already had stock, the hottest

442
00:24:32,880 --> 00:24:35,520
Speaker 1: company in the world at the highest point of the

443
00:24:35,560 --> 00:24:38,439
Speaker 1: stock had ever achieved and would ever achieve even for

444
00:24:38,480 --> 00:24:43,960
Speaker 1: the next seventeen years. Okay, uh. And these adventurous people,

445
00:24:44,280 --> 00:24:47,800
Speaker 1: these courageous people, they came out of Microsoft with me

446
00:24:47,840 --> 00:24:50,560
Speaker 1: and we took Expedia public and within a year we

447
00:24:50,560 --> 00:24:53,120
Speaker 1: were twice the size of the number two guy. Wow,

448
00:24:53,119 --> 00:24:56,720
Speaker 1: that's fantastic. And Expedia still is a free standing company,

449
00:24:56,800 --> 00:25:01,760
Speaker 1: still publicly, probably traded dollar market cap, doing extremely well,

450
00:25:01,840 --> 00:25:04,760
Speaker 1: one of the two kind of globally dominant players in

451
00:25:04,760 --> 00:25:07,720
Speaker 1: the online travel So how do you look at this

452
00:25:08,080 --> 00:25:11,080
Speaker 1: as your baby which is now off on its own,

453
00:25:11,119 --> 00:25:14,320
Speaker 1: and what's your involvement with pride? It's like the kid

454
00:25:14,359 --> 00:25:16,280
Speaker 1: that went to college and now he's married and he's

455
00:25:16,320 --> 00:25:18,760
Speaker 1: got a happy family and the family is growing up.

456
00:25:18,800 --> 00:25:21,600
Speaker 1: I really, you know, it makes me really proud to

457
00:25:21,600 --> 00:25:24,600
Speaker 1: to to see what Barry Diller and dark Kasra Shah

458
00:25:24,680 --> 00:25:28,320
Speaker 1: he the current CEO have done since I left. I'm

459
00:25:28,440 --> 00:25:32,199
Speaker 1: really impressed. Was it difficult to to turn over the

460
00:25:32,320 --> 00:25:34,800
Speaker 1: keys to the kingdom that easily? Like some there are

461
00:25:34,840 --> 00:25:38,000
Speaker 1: some entrepreneurs who have a hard time passing the reins?

462
00:25:38,040 --> 00:25:40,440
Speaker 1: Did did you have any issues with that? I mean,

463
00:25:40,600 --> 00:25:42,800
Speaker 1: of course there was some. I had some issues, But

464
00:25:43,160 --> 00:25:46,800
Speaker 1: my personality is a little bit more. Um. You know,

465
00:25:46,840 --> 00:25:48,880
Speaker 1: I don't need to own and control and do everything.

466
00:25:49,280 --> 00:25:51,960
Speaker 1: And I like to think and do about I like

467
00:25:52,000 --> 00:25:53,919
Speaker 1: to think about a lot of things. I have a

468
00:25:53,920 --> 00:25:58,119
Speaker 1: diverse set of interests. And when Barry Diller bought it

469
00:25:58,520 --> 00:26:01,240
Speaker 1: from the Republic for three four years and Barry Dillar

470
00:26:01,280 --> 00:26:04,719
Speaker 1: bought us out at a really nice price, is interactive?

471
00:26:04,800 --> 00:26:07,600
Speaker 1: This is? This was I a c H. I see

472
00:26:07,600 --> 00:26:10,800
Speaker 1: it was known as USA Interactive at the time. Um,

473
00:26:10,960 --> 00:26:15,600
Speaker 1: they bought Expedia at a nice price. Um, that seemed

474
00:26:15,640 --> 00:26:18,480
Speaker 1: like a good time for me to to go think

475
00:26:18,520 --> 00:26:21,080
Speaker 1: about doing other things. Let's talk a little bit about

476
00:26:21,119 --> 00:26:24,480
Speaker 1: glass Door. In two thousand and seven, you found the

477
00:26:24,520 --> 00:26:30,639
Speaker 1: company which helps people find jobs, understand the salary and

478
00:26:30,840 --> 00:26:35,040
Speaker 1: compensation they're getting, and gives folks a better sense of

479
00:26:35,640 --> 00:26:39,119
Speaker 1: what what they should be getting for their role. In

480
00:26:39,160 --> 00:26:42,080
Speaker 1: the marketplace. Is that a fair assessment? That's right. I

481
00:26:42,520 --> 00:26:46,520
Speaker 1: actually co founded it with a guy named Bob Holman

482
00:26:47,000 --> 00:26:50,480
Speaker 1: who worked for me at Expedia. He was a development manager.

483
00:26:51,320 --> 00:26:53,720
Speaker 1: Most all of my companies that I'm involved with, I've

484
00:26:53,760 --> 00:26:57,080
Speaker 1: co founded with people who worked for me at Expedia

485
00:26:57,119 --> 00:27:01,480
Speaker 1: and Zillo as well. So your co founder, I found

486
00:27:01,520 --> 00:27:05,600
Speaker 1: an Expedia, um, you know, inside of Microsoft. And I

487
00:27:05,640 --> 00:27:10,720
Speaker 1: co founded Zillo with an Expedia guy and a Microsoft guy,

488
00:27:10,720 --> 00:27:12,640
Speaker 1: a guy went to college with. And then I did

489
00:27:12,640 --> 00:27:14,119
Speaker 1: the same with glass Door, and I've done it with

490
00:27:14,119 --> 00:27:16,560
Speaker 1: a few others as well anyway, But to glass Door,

491
00:27:17,480 --> 00:27:20,520
Speaker 1: uh so, yes. The idea with glass Door is not

492
00:27:20,680 --> 00:27:23,720
Speaker 1: too dissimilar from Expedia and Zillo, and that is to

493
00:27:23,760 --> 00:27:26,280
Speaker 1: turn on the lights in the room to get people

494
00:27:26,440 --> 00:27:30,280
Speaker 1: information they didn't have before and specifically, what is it

495
00:27:30,320 --> 00:27:33,080
Speaker 1: really like to work there? Do people who work there

496
00:27:33,080 --> 00:27:36,240
Speaker 1: approve of the CEO and leadership? What do people make?

497
00:27:36,320 --> 00:27:38,159
Speaker 1: What do they really make? What are their what are

498
00:27:38,160 --> 00:27:42,160
Speaker 1: their real salaries and job titles in ten years? Um?

499
00:27:42,400 --> 00:27:44,879
Speaker 1: How do you ensure the integrity of that information? How

500
00:27:44,880 --> 00:27:48,720
Speaker 1: do you make sure so there's a site rate? My professor,

501
00:27:48,720 --> 00:27:52,200
Speaker 1: there are other educator related and you know, it's really

502
00:27:52,200 --> 00:27:56,439
Speaker 1: easy for one I rate student to skew everything. How

503
00:27:56,480 --> 00:27:59,480
Speaker 1: do you avoid that happening when people are rating either

504
00:27:59,600 --> 00:28:03,280
Speaker 1: CEO or a business. Door. So this information has turned

505
00:28:03,320 --> 00:28:06,320
Speaker 1: out to be really intriguing to people. We get twenty

506
00:28:06,400 --> 00:28:08,960
Speaker 1: or thirty million people a month now that come to

507
00:28:08,960 --> 00:28:11,119
Speaker 1: glass Door, and it's become one of the largest job

508
00:28:11,200 --> 00:28:13,600
Speaker 1: sites in the world. It's really it's really good, and

509
00:28:13,600 --> 00:28:16,560
Speaker 1: it's gotten there because the information is good. The information

510
00:28:16,640 --> 00:28:19,359
Speaker 1: is good because we work really hard to ensure that

511
00:28:19,400 --> 00:28:22,160
Speaker 1: the people who are leaving reviews actually work or worked

512
00:28:22,160 --> 00:28:26,359
Speaker 1: at the company. And you can validate that we cantronically.

513
00:28:26,480 --> 00:28:28,400
Speaker 1: We can do that with email addresses, and we can

514
00:28:28,400 --> 00:28:31,240
Speaker 1: do that why with and forth communications, So we work

515
00:28:31,320 --> 00:28:34,760
Speaker 1: really hard to make sure they're legit. Secondly, anything that's

516
00:28:34,800 --> 00:28:37,440
Speaker 1: too strident to use his profanity or people's names or

517
00:28:37,480 --> 00:28:40,920
Speaker 1: as personal we just don't. We just don't post. Okay,

518
00:28:40,960 --> 00:28:42,880
Speaker 1: so we should have a chat with Jack Dorsey at

519
00:28:42,880 --> 00:28:46,480
Speaker 1: Twitter and maybe you could get them straightened out. You know,

520
00:28:46,840 --> 00:28:48,960
Speaker 1: it would be interesting. We might you know, we might

521
00:28:49,040 --> 00:28:53,400
Speaker 1: have a different president if that anyway. Uh So we

522
00:28:53,400 --> 00:28:56,840
Speaker 1: we clipped the tails. Basically, anything that that is too

523
00:28:57,080 --> 00:28:59,440
Speaker 1: stride in one way or another. We clipped clip. That's

524
00:28:59,440 --> 00:29:01,680
Speaker 1: part of the We did that part of the curve.

525
00:29:02,040 --> 00:29:05,000
Speaker 1: And I would also contend trip Advisor was something we

526
00:29:05,040 --> 00:29:07,520
Speaker 1: owned it expedient. We learned a lot from trip Advisor

527
00:29:07,680 --> 00:29:11,600
Speaker 1: with people leaving anonymous reviews about hotels right uh, And

528
00:29:11,800 --> 00:29:14,920
Speaker 1: I would say over time that trip Advisor, YELP, glass

529
00:29:14,960 --> 00:29:23,040
Speaker 1: door reviews on Amazon have actually taught consumers to uh,

530
00:29:23,160 --> 00:29:26,120
Speaker 1: to kind of tune out the noise. Anyway, when something

531
00:29:26,160 --> 00:29:29,200
Speaker 1: looks promotional, they think, oh, that's promotional that person, or

532
00:29:29,200 --> 00:29:31,080
Speaker 1: when something looks like they have an act to grind,

533
00:29:31,520 --> 00:29:35,280
Speaker 1: you know, people intuitively say, all right, well that person

534
00:29:35,400 --> 00:29:38,440
Speaker 1: is a disgruntled ex employee and and that's going on.

535
00:29:38,480 --> 00:29:40,800
Speaker 1: And so we as humans are tuning up to kind

536
00:29:40,800 --> 00:29:42,600
Speaker 1: of clip to kind of clip the tails too. So

537
00:29:42,640 --> 00:29:46,960
Speaker 1: what's turned out is that the the information is really accurate.

538
00:29:47,160 --> 00:29:50,400
Speaker 1: We have information on over seven thousand companies around the world.

539
00:29:50,480 --> 00:29:53,280
Speaker 1: We have tens of millions of pieces of data and reviews.

540
00:29:53,840 --> 00:29:57,960
Speaker 1: When when you work at a place I don't know Bloomberg,

541
00:29:58,640 --> 00:30:01,160
Speaker 1: go on to glass door, read the reviews, look at

542
00:30:01,200 --> 00:30:04,680
Speaker 1: the salaries, see what people think of the ceo. UH

543
00:30:04,720 --> 00:30:07,680
Speaker 1: and you're gonna read that and you're gonna say, yeah,

544
00:30:07,760 --> 00:30:12,840
Speaker 1: that's that sounds right to fascinating thing is if you're

545
00:30:12,960 --> 00:30:17,520
Speaker 1: a obviously someone who's just starting with someone who's very senior,

546
00:30:17,920 --> 00:30:22,000
Speaker 1: those are really specific salary pricing. But in the middle

547
00:30:22,080 --> 00:30:25,040
Speaker 1: of it, a lot of people have no idea what

548
00:30:25,280 --> 00:30:28,440
Speaker 1: is the standard salary at least in their part of

549
00:30:28,440 --> 00:30:30,680
Speaker 1: the country. I know they vary from city to city.

550
00:30:31,080 --> 00:30:34,000
Speaker 1: And it's kind of fascinating clicking around and looking at stuff.

551
00:30:34,280 --> 00:30:36,520
Speaker 1: And how about and how about gender splits. I mean,

552
00:30:36,560 --> 00:30:38,600
Speaker 1: we we have all this data, we know the we

553
00:30:38,680 --> 00:30:41,760
Speaker 1: know the gender of the people with salaries, and we've

554
00:30:41,760 --> 00:30:45,720
Speaker 1: been able to uncover gender paid disparities. Really, yes, it's

555
00:30:45,760 --> 00:30:49,560
Speaker 1: really it's really interesting. But what's the takeaway from that? Then? Uh,

556
00:30:49,600 --> 00:30:52,880
Speaker 1: the takeaway it's it's difficult really to get a handle

557
00:30:52,920 --> 00:30:56,959
Speaker 1: on the gender stuff because even if stuff looks equal,

558
00:30:57,040 --> 00:30:59,760
Speaker 1: you don't really know how quickly a promotional path has

559
00:30:59,760 --> 00:31:02,320
Speaker 1: had under what promotional opportunities. A lot of nuance there,

560
00:31:02,360 --> 00:31:04,200
Speaker 1: there's a lot there is a lot of nuance. But

561
00:31:04,520 --> 00:31:08,960
Speaker 1: generally speaking, uh, women have been paid less than men

562
00:31:09,200 --> 00:31:11,600
Speaker 1: for the same role. That is a that is a

563
00:31:11,640 --> 00:31:15,160
Speaker 1: conclusion that we come to um. Interestingly, investment managers use

564
00:31:15,400 --> 00:31:18,280
Speaker 1: glass door quite a bit. Oh really, yeah, I have

565
00:31:18,400 --> 00:31:20,680
Speaker 1: I know several hedge fund guys and investment managers that

566
00:31:20,840 --> 00:31:24,240
Speaker 1: use glass door to figure out what's going on at

567
00:31:24,240 --> 00:31:27,360
Speaker 1: the companies they're investing. Are they spreading the wealth around?

568
00:31:27,360 --> 00:31:30,040
Speaker 1: They must be really doing really well and that may

569
00:31:30,040 --> 00:31:32,480
Speaker 1: not have showed up yet in the official doctor. What

570
00:31:32,480 --> 00:31:35,840
Speaker 1: do people think of the CEO? You know, that's quite fast.

571
00:31:36,080 --> 00:31:38,800
Speaker 1: Let's talk a little bit about Netflix. So you join

572
00:31:38,880 --> 00:31:42,000
Speaker 1: the board win when it was private? Come on, I've

573
00:31:42,000 --> 00:31:44,880
Speaker 1: been on that board for really, I can't remember the

574
00:31:44,920 --> 00:31:48,840
Speaker 1: year fifteen years at least I remember when they first launched,

575
00:31:48,960 --> 00:31:52,560
Speaker 1: and the kids listening aren't gonna remember that. They used

576
00:31:52,560 --> 00:31:56,080
Speaker 1: to mail DVDs through the US postal. And the first

577
00:31:56,120 --> 00:31:58,400
Speaker 1: time I heard about it's like, why do I want

578
00:31:58,440 --> 00:32:01,200
Speaker 1: to wait for dv eased to show up in the mail?

579
00:32:01,920 --> 00:32:03,400
Speaker 1: And then it's like, oh wait, I don't have to

580
00:32:03,440 --> 00:32:06,840
Speaker 1: go to Blockbuster and there's always two or three in transit.

581
00:32:07,160 --> 00:32:10,560
Speaker 1: Oh that makes a lot of sense. And once they

582
00:32:10,640 --> 00:32:12,840
Speaker 1: moved to all digital, and it was clear that was

583
00:32:12,880 --> 00:32:16,440
Speaker 1: the goal eventually to go all digital, there was no

584
00:32:16,560 --> 00:32:19,560
Speaker 1: looking back. What was that process like, okay, well, I

585
00:32:19,600 --> 00:32:22,280
Speaker 1: had the same reaction unit. So there weren't many web

586
00:32:22,320 --> 00:32:25,000
Speaker 1: guys back when I went to when I joined the

587
00:32:25,040 --> 00:32:26,520
Speaker 1: board of Netflix, and I was one of them. I

588
00:32:26,560 --> 00:32:28,800
Speaker 1: was running Expedia and by the way, how did you

589
00:32:29,040 --> 00:32:31,160
Speaker 1: get the invite? Okay, So there were many of us,

590
00:32:31,280 --> 00:32:36,040
Speaker 1: and and we read Hastings, who has incredible CEO of Netflix,

591
00:32:36,400 --> 00:32:39,480
Speaker 1: you know, running an incredible leadership team over there. He

592
00:32:39,720 --> 00:32:41,719
Speaker 1: and I had a mutual friend and investor, a guy

593
00:32:41,800 --> 00:32:44,480
Speaker 1: named Jay Hog, who is the founding partner of TCV

594
00:32:44,680 --> 00:32:51,280
Speaker 1: Technology Crossover Ventures. He's a really well regarded historical figure

595
00:32:51,520 --> 00:32:55,200
Speaker 1: in in technology, isn't it. He is not well known public,

596
00:32:55,280 --> 00:32:59,480
Speaker 1: low key legend, low key legend. I sit on a

597
00:32:59,520 --> 00:33:02,520
Speaker 1: few board with him, and I learned. I learned every

598
00:33:02,520 --> 00:33:04,680
Speaker 1: time I'm in a room with him. I really think

599
00:33:04,760 --> 00:33:08,440
Speaker 1: he's outstanding. He knew both of us, both Read and me,

600
00:33:08,600 --> 00:33:10,520
Speaker 1: and Reid was looking to build out his board, getting

601
00:33:10,520 --> 00:33:12,800
Speaker 1: ready to go public, and so Read and I had

602
00:33:12,800 --> 00:33:16,800
Speaker 1: dinner and I said, I gotta admit, like this, this

603
00:33:16,880 --> 00:33:18,920
Speaker 1: seems like there's an end of the road here. Like

604
00:33:18,960 --> 00:33:21,560
Speaker 1: the DVD thing, the form factor, it's gonna go with

605
00:33:21,840 --> 00:33:24,200
Speaker 1: the services. Cool I grant you that, but the form

606
00:33:24,240 --> 00:33:26,880
Speaker 1: factor is gonna go away. Read You're gonna hit the wall.

607
00:33:26,960 --> 00:33:28,480
Speaker 1: And then what's the future. How are we ever going

608
00:33:28,520 --> 00:33:30,480
Speaker 1: to get any value out of this thing? It's gonna die?

609
00:33:31,440 --> 00:33:35,000
Speaker 1: Um And he's like, he said, did you see the

610
00:33:35,120 --> 00:33:38,520
Speaker 1: name of the We didn't name it DVD by mail flicks,

611
00:33:38,680 --> 00:33:43,479
Speaker 1: Rich named it Netflix. And I'm like, okay, well that's okay,

612
00:33:43,520 --> 00:33:46,400
Speaker 1: good point, good point. But how are you gonna how

613
00:33:46,400 --> 00:33:48,240
Speaker 1: are you going to cross the chasm? How are you

614
00:33:48,240 --> 00:33:49,920
Speaker 1: going to get over the wall? You know that, you

615
00:33:49,960 --> 00:33:53,960
Speaker 1: know that chasm is there? And he said, I don't know,

616
00:33:55,000 --> 00:33:58,800
Speaker 1: He said, I don't know. We we It could be downloads,

617
00:33:58,840 --> 00:34:02,440
Speaker 1: it could be streaming. Who knows. We really don't know.

618
00:34:02,840 --> 00:34:05,360
Speaker 1: But here's what I do know that if we can

619
00:34:05,400 --> 00:34:08,880
Speaker 1: build up a big enough subscriber base before the chasm

620
00:34:09,000 --> 00:34:12,080
Speaker 1: is upon us, then we'll be in an incredibly good

621
00:34:12,120 --> 00:34:15,360
Speaker 1: position to cross the chasm with our subscriber base into

622
00:34:15,360 --> 00:34:17,920
Speaker 1: the new world, whatever that new world. That's some serious

623
00:34:17,960 --> 00:34:21,759
Speaker 1: long ball. Right before he even launches the I p

624
00:34:21,840 --> 00:34:26,400
Speaker 1: O for mailing DVDs, he's thinking, eventually, we have to

625
00:34:26,400 --> 00:34:30,920
Speaker 1: be digital. He is truly a long long ball player.

626
00:34:31,000 --> 00:34:34,719
Speaker 1: He is a visionary, long term guy. Um, there are

627
00:34:34,840 --> 00:34:36,400
Speaker 1: very few people that can do this. You've had a

628
00:34:36,400 --> 00:34:40,800
Speaker 1: lot on your show. Jeff Bezos is another one who

629
00:34:40,400 --> 00:34:44,560
Speaker 1: twenty years ago he's thinking yeah, yeah, eventually. Granted we

630
00:34:44,640 --> 00:34:48,440
Speaker 1: talked about Amazon Cloud service. That probably wasn't in his

631
00:34:48,520 --> 00:34:51,600
Speaker 1: original plan of hey, we have all these service let's

632
00:34:51,640 --> 00:34:54,719
Speaker 1: use him for something. But it was clear that he

633
00:34:54,840 --> 00:34:59,839
Speaker 1: had an idea about just not competing with retailers, just

634
00:35:00,000 --> 00:35:04,719
Speaker 1: completely bypassing them, being the right, right entire by the way,

635
00:35:04,760 --> 00:35:10,640
Speaker 1: I just got Alexa the echo delivered yesterday from from

636
00:35:10,680 --> 00:35:14,000
Speaker 1: Amazon Prime Day. Um, you know I didn't want to

637
00:35:14,000 --> 00:35:17,560
Speaker 1: experiment with it a two bucks but at yeah, okay,

638
00:35:17,600 --> 00:35:21,319
Speaker 1: let me try it. And you could see I just

639
00:35:21,640 --> 00:35:25,520
Speaker 1: opened the box, you could. I already can see how

640
00:35:26,200 --> 00:35:31,719
Speaker 1: if this thing continues to develop the way it does. Um,

641
00:35:31,760 --> 00:35:36,680
Speaker 1: this is another monster, another and you have to stop

642
00:35:36,719 --> 00:35:40,840
Speaker 1: and worry. Wonder how did Apple blow this with Siri?

643
00:35:41,040 --> 00:35:43,600
Speaker 1: I mean they were really way out of It's not

644
00:35:43,680 --> 00:35:46,760
Speaker 1: over yet. It's not over yet, but but well, Siria

645
00:35:46,800 --> 00:35:48,640
Speaker 1: has to get a lot that Amazon, right, Siria has

646
00:35:48,640 --> 00:35:52,040
Speaker 1: to get a lot better. And what subtle and interesting

647
00:35:52,160 --> 00:35:56,640
Speaker 1: is just how important response time is in using and

648
00:35:56,719 --> 00:36:00,839
Speaker 1: using these things and using these voice intelligent agents. Uh.

649
00:36:01,000 --> 00:36:03,799
Speaker 1: Sirius was okay, but not many people used it when

650
00:36:03,800 --> 00:36:05,640
Speaker 1: it first came out because it just, you know, it

651
00:36:05,760 --> 00:36:08,040
Speaker 1: just didn't respond very quickly, and you had to be

652
00:36:08,239 --> 00:36:10,960
Speaker 1: you had to be near a network that actually worked.

653
00:36:10,960 --> 00:36:14,840
Speaker 1: And in the United States, that's an iffy proposition on

654
00:36:15,040 --> 00:36:17,279
Speaker 1: in mobile. But if you're at home, if you're at

655
00:36:17,280 --> 00:36:20,160
Speaker 1: home and there's a dedicated device that all it does

656
00:36:20,640 --> 00:36:24,080
Speaker 1: is listen for you to say its name so that

657
00:36:24,160 --> 00:36:27,240
Speaker 1: it can respond really, really quickly, all of a sudden

658
00:36:27,440 --> 00:36:30,760
Speaker 1: people started using it. And you know, when you're busy

659
00:36:30,760 --> 00:36:32,319
Speaker 1: with your hands in the kitchen and you want to

660
00:36:32,320 --> 00:36:34,279
Speaker 1: set a timer, are you going to reach for your

661
00:36:34,440 --> 00:36:36,960
Speaker 1: iPhone to set the timer with four or five clicks?

662
00:36:37,560 --> 00:36:41,000
Speaker 1: Are you going to say, Alexa, set timer for thirteen

663
00:36:41,000 --> 00:36:44,239
Speaker 1: minutes and it and it just works. I'm you know,

664
00:36:44,360 --> 00:36:48,160
Speaker 1: I know Jeff a little bit. We're both in Seattle, Um.

665
00:36:48,280 --> 00:36:52,960
Speaker 1: You know, he is really the most impressive business guy

666
00:36:53,040 --> 00:36:56,359
Speaker 1: of the kind of of the new millennium. I mean

667
00:36:56,719 --> 00:36:59,640
Speaker 1: just so so so we've mentioned a couple of CEOs

668
00:36:59,680 --> 00:37:03,759
Speaker 1: we've made and read Hastings and Jeff Bezos what other

669
00:37:03,880 --> 00:37:08,760
Speaker 1: CEOs impress you and what characteristics do you think define

670
00:37:09,440 --> 00:37:15,239
Speaker 1: an outstanding CEO in the modern era. Well, because I'm

671
00:37:15,800 --> 00:37:20,480
Speaker 1: a startup oriented person, I tend to be focused on

672
00:37:20,560 --> 00:37:24,280
Speaker 1: the founder CEOs. I believe that the found the great

673
00:37:24,640 --> 00:37:31,480
Speaker 1: the greatest CEOs tend to be the founder CEOs. I think, Um,

674
00:37:31,520 --> 00:37:35,640
Speaker 1: there are terrific professional management CEOs that come in later,

675
00:37:35,760 --> 00:37:40,279
Speaker 1: there's no question, but a professional CEO never quite has

676
00:37:40,520 --> 00:37:47,360
Speaker 1: the credibility of the founder. Isn't usually isn't able to

677
00:37:47,360 --> 00:37:50,799
Speaker 1: to think really long term and and and carry the

678
00:37:50,840 --> 00:37:54,600
Speaker 1: employee base in the investor base along for a the

679
00:37:54,640 --> 00:37:58,279
Speaker 1: long ride and say, hey, we're going to invest billions

680
00:37:58,280 --> 00:38:02,239
Speaker 1: of dollars in producing our own streaming content even though

681
00:38:02,280 --> 00:38:05,399
Speaker 1: we've never done that before. Come along, I'm gonna hold

682
00:38:05,400 --> 00:38:07,000
Speaker 1: your hand, and you're gonna hold my hand, and we're

683
00:38:07,000 --> 00:38:10,279
Speaker 1: gonna get through this because on the other side it's glorious.

684
00:38:10,400 --> 00:38:13,480
Speaker 1: But in the intervening six or seven years, the P

685
00:38:13,640 --> 00:38:18,560
Speaker 1: and L and the cash flow is going to be ugly. Okay, Really,

686
00:38:18,920 --> 00:38:23,399
Speaker 1: founders have license to do that, uh, And afterwards it's

687
00:38:23,480 --> 00:38:25,719
Speaker 1: it's a little bit harder. Uh. There are lots of

688
00:38:25,719 --> 00:38:28,719
Speaker 1: bad founder CEO s too, for sure, But I think

689
00:38:28,760 --> 00:38:31,760
Speaker 1: the the true Grates in my mind, like Bill Gates

690
00:38:31,760 --> 00:38:35,800
Speaker 1: and Steve Jobs and Jeff Bezos and Read Hastings. Um,

691
00:38:35,840 --> 00:38:38,480
Speaker 1: what do you think of Zuckerberg at Facebook? Zuckerberg, He's

692
00:38:38,600 --> 00:38:42,520
Speaker 1: He's amazing. He's really an incredible andreason said. He is

693
00:38:42,640 --> 00:38:47,560
Speaker 1: just a human learning machine and just is constantly observed

694
00:38:47,719 --> 00:38:51,040
Speaker 1: if there's something to be learned in any environment. He's

695
00:38:51,120 --> 00:38:53,560
Speaker 1: just a sponge knowledge. He's a sponging you know what.

696
00:38:53,600 --> 00:38:55,960
Speaker 1: Young Bill Bill Gates is still that way really, but

697
00:38:56,000 --> 00:38:58,960
Speaker 1: young Bill Gates was was exactly the same, no kidding.

698
00:38:59,080 --> 00:39:03,279
Speaker 1: It's just cons instantly hard learning about everything, not just

699
00:39:03,360 --> 00:39:05,120
Speaker 1: the stuff you think that they ought to be learning

700
00:39:05,120 --> 00:39:08,840
Speaker 1: about based on their businesses, but wanting to know everything.

701
00:39:09,920 --> 00:39:12,840
Speaker 1: We have been speaking with Rich Barton. He is the

702
00:39:12,960 --> 00:39:16,680
Speaker 1: co founder of Zillo and glass Door and the founder

703
00:39:16,680 --> 00:39:21,200
Speaker 1: of Expedia and numerous other entities. Be sure and check

704
00:39:21,200 --> 00:39:24,560
Speaker 1: out my daily column. It's at Bloomberg View dot com.

705
00:39:24,680 --> 00:39:27,640
Speaker 1: You can follow me on Twitter at Rid Halts. We

706
00:39:27,800 --> 00:39:32,040
Speaker 1: love your comments, feedback and suggestions right to us at

707
00:39:32,480 --> 00:39:36,560
Speaker 1: m IB podcast at Bloomberg dot net. I'm very Rid Halts.

708
00:39:36,719 --> 00:39:51,040
Speaker 1: You're listening to masters and business on Bloomberg Radio. If

709
00:39:51,080 --> 00:39:53,840
Speaker 1: you're having a business dispute, the process can be slow

710
00:39:53,920 --> 00:39:56,840
Speaker 1: and drawn out, especially if you rely on litigation. In

711
00:39:56,880 --> 00:39:59,440
Speaker 1: the courts. You can wait for years before your case

712
00:39:59,560 --> 00:40:02,640
Speaker 1: is resolved, and the longer your case proceeds, the more

713
00:40:02,680 --> 00:40:06,840
Speaker 1: your case can cost. Not with the American Arbitration Association.

714
00:40:07,239 --> 00:40:11,360
Speaker 1: Arbitration or mediation with the American Arbitration Association is faster.

715
00:40:11,640 --> 00:40:15,320
Speaker 1: In fact, nearly fifty of our cases settled prior to hearings.

716
00:40:16,040 --> 00:40:24,040
Speaker 1: A d r dot org resolve faster. Welcome to the podcast, Briche.

717
00:40:24,080 --> 00:40:25,919
Speaker 1: Thank you so much for doing this. I am I've

718
00:40:26,000 --> 00:40:29,480
Speaker 1: I've said I'm a giant fan of Zillo, and as

719
00:40:29,520 --> 00:40:33,359
Speaker 1: I started researching who you are and what you've done,

720
00:40:33,360 --> 00:40:35,239
Speaker 1: I'm like, oh, I know that company. Oh wait, I

721
00:40:35,280 --> 00:40:37,920
Speaker 1: know that company. Oh wait, I know who those like?

722
00:40:38,320 --> 00:40:40,200
Speaker 1: Oh I I how did I not know who this

723
00:40:40,239 --> 00:40:43,759
Speaker 1: person was many many years ago. All the things that

724
00:40:43,800 --> 00:40:48,080
Speaker 1: you're involved in are some of my favorite and most

725
00:40:48,160 --> 00:40:53,919
Speaker 1: regularly used technologies, So that that's pretty interesting. You've had

726
00:40:54,160 --> 00:40:59,880
Speaker 1: quite a fascinating career in Seattle. Can we say Seattle

727
00:41:00,280 --> 00:41:03,880
Speaker 1: is rapidly gaining on Silicon Valley? Is that a fair statement.

728
00:41:04,800 --> 00:41:06,520
Speaker 1: By the way, you're the second person I've had from

729
00:41:06,560 --> 00:41:10,399
Speaker 1: Silicon Valley from I'm sorry, from Seattle, the first being

730
00:41:10,520 --> 00:41:14,359
Speaker 1: Nick Hannahur all right, Nick, my friend. Yeah, Seattle is

731
00:41:14,600 --> 00:41:22,080
Speaker 1: a boomtown. Boomtown, sixty two cranes, leading city in the US.

732
00:41:23,000 --> 00:41:29,040
Speaker 1: I just had that graphics three day. Clearly does the

733
00:41:29,160 --> 00:41:36,280
Speaker 1: cranes everywhere. This is because the flywheel, the tech flywheel

734
00:41:36,560 --> 00:41:39,800
Speaker 1: is spinning and it's just get the snowball is rolling

735
00:41:39,880 --> 00:41:42,680
Speaker 1: left and nobody cared. No, well no, I mean there's

736
00:41:42,719 --> 00:41:45,920
Speaker 1: a lot of boeing still there, Okay, but the headquarters

737
00:41:45,960 --> 00:41:50,120
Speaker 1: left and nobody cared. I mean it started with you know, Microsoft,

738
00:41:50,239 --> 00:41:55,280
Speaker 1: probably Microsoft was this incredible gravitational force for tech talent

739
00:41:55,360 --> 00:41:59,400
Speaker 1: from around the world, sucking it in and as micros

740
00:41:59,520 --> 00:42:03,239
Speaker 1: as is, that talent kind of scattered. It started all

741
00:42:03,320 --> 00:42:09,080
Speaker 1: kinds of New Spain, Expedia. So Amazon especially I think

742
00:42:09,200 --> 00:42:12,640
Speaker 1: is driving uh the the new is the new magnet

743
00:42:12,680 --> 00:42:15,719
Speaker 1: for talent coming into town. The new HQ downtown is

744
00:42:15,760 --> 00:42:18,719
Speaker 1: supposed to be spectacular. Well, it's multiple buildings. I mean,

745
00:42:18,760 --> 00:42:20,880
Speaker 1: I would say five or six of those cranes are Amazon,

746
00:42:21,520 --> 00:42:24,400
Speaker 1: and they've they've revived a whole part of town. Um,

747
00:42:24,440 --> 00:42:26,640
Speaker 1: it's it's truly, it's truly phenomenal. I guess I would

748
00:42:26,640 --> 00:42:30,320
Speaker 1: add that the University of Washington is an incredible university,

749
00:42:31,000 --> 00:42:35,320
Speaker 1: and most especially the computer science department, where I actually

750
00:42:35,360 --> 00:42:38,040
Speaker 1: gave the commencement this year, which is quite good fun.

751
00:42:38,200 --> 00:42:41,000
Speaker 1: We get a lot of computer scientists, engineers out of

752
00:42:41,040 --> 00:42:43,120
Speaker 1: out of you dub It is a top five program

753
00:42:43,200 --> 00:42:47,279
Speaker 1: in the country. And so this this mix of you know,

754
00:42:47,440 --> 00:42:52,160
Speaker 1: education and talent and money and and it being a

755
00:42:52,239 --> 00:42:56,360
Speaker 1: terrific place to live, means that Seattle is really rising rapidly.

756
00:42:56,600 --> 00:42:59,000
Speaker 1: Now is it gaining on Silicon Valley. No, that snowball

757
00:42:59,080 --> 00:43:01,239
Speaker 1: is probably you know, it's even bigger, and that thing

758
00:43:01,320 --> 00:43:03,879
Speaker 1: is still rolling to I spent I spent about half

759
00:43:03,960 --> 00:43:06,759
Speaker 1: my business time down in Silicon Valley as well, And

760
00:43:06,840 --> 00:43:11,400
Speaker 1: it's just it's about so. We were justin in Silicon

761
00:43:11,520 --> 00:43:15,640
Speaker 1: Valley earlier this year. My last trip to Seattle, I

762
00:43:15,680 --> 00:43:18,160
Speaker 1: spoke at you dubbed in the Finance department, which was

763
00:43:18,200 --> 00:43:22,640
Speaker 1: really kind of fascinating. Um, And I was spoke at

764
00:43:22,680 --> 00:43:26,000
Speaker 1: the Treasury department of Microsoft who showed me the most

765
00:43:26,040 --> 00:43:29,560
Speaker 1: amazing thing on the wall behind them. They flip a

766
00:43:29,680 --> 00:43:33,880
Speaker 1: panel and it spins around and there's a white phone

767
00:43:34,040 --> 00:43:36,279
Speaker 1: on a shelf. It all it is a white fie.

768
00:43:36,960 --> 00:43:39,520
Speaker 1: What's the white phone? Oh, that's the bad phone that

769
00:43:39,880 --> 00:43:43,000
Speaker 1: Bill used to call. And they literally showed me a

770
00:43:43,000 --> 00:43:46,400
Speaker 1: guy who's got a keyboard. Who's the guy who handles

771
00:43:46,480 --> 00:43:52,360
Speaker 1: the Microsoft stock repurchase program for new option issuance to

772
00:43:52,400 --> 00:43:55,239
Speaker 1: new employees. And they also, long before Apple, they had

773
00:43:55,280 --> 00:44:00,239
Speaker 1: a massive stock repurchase program. And I jokingly said, what

774
00:44:00,320 --> 00:44:02,440
Speaker 1: it has a keyboard with like one key just to

775
00:44:02,480 --> 00:44:04,600
Speaker 1: buy thing on it. It was a joke, and they

776
00:44:04,680 --> 00:44:08,520
Speaker 1: someone pulled out a keyboard that they had given him.

777
00:44:08,560 --> 00:44:15,000
Speaker 1: It was. It was pretty hilarious. The Microsoft was another building,

778
00:44:15,000 --> 00:44:18,000
Speaker 1: the Treasury department. There, it's thirty people in the running

779
00:44:18,000 --> 00:44:21,040
Speaker 1: a hundred billion dollars. It's an incredible amount of I

780
00:44:21,040 --> 00:44:22,839
Speaker 1: think at the time it was eighty nine billion. It's

781
00:44:22,840 --> 00:44:25,680
Speaker 1: close to a hundred now or if it's not past it.

782
00:44:26,120 --> 00:44:30,959
Speaker 1: That campus was astonishing for people who have never been there.

783
00:44:32,480 --> 00:44:35,759
Speaker 1: It's like the size of any four colleges combined. And

784
00:44:35,840 --> 00:44:39,000
Speaker 1: none of the buildings are in numerical order. It's not

785
00:44:39,080 --> 00:44:42,879
Speaker 1: like one to three. So you even have this, it's

786
00:44:42,920 --> 00:44:46,160
Speaker 1: even bigger than you imagine coast. You can't just extrapolate

787
00:44:46,160 --> 00:44:50,440
Speaker 1: one five. Wait, what is building one seven? We've just

788
00:44:50,560 --> 00:44:54,400
Speaker 1: been building eight. The highlight of that trip was visiting

789
00:44:54,440 --> 00:44:58,520
Speaker 1: the Microsoft Office office. Will you walk into the room

790
00:44:58,560 --> 00:45:01,040
Speaker 1: and it's into the building and it's Microsoft. The logo

791
00:45:01,480 --> 00:45:04,960
Speaker 1: Microsoft Office on the wall behind you, and it's like, so,

792
00:45:05,040 --> 00:45:08,719
Speaker 1: what's in this building? She points to the logo this

793
00:45:08,800 --> 00:45:12,640
Speaker 1: is the office office, which was hilarious to me. But

794
00:45:12,800 --> 00:45:16,080
Speaker 1: they were dead. So what was it like watching that

795
00:45:16,160 --> 00:45:19,120
Speaker 1: get built out? Because in the early nineties it was small.

796
00:45:19,200 --> 00:45:22,640
Speaker 1: We were small, I mean small relatively speaking, but as

797
00:45:22,640 --> 00:45:25,160
Speaker 1: opposed to what is it like? I knew all the buildings, right,

798
00:45:25,280 --> 00:45:28,080
Speaker 1: and I knew a lot of like not everybody, but

799
00:45:28,280 --> 00:45:30,680
Speaker 1: most of the people in Seattle by the time you left,

800
00:45:30,880 --> 00:45:33,880
Speaker 1: By the time I left, I left in I couldn't

801
00:45:33,920 --> 00:45:39,520
Speaker 1: possibly know everyone. Watching it get built was um, you know,

802
00:45:39,760 --> 00:45:42,360
Speaker 1: it was miraculous. It really was. And it's a testament

803
00:45:42,400 --> 00:45:45,319
Speaker 1: to Bill Gates. I'm a big believer in the Jim

804
00:45:45,360 --> 00:45:50,400
Speaker 1: Collins be hag carry audacious goal uh and Bill Gates

805
00:45:51,040 --> 00:45:54,680
Speaker 1: put forth one of the great begs. You know, a

806
00:45:54,719 --> 00:45:59,480
Speaker 1: computer on every desk and in every home running Microsoft software.

807
00:45:59,840 --> 00:46:02,120
Speaker 1: So the knock on Ballmer. And I want to ask

808
00:46:02,120 --> 00:46:04,239
Speaker 1: you if this is fair, is that he wasn't a

809
00:46:04,280 --> 00:46:07,719
Speaker 1: b hag guy. He didn't have these audacious goals. They

810
00:46:07,719 --> 00:46:11,520
Speaker 1: were really a series of small goals as opposed to

811
00:46:11,560 --> 00:46:16,840
Speaker 1: throw in the hail. Mary. Well, you know, a great

812
00:46:16,920 --> 00:46:21,319
Speaker 1: bee hag needs a great field general and a great

813
00:46:21,360 --> 00:46:24,680
Speaker 1: operator to you can't, you can't. You don't just magically

814
00:46:24,760 --> 00:46:27,120
Speaker 1: get to your b hag because you have it, even

815
00:46:27,120 --> 00:46:29,840
Speaker 1: though I believe to a certain extent they are self fulfilling,

816
00:46:30,160 --> 00:46:34,680
Speaker 1: but you need operationally to get there. And I don't

817
00:46:34,719 --> 00:46:36,960
Speaker 1: know that I've encountered anyone better than Balmer from an

818
00:46:37,000 --> 00:46:39,839
Speaker 1: operational person as a field general. But what about as

819
00:46:39,880 --> 00:46:42,560
Speaker 1: a CEO? As a CEO, you know, as that's what

820
00:46:42,600 --> 00:46:44,520
Speaker 1: I mean. As a CEO, he grew, I mean he grew,

821
00:46:44,640 --> 00:46:48,160
Speaker 1: He grew the company. The company grew quite nicely under

822
00:46:48,280 --> 00:46:52,680
Speaker 1: his uh you under his leadership. Um My guess is

823
00:46:52,719 --> 00:46:55,839
Speaker 1: that that Bill probably stepped back a little too much,

824
00:46:56,880 --> 00:47:01,360
Speaker 1: uh in providing that visionary overlay kind of pointing to

825
00:47:01,400 --> 00:47:06,080
Speaker 1: the mountain in the distance when Steve took over. And um,

826
00:47:06,920 --> 00:47:08,680
Speaker 1: and that's it is. And and so it's hard to

827
00:47:08,719 --> 00:47:11,719
Speaker 1: know how to step back, okay, and so you know

828
00:47:11,920 --> 00:47:13,960
Speaker 1: there were there were some issues, but overall, I mean

829
00:47:14,000 --> 00:47:16,279
Speaker 1: Microsoft's a company in very good shape. Yeah, no doubt

830
00:47:16,280 --> 00:47:18,920
Speaker 1: about it. There are no courses at Harvard Business School

831
00:47:18,960 --> 00:47:20,880
Speaker 1: that's say, by the way, when you build what was

832
00:47:20,920 --> 00:47:23,200
Speaker 1: then the biggest company in the world, here's how to

833
00:47:23,280 --> 00:47:25,319
Speaker 1: ease back a little bit. I don't think that's a

834
00:47:26,000 --> 00:47:28,800
Speaker 1: that's in the manual. You know, so even though it

835
00:47:28,840 --> 00:47:33,000
Speaker 1: should be so you you you referenced um what it

836
00:47:33,080 --> 00:47:37,719
Speaker 1: was like, meaning read Hastings and working with um. Netflix.

837
00:47:40,000 --> 00:47:45,799
Speaker 1: Amazon theoretically is the big competitor to Netflix. Is are

838
00:47:45,840 --> 00:47:50,359
Speaker 1: they perceived that way or are they Yeah, they're really

839
00:47:50,360 --> 00:47:54,000
Speaker 1: a real retailer, and video isn't what they do. You know,

840
00:47:54,080 --> 00:47:57,960
Speaker 1: the Amazons are doing quite well, uh with their prime

841
00:47:58,080 --> 00:48:01,360
Speaker 1: video and they appear to be really serious about it.

842
00:48:01,400 --> 00:48:05,120
Speaker 1: They're getting good content, they're getting good viewers. Uh. My

843
00:48:05,200 --> 00:48:09,640
Speaker 1: personal opinion, uh is roomful, lots of players in the space. Well,

844
00:48:10,120 --> 00:48:12,880
Speaker 1: it's it's even better than that from my perspective. And

845
00:48:12,920 --> 00:48:15,480
Speaker 1: I would say this is probably reads perspective as well.

846
00:48:15,520 --> 00:48:18,080
Speaker 1: And that is O T T over the top. So

847
00:48:18,200 --> 00:48:24,000
Speaker 1: Internet TV, the more Internet TV there is, the more

848
00:48:24,239 --> 00:48:28,360
Speaker 1: Internet TV there is Okay, So this is a pie

849
00:48:28,480 --> 00:48:31,520
Speaker 1: that just keeps getting bigger as more and better content

850
00:48:31,640 --> 00:48:34,520
Speaker 1: is available on the Internet and on your on your smartphone.

851
00:48:35,400 --> 00:48:40,480
Speaker 1: And what's really happening is that time is being taken

852
00:48:41,200 --> 00:48:44,120
Speaker 1: from other things you were doing, whether whether you were

853
00:48:44,120 --> 00:48:48,839
Speaker 1: watching traditional linear TV or whether you were doing something else. Uh,

854
00:48:49,080 --> 00:48:53,000
Speaker 1: and that time is moving at a really rapid pace

855
00:48:53,360 --> 00:48:58,719
Speaker 1: to internet television viewing. Um. Netflix is great. You know.

856
00:48:59,320 --> 00:49:02,799
Speaker 1: One of reads great geniuses is focus and clarity and

857
00:49:02,880 --> 00:49:05,480
Speaker 1: long term and not getting distracted. The company has never

858
00:49:05,480 --> 00:49:08,800
Speaker 1: done an acquisition, is that true? Never done an acquisition.

859
00:49:08,920 --> 00:49:12,160
Speaker 1: That's amazing. I had no idea, and I'm the kind

860
00:49:12,160 --> 00:49:14,080
Speaker 1: of guy that always has new ideas. I'm like, what

861
00:49:14,120 --> 00:49:17,480
Speaker 1: about sports? What about music? What about video games? And

862
00:49:17,520 --> 00:49:20,000
Speaker 1: reads like yeah, well we're gonna keep our eye on

863
00:49:20,000 --> 00:49:22,400
Speaker 1: the ball. Rich, thank you, we thought about that and

864
00:49:22,960 --> 00:49:24,759
Speaker 1: we're going to keep all right on the ball. Their

865
00:49:24,760 --> 00:49:29,680
Speaker 1: original content is great. Yeh. Master of None is fabulous.

866
00:49:29,920 --> 00:49:32,640
Speaker 1: This run of stand up specials that they've been paying

867
00:49:32,640 --> 00:49:36,480
Speaker 1: people boatloads of money to do. The Chappelle one is justice,

868
00:49:37,160 --> 00:49:44,719
Speaker 1: It's genius. That was just unbelievable. Um, things you see

869
00:49:44,760 --> 00:49:47,719
Speaker 1: stranger things, no that's a good one. You should see it.

870
00:49:48,440 --> 00:49:51,920
Speaker 1: If I have any complaints about Netflix, and I really

871
00:49:51,960 --> 00:49:55,400
Speaker 1: don't have a lot. That they fixed their connectivity issues,

872
00:49:55,400 --> 00:49:58,200
Speaker 1: they fixed their bandwidth problems, that there's a ton of

873
00:49:58,280 --> 00:50:00,880
Speaker 1: stuff that they worked out. The original contents is great.

874
00:50:01,360 --> 00:50:06,120
Speaker 1: I find the old organization on the website so much

875
00:50:06,200 --> 00:50:11,040
Speaker 1: better than the current organization. As an app on a TV,

876
00:50:12,000 --> 00:50:15,520
Speaker 1: it's not easy to find something when you're looking for it,

877
00:50:15,680 --> 00:50:18,640
Speaker 1: or God forbid, you say, I don't know what I

878
00:50:18,640 --> 00:50:21,480
Speaker 1: want to watch tonight, but let's chill with Let's Netflix

879
00:50:21,480 --> 00:50:25,680
Speaker 1: and chill. But really watch Netflix, not Netflix until, by

880
00:50:25,719 --> 00:50:29,120
Speaker 1: the way, tell him whatever genius in the PR department

881
00:50:29,160 --> 00:50:32,400
Speaker 1: came up with that phrase spectacular. I think it was

882
00:50:32,440 --> 00:50:35,480
Speaker 1: an emergent, that it was emerging. But I do think

883
00:50:35,520 --> 00:50:38,360
Speaker 1: it was emerging the company grabbed onto it. I would

884
00:50:38,400 --> 00:50:41,200
Speaker 1: love to find out that that was some evil marketing

885
00:50:41,239 --> 00:50:45,560
Speaker 1: genius sort of fed that out. That would just bemo

886
00:50:45,640 --> 00:50:47,919
Speaker 1: Kelly Bennett. Hey, Kelly, where did that really come from?

887
00:50:47,960 --> 00:50:49,960
Speaker 1: If they're smart, they'll never admit it. Oh no, that

888
00:50:50,040 --> 00:50:53,160
Speaker 1: was purely But but if you just say, let's love

889
00:50:53,239 --> 00:50:56,240
Speaker 1: for something, it's a different it's a different form factor.

890
00:50:56,400 --> 00:50:58,680
Speaker 1: You're totally. You're a different distance from the screen when

891
00:50:58,680 --> 00:51:02,160
Speaker 1: you're on your couch, you have a different control mechanism,

892
00:51:02,239 --> 00:51:06,400
Speaker 1: and so tons and tons of i Q and sweat

893
00:51:06,760 --> 00:51:11,040
Speaker 1: has gone into trying to anticipate what you might like

894
00:51:11,160 --> 00:51:13,400
Speaker 1: to watch next and putting that in front of you

895
00:51:13,560 --> 00:51:18,560
Speaker 1: rather than being a hunt for you know, a search

896
00:51:18,640 --> 00:51:23,160
Speaker 1: and find paradigm on a big screen at twelve feet

897
00:51:23,480 --> 00:51:27,480
Speaker 1: does not work very well. And so what what the

898
00:51:27,920 --> 00:51:29,880
Speaker 1: designers are trying to in the in the coders are

899
00:51:29,880 --> 00:51:33,920
Speaker 1: trying to do is use really really smart software to

900
00:51:33,960 --> 00:51:37,240
Speaker 1: anticipate what you might want and put it there and uh,

901
00:51:37,320 --> 00:51:40,680
Speaker 1: you know, it's it's it's actually quite we do quite well.

902
00:51:40,960 --> 00:51:43,960
Speaker 1: It may not feel it to an old uh you know,

903
00:51:44,360 --> 00:51:47,000
Speaker 1: search and find type paradigm person who's used to it

904
00:51:47,040 --> 00:51:50,480
Speaker 1: on their laptop. It's it's different when you have a keyboard.

905
00:51:50,520 --> 00:51:53,880
Speaker 1: You find stuff more easily, No, for sure. And I

906
00:51:53,920 --> 00:51:56,719
Speaker 1: would make the same complaint about Amazon's interface for it.

907
00:51:57,000 --> 00:51:59,400
Speaker 1: And by the way, it's not just me as a

908
00:51:59,440 --> 00:52:05,600
Speaker 1: fifty five year old, I've heard similar discussions amongst um

909
00:52:05,960 --> 00:52:10,680
Speaker 1: younger niece's nephews, millennial co workers. There is a holy

910
00:52:10,719 --> 00:52:14,120
Speaker 1: grail of how can you combine both so that we're

911
00:52:14,160 --> 00:52:18,360
Speaker 1: making suggestions of things that based on your your previous

912
00:52:18,440 --> 00:52:21,560
Speaker 1: viewing history, we can figure out you want, but also

913
00:52:21,719 --> 00:52:24,120
Speaker 1: make it easy for what in the mood for Let's

914
00:52:24,120 --> 00:52:25,719
Speaker 1: find a way to do that, and that should be

915
00:52:25,719 --> 00:52:28,480
Speaker 1: a voice based intelligent interaction in the future, and it

916
00:52:28,840 --> 00:52:32,719
Speaker 1: eventually will you know, the advantage you mentioned, um how

917
00:52:32,800 --> 00:52:38,319
Speaker 1: Echo is an emergent technology, the advantage with Alexa, Hey

918
00:52:38,400 --> 00:52:41,520
Speaker 1: find me something good to watch on on Amazon Prime.

919
00:52:42,200 --> 00:52:46,399
Speaker 1: That's a monstrous thing. It's not there yet, but at

920
00:52:46,440 --> 00:52:50,440
Speaker 1: one day in the future. Um. Somebody just said, if

921
00:52:50,480 --> 00:52:53,960
Speaker 1: you extrapolate what Amazon is doing twenty years into the future,

922
00:52:54,440 --> 00:52:57,000
Speaker 1: their a monopoly at that point and someone's gonna have

923
00:52:57,000 --> 00:52:59,920
Speaker 1: to break them up, which coming from Microsoft, that has

924
00:53:00,080 --> 00:53:03,640
Speaker 1: to be an interesting Yeah. I lived through that d

925
00:53:03,880 --> 00:53:06,759
Speaker 1: J thing. It was really rough. It's not you know,

926
00:53:06,960 --> 00:53:11,279
Speaker 1: no fun, it's no fun, um, you know. And I

927
00:53:11,320 --> 00:53:15,840
Speaker 1: think it is inevitable as companies get really really huge,

928
00:53:16,120 --> 00:53:19,640
Speaker 1: and I think Jeff is probably well aware of that

929
00:53:20,120 --> 00:53:23,560
Speaker 1: and is trying to do everything he can to um,

930
00:53:23,800 --> 00:53:26,080
Speaker 1: you know, to delay that as long as possible and

931
00:53:26,120 --> 00:53:29,360
Speaker 1: to play within the rules. But eventually the tall poppy

932
00:53:29,400 --> 00:53:31,640
Speaker 1: gets chopped down you have to say to say the list,

933
00:53:31,840 --> 00:53:37,560
Speaker 1: you know, my fantasy or my imagination. I should say,

934
00:53:37,640 --> 00:53:41,200
Speaker 1: in the ninety nineties, when there were a number of

935
00:53:41,320 --> 00:53:43,839
Speaker 1: small tech companies that I was kicking the tires on

936
00:53:44,160 --> 00:53:47,680
Speaker 1: and a number of new ideas going around, I pictured

937
00:53:47,719 --> 00:53:51,400
Speaker 1: every venture meeting having the founders come in giving their pitch,

938
00:53:51,440 --> 00:53:55,000
Speaker 1: explaining the technology, explaining the market, explaining they have to

939
00:53:55,320 --> 00:53:57,640
Speaker 1: what they need to do to capture critical mass, and

940
00:53:57,680 --> 00:54:01,000
Speaker 1: then someone saying, what prevents my acrosoft from building this

941
00:54:01,120 --> 00:54:03,720
Speaker 1: into Windows? And if you didn't have the right answer

942
00:54:03,719 --> 00:54:06,360
Speaker 1: to that, thanks for coming by. I mean it was

943
00:54:06,360 --> 00:54:11,320
Speaker 1: pretty much that until really combination of d J slowed

944
00:54:11,360 --> 00:54:14,879
Speaker 1: Microsoft down a few years, and then the web exploded

945
00:54:15,160 --> 00:54:17,239
Speaker 1: and that question went away. It's like, well, no, they

946
00:54:17,239 --> 00:54:19,279
Speaker 1: can build it into Windows, but we're gonna be on

947
00:54:19,320 --> 00:54:21,200
Speaker 1: the web. We're gonna be on any platform. There's a

948
00:54:21,239 --> 00:54:24,839
Speaker 1: new question now, though. Let's hear it Amazon, right, what's

949
00:54:24,840 --> 00:54:28,839
Speaker 1: going to prevent Amazon from Amazon cast a shadow every

950
00:54:28,920 --> 00:54:32,000
Speaker 1: all directions now and a long one in all directions,

951
00:54:32,000 --> 00:54:36,959
Speaker 1: which doesn't seem physically possible. But so the metaphor breaks down.

952
00:54:36,960 --> 00:54:39,360
Speaker 1: But that is the question being asked by venture capitalist

953
00:54:39,360 --> 00:54:44,120
Speaker 1: they're doing devices, They're they're obviously dominating retail. And I

954
00:54:44,280 --> 00:54:50,000
Speaker 1: read recently that something like of all searches take place

955
00:54:50,120 --> 00:54:55,359
Speaker 1: within Amazon, well maybe overall, but even even higher percentage

956
00:54:55,600 --> 00:54:59,080
Speaker 1: of uh commercial searches, which are the valuable ones that

957
00:54:59,440 --> 00:55:03,600
Speaker 1: results deeper center. More of commercial searches happen, Uh happened

958
00:55:03,600 --> 00:55:09,760
Speaker 1: to Amazon? Um? You know Amazon is Uh, Yeah, they're

959
00:55:09,800 --> 00:55:13,000
Speaker 1: They're They're everywhere. Who else is in Seattle? That that's

960
00:55:13,080 --> 00:55:15,719
Speaker 1: of note? Who else should we be aware of as

961
00:55:15,760 --> 00:55:18,200
Speaker 1: a up and coming company? Because I know there are

962
00:55:18,200 --> 00:55:21,440
Speaker 1: a few companies out there. Concur is there. Um, I

963
00:55:21,440 --> 00:55:23,319
Speaker 1: don't know if that's up and coming, but that's it's

964
00:55:23,360 --> 00:55:26,080
Speaker 1: been around a while. Um. Back to the Amazon thing

965
00:55:26,080 --> 00:55:29,040
Speaker 1: for a second. It may not be obvious, but they're

966
00:55:29,080 --> 00:55:33,120
Speaker 1: they're really the major force in enterprise software as well

967
00:55:33,160 --> 00:55:38,440
Speaker 1: now because of a WS and because pretty much most

968
00:55:38,640 --> 00:55:44,000
Speaker 1: computing is moving into the cloud, into a remote cloud. UM.

969
00:55:44,120 --> 00:55:47,640
Speaker 1: The whole software enterprise software stack, which is not my forte.

970
00:55:47,440 --> 00:55:52,080
Speaker 1: I do consumer internet marketplaces, um, but the whole enterprise

971
00:55:52,120 --> 00:55:54,840
Speaker 1: software stack is moving out of these premise based systems

972
00:55:55,520 --> 00:55:57,960
Speaker 1: as sold by Oracle and other you know, more like

973
00:55:59,320 --> 00:56:03,120
Speaker 1: IBM one, but they're also moving to the cloud as well. Okay, well,

974
00:56:03,360 --> 00:56:06,799
Speaker 1: here's the thing. Amazon is the cloud, and Amazon keeps

975
00:56:06,800 --> 00:56:09,320
Speaker 1: adding more and more functionality. Embet it as a service

976
00:56:09,360 --> 00:56:12,600
Speaker 1: within the cloud, and it's taking over chunk by chunk,

977
00:56:12,760 --> 00:56:16,080
Speaker 1: is taking over these enterprise software categories. You know, Oracle

978
00:56:16,120 --> 00:56:19,760
Speaker 1: as a pretty big database business. Amazon has a really

979
00:56:19,800 --> 00:56:22,279
Speaker 1: big database business that's a little bit lower end, but

980
00:56:22,440 --> 00:56:25,760
Speaker 1: is eating eating the big database. Really has no idea

981
00:56:25,920 --> 00:56:27,480
Speaker 1: and it can. It's going to be the same for

982
00:56:27,920 --> 00:56:31,640
Speaker 1: you know, HR software for sales software. I mean, it's

983
00:56:31,760 --> 00:56:35,719
Speaker 1: a legitimate competitor to salesforce type I think, you know,

984
00:56:35,719 --> 00:56:38,040
Speaker 1: I don't know that one specifically. I haven't sat in

985
00:56:38,080 --> 00:56:40,400
Speaker 1: on a pitch on that, but it certainly wouldn't surprise me.

986
00:56:40,480 --> 00:56:43,200
Speaker 1: And and I bring it up because that's part of

987
00:56:43,200 --> 00:56:46,239
Speaker 1: the shadow that overhangs any kind of venture pitch, right see.

988
00:56:46,239 --> 00:56:49,960
Speaker 1: I see it from the other end of the market spectrum,

989
00:56:50,000 --> 00:56:52,680
Speaker 1: which is it used to be if you wanted to

990
00:56:52,719 --> 00:56:55,840
Speaker 1: set up a company, well you had to hire a CTO,

991
00:56:55,920 --> 00:56:57,160
Speaker 1: and you had to get a bunch of service and

992
00:56:57,160 --> 00:56:58,920
Speaker 1: you get a bunch of web designs. You had to

993
00:56:58,920 --> 00:57:04,040
Speaker 1: do all this startup stuff, which twenty years ago cost

994
00:57:04,600 --> 00:57:08,320
Speaker 1: million dollars and today again to quote and Rees and

995
00:57:08,440 --> 00:57:14,080
Speaker 1: it's you know, the total cost is the founders laptops

996
00:57:14,239 --> 00:57:18,040
Speaker 1: and their access to Amazon. Absolutely, and it's pretty similar

997
00:57:18,040 --> 00:57:20,320
Speaker 1: to what we were talking about with your with Ridholts

998
00:57:20,760 --> 00:57:26,280
Speaker 1: Wealth Management right where schwab or more fidelity, Uh is

999
00:57:26,600 --> 00:57:31,320
Speaker 1: a WS for you you can have a turnkey exactly.

1000
00:57:31,320 --> 00:57:34,720
Speaker 1: That Times article was great. It is it really, that

1001
00:57:35,000 --> 00:57:38,080
Speaker 1: is what a WS is and that is what a

1002
00:57:38,080 --> 00:57:40,600
Speaker 1: a w S is going to grow into. Stuff like that.

1003
00:57:41,520 --> 00:57:44,120
Speaker 1: It's pretty it's pretty interesting. So other companies in Seattle.

1004
00:57:44,280 --> 00:57:49,480
Speaker 1: There's a really hot data one called Tableau. It's interesting. Um,

1005
00:57:49,520 --> 00:57:51,640
Speaker 1: you know, there's a history that F five was there.

1006
00:57:51,760 --> 00:57:56,160
Speaker 1: There's a histor long history of um, you know, biomedical

1007
00:57:56,240 --> 00:57:59,840
Speaker 1: stuff coming out of Seattle. M jen Lee Hood is there.

1008
00:58:00,440 --> 00:58:02,760
Speaker 1: So it's a really rich kind of biotech and medical

1009
00:58:03,120 --> 00:58:05,320
Speaker 1: community and that is a going to be a big

1010
00:58:05,360 --> 00:58:09,080
Speaker 1: growth area. UM. It's becoming known as cloud City though

1011
00:58:09,360 --> 00:58:12,640
Speaker 1: you know cloud City because of AWS and because of

1012
00:58:12,720 --> 00:58:16,440
Speaker 1: the Microsoft Azure which is Microsoft Cloud is also there

1013
00:58:16,520 --> 00:58:21,000
Speaker 1: is also there. Uh, there are all of these new

1014
00:58:21,240 --> 00:58:25,960
Speaker 1: cloud based enterprise software companies and cloud based tech companies

1015
00:58:26,080 --> 00:58:27,960
Speaker 1: that are growing up in Seattle. I would say that's

1016
00:58:28,000 --> 00:58:32,240
Speaker 1: probably the most exciting area of tech growth in Seattle.

1017
00:58:32,360 --> 00:58:34,600
Speaker 1: Not an area that I personally participate in. I like

1018
00:58:34,680 --> 00:58:38,280
Speaker 1: to build brands, but it's interesting. So you have people

1019
00:58:38,320 --> 00:58:40,720
Speaker 1: who are working in Amazon with people working in Microsoft.

1020
00:58:41,080 --> 00:58:44,280
Speaker 1: They get the startup bitch, sometimes with the blessing of

1021
00:58:44,280 --> 00:58:46,960
Speaker 1: the company, sometimes without. They go out and do what

1022
00:58:47,040 --> 00:58:50,920
Speaker 1: they want to do. And that was the other question

1023
00:58:51,000 --> 00:58:52,880
Speaker 1: is what vcs are up there or do all the

1024
00:58:52,880 --> 00:58:57,040
Speaker 1: big vcs have a branch up there? Now? Both basically

1025
00:58:57,040 --> 00:58:59,840
Speaker 1: we have some. We have some native Seattle venture capitalists

1026
00:59:00,040 --> 00:59:02,560
Speaker 1: are that are that are good. It's not as broader,

1027
00:59:02,600 --> 00:59:06,840
Speaker 1: as deep or as experienced a venture community, but companies

1028
00:59:06,920 --> 00:59:13,080
Speaker 1: like Madrona and Ignition and Mavarn these are in Seattle.

1029
00:59:13,360 --> 00:59:16,720
Speaker 1: Uh is Ingnitial, Ignition a combinator or is that a

1030
00:59:16,920 --> 00:59:19,800
Speaker 1: straight up These straight up VC A lot of cloud stuff.

1031
00:59:19,920 --> 00:59:24,000
Speaker 1: They do X X largely X Microsoft guys UM and

1032
00:59:24,040 --> 00:59:27,240
Speaker 1: they've they've done pretty well. I would say when I

1033
00:59:27,360 --> 00:59:30,120
Speaker 1: raise money for my startups, when I have done that,

1034
00:59:30,360 --> 00:59:34,000
Speaker 1: I generally I look to Seattle vcs, but because of

1035
00:59:34,040 --> 00:59:37,200
Speaker 1: my connections in Silicon Valley, I tend to take my

1036
00:59:37,320 --> 00:59:42,000
Speaker 1: A rounds from Silicon Valley. I find that especially in

1037
00:59:42,360 --> 00:59:47,600
Speaker 1: consumer internet, software space and media, the Valley is a

1038
00:59:47,800 --> 00:59:52,000
Speaker 1: much deeper, richer, more experienced ecosystem. Uh so I can

1039
00:59:52,080 --> 00:59:55,000
Speaker 1: learn a lot more from from from the Valley. So so,

1040
00:59:55,040 --> 00:59:56,800
Speaker 1: what are the next startups we're going to see coming

1041
00:59:56,800 --> 01:00:00,280
Speaker 1: out of rich Bond? You know, I'm not actively looking

1042
01:00:00,280 --> 01:00:03,240
Speaker 1: for him. I'm pretty busy. I have eight or nine boards. Um,

1043
01:00:03,400 --> 01:00:07,560
Speaker 1: I really enjoy being a coach and I have some

1044
01:00:07,800 --> 01:00:11,040
Speaker 1: terrific players that I'm coaching and I really take pride

1045
01:00:11,040 --> 01:00:14,880
Speaker 1: in in in watching them succeed and hit hit singles,

1046
01:00:14,920 --> 01:00:18,400
Speaker 1: doubles and an occasional home run. Um, I enjoy that

1047
01:00:19,760 --> 01:00:22,360
Speaker 1: scratch that itch or does it if you if you

1048
01:00:22,400 --> 01:00:27,160
Speaker 1: ignore it long enough, does it does it start up again? Um?

1049
01:00:27,200 --> 01:00:30,880
Speaker 1: It's there, It's certainly there. But I'm moving into a

1050
01:00:30,880 --> 01:00:33,080
Speaker 1: phase of my life where there are a lot of

1051
01:00:33,080 --> 01:00:35,560
Speaker 1: other things I like to do, and so I'm not

1052
01:00:35,640 --> 01:00:40,240
Speaker 1: actively looking for new startups that said they come along. Um.

1053
01:00:40,280 --> 01:00:42,320
Speaker 1: You know, I recently joined the board of Artsy here

1054
01:00:42,320 --> 01:00:44,040
Speaker 1: in New York City, which is why I'm why I'm

1055
01:00:44,080 --> 01:00:47,920
Speaker 1: here Arts Arts. Yeah, it's going to be the the

1056
01:00:49,520 --> 01:00:53,360
Speaker 1: digital marketplace for fine art secondary market for fine arts.

1057
01:00:53,600 --> 01:00:56,240
Speaker 1: I just wrote a column somewhere. Was it Bloomberg or

1058
01:00:56,240 --> 01:00:59,040
Speaker 1: somewhere else? Probably there was. Just just the other day,

1059
01:00:59,200 --> 01:01:04,280
Speaker 1: we just raised a fifty million dollar d round I

1060
01:01:04,360 --> 01:01:07,760
Speaker 1: invested as an angel investor years ago. It's a marketplace build.

1061
01:01:07,760 --> 01:01:10,560
Speaker 1: I love marketplace builds. It's a power to the people play.

1062
01:01:11,160 --> 01:01:15,160
Speaker 1: You know, the art market worldwide is we estimate three trillion.

1063
01:01:15,320 --> 01:01:18,680
Speaker 1: There's three trillion of art stock, fine art stock on

1064
01:01:18,800 --> 01:01:22,280
Speaker 1: walls and in warehouses and in museums all over the world.

1065
01:01:22,640 --> 01:01:27,560
Speaker 1: But that's a really fat head, long tail distribution. You

1066
01:01:27,680 --> 01:01:32,720
Speaker 1: have a thousand grand Master's painting, and that's a trillion

1067
01:01:32,920 --> 01:01:35,080
Speaker 1: or a couple of trillions, probably not quite a trillion.

1068
01:01:35,120 --> 01:01:36,880
Speaker 1: There aren't as many. It doesn't go quite as deep

1069
01:01:36,920 --> 01:01:39,200
Speaker 1: as you think it does. There aren't. There aren't as many.

1070
01:01:39,280 --> 01:01:42,880
Speaker 1: There aren't that many hundred million dollar plus value value

1071
01:01:42,920 --> 01:01:46,280
Speaker 1: works out there. But but you lots of your point

1072
01:01:46,360 --> 01:01:48,440
Speaker 1: is taken, yes, but the bulk of the transactions in

1073
01:01:48,480 --> 01:01:50,240
Speaker 1: the art mark in the fine art markets have been

1074
01:01:50,320 --> 01:01:54,920
Speaker 1: between five thousand and fifty thousand dollars of the three trillion.

1075
01:01:55,680 --> 01:01:58,320
Speaker 1: Just grant that to me for a secure forty four

1076
01:01:58,360 --> 01:02:02,320
Speaker 1: billion dollars of art trade a year. Really decent chunk

1077
01:02:02,400 --> 01:02:04,400
Speaker 1: of money. Okay, it's a big chunk of money, but

1078
01:02:04,480 --> 01:02:08,040
Speaker 1: it's a percent a percent and a half of the

1079
01:02:08,160 --> 01:02:11,360
Speaker 1: art stock. I know of no other business where that

1080
01:02:12,400 --> 01:02:15,520
Speaker 1: asset based business where that lower percentage of the of

1081
01:02:15,560 --> 01:02:19,840
Speaker 1: the stock trades value changes hands every year. Housing, for instance,

1082
01:02:19,880 --> 01:02:23,320
Speaker 1: about fifteen six of the housing stock turns over every year.

1083
01:02:23,840 --> 01:02:26,200
Speaker 1: Automo that global or just in the US. In the US.

1084
01:02:26,280 --> 01:02:29,040
Speaker 1: But and let's call that two hundred trillion in total

1085
01:02:29,040 --> 01:02:32,120
Speaker 1: assets something. I think it's a hundred in land and

1086
01:02:32,120 --> 01:02:36,680
Speaker 1: a hundred in improvement on those lines. Automobiles. I don't

1087
01:02:36,680 --> 01:02:40,000
Speaker 1: know the exact numbers, but my guess is of the

1088
01:02:40,000 --> 01:02:43,240
Speaker 1: automobile stock trades every year or is purchased in the

1089
01:02:43,240 --> 01:02:46,919
Speaker 1: primary market. Leasing is so big three years, its okay.

1090
01:02:46,960 --> 01:02:50,000
Speaker 1: So if it's three years, it's even more, it's okay.

1091
01:02:50,120 --> 01:02:57,440
Speaker 1: Uh so uh At one, I see real opportunity to

1092
01:02:57,520 --> 01:03:03,240
Speaker 1: create a marketplace where that's that stagnant pool can start moving.

1093
01:03:03,320 --> 01:03:05,800
Speaker 1: We can bring we can bring some motion to the water.

1094
01:03:05,960 --> 01:03:09,920
Speaker 1: And there's been no real marketplace for these things to

1095
01:03:09,920 --> 01:03:12,400
Speaker 1: to trade hands. It just hasn't really existed. There's been

1096
01:03:12,440 --> 01:03:14,000
Speaker 1: no way to do it. So I kind of view

1097
01:03:14,000 --> 01:03:19,440
Speaker 1: it as our artsy as Airbnb for art, Airbnb for art,

1098
01:03:19,560 --> 01:03:22,600
Speaker 1: or really more Etsy for art. Well Carter, the CEO,

1099
01:03:22,680 --> 01:03:26,080
Speaker 1: likes to say Uber for art. Um I would say

1100
01:03:26,080 --> 01:03:28,560
Speaker 1: more air, but I guess the analogy for Airbnbs for

1101
01:03:28,640 --> 01:03:32,680
Speaker 1: me is that there's all this fallow, dark inventory over

1102
01:03:32,760 --> 01:03:34,880
Speaker 1: the world, and I'm gonna light it up. We're gonna

1103
01:03:35,000 --> 01:03:37,320
Speaker 1: light that up, and we're gonna bring it into the light.

1104
01:03:37,480 --> 01:03:40,880
Speaker 1: The museums are the are the Marriott's and Hilton's and

1105
01:03:41,040 --> 01:03:44,560
Speaker 1: all the individual homes that are availed from rents is

1106
01:03:44,680 --> 01:03:47,200
Speaker 1: everybody below the grand masters that may or may not

1107
01:03:47,240 --> 01:03:49,320
Speaker 1: want to sell a painting and I'm gonna collect. My

1108
01:03:49,320 --> 01:03:51,320
Speaker 1: wife is a collector. We have a lot of contemporary

1109
01:03:51,320 --> 01:03:54,720
Speaker 1: pieces in that price range, and we never sell pieces

1110
01:03:54,720 --> 01:03:56,680
Speaker 1: even though we'd like to, because there's really no way

1111
01:03:56,720 --> 01:03:58,800
Speaker 1: to do it. How do you do it? There? They

1112
01:03:58,840 --> 01:04:01,360
Speaker 1: are A horrible article is about, I mean really good

1113
01:04:01,760 --> 01:04:04,800
Speaker 1: articles about how horrible it is if you purchase something

1114
01:04:04,960 --> 01:04:11,480
Speaker 1: from UM an art dealer. They pretty much control that artist,

1115
01:04:11,840 --> 01:04:14,440
Speaker 1: control that market if you want to sell it. If

1116
01:04:14,480 --> 01:04:16,960
Speaker 1: you don't go through them, they're very really upset at you,

1117
01:04:17,360 --> 01:04:19,360
Speaker 1: and it's like, Okay, that's it. No more x y

1118
01:04:19,440 --> 01:04:22,120
Speaker 1: Z artist for you. Wait, I bought this, You're now

1119
01:04:22,200 --> 01:04:25,360
Speaker 1: out of the process. But apparently not is a little

1120
01:04:25,440 --> 01:04:27,959
Speaker 1: mini monopoly in that space. So you guys are gonna

1121
01:04:27,960 --> 01:04:31,640
Speaker 1: break that. Well, we're gonna bring We're gonna bring empowerment

1122
01:04:31,840 --> 01:04:36,320
Speaker 1: and light and liquidity to to a market that's been

1123
01:04:36,360 --> 01:04:38,800
Speaker 1: a little stagnant. And here's the thing, here's the good

1124
01:04:38,800 --> 01:04:44,040
Speaker 1: news for art dealers and artists, uh, is that when

1125
01:04:44,160 --> 01:04:48,360
Speaker 1: you bring liquidity and you get motion, and you get movement,

1126
01:04:49,200 --> 01:04:53,240
Speaker 1: it grows. That billion out of the three trillion is

1127
01:04:53,280 --> 01:04:56,040
Speaker 1: going to go to that one percent of the market

1128
01:04:56,040 --> 01:04:57,680
Speaker 1: that's trading is going to go to two. Then it's

1129
01:04:57,680 --> 01:04:59,080
Speaker 1: going to go to five, then it's going to go

1130
01:04:59,120 --> 01:05:02,160
Speaker 1: to fift end. And so what happens to the overall market,

1131
01:05:02,720 --> 01:05:05,360
Speaker 1: it's a multiple. It's an into your multiple of the

1132
01:05:05,400 --> 01:05:08,160
Speaker 1: size it is today. And so everybody's actually going to win,

1133
01:05:08,560 --> 01:05:13,400
Speaker 1: especially artists, collectors, anybody, the dealers, every everybody's gonna win.

1134
01:05:13,520 --> 01:05:15,160
Speaker 1: Everybody's gonna win. An arts he is going to be

1135
01:05:15,240 --> 01:05:17,840
Speaker 1: the marketplace that that enables that. So I see it

1136
01:05:17,880 --> 01:05:22,000
Speaker 1: as a really big opportunity. The mission The kind of

1137
01:05:22,360 --> 01:05:26,200
Speaker 1: mission of the company from the founder Carter is to

1138
01:05:26,320 --> 01:05:30,760
Speaker 1: make art as ubiquitous as music. Mhm. That's fascinating, although

1139
01:05:30,880 --> 01:05:33,200
Speaker 1: music is less ubiquitous than it used to be. M

1140
01:05:33,920 --> 01:05:37,480
Speaker 1: you think, um no, but you know it's hard because

1141
01:05:38,040 --> 01:05:41,160
Speaker 1: I'm old school and I still buy CDs and then

1142
01:05:41,160 --> 01:05:44,160
Speaker 1: I immediately get the Amazon download, so I have both.

1143
01:05:44,240 --> 01:05:45,960
Speaker 1: I don't stand why I'm gonna pay ten dollars for

1144
01:05:46,000 --> 01:05:50,480
Speaker 1: digital without the CDC for the stereo. I want the

1145
01:05:50,520 --> 01:05:54,880
Speaker 1: actual Try asking your new Alexa to play Jack Johnson,

1146
01:05:55,200 --> 01:05:58,400
Speaker 1: to say, hey, Alex, Alexa played Jack Johnson or whatever

1147
01:05:58,400 --> 01:06:04,760
Speaker 1: it is. And what happens? You try it? Okay, I

1148
01:06:05,040 --> 01:06:08,240
Speaker 1: literally just just it? Does it? Okay? Barry? It does

1149
01:06:08,280 --> 01:06:11,360
Speaker 1: it instantly? It does it instantly. Did you get the

1150
01:06:11,680 --> 01:06:13,520
Speaker 1: one with the good the big one? Okay, you got

1151
01:06:13,520 --> 01:06:15,360
Speaker 1: the big one? Good? By the way, that was the better.

1152
01:06:15,440 --> 01:06:17,760
Speaker 1: The forty nine dollars reduced to thirty nine didn't make

1153
01:06:17,800 --> 01:06:20,520
Speaker 1: sense to me, but one seventy nine to eighty nine

1154
01:06:20,520 --> 01:06:22,640
Speaker 1: that made more sense. So the speaker is not great,

1155
01:06:22,680 --> 01:06:25,000
Speaker 1: even in the big one. It's not awesome. Okay, my

1156
01:06:25,080 --> 01:06:26,560
Speaker 1: son knows that I have in my house. I was

1157
01:06:26,560 --> 01:06:29,360
Speaker 1: gonna say way better, way better. Do they work? But

1158
01:06:29,440 --> 01:06:32,439
Speaker 1: here's what here's what happened. Now, here's what happened. Here's

1159
01:06:32,440 --> 01:06:34,840
Speaker 1: what happens in my kitchen in Seattle in the morning

1160
01:06:34,840 --> 01:06:38,160
Speaker 1: with my three kids. Okay, we have the fancy son Nos.

1161
01:06:38,520 --> 01:06:41,400
Speaker 1: We got the app for sons to control Sanos. It's

1162
01:06:41,480 --> 01:06:45,760
Speaker 1: literally next to Zillo. Okay, I'm not exaggerating. I'm literally right.

1163
01:06:45,800 --> 01:06:49,880
Speaker 1: I'm exactly the same way. Let me get that picture.

1164
01:06:49,920 --> 01:06:51,880
Speaker 1: I gotta get a picture of that. That's so funny.

1165
01:06:52,560 --> 01:06:56,360
Speaker 1: That is literally with Starbucks, fit Bit and Uber on

1166
01:06:56,400 --> 01:06:59,400
Speaker 1: the home page. I'm gonna get you in the photo too,

1167
01:06:59,640 --> 01:07:02,320
Speaker 1: all right, got it? Okay, here's the thing what my

1168
01:07:02,440 --> 01:07:05,400
Speaker 1: kids do and my wife, which drives me batty. It's

1169
01:07:05,400 --> 01:07:08,280
Speaker 1: a little too hard to open the Sonos up really

1170
01:07:08,440 --> 01:07:13,160
Speaker 1: select whatever music services. What they do. They say, Alexa

1171
01:07:13,240 --> 01:07:16,200
Speaker 1: play the new Drake and that's it. And it does.

1172
01:07:16,360 --> 01:07:18,120
Speaker 1: And they don't care that the quality is crappy. It's

1173
01:07:18,120 --> 01:07:19,680
Speaker 1: sitting in the corner of the kitchen. They don't care.

1174
01:07:19,680 --> 01:07:21,680
Speaker 1: The quality good listening to drink it's good And I'm like,

1175
01:07:22,040 --> 01:07:26,880
Speaker 1: what are you doing? Why are you doing? That just

1176
01:07:26,960 --> 01:07:30,000
Speaker 1: works with Sonos now yet, well, not that I've seen

1177
01:07:30,000 --> 01:07:32,080
Speaker 1: I've been watching for and I was I thought it

1178
01:07:32,120 --> 01:07:36,000
Speaker 1: was on the list of skills. And if you could say,

1179
01:07:36,000 --> 01:07:39,360
Speaker 1: Alexa play x y Z and son. Nos, now you're

1180
01:07:39,400 --> 01:07:42,240
Speaker 1: really talking about I've been I've been like on Twitter

1181
01:07:42,320 --> 01:07:44,600
Speaker 1: asking that question for quite some time with the people

1182
01:07:44,600 --> 01:07:47,560
Speaker 1: in charge, and it ain't. It ain't million yet. You know,

1183
01:07:47,840 --> 01:07:50,440
Speaker 1: I think that's an eventuality. It should be. It should be.

1184
01:07:50,560 --> 01:07:52,520
Speaker 1: So I only have you for another ten fifteen minutes.

1185
01:07:52,560 --> 01:07:55,680
Speaker 1: Let me get to some of my um favorite questions.

1186
01:07:56,240 --> 01:07:59,840
Speaker 1: Let's start with what's the most important thing that people

1187
01:08:00,160 --> 01:08:04,080
Speaker 1: don't know about your background? I was the ice cream

1188
01:08:04,080 --> 01:08:06,080
Speaker 1: Man when I was a kid. I drove the ice

1189
01:08:06,080 --> 01:08:07,920
Speaker 1: cream truck, the good you a truck, the white truck

1190
01:08:08,000 --> 01:08:11,080
Speaker 1: was it was it was the blue blue skybar, Blue

1191
01:08:11,080 --> 01:08:14,400
Speaker 1: sky bar anyway, it was increaching in New Canyon, New Canty, Connecticut.

1192
01:08:14,400 --> 01:08:16,439
Speaker 1: It was. It was not a Was that a summer job?

1193
01:08:16,640 --> 01:08:18,559
Speaker 1: Summer job? I drove it one summer. A great way

1194
01:08:18,600 --> 01:08:24,200
Speaker 1: to meet young girls. I'm sure, um, what else I know?

1195
01:08:24,240 --> 01:08:28,439
Speaker 1: That's no, that's a winner, driving the ice cream Man, right,

1196
01:08:29,439 --> 01:08:32,439
Speaker 1: Nobody everybody likes the ice cream Man. Next time you buy,

1197
01:08:32,760 --> 01:08:35,439
Speaker 1: Alexa asked him to play the Van Halen song. Ice

1198
01:08:35,439 --> 01:08:39,720
Speaker 1: cream Man, ice cream Man? Um early mentors. Who were

1199
01:08:39,800 --> 01:08:44,960
Speaker 1: some of your early mentors? Um, my early mentors. Uh,

1200
01:08:45,360 --> 01:08:49,360
Speaker 1: I've always been interested in stocks. Actually. Um My dad

1201
01:08:49,400 --> 01:08:51,800
Speaker 1: was one of these guys that subscribe to Value Line.

1202
01:08:51,880 --> 01:08:54,759
Speaker 1: He was he was a corporate executive, but his dad

1203
01:08:55,280 --> 01:08:58,400
Speaker 1: had been a Wall Street had been a bond salesman actually,

1204
01:08:59,040 --> 01:09:01,720
Speaker 1: and some my dad subscribed to Value Line. And I

1205
01:09:01,720 --> 01:09:03,880
Speaker 1: remember pouring over Value Line with my dad as I

1206
01:09:03,960 --> 01:09:06,760
Speaker 1: was learning how to do stocks and learning about pe ratios.

1207
01:09:06,800 --> 01:09:09,800
Speaker 1: I remember buying abbed Labs when I was a kid,

1208
01:09:10,200 --> 01:09:12,000
Speaker 1: as well as Sony the Walkman had just come out,

1209
01:09:12,000 --> 01:09:13,960
Speaker 1: and I'm like, Sony's gonna kill it. That was a

1210
01:09:13,960 --> 01:09:16,800
Speaker 1: bad one. Last was a good one. I see when

1211
01:09:16,800 --> 01:09:19,960
Speaker 1: the iPod came out, I had that same phot about Apple. Oh,

1212
01:09:20,000 --> 01:09:22,760
Speaker 1: this is the Sony Walkman for the digital era. And

1213
01:09:22,800 --> 01:09:25,320
Speaker 1: when I cut my teeth on Value Line and I

1214
01:09:25,360 --> 01:09:28,280
Speaker 1: remember thinking, why isn't this updated instantly? Why am I

1215
01:09:28,320 --> 01:09:35,240
Speaker 1: getting pages? And then was already there They scratched that itch. Yeah,

1216
01:09:35,320 --> 01:09:38,559
Speaker 1: that's amazing. Um My dad, my dad was. Yeah, my

1217
01:09:38,640 --> 01:09:43,200
Speaker 1: dad was was like a great one for me. Um,

1218
01:09:43,360 --> 01:09:45,800
Speaker 1: you know, I'm really lucky to have had exposure to

1219
01:09:45,920 --> 01:09:50,360
Speaker 1: some some of the greats, really Steve Bomber. But you know,

1220
01:09:50,920 --> 01:09:53,280
Speaker 1: Bill taught me a lot. And Steve has taught me

1221
01:09:53,360 --> 01:09:58,960
Speaker 1: a lot about about about passion and storytelling. Uh. Bill

1222
01:09:59,000 --> 01:10:02,640
Speaker 1: has taught me a lot of a long term vision. Um.

1223
01:10:02,680 --> 01:10:07,280
Speaker 1: I'm really lucky I have had the pleasure of working

1224
01:10:07,280 --> 01:10:09,479
Speaker 1: with a couple of the best venture capitalists I think

1225
01:10:09,520 --> 01:10:12,919
Speaker 1: of all time, who I kind of view as my mentors. Obviously,

1226
01:10:13,479 --> 01:10:19,280
Speaker 1: Well Jhog we talked about TCV, you know. Uh, he's

1227
01:10:19,320 --> 01:10:22,920
Speaker 1: around our age, you know, maybe closer to your age

1228
01:10:22,960 --> 01:10:29,960
Speaker 1: than my age, maybe a little older. Um. He's a calm, experienced,

1229
01:10:30,320 --> 01:10:34,680
Speaker 1: level headed, well informed. Um. He's the guy that can

1230
01:10:34,720 --> 01:10:37,439
Speaker 1: ask the really hard question in the really non threatening way.

1231
01:10:38,680 --> 01:10:42,200
Speaker 1: I've learned learned that from him, and and that is

1232
01:10:42,240 --> 01:10:46,120
Speaker 1: a good skill. Bill Gurley at Benchmark Capital. Bill is

1233
01:10:46,600 --> 01:10:50,880
Speaker 1: you know, perhaps the most successful, well known venture cat,

1234
01:10:50,880 --> 01:10:53,280
Speaker 1: well well published. You know, Bill, he we were supposed

1235
01:10:53,320 --> 01:10:56,920
Speaker 1: to do a show and he unfortunately had a family

1236
01:10:56,960 --> 01:11:00,719
Speaker 1: emergency and were rescheduled for September or so. Should really

1237
01:11:00,800 --> 01:11:04,160
Speaker 1: he's excited. He's going to be great on here, and

1238
01:11:04,160 --> 01:11:07,760
Speaker 1: and he is quite an intellectual finance guy. I mean,

1239
01:11:07,800 --> 01:11:10,439
Speaker 1: he really is. But what and what I've learned from him?

1240
01:11:10,479 --> 01:11:14,559
Speaker 1: He was an analyst before he was a venture capitalist. Um,

1241
01:11:14,600 --> 01:11:17,759
Speaker 1: and what I've learned from him, I guess he's really

1242
01:11:18,200 --> 01:11:23,880
Speaker 1: He's taught me how to think uh competitive strategy, you know,

1243
01:11:23,920 --> 01:11:29,920
Speaker 1: in the Michael Porter sense. He's really an unbelievable strategic thinker.

1244
01:11:30,920 --> 01:11:33,679
Speaker 1: And he has helped me figure out how to break

1245
01:11:33,720 --> 01:11:38,479
Speaker 1: down product situations, businesses, industries and look at it through

1246
01:11:38,720 --> 01:11:42,280
Speaker 1: a competitive advantage from a competitive advantage perspective, and where

1247
01:11:42,280 --> 01:11:47,400
Speaker 1: are the moats? Where's the value created? Um? And ultimately

1248
01:11:47,400 --> 01:11:51,240
Speaker 1: how it all ultimately comes down to a DCF you

1249
01:11:51,439 --> 01:11:55,720
Speaker 1: UM had had these mentors, who was the person that,

1250
01:11:55,840 --> 01:12:00,719
Speaker 1: as a business person, most influenced the way you build

1251
01:12:00,880 --> 01:12:05,519
Speaker 1: and manage companies? All of those guys have helped me.

1252
01:12:05,640 --> 01:12:07,519
Speaker 1: I'd say Greg Mafas helped me quite a bit too

1253
01:12:07,520 --> 01:12:10,160
Speaker 1: with financial engineering, and Barry Diller he's quite quite a

1254
01:12:10,200 --> 01:12:13,519
Speaker 1: financial engineer. By the way, You've given me a list

1255
01:12:13,560 --> 01:12:16,000
Speaker 1: of a year's worth of future podcasts. You know theyre

1256
01:12:16,040 --> 01:12:21,479
Speaker 1: in there. They'd all be great. Um. You know, I

1257
01:12:21,520 --> 01:12:25,760
Speaker 1: think my business ethos is probably an amalgamation of a

1258
01:12:25,840 --> 01:12:28,160
Speaker 1: lot of these folks. My most my my my most

1259
01:12:28,240 --> 01:12:31,080
Speaker 1: recent job had been Microsoft, and so that's the culture

1260
01:12:31,160 --> 01:12:34,120
Speaker 1: I knew. And when we started an expedia and spun

1261
01:12:34,240 --> 01:12:36,559
Speaker 1: it out, I knew what I wanted to bring from

1262
01:12:36,600 --> 01:12:39,160
Speaker 1: the Microsoft culture and what I wanted to leave behind,

1263
01:12:39,920 --> 01:12:42,000
Speaker 1: what I wanted a little short elbow. Back in the

1264
01:12:42,120 --> 01:12:46,680
Speaker 1: day it was there. There was a it was very aggressive,

1265
01:12:46,760 --> 01:12:51,920
Speaker 1: challenging environment. Uh. That wasn't for everybody, and turn a

1266
01:12:51,960 --> 01:12:55,519
Speaker 1: lot of turn some people off. Uh. And the engineer

1267
01:12:55,600 --> 01:12:59,280
Speaker 1: was God eight and everybody else was a supplicant. Not

1268
01:12:59,400 --> 01:13:03,080
Speaker 1: a bad approach if you're building technology, it can absolutely work.

1269
01:13:03,120 --> 01:13:07,320
Speaker 1: But my opinion is that that building companies for the

1270
01:13:07,400 --> 01:13:09,960
Speaker 1: long term requires all the spokes on the wheel to

1271
01:13:10,000 --> 01:13:13,439
Speaker 1: be tightened to the same to the same pressure, more

1272
01:13:13,479 --> 01:13:15,080
Speaker 1: of a team effort, in other words, so that the

1273
01:13:15,120 --> 01:13:17,800
Speaker 1: wheel rolls true. Otherwise the wheel kind of bumps along

1274
01:13:17,840 --> 01:13:20,560
Speaker 1: and you have problems, and companies that are too lopsided

1275
01:13:20,600 --> 01:13:24,680
Speaker 1: culturally they all end up with problems. Uh. And so

1276
01:13:24,680 --> 01:13:28,280
Speaker 1: so you know, kind of having a no assholes policy

1277
01:13:28,680 --> 01:13:31,840
Speaker 1: and a respect you know, a respectful culture that's challenging

1278
01:13:31,920 --> 01:13:36,519
Speaker 1: and competitive, but not to the exclusion of you know,

1279
01:13:36,760 --> 01:13:40,040
Speaker 1: being respectful I share your philosophy, and and it's always

1280
01:13:40,080 --> 01:13:43,920
Speaker 1: difficult to tactfully explain to somebody why they're not a

1281
01:13:43,960 --> 01:13:48,439
Speaker 1: good fit for that reason. But it's really important, it's

1282
01:13:48,439 --> 01:13:51,800
Speaker 1: really important. All Right, this may be my single favorite question,

1283
01:13:51,840 --> 01:13:54,640
Speaker 1: or at least reader's favorite questions. Tell us about some

1284
01:13:54,720 --> 01:13:58,800
Speaker 1: of your favorite books. Okay, uh, yeah, I love this

1285
01:13:58,840 --> 01:14:01,599
Speaker 1: part of your your interview is people. I get emails

1286
01:14:01,640 --> 01:14:03,280
Speaker 1: all the time. I'm always looking for a new book

1287
01:14:03,320 --> 01:14:06,479
Speaker 1: to read. Yeah, don't forget to ask that question. Yeah,

1288
01:14:06,560 --> 01:14:09,200
Speaker 1: so I told you I'm not a big business book reader.

1289
01:14:09,640 --> 01:14:12,799
Speaker 1: I live business. When I'm in bed at night reading,

1290
01:14:13,000 --> 01:14:14,960
Speaker 1: I'm not. I don't. I don't want to read about

1291
01:14:15,120 --> 01:14:17,559
Speaker 1: my life. I want to escape. I tend to want

1292
01:14:17,560 --> 01:14:23,559
Speaker 1: to escape. Um, I read a lot. I mainly read fiction. Really,

1293
01:14:23,600 --> 01:14:26,519
Speaker 1: that's fascinating. And I would say that my passion category

1294
01:14:26,520 --> 01:14:30,080
Speaker 1: of fiction is probably science fiction both not surprisingly. What

1295
01:14:30,160 --> 01:14:32,679
Speaker 1: have you read recently that you really like? Well, I really,

1296
01:14:32,720 --> 01:14:35,200
Speaker 1: I really love Neil Stevenson. Who's the Seattle guy. He

1297
01:14:35,240 --> 01:14:38,679
Speaker 1: wrote snow Crash. You know that was that's William Gibson.

1298
01:14:38,840 --> 01:14:42,360
Speaker 1: William Gibson. That's right, I love snow Crash. Was was

1299
01:14:42,760 --> 01:14:46,080
Speaker 1: Neil Stevenson's kind of breakthrough work from fifteen or twenty

1300
01:14:46,160 --> 01:14:50,639
Speaker 1: years ago where he envisioned the internet. He envisioned um.

1301
01:14:50,680 --> 01:14:53,439
Speaker 1: He called it the metaverse. Uh, snow Crash is a

1302
01:14:53,439 --> 01:14:55,799
Speaker 1: real winner. In fact, Mark Andreeson and Your show refrosched

1303
01:14:55,800 --> 01:15:00,000
Speaker 1: it too, but snow Crash again but he neil. Recently

1304
01:15:00,080 --> 01:15:03,959
Speaker 1: we published a book called seven Eves. Somebody else referenced.

1305
01:15:05,040 --> 01:15:09,040
Speaker 1: Maybe it's Chris Anderson somebody else reference Okay, Uh, seven

1306
01:15:09,080 --> 01:15:13,439
Speaker 1: Eaves is just off the hook. Now. It's dense, uh,

1307
01:15:13,479 --> 01:15:17,080
Speaker 1: and it's a little technical, but it's approachable by anybody. Um.

1308
01:15:17,520 --> 01:15:21,000
Speaker 1: But the conceits the setup for seven Eaves is a agent,

1309
01:15:21,080 --> 01:15:24,280
Speaker 1: and unseen agent of some kind hits our moon and

1310
01:15:24,360 --> 01:15:28,480
Speaker 1: breaks it into seven pieces just kind of like you know, crumbles,

1311
01:15:28,520 --> 01:15:30,720
Speaker 1: just doesn't crumble, just kind of breaks apart. And they

1312
01:15:30,760 --> 01:15:32,960
Speaker 1: all kind of are moving together, and they're still orbiting

1313
01:15:33,000 --> 01:15:36,439
Speaker 1: the Earth. And everybody's worried at first, and they're like, oh, well,

1314
01:15:36,640 --> 01:15:39,559
Speaker 1: you know, gravitational centers about the same. It's orbiting the Earth.

1315
01:15:39,600 --> 01:15:43,280
Speaker 1: It's all good. Um, nothing bad is going to happen, right. Well,

1316
01:15:43,600 --> 01:15:46,200
Speaker 1: then the pieces start to bump into each other, okay,

1317
01:15:46,360 --> 01:15:49,000
Speaker 1: and little pieces break off, and the more collisions cause

1318
01:15:49,040 --> 01:15:51,519
Speaker 1: more collisions, and of course we know what happens that

1319
01:15:51,760 --> 01:15:54,040
Speaker 1: the bits of the Moon start falling into the Earth,

1320
01:15:54,080 --> 01:15:56,160
Speaker 1: and it becomes apparent to the smart people on Earth

1321
01:15:56,240 --> 01:15:59,200
Speaker 1: that the Earth is gonna end. And so they knew

1322
01:15:59,240 --> 01:16:00,880
Speaker 1: exactly how long it was going to take because they

1323
01:16:00,920 --> 01:16:03,360
Speaker 1: did the statistical models. And now it's a race to

1324
01:16:03,400 --> 01:16:06,120
Speaker 1: get as much stuff up into as much of humanity

1325
01:16:06,479 --> 01:16:09,400
Speaker 1: up into space before the Earth dies. And that's the

1326
01:16:09,479 --> 01:16:12,160
Speaker 1: kind of set up for this wonderful, this, this wonderful

1327
01:16:12,760 --> 01:16:16,040
Speaker 1: to add to the list. Sure, he also wrote Cryptanomicon,

1328
01:16:16,280 --> 01:16:20,639
Speaker 1: That's the one I was thinking, which completely is prescient

1329
01:16:21,360 --> 01:16:24,040
Speaker 1: around the whole cryptocurrency thing that's going on right now

1330
01:16:24,080 --> 01:16:27,200
Speaker 1: and is a is a very dense work that I'm

1331
01:16:27,240 --> 01:16:29,479
Speaker 1: that my fifteen year old son is working his way

1332
01:16:29,479 --> 01:16:32,680
Speaker 1: through right now. He's kind of a geeky kid. Um.

1333
01:16:32,720 --> 01:16:36,639
Speaker 1: But it it tells a terrific kind of Alan touring

1334
01:16:36,640 --> 01:16:40,439
Speaker 1: age story, a World War two story, and stashing Nazi

1335
01:16:40,680 --> 01:16:45,960
Speaker 1: stolen gold in Japanese islands and ties that somehow into

1336
01:16:46,040 --> 01:16:50,040
Speaker 1: what's happening with cryptocurrency today. That's interesting. Um, I know,

1337
01:16:50,120 --> 01:16:52,600
Speaker 1: we only have a few minutes left. I got to

1338
01:16:52,640 --> 01:16:57,240
Speaker 1: do the I have to do the last three questions. Oh,

1339
01:16:57,400 --> 01:16:59,920
Speaker 1: so let me do the last two questions then. Um,

1340
01:17:01,360 --> 01:17:03,720
Speaker 1: what sort of advice would you give to a millennial

1341
01:17:04,080 --> 01:17:12,200
Speaker 1: or someone just beginning their career? Interesting question, absolutely, someone

1342
01:17:12,240 --> 01:17:16,040
Speaker 1: interested in technology and or digital Take big swings. Take

1343
01:17:16,840 --> 01:17:21,040
Speaker 1: that goes back to take swings, the big Harry carry audiations,

1344
01:17:21,320 --> 01:17:24,439
Speaker 1: Dream big, Dream big. It's just as easy to swing

1345
01:17:24,600 --> 01:17:27,800
Speaker 1: for the fences that as it is the bunt right.

1346
01:17:27,800 --> 01:17:30,760
Speaker 1: You're at bat, Take big swings, especially young people. You

1347
01:17:30,760 --> 01:17:33,840
Speaker 1: you you're at a point in your lives that, whether

1348
01:17:33,920 --> 01:17:36,040
Speaker 1: or not you know it, you're at your most risk. Loving.

1349
01:17:38,520 --> 01:17:41,040
Speaker 1: Don't have kids, you don't have family, hopeful a ton

1350
01:17:41,080 --> 01:17:43,280
Speaker 1: of dad. Hopefully you don't have a ton of a

1351
01:17:43,320 --> 01:17:48,360
Speaker 1: ton of dad. Um, you know, take big swings. Hang

1352
01:17:48,400 --> 01:17:50,880
Speaker 1: around the hoop as well. Find the hoop, find where

1353
01:17:50,880 --> 01:17:53,080
Speaker 1: you think the next big thing is, and go hang

1354
01:17:53,120 --> 01:17:54,840
Speaker 1: around that hoop. The way to score points is to

1355
01:17:54,840 --> 01:17:57,120
Speaker 1: hang around the hoop. That's the best way. And our

1356
01:17:57,160 --> 01:18:00,240
Speaker 1: final question, what is it that you know about out

1357
01:18:00,520 --> 01:18:05,040
Speaker 1: venture capital technology startups today that you wish you knew

1358
01:18:05,760 --> 01:18:16,160
Speaker 1: back in? Um? I think that, uh, you know, I

1359
01:18:16,240 --> 01:18:18,559
Speaker 1: kind of I think about it as an investing question.

1360
01:18:18,600 --> 01:18:24,439
Speaker 1: Actually is okay, And you know, the mistakes I made,

1361
01:18:25,000 --> 01:18:27,640
Speaker 1: the mistakes I've made investing are all the follow of

1362
01:18:27,640 --> 01:18:31,280
Speaker 1: the ticker mistakes. Okay, they're all the d D mistakes

1363
01:18:31,320 --> 01:18:33,040
Speaker 1: where we feel like we need to do something. I'm

1364
01:18:33,080 --> 01:18:38,720
Speaker 1: watching the ticker, and I wish i'd known back then.

1365
01:18:39,320 --> 01:18:41,400
Speaker 1: I kind of logically knew that you just got to

1366
01:18:41,439 --> 01:18:44,040
Speaker 1: pick a few stocks and hold them forever. And I

1367
01:18:44,080 --> 01:18:47,000
Speaker 1: knew that I'd read you know, I'd read about Buffett

1368
01:18:47,040 --> 01:18:49,679
Speaker 1: for sure, like that Carol Loomis book on tap Dancy

1369
01:18:49,720 --> 01:18:51,800
Speaker 1: to Work is a great book. So I logically knew

1370
01:18:51,840 --> 01:18:54,439
Speaker 1: all this stuff. I watched my dad. But the truth is,

1371
01:18:54,640 --> 01:18:57,440
Speaker 1: human nature doesn't let us really do those things. But

1372
01:18:57,439 --> 01:18:59,960
Speaker 1: but I wish I had really known that the Amazon

1373
01:19:00,040 --> 01:19:02,240
Speaker 1: stock that I had pre I p o that I

1374
01:19:02,240 --> 01:19:06,040
Speaker 1: should have just helped. Okay, um, But that long term

1375
01:19:06,280 --> 01:19:09,840
Speaker 1: calm orientation is the same thing I wish i'd i'd

1376
01:19:09,840 --> 01:19:12,880
Speaker 1: really known about starting businesses too. I mean, that is

1377
01:19:12,920 --> 01:19:15,439
Speaker 1: the most important things, to have that long term vision

1378
01:19:15,800 --> 01:19:18,640
Speaker 1: and stick to it. It's that big dream that attracts

1379
01:19:18,760 --> 01:19:21,839
Speaker 1: the best people to you, and you know, stay on target.

1380
01:19:22,479 --> 01:19:25,240
Speaker 1: We have been speaking with Rich Barton. He is the

1381
01:19:25,520 --> 01:19:29,760
Speaker 1: founder of Expedia, co founder of Zillo, glass Door, and

1382
01:19:29,880 --> 01:19:33,840
Speaker 1: numerous other startups. If you have enjoyed this conversation, be

1383
01:19:33,880 --> 01:19:35,519
Speaker 1: sure and look up an inch or down an inch

1384
01:19:35,560 --> 01:19:38,639
Speaker 1: at any of the other hundred and fifty or so

1385
01:19:38,720 --> 01:19:42,240
Speaker 1: such conversations we've had over the past three years. You

1386
01:19:42,280 --> 01:19:48,559
Speaker 1: can find us on Apple, iTunes, SoundCloud, Overcast, and we're

1387
01:19:48,560 --> 01:19:51,920
Speaker 1: adding a few other technologies, so by the time you

1388
01:19:52,000 --> 01:19:54,679
Speaker 1: hear this, you might be listening to it on on

1389
01:19:54,760 --> 01:19:59,800
Speaker 1: something else. We love your comments, feedback and suggestions right

1390
01:20:00,000 --> 01:20:02,960
Speaker 1: to us at m IB podcast at Bloomberg dot net.

1391
01:20:03,680 --> 01:20:05,920
Speaker 1: I would be remiss if I did not thank the

1392
01:20:05,960 --> 01:20:09,840
Speaker 1: wonderful team that helps put these podcasts together. Taylor Riggs

1393
01:20:09,880 --> 01:20:14,519
Speaker 1: is my producer in the recording studio today is Charlie Valmer.

1394
01:20:14,880 --> 01:20:18,240
Speaker 1: Medina Parwana is our audio engineer. Mike Batnick is my

1395
01:20:18,320 --> 01:20:22,439
Speaker 1: head of research. I'm Barry Ridults Human listening to Masters

1396
01:20:22,439 --> 01:20:34,200
Speaker 1: in Business on Bloomberg Radio. Masters in Business is brought

1397
01:20:34,240 --> 01:20:38,520
Speaker 1: to you by the American Arbitration Association. Business disputes are inevitable,

1398
01:20:38,760 --> 01:20:43,160
Speaker 1: resolve faster with the American Arbitration Association, the global leader

1399
01:20:43,280 --> 01:20:47,519
Speaker 1: in alternative dispute resolution for over ninety years. Learn more

1400
01:20:47,800 --> 01:20:49,639
Speaker 1: at a d R dot org.